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Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a... Global Health Metrics Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019 GBD 2019 Diseases and Injuries Collaborators* Summary Lancet 2020; 396: 1204–22 Background In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries This online publication has been along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global corrected. The corrected version Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, first appeared at thelancet.com publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and on October 23, 2020 collectively exhaustive list of diseases and injuries. *For the list of Collaborators see Viewpoint Lancet 2020; Methods GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and 396: 1135–59 disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Correspondence to: Prof Christopher J L Murray, Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, Institute for Health Metrics and health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific Evaluation, University of death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian Washington, Seattle, WA 98195, process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD USA [email protected] population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and development investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. 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. 1204 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics Research in context Evidence before this study distributions used in DisMod-MR, the Bayesian meta-regression The Global Burden of Diseases, Injuries, and Risk Factors Study tool used to simultaneously estimate incidence, prevalence, (GBD) 2017 reported on incidence, prevalence, and mortality remission, excess mortality, and cause-specific mortality, from 359 diseases and injuries. Information on prevalence and were revised on the basis of simulation studies showing that mortality was also analysed in terms of summary measures: less informative priors helped to improve the coverage of years of life lost (YLLs), years lived with disability (YLDs), uncertainty intervals. (5) Redistribution algorithms for sepsis, disability-adjusted life-years (DALYs), and healthy life heart failure, pulmonary embolism, acute kidney injury, hepatic expectancy. GBD is the only comprehensive assessment failure, acute respiratory failure, pneumonitis, and five providing time trends for a mutually exclusive and collectively intermediate causes in the central nervous system were revised exhaustive list of diseases and injuries. For the first time, according to an analysis of 116 million deaths that were GBD 2017 also produced internally consistent estimates of attributed to multiple causes. (6) Processing of clinical population, fertility, mortality, and migration by age, sex, informatics data on hospital and clinic visits was revised to and year for 1950–2017. GBD 2017 also included subnational better take into account differential access across locations to assessments for 16 countries at administrative level 1 and for health-care facilities. (7) To enhance the stability of models in local authorities in England. the presence of the addition of subnational data in different GBD cycles, we adopted a set of standard locations for the Added value of this study estimation of covariate effects in models. (8) 7333 national and GBD 2019 updates and expands beyond GBD 2017 in ten ways. 24 657 subnational vital registration systems, 16 984 published (1) The number of countries for which subnational assessments studies, and 1654 household surveys were used in the analysis, have been undertaken was expanded to include Italy, Nigeria, including many newly available data sources. (9) Results are Pakistan, the Philippines, and Poland. (2) 12 new causes were presented so as to integrate causes of death, incidence, added to the GBD modelling framework, including pulmonary prevalence, YLDs, YLLs, and DALYs into a comprehensive arterial hypertension, nine new sites of cancer, and two new assessment of each disease and injury. (10) Closer technical sites of osteoarthritis (hand and other joints). (3) For each coordination with WHO has led to the addition of nine WHO disease, the preferred or reference case definition or member states to the analysis and revisions of the analytical measurement method was clearly defined and stored in a approach for select diseases. database. For both risks and diseases, the statistical relationship between the alternative and reference measurement method Implications of all the available evidence was analysed using network meta-regression using only data GBD 2019 provides the most up-to-date assessment of the where two different approaches were measured in the same descriptive epidemiology of a mutually exclusive and collectively location–time period. Although statistical cross-walking exhaustive list of diseases and injuries for 204 countries and between alternative and reference definitions and territories from 1990 to 2019. The comprehensive nature of the measurement methods has been a feature in all GBD studies, assessment provides policy-relevant information on the trends the approach in GBD 2019 was highly standardised and used of major causes of burden globally, regionally, and by country or improved methods across diseases and risks. (4) Some prior territory. Introduction been released, to our knowledge. In this study, we The Global Burden of Diseases, Injuries, and Risk Factors summarise GBD methods and present integrated results Study (GBD) provides a systematic scientific assess ment on fatal and non-fatal outcomes for the GBD disease of published, publicly available, and contributed data on and injury hierarchical cause list. GBD 2019 includes disease and injury incidence, prevalence, and mortality estimation of numerous different models for disease and for a mutually exclusive and collectively exhaustive list injury outcomes. This Article provides a high-level over- 1–3 of diseases and injuries. In an era of shifting global view of our findings. Results are presented both broadly agendas and expanded emphasis on non-communicable and in detail for a selection of diseases, injuries, and diseases and injuries along with communicable diseases, impairments in two-page summaries with a standard set sound and up-to-date evidence on trends—both progress of tables and figures. and adverse patterns—by cause at the national level is essential to reflect effects of public health policy and Methods 4–7 medical care delivery. Overview GBD 2019 provides an opportunity to incorporate The general approach to estimating causes of death and newly available datasets, enhance method performance disease incidence and prevalence for GBD 2019 is the 2,3 and standardisation, and reflect changes in scientific same as for GBD 2017. Appendix 1 provides details on See Online for appendix 1 1–3 understanding. Since GBD 2017, no comprehensive the methods used to model each disease and injury. update of descriptive epidemiology levels and trends has Here, we provide an overview of the methods, with an www.thelancet.com Vol 396 October 17, 2020 1205 Global Health Metrics emphasis on the main methodology changes since framework: pulmonary arterial hypertension, eye cancer, GBD 2017. soft tissue and other extraosseous sarcomas, malignant For each iteration of GBD, the estimates for the whole neoplasm of bone and articular cartilage, and neuro- time series are updated on the basis of addition of new blastoma and other peripheral nervous cell tumours at data and change in methods where appropriate. Thus, Level 3, and hepatoblastoma, Burkitt lymphoma, other the GBD 2019 results supersede those from previous non-Hodgkin lymphoma, retinoblastoma, other eye can- rounds of GBD. cers, and two sites of osteoarthritis (hand and other joints) GBD 2019 complies with the Guidelines for Accurate at Level 4. and Transparent Health Estimates Reporting (GATHER) statement (appendix 1 section 1.4). Analyses were com- Data pleted with Python version 3.6.2, Stata version 13, and The GBD estimation process is based on identifying For the statistical code see R version 3.5.0. Statistical code used for GBD estimation multiple relevant data sources for each disease or injury http://ghdx.healthdata.org/gbd- is publicly available online. including censuses, household surveys, civil registration 2019/code and vital statistics, disease registries, health service use, Geographical units, age groups, time periods, and cause air pollution monitors, satellite imaging, disease notifi - levels cations, and other sources. Each of these types of data are GBD 2019 estimated each epidemiological quantity of identified from systematic review of published studies, interest—incidence, prevalence, mortality, years lived searches of government and international organisation with disability (YLDs), years of life lost (YLLs), and websites, published reports, primary data sources such as disability-adjusted life-years (DALYs)—for 23 age the Demographic and Health Surveys, and contributions groups; males, females, and both sexes combined; and of datasets by GBD collaborators. 86 249 sources were 204 countries and territories that were grouped into used in this analysis, including 19 354 sources reporting 21 regions and seven super-regions. For GBD 2019, deaths, 31 499 reporting incidence, 19 773 reporting prev- nine countries and territories (Cook Islands, Monaco, alence, and 26 631 reporting other metrics. Each newly San Marino, Nauru, Niue, Palau, Saint Kitts and Nevis, identified and obtained data source is given a unique Tokelau, and Tuvalu) were added, such that the GBD identifier by a team of librarians and included in the For the GHDx see http://ghdx. location hierarchy now includes all WHO member Global Health Data Exchange (GHDx). The GHDx makes healthdata.org states. GBD 2019 includes subnational analyses for publicly available the metadata for each source included in Italy, Nigeria, Pakistan, the Philippines, and Poland, GBD as well as the data, where allowed by the data For the GHDx source tool see and 16 countries previously estimated at subnational provider. Readers can use the GHDx source tool to identify http://ghdx.healthdata.org/gbd- levels (Brazil, China, Ethiopia, India, Indonesia, Iran, which sources were used for estimating any disease or 2019/data-input-sources Japan, Kenya, Mexico, New Zealand, Norway, Russia, injury outcome in any given location. South Africa, Sweden, the UK, and the USA). All subnational analyses are at the first level of admin - Data processing istrative organisation within each country except for A crucial step in the GBD analytical process is correcting New Zealand (by Māori ethnicity), Sweden (by for known bias by redistributing deaths from unspecified Stockholm and non-Stockholm), the UK (by local gov- codes to more specific disease categories, and by adjusting ernment authorities), and the Philippines (by province). data with alternative case definitions or measurement In this publication, we present subnational estimates methods to the reference method. We highlight several for Brazil, India, Indonesia, Japan, Kenya, Mexico, major changes in data processing that in some cases have Sweden, the UK, and the USA; given space constraints, affected GBD results. See Online for appendix 2 these results are presented in appendix 2. At the most detailed spatial resolution, we generated estimates for Cause of death redistribution 990 locations. The GBD diseases and injuries analytical Vital registration with medical certification of cause of framework generated estimates for every year from death is a crucial resource for the GBD cause of death 1990 to 2019. analysis in many countries. Cause of death data obtained Diseases and injuries were organised into a levelled using various revisions of the International Classification cause hierarchy from the three broadest causes of death of Diseases and Injuries (ICD) were mapped to the and disability at Level 1 to the most specific causes at GBD cause list. Many deaths, however, are assigned to Level 4. Within the three Level 1 causes—communicable, causes that cannot be the underlying cause of death maternal, neonatal, and nutritional diseases; non-com mu- (eg, cardiopulmonary failure) or are inadequately speci- nicable diseases; and injuries—there are 22 Level 2 causes, fied (eg, injury from undetermined intent). These deaths 174 Level 3 causes, and 301 Level 4 causes (including were reassigned to the most probable underlying causes 131 Level 3 causes that are not further disaggregated at of death as part of the data processing for GBD. Level 4; see appendix 1 sections 3.4 and 4.12 for the full list Redistribution algorithms can be divided into three of causes). 364 total causes are non-fatal and 286 are fatal. categories: proportionate redistribution, fixed proportion For GBD 2019, 12 new causes were added to the modelling redistribution based on published studies or expert 1206 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics judgment, or statistical algorithms. For GBD 2019, data in the USA, Taiwan (province of China), New Zealand, for 116 million deaths attributed to multiple causes were and the Philippines, as well as USA Healthcare Cost and analysed to produce more empirical redistribution algo- Utilization Project inpatient data. Ratios of outpatient to rithms for sepsis, heart failure, pulmonary embolism, inpatient care for each cause were extracted from claims acute kidney injury, hepatic failure, acute respiratory data from the USA and Taiwan (province of China). failure, pneumonitis, and five intermediate causes The log of the ratios for each cause were modelled by (hydrocephalus, toxic encephalopathy, compres sion of age and sex using MR-BRT (Meta-Regression-Bayesian brain, encephalopathy, and cerebral oedema) in the Regularised Trimmed), the Bayesian meta-regression central nervous system. To redistribute unspecified tool. To account for the incomplete health-care access in injuries, we used a method similar to that of intermediate populations where not every person with a disease or cause redistribution, using the pattern of the nature of injury would be accounted for in administrative clinical injury codes in the causal chain where the ICD codes X59 records, we transformed the adjusted admission rates (“exposure to unspecified factor”) and Y34 (“unspecified using a scalar derived from the Healthcare Access and event, undetermined intent”) and GBD injury causes Quality Index. We used this approach to produce were the underlying cause of death. These new adjusted, standardised clinical data inputs. More details algorithms led to important changes in the causes to are provided in appendix 1 (section 4.3). which these intermediate outcomes were redistributed. Additionally, data on deaths from diabetes and stroke Modelling lack the detail on subtype in many countries; we ran For most diseases and injuries, processed data are regressions on vital registration data with at least 50% modelled using standardised tools to generate estimates of deaths coded specifically to type 1 or 2 diabetes and of each quantity of interest by age, sex, location, and year. ischaemic, haemorrhagic, or subarachnoid stroke to There are three main standardised tools: Cause of Death predict deaths by these subtypes when these were coded Ensemble model (CODEm), spatiotemporal Gaussian to unspecified diabetes or stroke. process regression (ST-GPR), and DisMod-MR. Previous 2,3,12 publications and the appendix provide more details on Correcting for non-reference case definitions or measurement these general GBD methods. Briefly, CODEm is a highly methods systematised tool to analyse cause of death data using an In previous cycles of GBD, data reported using alternative ensemble of different modelling methods for rates or case definitions or measurement methods were corrected cause fractions with varying choices of covariates that to the reference definition or measurement method perform best with out-of-sample predictive validity primarily as part of the Bayesian meta-regression models. testing. DisMod-MR is a Bayesian meta-regression tool For example, in DisMod-MR, the population data were that allows evaluation of all available data on incidence, simultaneously modelled as a function of country covar- prevalence, remission, and mortality for a disease, iates for variation in true rates and as a function of enforcing consistency between epidemiological para- indicator variables capturing alternative measurement meters. ST-GPR is a set of regression methods that methods. To enhance transparency and to standardise borrow strength between locations and over time for and improve methods in GBD 2019, we estimated single metrics of interest, such as risk factor exposure or correction factors for alternative case definitions or mortality rates. In addition, for select diseases, particularly measurement methods using network meta-regression, for rarer outcomes, alternative modelling strategies including only data where two methods were assessed in have been dev eloped, which are described in appendix 1 the same location–time period or in the exact same (section 3.2). population. This included validation studies where two In GBD 2019, we designated a set of standard locations methods had been compared in populations that were that included all countries and territories as well as not necessarily random samples of the general popu- the subnational locations for Brazil, China, India, and lation. Details on the correction factors from alternative the USA. Coefficients of covariates in the three main to reference measurement methods are provided in modelling tools were estimated for these standard appendix 1 (section 4.4.2). locations only—ie, we ignored data from subnational locations other than for Brazil, China, India, and the USA Clinical informatics (appendix 1 section 1.1). Using this set of standard Clinical informatics data include inpatient admissions, locations will prevent changes in regression coefficients outpatient (including general practitioner) visits, and from one GBD cycle to the next that are solely due to the health insurance claims. Several data processing steps addition of new subnational units in the analysis that were undertaken. Inpatient hospital data with a single might have lower quality data or small populations diagnosis only were adjusted to account for non- (appendix 1 section 1.1). Changes to CODEm for GBD 2019 primary diagnoses as well as outpatient care. For each included the addition of count models to the model GBD cause that used clinical data, ratios of non-primary ensemble for rarer causes. We also modified DisMod-MR to primary diagnosis rates were extracted from claims priors to effectively increase the out-of-sample coverage of www.thelancet.com Vol 396 October 17, 2020 1207 Age-standardised DALY rate Global Health Metrics uncertainty intervals (UIs) as assessed in simulation some parts of the world would have obscured the trends testing (appendix 1 section 4.5). in all other causes; we also subtracted out DALY rates For the cause Alzheimer’s disease and other dementias, from natural disasters and war and conflict to avoid we changed the method of addressing large variations trends in disease burden in some countries being between locations and over time in the assignment of dominated by these sudden and dramatic changes. As a dementia as the underlying cause of death. Based on measure of the epidemiological transition, we present a systematic review of published cohort studies, we the ratio of YLDs due to non-communicable diseases estimated the relative risk of death in individuals with and injuries, and due to total burden in DALYs. We dementia. We identified the proportion of excess deaths present 95% UIs for every metric based on the 25th and in patients with dementia where dementia is the under- 975th ordered values of 1000 draws of the posterior lying cause of death as opposed to a correlated risk factor distribution. (appendix 1 section 2.6.2). We changed the strategy of modelling deaths for acute hepatitis A, B, C, and E from Role of the funding source a natural history model relying on inpatient case fatality The funders of this study had no role in study design, rates to CODEm models after predicting type-specific data collection, data analysis, data interpretation, or the acute hepatitis deaths from vital registration data with writing of the report. The corresponding author had full specified hepatitis type. access to the data in the study and final responsibility for DisMod-MR was used to estimate deaths from three the decision to submit for publication. outcomes (dementia, Parkinson’s, and atrial fibrillation), and to determine the proportions of deaths by underlying Results aetiologies of cirrhosis, liver cancer, and chronic kidney Global trends disease deaths. Between 1990 and 2019, the number of global DALYs remained almost constant, but once the effects of Socio-demographic Index, annual rate of change, and population growth and ageing were removed by con- data presentation verting counts to age-standardised rates, there were clear The Socio-demographic Index (SDI) is a composite improvements in overall health (figure 1). Over the past indicator of a country’s lag-distributed income per decade, the pace of decline in global age-standardised capita, average years of schooling, and the fertility rate in DALY rates accelerated in age groups younger than females under the age of 25 years (appendix 1 section 6). 50 years compared with the 1990–2010 time period For changes over time, we present annualised rates of (table). The annualised rate of decline was greatest in the change as the difference in the natural log of the values 0–9-year age group. In the population aged 50 years and at the start and end of the time interval divided by the older, the rate of change was slower from 2010 to 2019 number of years in the interval. We examine the compared with the earlier time period. relationship between SDI and the annualised rate of These general trends are made up of complex trends for change in age-standardised DALY rates for all causes, specific diseases and injuries. Overall trends in the number apart from HIV/AIDS, natural disasters, and war and of DALYs across the different age groups between conflict, by country or territory, for the time periods 1990 and 2019 are driven by some key diseases and injuries 1990–2010 and 2010–19. We deliberately subtracted out (figure 2). The ten most important drivers of increasing DALYs due to HIV/AIDS because their magnitude in burden (ie, the causes that had the largest absolute increases in number of DALYs between 1990 and 2019) include six causes that largely affect older adults (ischaemic 3000 60 000 heart disease, diabetes, stroke, chronic kidney disease, lung cancer, and age-related hearing loss), whereas the 50 000 other four causes (HIV/AIDS, other musculoskeletal disorders, low back pain, and depressive disorders) are 2000 40 000 common from teenage years into old age (figure 2). Despite these ten conditions contributing the largest 30 000 number of additional DALYs over the 30-year period, only HIV/AIDS, other musculoskeletal disorders, and diabetes 1000 20 000 saw large increases in age-standardised DALY rates, with 10 000 an increase of 58·5% (95% UI 37·1–89·2) for HIV/AIDS, Age-standardised DALY rate DALY count 30·7% (27·6–34·3) for other musculoskeletal disorders, 0 0 and 24·4% (18·5–29·7) for diabetes. The burden of 1990 2000 2010 2019 Year HIV/AIDS, however, peaked in 2004 and has dropped substantially after the global scale-up of antiretroviral Figure 1: Global DALYs and age-standardised DALY rates, 1990–2019 treatment (ART). The changes in age-standardised rates Shaded sections indicate 95% uncertainty intervals. DALY=disability-adjusted life-year. for chronic kidney disease, age-related hearing loss, and 1208 www.thelancet.com Vol 396 October 17, 2020 DALY count (millions) Global Health Metrics DALYs 2019 Annualised rate of change, 1990–2010 Annualised rate of change, 2010–19 Count Age-standardised rate DALYs Age-standardised rate DALYs Age-standardised rate (millions) (per 100 000) 0–9 years 531 (458 to 621) 19 125·7 (16 495·1 to 22 382·5) −2·3% (−2·5 to −2·2) −2·5% (−2·6 to −2·3) −3·7% (−4·4 to −2·9) −4·0% (−4·7 to −3·2) 10–24 years 229 (194 to 270) 12 313·0 (10 399·9 to 14 478·3) 0·2% (0·1 to 0·2) −0·7% (−0·8 to −0·6) −1·1% (−1·4 to −0·9) −1·3% (−1·5 to −1·1) 25–49 years 616 (533 to 709) 22 691·2 (19 613·7 to 26 116·3) 1·4% (1·4 to 1·5) −0·4% (−0·4 to −0·3) −0·0% (−0·2 to 0·1) −1·2% (−1·4 to −1·0) 50–74 years 832 (752 to 919) 28 263·2 (25 527·6 to 31 213·4) 1·3% (1·2 to 1·3) −1·0% (−1·0 to −0·9) 2·0% (1·8 to 2·1) −0·9% (−1·1 to −0·8) ≥75 years 329 (308 to 351) 77 320·5 (72 372·5 to 82 440·3) 2·2% (2·2 to 2·2) −0·9% (−0·9 to −0·9) 2·3% (2·3 to 2·4) −0·8% (−0·9 to −0·8) All ages 2540 (2290 to 2810) 32 801·7 (29 535·1 to 36 319·5) −0·0% (−0·1 to 0·0) −1·4% (−1·5 to −1·3) −0·2% (−0·4 to 0·0) −1·3% (−1·5 to −1·1) DALY=disability-adjusted life-year. Table: Global DALYs in 2019 and annualised rate of change in DALYs and age-standardised DALY rates over 1990–2010 and 2010–19, by age group and for all ages depressive disorders were small (figure 2). Substantial accounted for by congenital syphilis in this age group; declines in age-standardised rates were seen in ischaemic tenth). Congenital birth defects (ranked fourth) as well heart disease (28·6%, 95% UI 24·2–33·3), stroke (35·2%, as two nutritional disorders—dietary iron deficiency 30·5–40·5), and lung cancer (16·1%, 8·2–24·0). (seventh) and protein-energy malnutrition (eighth)— The ten most important contri butors to declining completed the top ten. The percentage change in age- burden (ie, the causes that had the largest absolute standardised DALY rates for eight of the ten leading decreases in number of DALYs between 1990 and 2019) causes was large, ranging from a 35·4% (23·8–44·8) include nine that predominantly affect children (lower decline for neonatal disorders to 78·3% (69·9–85·5) respir atory infections, diarrhoeal diseases, neonatal dis- decline for protein-energy malnutrition over the study orders, measles, protein-energy malnutrition, congenital period. The decreases for the remaining two top-ten birth defects, drowning, tetanus, and malaria), as well causes, sexually transmitted infections and dietary iron as tuberculosis, which largely affects adults. All of deficiency, were much more modest. Sub-Saharan Africa these causes with declining burden also had substantial experienced nearly half of the total DALYs (49·9% decreases in age-standardised DALY rates, ranging [47·6–52·3]) for this age group in 2019. from 32·6% (21·2–42·1) decline for neonatal disorders The change in disease burden in adolescents aged to 90·4% (87·5–92·8) decline for measles, not just 10–24 years was much more modest (figure 2). DALYs decreases in the absolute number of DALYs due to declined by 6·2% (95% UI 2·1–10·5) overall between demographic changes (figure 2A). Although most of the 1990 and 2019. DALYs for non-communicable diseases ten leading Level 3 causes of DALYs were the same for increased by 13·1% (9·5–16·3), whereas injuries declined both sexes in 2019, road injuries (ranked fourth for by 24·8% (19·7–29·3) and infectious diseases by 18·7% males), cirrhosis (ninth), and lung cancer (tenth) were in (13·4–24·0). Three injury causes were among the top ten the top ten for males only, and were replaced by low back causes of global DALYs in this age group in 2019: road pain (ranked sixth for females), gynaecological diseases injuries (ranked first), self-harm (third), and interpersonal (ninth), and headache disorders (tenth) for females violence (fifth; figure 2). Headache disor ders, two mental (appendix 2 figure S5 and tables S2–5, S7, S8, S12, S13, disorders (depression and anxiety), low back pain, dietary S16). Congenital defects were ranked tenth for both sexes iron deficiency, HIV/AIDS, and diarrhoeal disease were combined in 2019 but did not make the top ten for either the other causes in the top ten for adolescents. Among sex separately. the top ten causes in this age group, age-standardised The burden for children younger than 10 years declined DALY rates for road injuries, self-harm, and diarrhoeal profoundly between 1990 and 2019, by 57·5% (95% UI diseases decreased by more than a third each between 50·3–63·1). Key drivers of this progress included large 1990 and 2019. As in the 0–9-year age group, the large reductions in major infectious diseases affecting increase in burden due to HIV/AIDS in the 10–24-year age children—namely, lower respiratory infections, diarrhoeal group reflects a rapid increase in the first half of the study diseases, and meningitis, each of which declined by period followed by a decline after the global scale-up of more than 60% between 1990 and 2019 (figure 2). In 2019, ART; despite declining in recent years, the HIV/AIDs neonatal disorders were the leading cause of burden in burden has not yet returned to 1990 levels. The other this age group, accounting for 32·4% (30·7–34·1) of the causes in the top ten showed small or insignificant change group’s global DALYs, increasing from 23·0% (22·0–24·1) (figure 2). The sex differences in the top ten rankings are in 1990. Six infectious diseases were also among the striking. The three previously mentioned injuries were top ten causes of burden in children: lower respiratory the top-ranked causes of DALYs among male adolescents infections (ranked second), diarrhoeal diseases (third), (appendix 2 figure S9), whereas headaches, depressive malaria (fifth), meningitis (sixth), whooping cough (ninth), disorders, and anxiety disorders were the top three causes and sexually transmitted infections (which were fully of DALYs among females (appendix 2 figure S10). www.thelancet.com Vol 396 October 17, 2020 1209 Global Health Metrics A All ages Leading causes 1990 Percentage of DALYs Leading causes 2019 Percentage of DALYs Percentage change in Percentage change in 1990 2019 number of DALYs, age-standardised DALY 1990–2019 rate, 1990–2019 1 Neonatal disorders 10·6 (9·9 to 11·4) 1 Neonatal disorders 7·3 (6·4 to 8·4) –32·3 (–41·7 to –20·8) –32·6 (–42·1 to –21·2) 2 Lower respiratory infections 8·7 (7·6 to 10·0) 2 Ischaemic heart disease 7·2 (6·5 to 7·9) 50·4 (39·9 to 60·2) –28·6 (–33·3 to –24·2) 3 Diarrhoeal diseases 7·3 (5·9 to 8·8) 3 Stroke5·7 (5·1 to 6·2) 32·4 (22·0 to 42·2) –35·2 (–40·5 to –30·5) 4 Ischaemic heart disease 4·7 (4·4 to 5·0) 4 Lower respiratory infections 3·8 (3·3 to 4·3) –56·7 (–64·2 to –47·5) –62·5 (–69·0 to –54·9) 5 Stroke 4·2 (3·9 to 4·5)5 Diarrhoeal diseases 3·2 (2·6 to 4·0) –57·5 (–66·2 to –44·7) –64·6 (–71·7 to –54·2) 6 Congenital birth defects 3·2 (2·3 to 4·8) 6 COPD 2·9 (2·6 to 3·2) 25·6 (15·1 to 46·0) –39·8 (–44·9 to –30·2) 7 Tuberculosis 3·1 (2·8 to 3·4) 7 Road injuries 2·9 (2·6 to 3·0) 2·4 (–6·9 to 10·8) –31·0 (–37·1 to –25·4) 8 Road injuries 2·7 (2·6 to 3·0) 8 Diabetes 2·8 (2·5 to 3·1) 147·9 (135·9 to 158·9) 24·4 (18·5 to 29·7) 9 Measles 2·7 (0·9 to 5·6) 9 Low back pain 2·5 (1·9 to 3·1) 46·9 (43·3 to 50·5) –16·3 (–17·1 to –15·5) 10 Malaria 2·5 (1·4 to 4·1) 10 Congenital birth defects 2·1 (1·7 to 2·6) –37·3 (–50·6 to –12·8) –40·0 (–52·7 to –17·1) 11 COPD 2·3 (1·9 to 2·5) 11 HIV/AIDS1·9 (1·6 to 2·2) 127·7 (97·3 to 171·7) 58·5 (37·1 to 89·2) 12 Protein-energy malnutrition 2·0 (1·6 to 2·7) 12 Tuberculosis 1·9 (1·7 to 2·0) –41·0 (–47·2 to –33·5) –62·8 (–66·6 to –58·0) 13 Low back pain 1·7 (1·2 to 2·1) 13 Depressive disorders 1·8 (1·4 to 2·4) 61·1 (56·9 to 65·0) –1·8 (–2·9 to –0·8) 14 Self-harm 1·4 (1·2 to 1·5) 14 Malaria 1·8 (0·9 to 3·1) –29·4 (–56·9 to 6·6) –37·8 (–61·9 to –6·2) 15 Cirrhosis 1·3 (1·2 to 1·5) 15 Headache disorders 1·8 (0·4 to 3·8) 56·7 (52·4 to 62·1) 1·1 (–4·2 to 2·9) 16 Meningitis 1·3 (1·1 to 1·5) 16 Cirrhosis 1·8 (1·6 to 2·0) 33·0 (22·4 to 48·2) –26·8 (–32·5 to –19·0) 17 Drowning 1·3 (1·1 to 1·4) 17 Lung cancer 1·8 (1·6 to 2·0) 69·1 (53·1 to 85·4) –16·2 (–24·0 to –8·2) 18 Headache disorders 1·1 (0·2 to 2·4) 18 Chronic kidney disease 1·6 (1·5 to 1·8) 93·2 (81·6 to 105·0) 6·3 (0·2 to 12·4) 19 Depressive disorders 1·1 (0·8 to 1·5) 19 Other musculoskeletal 1·6 (1·2 to 2·1) 128·9 (122·0 to 136·3) 30·7 (27·6 to 34·3) 20 Diabetes 1·1 (1·0 to 1·2) 20 Age-related hearing loss 1·6 (1·2 to 2·1) 82·8 (75·2 to 88·9) –1·8 (–3·7 to –0·1) 21 Lung cancer 1·0 (1·0 to 1·1) 21 Falls 1·5 (1·4 to 1·7) 47·1 (31·5 to 61·0) –14·5 (–22·5 to –7·4) 22 Falls 1·0 (0·9 to 1·2) 22 Self-harm 1·3 (1·2 to 1·5) –5·6 (–14·2 to 3·7) –38·9 (–44·3 to –33·0) 23 Dietary iron deficiency 1·0 (0·7 to 1·3) 23 Gynaecological diseases 1·2 (0·9 to 1·5) 48·7 (45·8 to 51·8) –6·8 (–8·7 to –4·9) 24 Interpersonal violence 0·9 (0·9 to 1·0) 24 Anxiety disorders 1·1 (0·8 to 1·5) 53·7 (48·8 to 59·1) –0·1 (–1·0 to 0·7) 25 Whooping cough 0·9 (0·4 to 1·7) 25 Dietary iron deficiency 1·1 (0·8 to 1·5) 13·8 (10·5 to 17·2) –16·4 (–18·7 to –14·0) 27 Age-related hearing loss 0·8 (0·6 to 1·1) 26 Interpersonal violence 1·1 (1·0 to 1·2) 10·2 (3·2 to 19·2) –23·8 (–28·6 to –17·8) 29 Chronic kidney disease 0·8 (0·8 to 0·9) 40 Meningitis 0·6 (0·5 to 0·8) –51·3 (–59·4 to –42·0) –57·2 (–64·4 to –48·6) 30 HIV/AIDS 0·8 (0·6 to 1·0) 41 Protein-energy malnutrition 0·6 (0·5 to 0·7) –71·1 (–79·6 to –59·7) –74·5 (–82·0 to –64·5) 32 Gynaecological diseases 0·8 (0·6 to 1·0) 46 Drowning 0·5 (0·5 to 0·6) –60·6 (–65·2 to –53·6) –68·2 (–71·9 to –62·8) 34 Anxiety disorders 0·7 (0·5 to 1·0) 55 Whooping cough 0·4 (0·2 to 0·7) –54·5 (–74·6 to –16·9) –56·3 (–75·6 to –20·3) 35 Other musculoskeletal 0·7 (0·5 to 1·0) 71 Measles 0·3 (0·1 to 0·6) –89·8 (–92·3 to –86·8) –90·4 (–92·8 to –87·5) B 0–9 years 1 Neonatal disorders 23·0 (22·0 to 24·1) 1 Neonatal disorders 32·4 (30·7 to 34·1) –36·2 (–45·4 to –24·7) –35·4 (–44·8 to –23·8) 2 Lower respiratory infections 17·0 (14·9 to 19·7) 2 Lower respiratory infections 11·6 (10·5 to 12·6) –69·1 (–75·9 to –60·9) –69·6 (–76·3 to –61·6) 3 Diarrhoeal diseases 13·1 (10·7 to 15·1) 3 Diarrhoeal diseases 9·3 (7·9 to 10·8) –67·8 (–75·3 to –57·2) –68·5 (–75·9 to –58·4) 4 Congenital birth defects 6·6 (4·6 to 10·0) 4 Congenital birth defects 8·6 (7·4 to 10·7) –41·6 (–54·6 to –17·4) –40·1 (–55·1 to –17·9) 5 Measles 5·7 (2·0 to 11·8) 5 Malaria 6·4 (3·3 to 10·8) –36·9 (–61·4 to –2·2) –38·5 (–63·1 to –6·5) 6 Malaria 4·6 (2·5 to 7·5) 6 Meningitis 2·1 (1·8 to 2·5) –59·7 (–68·1 to –49·3) –61·0 (–69·2 to –51·1) 7 Protein-energy malnutrition 4·1 (3·1 to 5·5) 7 Dietary iron deficiency 2·0 (1·3 to 2·9) –0·8 (–5·3 to 3·6) –8·2 (–12·3 to –4·1) 8 Meningitis 2·3 (2·0 to 2·7) 8 Protein-energy malnutrition 2·0 (1·7 to 2·3) –78·1 (–85·0 to –68·9) –78·3 (–85·5 to –69·9) 9 Whooping cough 1·9 (0·8 to 3·8) 9 Whooping cough 1·9 (0·9 to 3·3) –54·7 (–74·7 to –17·3) –53·2 (–75·6 to –20·4) 10 Drowning 1·8 (1·5 to 2·1) 10 STIs 1·4 (0·5 to 2·8) –16·3 (–30·7 to 1·7) –14·9 (–30·1 to 2·5) 11 Tuberculosis 1·8 (1·5 to 2·1) 11 Measles 1·3 (0·4 to 2·7) –90·0 (–92·6 to –86·9) –90·5 (–92·9 to –87·6) 12 Tetanus 1·7 (1·4 to 1·9) 12 Road injuries 1·1 (1·0 to 1·4) –61·5 (–68·7 to –45·0) –63·7 (–70·8 to –48·8) 13 Road injuries 1·3 (1·1 to 1·5) 13 Tuberculosis 1·0 (0·9 to 1·2) –74·5 (–79·8 to –67·8) –75·5 (–80·6 to –69·2) 14 Dietary iron deficiency 0·9 (0·6 to 1·3) 14 HIV/AIDS1·0 (0·9 to 1·2) –18·6 (–35·6 to 3·6) –25·0 (–35·3 to –13·6) 15 STIs 0·7 (0·2 to 1·5) 15 iNTS 1·0 (0·6 to 1·5) 68·3 (27·4 to 121·2) 61·4 (20·6 to 109·3) 16 Typhoid and paratyphoid 0·7 (0·3 to 1·3) 16 Drowning 0·9 (0·8 to 1·1) –77·6 (–81·3 to –70·1) –79·0 (–82·6 to –72·2) 17 Foreign body 0·6 (0·5 to 0·7) 17 Haemoglobinopathies 0·9 (0·7 to 1·0) –10·3 (–30·3 to 22·5) –13·7 (–34·3 to 14·7) 18 HIV/AIDS 0·6 (0·5 to 0·7) 18 Typhoid and paratyphoid 0·8 (0·4 to 1·5) –46·7 (–59·1 to –31·1) –50·7 (–62·5 to –36·9) 19 Encephalitis 0·5 (0·4 to 0·7) 19 Asthma 0·5 (0·4 to 0·8) –32·2 (–46·2 to –14·5) –37·5 (–50·0 to –21·5) 20 Acute hepatitis 0·5 (0·4 to 0·5) 20 Foreign body 0·5 (0·4 to 0·5) –62·9 (–69·6 to –56·2) –63·6 (–70·2 to –57·1) 21 Haemoglobinopathies 0·4 (0·3 to 0·6) 21 EMBID 0·5 (0·4 to 0·6) –18·9 (–33·3 to –0·9) –22·1 (–36·1 to –6·0) 22 Leukaemia 0·4 (0·3 to 0·6) 22 Sudden infant death 0·5 (0·2 to 1·0) –50·6 (–61·6 to –29·8) –46·9 (–61·7 to –30·0) 23 Sudden infant death 0·4 (0·2 to 0·9) 23 Idiopathic epilepsy 0·5 (0·3 to 0·6) –30·7 (–45·8 to 3·6) –34·0 (–49·1 to –3·8) 24 Asthma 0·4 (0·3 to 0·5) 24 Other unspecified infectious 0·4 (0·3 to 0·6) –28·4 (–48·3 to 7·8) –29·3 (–50·3 to 3·3) 25 Falls 0·4 (0·3 to 0·5)25 Dermatitis 0·4 (0·2 to 0·7) 2·7 (1·7 to 3·7) –6·0 (–6·9 to –5·1) 28 Idiopathic epilepsy 0·3 (0·2 to 0·4) 26 Leukaemia 0·4 (0·4 to 0·5) –54·8 (–67·7 to –32·9) –55·3 (–69·5 to –37·0) 30 Other unspecified infectious 0·3 (0·2 to 0·4) 27 Falls 0·4 (0·3 to 0·5) –48·3 (–68·7 to –22·6) –47·2 (–67·0 to –18·0) 33 iNTS 0·3 (0·1 to 0·4) 28 Encephalitis 0·4 (0·3 to 0·5) –67·6 (–76·7 to –47·6) –68·5 (–77·9 to –50·2) 34 EMBID 0·3 (0·2 to 0·3) 32 Tetanus 0·3 (0·3 to 0·5) –91·3 (–93·8 to –85·6) –91·2 (–93·8 to –85·6) 44 Dermatitis 0·2 (0·1 to 0·3) 39 Acute hepatitis 0·3 (0·2 to 0·3) –73·1 (–81·7 to –59·1) –74·1 (–82·6 to –61·1) Communicable, maternal, neonatal, and nutritional diseases Non-communicable diseases Injuries (Figure 2 continues on next page) 1210 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics Maternal disorders, gynaecological disorders, and dietary pain (fourth), headache disorders (fifth), and depressive iron deficiency were also in the top ten causes for females disorders (sixth; figure 2). Tuberculosis and four non- in this relatively young age group (appendix 2 figure S10). communicable causes—ischaemic heart disease, gynae- Five causes that were in the top ten for ages 10–24 cological disorders, other musculoskeletal disorders, and in 2019 were also in the top ten in the 25–49 age group: stroke—completed the top ten rankings. There were road injuries (ranked first), HIV/AIDS (second), low back substantial improvements since 1990 in DALY rates of C 10–24 years Leading causes 1990 Percentage of DALYs Leading causes 2019 Percentage of DALYs Percentage change in Percentage change in 1990 2019 number of DALYs, age-standardised DALY 1990–2019 rate, 1990–2019 1 Road injuries7·8 (6·9 to 8·8) 1 Road injuries6·6 (5·6 to 7·7) –20·1 (–28·3 to –12·9) –33·6 (–40·4 to –27·7) 24·6 (20·6 to 27·1) 3·3 (0·2 to 5·6) 2 Self-harm 4·9 (4·1 to 5·6) 2 Headache disorders 5·0 (0·6 to 10·9) 3 Headache disorders 3·8 (0·4 to 8·2) 3 Self-harm 3·7 (3·1 to 4·5) –28·4 (–36·3 to –18·9) –40·5 (–47·2 to –32·8) 4 Tuberculosis 3·6 (3·1 to 4·1) 4 Depressive disorders3·7 (2·6 to 5·0) 20·7 (17·4 to 23·5) 0·0 (–2·8 to 2·4) 5 Diarrhoeal diseases 3·2 (2·1 to 4·9) 5 Interpersonal violence 3·5 (2·9 to 4·1) 2·1 (–5·0 to 11·1) –15·4 (–21·3 to –7·9) 6 Interpersonal violence 3·2 (2·8 to 3·6) 6 Anxiety disorders3·3 (2·3 to 4·4) 17·9 (15·7 to 20·3) –2·0 (–3·8 to –0·1) 7 Maternal disorders 3·0 (2·6 to 3·4) 7 Low back pain 3·2 (2·2 to 4·3) 6·0 (4·4 to 7·6) –12·0 (–13·3 to –10·6) 8 Depressive disorders2·8 (2·0 to 3·9) 8 Dietary iron deficiency 2·6 (1·9 to 3·4) 15·9 (8·6 to 22·4) –3·5 (–9·5 to 2·0) 9 Low back pain 2·8 (1·9 to 3·8) 9 HIV/AIDS 2·6 (1·9 to 3·5) 159·0 (115·4 to 211·1) 112·8 (84·3 to 141·9) 10 Drowning 2·7 (2·3 to 3·2) 10 Diarrhoeal diseases 2·6 (1·9 to 3·6) –25·7 (–40·1 to –0·3) –37·0 (–50·2 to –17·0) 11 Typhoid and paratyphoid 2·6 (1·2 to 4·9) 11 Neonatal disorders 2·3 (1·8 to 2·8) 143·6 (114·3 to 174·6) 103·6 (78·4 to 128·5) 12 Anxiety disorders2·6 (1·8 to 3·5) 12 Tuberculosis 2·1 (1·8 to 2·5) –44·3 (–50·7 to –36·9) –53·8 (–59·1 to –47·7) 13 Dietary iron deficiency 2·1 (1·6 to 2·8) 13 Gynaecological diseases 1·9 (1·4 to 2·6) 19·1 (15·8 to 22·0) –1·4 (–4·2 to 1·0) 14 Malaria 2·1 (1·3 to 3·3) 14 Typhoid and paratyphoid 1·8 (0·8 to 3·3) –35·5 (–46·0 to –26·4) –46·2 (–54·9 to –38·5) 15 Lower respiratory infections 1·7 (1·4 to 2·0) 15 Maternal disorders 1·8 (1·5 to 2·2) –42·7 (–51·9 to –33·8) –52·5 (–60·2 to –45·3) 16 Conflict and terrorism 1·5 (1·3 to 1·9) 16 Malaria 1·8 (1·0 to 3·0) –19·4 (–50·8 to 15·8) –31·9 (–59·0 to –3·6) 17 Gynaecological diseases 1·5 (1·1 to 2·1) 17 Conduct disorder 1·8 (1·1 to 2·6) 24·7 (22·2 to 27·0) 4·4 (2·3 to 6·3) 18 Falls 1·5 (1·3 to 1·6) 18 Drug use disorders1·6 (1·3 to 2·1) 21·8 (15·2 to 28·7) 0·6 (–4·8 to 6·2) 19 Congenital birth defects 1·5 (1·3 to 1·7) 19 Acne vulgaris 1·6 (1·0 to 2·4) 41·5 (39·8 to 43·2) 18·1 (16·7 to 19·5) 20 Idiopathic epilepsy1·4 (1·1 to 1·8) 20 Idiopathic epilepsy1·6 (1·2 to 2·1) 6·5 (–7·1 to 25·7) –11·4 (–22·8 to 4·6) 21 Conduct disorder 1·3 (0·8 to 2·0) 21 Congenital birth defects 1·5 (1·3 to 1·7) –5·6 (–15·6 to 7·4) –21·2 (–29·7 to –10·5) 22 Drug use disorders1·3 (1·0 to 1·6) 22 Falls 1·4 (1·3 to 1·6) –8·4 (–16·9 to 0·4) –23·9 (–30·9 to –16·7) 23 Asthma 1·2 (1·0 to 1·6) 23 Drowning 1·4 (1·2 to 1·7) –50·7 (–55·9 to –44·7) –58·8 (–63·2 to –53·9) 24 Stroke 1·2 (1·0 to 1·3) 24 Lower respiratory infections1·4 (1·2 to 1·7) –20·9 (–29·9 to –10·5) –34·1 (–41·6 to –25·5) 25 Meningitis 1·1 (1·0 to 1·3) 25 Age-related hearing loss 1·3 (0·9 to 1·8) 18·6 (13·4 to 24·2) –1·2 (–5·7 to 3·2) 27 Acne vulgaris 1·1 (0·7 to 1·6) 27 Asthma 1·3 (1·0 to 1·8) –1·1 (–8·3 to 5·1) –18·0 (–23·8 to –12·4) 28 Age-related hearing loss 1·1 (0·7 to 1·5) 30 Stroke 1·1 (0·9 to 1·3) –12·8 (–21·5 to –2·9) –27·6 (–34·8 to –19·4) 33 HIV/AIDS 0·9 (0·6 to 1·5) 34 Meningitis 0·9 (0·7 to 1·1) –26·0 (–34·0 to –16·4) –38·3 (–45·0 to –30·4) 35 Neonatal disorders 0·9 (0·7 to 1·1) 46 Conflict and terrorism 0·6 (0·5 to 0·8) –62·1 (–65·7 to –57·9) –68·5 (–71·6 to –65·1) D 25–49 years 1 Road injuries 5·6 (5·1 to 6·1) 1 Road injuries5·1 (4·6 to 5·7) 23·2 (11·1 to 33·2) –22·5 (–30·1 to –16·2) 2 Tuberculosis 5·5 (4·8 to 6·2) 2 HIV/AIDS 4·8 (4·0 to 5·9) 176·2 (131·1 to 244·3) 72·2 (52·4 to 91·9) 3 Ischaemic heart disease 4·4 (3·8 to 4·9) 3 Ischaemic heart disease 4·7 (4·0 to 5·4) 42·7 (28·4 to 57·3) –18·5 (–26·7 to –10·1) 4 Low back pain 3·9 (2·9 to 5·1) 4 Low back pain3·9 (2·9 to 5·0) 33·0 (29·2 to 36·9) –19·2 (–20·5 to –18·0) 5 Self-harm 3·8 (3·3 to 4·4) 5 Headache disorders 3·7 (0·8 to 7·7) 61·2 (56·5 to 64·5) 0·2 (–3·7 to 2·3) 6 Stroke 3·5 (3·1 to 3·9) 6 Depressive disorders3·5 (2·5 to 4·5) 53·2 (49·3 to 56·8) –4·9 (–6·4 to –3·4) 7 Headache disorders 3·1 (0·7 to 6·4) 7 Gynaecological diseases 3·3 (2·5 to 4·2) 52·7 (49·7 to 56·0) –4·5 (–6·3 to –2·5) 8 Depressive disorders 3·0 (2·2 to 3·9) 8 Other musculoskeletal 3·2 (2·3 to 4·2) 107·1 (101·0 to 114·3) 26·7 (23·4 to 30·5) 9 Cirrhosis 2·8 (2·5 to 3·2) 9 Stroke 3·2 (2·8 to 3·6) 19·9 (8·0 to 31·1) –31·0 (–37·9 to –24·6) 10 Gynaecological diseases 2·8 (2·2 to 3·7) 10 Tuberculosis 3·0 (2·6 to 3·4) –27·0 (–34·7 to –18·7) –55·5 (–60·2 to –50·5) 11 Maternal disorders 2·6 (2·3 to 2·9) 11 Self-harm 2·9 (2·4 to 3·4) –0·9 (–10·3 to 9·1) –37·2 (–43·2 to –30·9) 12 Interpersonal violence 2·5 (2·3 to 2·8) 12 Cirrhosis 2·8 (2·4 to 3·2) 29·6 (19·0 to 44·5) –23·8 (–30·1 to –15·1) 13 HIV/AIDS 2·3 (1·6 to 3·2) 13 Interpersonal violence 2·3 (2·0 to 2·6) 18·1 (10·7 to 26·5) –24·4 (–29·0 to –19·0) 14 Other musculoskeletal 2·0 (1·5 to 2·8) 14 Diabetes 2·2 (1·9 to 2·5) 123·9 (110·1 to 135·3) 29·2 (21·1 to 36·0) 15 Diarrhoeal diseases 2·0 (1·3 to 3·1) 15 Anxiety disorders 2·0 (1·4 to 2·7) 61·6 (57·5 to 65·4) 1·1 (0·0 to 2·1) 16 Falls 1·8 (1·6 to 2·0) 16 Drug use disorders 1·9 (1·5 to 2·2) 92·0 (82·7 to 102·5) 25·4 (19·3 to 31·6) 17 Anxiety disorders 1·7 (1·2 to 2·2) 17 Falls 1·8 (1·6 to 2·0) 34·4 (25·8 to 41·7) –18·0 (–23·4 to –13·5) 18 Alcohol use disorders 1·7 (1·4 to 2·0) 18 Chronic kidney disease 1·6 (1·4 to 1·8) 67·3 (53·9 to 80·3) 0·7 (–7·3 to 8·4) 19 Neck pain 1·3 (0·9 to 2·0) 19 Neck pain1·6 (1·1 to 2·4) 60·2 (52·4 to 67·9) –3·6 (–6·0 to –1·5) 20 Diabetes 1·3 (1·2 to 1·5) 20 Alcohol use disorders 1·6 (1·3 to 1·9) 28·2 (22·9 to 33·2) –20·9 (–24·2 to –17·9) 21 Chronic kidney disease 1·3 (1·2 to 1·4) 21 Age-related hearing loss 1·5 (1·1 to 2·1) 64·3 (58·7 to 69·1) –0·5 (–3·1 to 1·9) 22 Drug use disorders 1·3 (1·0 to 1·6) 22 Schizophrenia 1·5 (1·1 to 1·9) 59·6 (57·5 to 61·9) –0·9 (–2·0 to 0·2) 23 Schizophrenia 1·3 (0·9 to 1·6) 23 Maternal disorders 1·4 (1·2 to 1·6) –28·9 (–39·6 to –19·2) –53·4 (–60·5 to –47·2) 24 Age-related hearing loss 1·3 (0·9 to 1·7) 24 Diarrhoeal diseases 1·3 (1·0 to 1·9) –13·5 (–32·6 to 15·5) –46·2 (–59·0 to –29·6) 25 Lower respiratory infections1·2 (1·1 to 1·4) 25 Oral disorders 1·2 (0·7 to 2·1) 70·7 (66·4 to 74·1) 2·8 (0·5 to 5·1) –23·1 (–30·2 to –16·0) 32 Oral disorders 1·0 (0·5 to 1·6) 27 Lower respiratory infections 1·2 (1·0 to 1·4) 26·8 (15·2 to 38·5) Communicable, maternal, neonatal, and nutritional diseases Non-communicable diseases Injuries (Figure 2 continues on next page) www.thelancet.com Vol 396 October 17, 2020 1211 Global Health Metrics E 50–74 years Leading causes 1990 Percentage of DALYs Leading causes 2019 Percentage of DALYs Percentage change in Percentage change in 1990 2019 number of DALYs, age-standardised DALY 1990–2019 rate, 1990–2019 1 Ischaemic heart disease 12·5 (11·6 to 13·4) 1 Ischaemic heart disease 11·8 (10·7 to 12·9) 46·1 (35·6 to 56·4) –29·1 (–34·2 to –24·1) 2 Stroke 10·9 (10·0 to 11·8) 2 Stroke9·3 (8·5 to 10·1) 31·5 (19·5 to 42·9) –36·3 (–42·1 to –30·8) 3 COPD 6·5 (5·5 to 7·1) 3 Diabetes 5·1 (4·6 to 5·7) 156·1 (143·4 to 167·9) 24·5 (18·5 to 30·4) 4 Tuberculosis 4·0 (3·6 to 4·4) 4 COPD 4·7 (4·2 to 5·2) 12·0 (0·9 to 32·3) –45·9 (–51·4 to –36·2) 5 Lung cancer 3·6 (3·3 to 3·9) 5 Lung cancer 3·9 (3·4 to 4·3) 64·3 (48·8 to 80·2) –19·8 (–27·3 to –12·1) 72·1 (70·0 to 74·3) 6 Diabetes 3·1 (2·8 to 3·4) 6 Low back pain 3·1 (2·3 to 4·0) –15·9 (–16·9 to –14·9) 7 Cirrhosis 2·8 (2·6 to 3·1) 7 Cirrhosis 2·7 (2·4 to 3·0) 44·6 (33·2 to 57·1) –29·1 (–34·7 to –23·0) 8 Low back pain 2·8 (2·1 to 3·7) 8 Chronic kidney disease 2·3 (2·1 to 2·5) 130·2 (113·0 to 145·6) 12·1 (3·7 to 19·5) –2·6 (–4·9 to –0·5) 9 Diarrhoeal diseases 2·6 (1·6 to 4·0) 9 Age-related hearing loss 2·2 (1·5 to 3·0) 100·8 (96·0 to 104·9) 10 Stomach cancer 2·4 (2·2 to 2·6) 10 Road injuries 2·1 (1·9 to 2·3) 72·9 (56·5 to 83·9) –15·2 (–23·2 to –9·9) 11 Road injuries 1·9 (1·8 to 2·0) 11 Other musculoskeletal 1·9 (1·4 to 2·6) 172·0 (160·6 to 187·4) 33·6 (280 to 40·2) –64·7 (–68·9 to –59·4) 12 Lower respiratory infections 1·8 (1·6 to 2·0) 12 Tuberculosis 1·9 (1·7 to 2·1) –27·8 (–36·2 to –16·9) 13 Age-related hearing loss 1·7 (1·2 to 2·3) 13 Lower respiratory infections 1·8 (1·6 to 1·9) 49·8 (37·9 to 62·4) –27·5 (–33·3 to –21·5) 14 Chronic kidney disease 1·6 (1·4 to 1·7) 14 Depressive disorders 1·7 (1·3 to 2·3) 107·3 (104·7 to 110·1) 1·5 (0·2 to 2·9) 15 Asthma 1·5 (1·2 to 1·9) 15 Colorectal cancer 1·7 (1·6 to 1·9) 95·1 (80v8 to 108·2) –5·1 (–12·1 to 1·2) 16 Hypertensive heart disease 16 Falls 1·7 (1·5 to 2·0) 88·3 (76·5 to 100·0) –8·4 (–14·1 to –2·6) 1·5 (1·2 to 1·7) 17 Falls 1·4 (1·3 to 1·6) 17 Stomach cancer 1·7 (1·5 to 1·9) 6·3 (–5·0 to 18·9) –48·1 (–536 to –42·0) 18 Colorectal cancer 1·4 (1·3 to 1·5) 18 Osteoarthritis 1·5 (0·8 to 2·9) 113·6 (110·9 to 116·4) 4·1 (28 to 5·4) 19 Depressive disorders 1·3 (0·9 to 1·7) 19 Blindness and vision loss 1·4 (1·1 to 2·0) 88·8 (81·9 to 95·8) –8·6 (–12·0 to –5·0) 20 Blindness and vision loss 1·2 (0·9 to 1·6) 20 Breast cancer 1·4 (1·3 to 1·5) 85·0 (69·9 to 99·4) –9·5 (–16·9 to –2·5) 21 Liver cancer 1·2 (1·0 to 1·3) 21 Diarrhoeal diseases 1·4 (0·9 to 2·1) –21·0 (–42·4 to 11·9) –61·0 (–72·1 to –45·8) 22 Breast cancer 1·2 (1·1 to 1·2) 22 Hypertensive heart disease 36·7 (20·8 to 58·8) 1·3 (1·0 to 1·5) –33·8 (–41·7 to –23·4) 23 Oesophageal cancer 1·1 (0·9 to 1·2) 23 Headache disorders 1·2 (0·4 to 2·5) 102·5 (88·7 to 108·2) –1·2 (–7·4 to 2·3) 90·5 (86·0 to 94·7) 24 Osteoarthritis 1·1 (0·6 to 2·2) 24 Oral disorders 1·2 (0·8 to 1·8) –7·4 (–9·6 to –5·1) 1·1 (0·7 to 1·7) 115·9 (110·5 to 122·2) 25 Self-harm 1·1 (1·0 to 1·2) 25 Neck pain 5·7 (3·0 to 8·5) 26 Other musculoskeletal 1·1 (0·7 to 1·5) 27 Oesophageal cancer 1·0 (0·9 to 1·1) 38·2 (18·9 to 71·9) –32·1 (–41·9 to –16·1) –1·3 (–14·3 to 11·2) 28 Oral disorders 1·0 (0·6 to 1·5) 28 Asthma 1·0 (0·8 to 1·1) –51·8 (–58·3 to –46·0) 29 Headache disorders 0·9 (0·3 to 1·9) 29 Liver cancer 0·9 (0·8 to 1·0) 22·2 (5·2 to 44·0) –39·9 (–48·5 to –29·5) 32 Neck pain 0·8 (0·5 to 1·2) 31 Self-harm 0·9 (0·8 to 1·0) 20·4 (11·3 to 33·7) –41·0 (–45·5 to –34·5) F 75 years and older 1 Ischaemic heart disease 18·6 (17·1 to 19·7) 1 Ischaemic heart disease 16·2 (14·6 to 17·6) 66·6 (57·7 to 74·2) –32·4 (–35·8 to –29·4) 2 Stroke 15·5 (14·3 to 16·7) 2 Stroke 13·0 (11·7 to 14·0) 60·5 (48·7 to 72·5) –33·4 (–38·3 to –28·5) 8·5 (7·5 to 9·2) 63·6 (49·1 to 86·1) 3 COPD 9·9 (8·6 to 10·7) 3 COPD –31·0 (–37·1 to –21·9) 4 Alzheimer's disease 3·8 (1·7 to 8·6) 4 Alzheimer's disease 5·6 (2·6 to 12·2) 180·0 (168·0 to 194·7) 2·6 (–2·1 to 6·6) 5 Lower respiratory infections 3·3 (3·0 to 3·6) 5 Diabetes 4·0 (3·6 to 4·3) 190·7 (179·4 to 201·0) 23·1 (18·6 to 27·5) 6 Diarrhoeal diseases 3·1 (2·0 to 4·5) 6 Lower respiratory infections3·3 (2·9 to 3·6) 87·4 (76·2 to 99·6) –25·3 (–29·3 to –20·4) 2·6 (2·4 to 2·9) 164·3 (143·6 to 183·8) 7 Diabetes 7 Lung cancer 2·6 (2·3 to 2·8) 16·4 (7·4 to 24·9) 8 Hypertensive heart disease 8 Falls 2·6 (2·2 to 2·9) 166·4 (151·1 to 183·4) 2·3 (1·9 to 2·5) 6·4 (0·4 to 13·3) 9 Age-related hearing loss 2·0 (1·5 to 2·7) 9 Chronic kidney disease 2·5 (2·3 to 2·7) 196·0 (173·9 to 211·1) 21·6 (12·6 to 27·4) 10 Lung cancer 1·9 (1·8 to 2·0) 10 Age-related hearing loss 2·5 (1·9 to 3·3) 137·8 (132·0 to 143·9) –2·2 (–4·3 to –0·2) 2·4 (1·8 to 2·7) 11 Falls 1·8 (1·6 to 2·1) 11 Hypertensive heart disease 106·0 (68·5 to 131·7) –15·1 (–31·5 to –5·0) 1·8 (1·6 to 2·1) 1·9 (1·2 to 3·0) 15·1 (–16·8 to 65·3) 12 Tuberculosis 12 Diarrhoeal diseases –51·0 (–64·9 to –30·4) 13 Low back pain 1·7 (1·2 to 2·3) 13 Low back pain 1·8 (1·3 to 2·4) 105·7 (100·2 to 111·4) –12·5 (–13·8 to –11·3) 14 Chronic kidney disease 1·6 (1·5 to 1·8) 14 Colorectal cancer 1·7 (1·5 to 1·8) 126·9 (113·4 to 138·3) –4·5 (–9·7 to 0·1) 15 Stomach cancer 1·6 (1·4 to 1·7) 15 Blindness and vision loss 1·7 (1·3 to 2·2) 124·7 (119·3 to 130·7) –7·4 (–9·9 to –4·8) 1·4 (1·1 to 1·8) 16 Blindness and vision loss 16 Atrial fibrillation 1·3 (1·1 to 1·5) 148·6 (134·8 to 161·9) –1·8 (–6·9 to 2·5) 17 Colorectal cancer 1·4 (1·3 to 1·5) 17 Stomach cancer 1·3 (1·1 to 1·4) 55·0 (43·8 to 66·6) –32·9 (–37·5 to –28·0) 18 Asthma 1·2 (1·0 to 1·7) 18 Prostate cancer 1·1 (1·0 to 1·4) 117·0 (102·1 to 142·3) –8·5 (–14·6 to 2·1) 19 Cirrhosis 1·2 (1·0 to 1·3) 19 Cirrhosis 1·1 (1·0 to 1·2) –21·3 (–30·2 to –13·5) 82·3 (62·1 to 100·9) 20 Prostate cancer 1·0 (0·8 to 1·2) 20 Parkinson's disease 1·1 (1·0 to 1·2) 153·7 (138·7 to 166·6) 6·0 (0·0 to 11·1) 1·0 (0·8 to 1·2) 1·1 (06 to 21) 21 Atrial fibrillation 21 Osteoarthritis 139·5 (136·5 to 142·6) 0·8 (–0·4 to 2·1) 22 Osteoarthritis 0·9 (0·5 to 1·7) 22 Oral disorders 0·9 (0·6 to 1·3) 112·0 (106·4 to 117·6) –10·9 (–12·9 to –8·8) 23 Oral disorders 0·8 (0·6 to 1·2) 23 Tuberculosis 0·9 (0·8 to 1·0) –6·3 (–16·9 to 14·6) –59·2 (–64·0 to –50·3) 24 Parkinson's disease 0·8 (0·8 to 0·9) 24 Asthma 0·8 (0·7 to 1·0) 25·2 (3·2 to 41·2) –46·2 (–55·9 to –39·8) 0·8 (0·7 to 0·9) –9·3 (–13·5 to –5·9) 25 Upper digestive diseases 0·8 (0·7 to 0·9) 25 Road injuries 110·0 (99·8 to 118·1) 26 Road injuries 0·7 (0·6 to 0·8) 32 Upper digestive diseases0·6 (0·5 to 0·6) 34·0 (22·8 to 46·2) –43·8 (–48·4 to –38·7) Communicable, maternal, neonatal, and nutritional diseases Non-communicable diseases Injuries Figure 2: Leading 25 Level 3 causes of global DALYs and percentage of total DALYs (1990 and 2019), and percentage change in number of DALYs and age-standardised DALY rates from 1990 to 2019 for both sexes combined for all ages (A), children younger than 10 years (B), and ages 10–24 years (C), 25–49 years (D), 50–74 years (E), and 75 years and older (F) Causes are connected by lines between time periods; solid lines are increases in rank and dashed lines are decreases. Age-related hearing loss=age-related and other hearing loss. Alzheimer’s disease=Alzheimer’s disease and other dementias. Atrial fibrillation=atrial fibrillation and flutter. Cirrhosis=cirrhosis and other chronic liver diseases. COPD=chronic obstructive pulmonary disease. EMBID=endocrine, metabolic, blood, and immune disorders. DALY=disability-adjusted life-year. iNTS=invasive non-typhoidal salmonella. Haemoglobinopathies=haemoglobinopathies and haemolytic anaemias. Lung cancer=tracheal, bronchus, and lung cancer. Other musculoskeletal=other musculoskeletal disorders. Other unspecified infectious=other unspecified infectious diseases. Sudden infant death=sudden infant death syndrome. STI=sexually transmitted infections excluding HIV. 1212 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics tuberculosis, road injuries, stroke, and, to a lesser extent, epidemiological transition ranged from 8·4% (95% UI low back pain and ischaemic heart disease. For similar 6·2–10·9) in Chad to 56·9% (48·7–64·3) in Qatar. reasons as in the previous age group, HIV/AIDS The values in 1990 ranged from 3·5% (2·6–4·7) in Niger DALY rates increased substantially. The increase in the to 47·5% (37·6–56·0) in Andorra. In 2019, non-commu- residual “other musculoskeletal disorder” category is nicable and injury YLDs contributed to more than half of more difficult to interpret, as it is a collection of several all disease burden in 11 countries. All but two countries, individual diseases. HIV/AIDS, ischaemic heart disease, Ukraine and Lesotho, had higher ratios in 2019 compared stroke, and head ache disorders appeared in the top-ten with 1990. rankings for DALYs for both males and females in 2019. When comparing the annualised rate of change in age- Three injury causes (road injuries, self-harm, and inter- standardised DALY rates for all causes except HIV/AIDS, personal violence) and cirrhosis ranked prominently natural disasters, and war and conflict between the time among males but not females. Among females, gynae- periods 1990–2010 and 2010–19 for each country and cological disorders, depressive disorders, other musculo- territory, the rate, as shown by a simple linear regression skeletal disorders, maternal disorders, and anxiety line, is steeper in the latter time period, suggesting that disorders were top ten causes (appendix 2 figures S9, S10). change has accelerated over the last decade in countries In 2019, the ten leading causes of DALYs in age groups and territories at the lower end of the SDI range (figure 4). 50–74 years and 75 years and older largely overlapped. Improvements have started to stagnate, or even reverse, Ischaemic heart disease and stroke were ranked first in countries with higher SDI, as is the case in Dominica, and second, respectively, in both age groups. Chronic the Dominican Republic, Guam, Jamaica, Saint Lucia, obstructive pulmonary disease (COPD), diabetes, lung Saint Vincent and the Grenadines, Ukraine, the USA, and cancer, chronic kidney disease, and age-related hearing Venezuela. Countries with greater than 2% annual reduc- loss appeared in the top ten in both age groups. For ages tions in age-standardised DALY rates over both time 50–74 years, low back pain, cirrhosis, and road injuries periods were Ethiopia, Angola, Burundi, Malawi, Sudan, were the remaining top-ten-ranking causes of DALYs, Myanmar, Laos, and Bangladesh. Four countries from the whereas Alzheimer’s disease and other dementias, lower former Soviet Union—Russia, Belarus, Kazakhstan, and respiratory infections, and falls appeared in the top ten for Uzbekistan—experienced increases in age-standardised those aged 75 years and older. The most notable changes DALY rates between 1990 and 2010, but recovered in in top ten causes in these two age groups between the following decade; Russia, Kazakhstan, and Belarus 1990 and 2019 were large declines in age-standardised experienced an estimated annual decline of 2% or greater DALY rates for ischaemic heart disease, stroke, COPD, between 2010 and 2019, and Uzbekistan experienced an cirrhosis, and road injuries, but increases in DALY rates estimated 1·5% annual decline. Another former Soviet for diabetes and chronic kidney disease. There was a Union republic, Ukraine, saw modest decline in the decline in age-standardised lung cancer rates for ages 1990 to 2010 period, but a worsening trend in the decade 50–74 years, but an increase in the oldest age category. The after. ten leading causes for DALYs by sex in both of these older age groups largely overlapped in 2019. Among 50–74-year- Cause-specific trends olds, breast cancer, other musculoskeletal disorders, and Two-page cause-specific summaries provide detailed depressive disorders appeared in the top ten for females results on mortality, prev alence, incidence, YLLs, YLDs, only, while road injuries, cirrhosis, and tuberculosis made and DALYs for a selection of diseases, injuries, and it into the top ten for males. For the oldest age group, falls impairments in the GBD cause hierarchy. These sum- and hypertensive heart disease ranked in the top ten maries include 2019 counts, age-standardised rates, and among females, but not males; lung cancer and prostate rankings; the fraction of DALYs attributed to risk factors; cancer ranked among the top ten in males (appendix 2 patterns over time and age; and the relationship between figures S9, S10). SDI and DALY rates by country or territory. They were written to increase the accessibility to and transparency National trends of GBD estimates for each cause. Summaries for select Countries and territories vary widely in their stages of the causes are highlighted in print (pp S2–213); summaries For all two-page summaries see https://www.thelancet.com/ epidemiological transition. With increasing SDI, we for all diseases, injuries, and impairments can be found gbd/summaries expect to see a shift in the burden of disease from commu- online. nicable, maternal, neonatal, and nutritional diseases towards non-communicable causes. We also expect to Discussion see a shift towards a larger fraction of the burden due to Main findings YLDs compared with YLLs. These two major trends can Global health has steadily improved over the past be summarised by the percentage of all-cause DALYs 30 years, as measured by changes in age-standardised made up of non-communicable disease and injury YLDs. DALY rates. While health has improved, after accounting Figure 3 shows this proportion across 204 countries and for population growth and ageing, the absolute number territories in 1990 and 2019. In 2019, this measure of the of DALYs has remained stable. The shift to a much www.thelancet.com Vol 396 October 17, 2020 1213 Global Health Metrics A 1990 YLD proportion of DALYs <10% 30% to 34% 10% to 14% 35% to 39% 15% to 19% 40% to 44% 20% to 24% 45% to 49% 25% to 29% ≥50% Eastern Caribbean and central America Persian Gulf Balkan Peninsula Southeast Asia West Africa Mediterranean Northern Europe B 2019 Eastern Caribbean and central America Persian Gulf Balkan Peninsula Southeast Asia West Africa Mediterranean Northern Europe Figure 3: Proportion of total DALYs contributed by injury and non-communicable disease YLDs, by country or territory, 2019 Proportions were rounded to the nearest whole number. DALY=disability-adjusted life-year. YLD=year lived with disability. 1214 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics GBD super-region Central Europe, eastern Europe, and central Asia High income Latin America and Caribbean North Africa and Middle East South Asia Sub-Saharan Africa AB 1990–2010 2010–19 0·01 UZB ZWE UKR LSO GUM DOM LCA NRU DMA SWZ VCT USA BLR RUS JAM KAZ VEN –0·01 SDN YEM IND MRT CMR ARE MOZ SGP TGO OMN –0·02 ERI AFG GTM CHN KGZ SDN NGA TUR GNB GHA BDI ZAF SOM BLR MWI AGO CIV ZMB KOR RUS KHM BOL MMR LAO MWI NER PER SWZ SLE MDA TLS NPL ETH BGD MMR LAO KAZ BGD –0·03 AGO LBR BTN COD ETH RWA MDV GNQ –0·04 0 50 100 0 50 100 SDI SDI Figure 4: Annualised rate of change in age-standardised DALY rates for all causes excluding HIV/AIDS, natural disasters, and war and conflict, and SDI by country or territory, for 1990–2010 (A) and 2010–19 (B) A simple linear regression line is shown in each figure for the relationship between annualised rate of change and the average SDI value of each country and territory for each time period. AFG=Afghanistan. AGO=Angola. ARE=United Arab Emirates. BDI=Burundi. BGD=Bangladesh. BLR=Belarus. BOL=Bolivia. BTN=Bhutan. CHN=China. CIV=Côte d’Ivoire. CMR=Cameroon. COD=Democratic Republic of the Congo. DALY=disability-adjusted life-year. DMA=Dominica. DOM=Dominican Republic. ERI=Eritrea. ETH=Ethiopia. GHA=Ghana. GNB=Guinea-Bissau. GNQ=Equatorial Guinea. GTM=Guatemala. GUM=Guam. IND=India. JAM=Jamaica. KAZ=Kazakhstan. KHM=Cambodia. KOR=South Korea. KNA=Saint Kitts and Nevis. LAO=Laos. LBR=Liberia. LCA=Saint Lucia. LSO=Lesotho. MDA=Moldova. MDV=Maldives. MMR=Myanmar. MOZ=Mozambique. MRT=Mauritania. MWI=Malawi. NER=Niger. NGA=Nigeria. NPL=Nepal. NRU=Nauru. OMN=Oman. PER=Peru. RUS=Russia. RWA=Rwanda. SDN=Sudan. SGP=Singapore. SLE=Sierra Leone. SOM=Somalia. SWZ=eSwatini. TGO=Togo. TLS=Timor-Leste. TUR=Turkey. UKR=Ukraine. UZB=Uzbekistan. VCT=Saint Vincent and the Grenadines. VEN=Venezuela. YEM=Yemen. ZAF=South Africa. ZWE=Zimbabwe. SDI=Socio-demographic Index. greater number of DALYs occurring at older ages, despite national screening programme for the whole population reductions in age-standardised DALY rates, illustrates aged 12 years and older was established in 2019. Egypt the importance of understanding how ageing shapes had the highest prevalence of chronic hepatitis C in the future health needs. Policy makers should remain aware world, ascribed to iatrogenic infection during treatment 20–22 that the number of DALYs represents the burden of campaigns for schistosomiasis in the 1960s and 1970s. disease that the world’s health systems must manage. The sharp decline in chronic infections in Egypt is Although most diseases showed a pattern of stable or expected to be reflected in a large decline in deaths slowly changing rates of death and disability over the from cirrhosis and liver cancer in coming years. Unlike study period, there are some notable exceptions. Deaths hepatitis B vaccination in children, where the effect of due to drug use disorders have risen sharply over the intervention cannot be expected until several decades past decade. In 2019, more than half of all global overdose later, removal of hepatitis C virus in the adult population deaths occurred in the USA. Liberal prescribing of high- leads to more immediate health impact. dose opioids, inadequate provision of opioid substitution In children younger than 10 years, the decline in therapy, and the lacing of street drugs with highly neonatal disorders was slower than for the major potent opioids such as fentanyl are considered major infectious diseases, thus increasing neonatal disorders’ 14–17 contributors to this public health crisis. By contrast, a share of total DALYs. Among injuries in this age group, positive, rapid change in disease rates has taken place in drowning saw the largest decline in DALYs. The position Egypt, where close to 80% of the population aged 12 years of congenital syphilis among the top ten causes of and older has been screened for hepatitis C, and those DALYs in children is indicative of health system failure. with detectable virus are treated with a low-cost treatment With testing and treatment in the second trimester of 18,19 23 regimen. We estimated that the number of cases of pregnancy, this cause could be eliminated. The main chronic hepatitis C has dropped by 65·9% (95% UI reasons for failure are limited access to health services, 51·1–79·7) since screening and treatment were initiated the low use of rapid diagnostic tests, the failure of through regular health services in 2014 and an enhanced antenatal clinics to screen or treat when a woman is www.thelancet.com Vol 396 October 17, 2020 1215 Annualised rate of change in DALY rate Global Health Metrics tested positive, and the recent global shortage of is mitigating the pathway to reducing the burden of 24 37,38 benzathine penicillin, the treatment of choice. Despite cardiovascular diseases. In the 25–49-year age group, the large health gains among children younger than tuberculosis that is not associated with HIV infection 10 years, considerable burdens still remain in sub- ranked among the top ten causes in 2019. There are Saharan Africa. Sustaining the global pace of progress similar worries about sustained global funding of will become more challenging as an ever-increasing tuberculosis control as mentioned for HIV/AIDS, let proportion of the global birth cohort is born in sub- alone having the additional resources and research Saharan Africa, with the highest rates of burden in development effort that would be required to reach these age groups. It is encouraging, however, that the WHO’s goals to reduce the 2015 levels of tuberculosis largest decreases in DALY rates globally have occurred in deaths and incidence by 90% and 80%, respectively, by 39–41 sub-Saharan African countries, such as Ethiopia, Angola, the year 2030. Rwanda, and Malawi, although there are others that The prominent rankings of COPD and lung cancer have seen much less progress. in the 50–74-year and 75-years-and-older age groups Among the top ten causes of DALYs in adolescents emphasise the continuing need for tobacco-control aged 10–24 years, self-harm had the largest decline measures and attention to reducing exposure to indoor (28·4% [95% UI 18·9–36·3]) over the study period. The and outdoor air pollution. Already, low-income and prevalence of depressive disorders and other mental middle-income countries account for 62·6% of the disorders, which are major underlying causes of self- global burden of COPD and lung cancer, and this share harm, did not change, sug gesting that the decline in is likely to increase sharply over coming decades due to self-harm deaths was largely due to other factors such as ageing populations and less successful tobacco and air better access to mental health services, urbanisation, and pollution control. The finding that lung cancer DALY 27–30 a reduction in access to more lethal means of suicide. rates are declining in the 50–74-year age group but not in The increase in DALY rates of neonatal disorders in this those aged 75 years and older is probably due to a cohort age group is a downside to the large improvements in effect; this could be encouraging if it reflects a greater neonatal survival, causing a greater proportion of the response to tobacco control in younger generations that surviving babies to have long-term neurological and will drive further declines in coming years. Chronic sensory deficits. kidney disease is strongly linked to cardiovascular In the 25–49-year age group, HIV/AIDS was the second diseases and diabetes, and shares common risks and leading cause of DALYs in 2019 despite a drop since 2005, intervention approaches. Given its prominent position when ART became more widely available. To be on in the top ten rankings of DALYs in older age groups and course to end HIV/AIDS as a public health threat the costs associated with end-stage kidney disease by 2030, UNAIDS estimates that a substantial increase in treatments, screening and low-cost treatments at earlier global funding would be required, whereas high-income stages of chronic kidney disease should be more widely 32 43 countries have reduced their funding. The prominent implemented. Cirrhosis ranked seventh among those position of headache disorders in the DALY rankings in aged 50–74 years in 2019. With low-cost treatments the 10–24-year and 25–49-year age groups has received available to low-income and middle-income countries, little attention in global health policy debates. While there is an opportunity to eradicate hepatitis C as an there is no cure for these disorders, there are effective underlying cause—a strategy that Egypt is well on the 33 19 symptomatic and preventive treatments available. way to achieving in coming years. Child hood vac- Ischaemic heart disease, stroke, and diabetes were not cinations for hepatitis B will eventually also reduce among the 25 leading causes in the two younger age cirrhosis (and liver cancer) outcomes, but the full effect groups, but emerged as major contributors to burden in will probably not be apparent for years. Alcohol is the the 25–49-year age group and, more prominently, in the third modifiable cause of cirrhosis; there is strong older age groups that follow. These diseases share many evidence that taxation and regulations can reduce alcohol common risk factors and treatment approaches. The use to less harmful levels. Age-related hearing loss is a burden in high-income countries has been rapidly top ten cause of DALYs in the two older age groups. declining since the 1980s, but a more recent downturn in While some reduction in burden can be achieved by this decline over the past 5 years has been noted as an control of loud noises during leisure or occupational important explanation for the slowdown in life expectancy activities, most of the burden cannot be prevented gains. Low-income and middle-income countries still through currently known strategies. For a large propor- have ample opportunity to make greater use of known tion of the elderly, hearing aids can relieve some of the effective intervention strategies (tobacco control, blood symptoms and associated social isolation. The quality of pressure-lowering and cholesterol-lowering treatments, hearing aids has improved over the past decade, but low- and emergency response and treatment for acute events) cost appliances are not readily available in low-income 35 45 that have been so effective in high-income countries. and middle-income countries. However, the rising prevalence of diabetes, linked to the Alzheimer’s disease and other dementias, and falls are almost ubiquitous increase in body-mass index globally, two causes that appear in the top ten ranking of DALYs 1216 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics only for those aged 75 years and older. The ability to violence in Latin American countries, in addition to intervene by prevention or treatment for dementia is still the decelerated decline of cardiovascular mortality, are limited despite a large research and development effort driving the patterns in these locations. The mix of to identify drugs, but efforts continue. There is good universal and more geographically specific influences on evidence that a range of modifiable risks (tobacco, health reinforces the need for regular, detailed reporting physical inactivity, metabolic risks, and hearing loss) on population health by underlying cause to help decision 47,48 contribute to the development of dementia, but little makers to identify success stories of disease control, as evidence of the effectiveness of interventions addressing well as opportunities to improve and emulate countries 47,49 these risk factors. Falls in the elderly are common and that are performing well. linked to psychotropic and cardiovascular medications, 51,52 cognitive impairment, depression, and general frailty. Limitations There is evidence for the effectiveness of multifactorial The major limitation of the GBD analysis of the burden interventions combining education, exercise, and home of diseases and injuries is the availability of primary data. safety modification interventions. Where data are not available, the results depend on the The trend towards disability as an increasing share of out-of-sample predictive validity of the modelling efforts. overall burden has continued. In 11 countries, more than While improvements to data processing and modelling half the burden was from YLDs of NCDs and injuries in can lead to incremental improvements in the accuracy of 2019. To some extent, the absence of a discernible trend our estimates, fundamental improvements require more in disability might be an artifact of the poor availability of and better primary data collection. Even when data are data on severity, and, therefore, an inability to quantify available, they might not have been obtained using the effect of health service interventions that modulate the preferred case definition or measurement method. severity. The larger issue, however, is that most of the The more explicit identification of the preferred and focus of global public health has been on life-saving alternative measurement method for each outcome, and 7,54,55 interventions directed at the main causes of death. the bias mapping from alternative to reference method The large contributors to disability, such as muscu- undertaken as part of GBD 2019, have led to greater loskeletal conditions and mental disorders, are associated stability in data adjustments. These improvements will with few deaths. As disability becomes an increasingly also aid in identifying priorities for data collection and large component of disease burden and, as importantly, in determining preferred case definitions and study a larger component of health expenditure, a greater methods. Moving to use of standard locations for research development investment is needed to identify estimating fixed effects in the models will aid in cycle-to- 56–58 new, and more effective, intervention strategies. With cycle stability of models. Through the use of standard a rapidly ageing global population, the demands on locations, the addition of more subnational units in a health services to deal with disabling outcomes, which given GBD cycle should not shift the regression model increase with age, will require policy makers to anticipate predictions as much as they previously would have. these changes. GBD provides key information on the Nevertheless, collinearity between covariates in some of changes in types of health services in terms of facilities these models might contribute to some instability in and adequately trained personnel that will be needed. fixed effects between cycles. Future work on ensemble The finding that health gains in countries at the lower models might help to solve the collinearity problem. Of end of the SDI scale have, on average, accelerated over the note, because the cause of death models developed using past decade compared with the two decades before CODEm are an ensemble of all high-performing possible indicates the potential for low-income countries to make models, they avoid the instability due to collinearity. a real difference by investing in health. Progress, however, Although our statistical modelling is designed to capture has been uneven. The more recent downturn in reduc- uncertainty from stochastic variation in input data, age tions in DALY rates in countries and territories with and sex splitting of data, corrections for alternative case higher SDI is striking and near universal, although an definitions or uninformative cause of death codes, other actual reversal into increases of age-standardised DALY data manipulations, and model choice, it remains a rates has only happened in a small number of countries challenge to fully represent the UIs around estimates, in the Caribbean and the USA. Plausible drivers of this particularly in locations with sparse or absent data. This change include obesity, diminishing potential for further will remain a major focus of GBD by tapping into existing reductions in smoking, and improvements in coverage of knowledge in other estimation fields as well as our own treatments for high blood pressure and cholesterol to development of methods. maintain the past declines in cardiovascular mortality. The shift to adjusting dementia deaths to reflect Inequalities in access to preventive and curative services only those with end-stage disease is conceptually more by lower socioeconomic groups might be a further appealing than the past crude adjustment for the large obstacle to continued improvements in cardiovascular variation in coding practices. We will, however, need to mortality. The large increase in drug overdose deaths in replicate the methods of determining the share of excess the USA and the increasing number of deaths from mortality in people with dementia who are in the last www.thelancet.com Vol 396 October 17, 2020 1217 Global Health Metrics stages of the disease and for whom an assignment of some high SDI countries points out that further gains dementia as the underlying cause of death is therefore are not inevitable. Close monitoring of health trends and justified. A greater focus in future rounds of GBD will careful policy evaluation of the options to counteract need to be directed to identifying data of treatment effects adverse trends is required. Leading causes of DALYs, as on severity distributions of the large contributors to well as solutions, differ substantially across age groups, YLDs, such as mental, neurological, and musculoskeletal highlighting the need to formulate policy for different disorders, for which we currently do not distinguish phases of the life course. geographical variation in severity. This is of particular Contributors Please see appendix 1 for more detailed information about individual importance as these conditions represent an increasing authors’ contributions to the research, divided into the following share of total burden. Our effort to improve the consis - categories: managing the estimation process; writing the first draft of tency between mortality rates, prevalence, and incidence the manuscript; providing data or critical feedback on data sources; for selected conditions by providing more explicit developing methods or computational machinery; applying analytical methods to produce estimates; providing critical feedback on methods or guidance on excess mortality rates in DisMod-MR has results; drafting the work or revising it critically for important revealed that more attention will be required in future intellectual content; extracting, cleaning, or cataloguing data; designing rounds of GBD. After imposing a pattern of excess or coding figures and tables; and managing the overall research mortality that follows an expected pattern of lower rates enterprise. in countries with better health systems, the models might Declaration of interests predict prevalence or incidence estimates that are far I N Ackerman reports grants from Victorian Government, outside of the submitted work. C A T Antonio reports personal fees from Johnson & removed from observed data. The challenge is then to Johnson (Philippines), outside of the submitted work. E Beghi reports identify whether the inconsistency is due to error in the grants from Italian Ministry of Health and SOBI and personal fees from cause of death estimates, the non-fatal data sources, or a Arvelle Therapeutics, outside of the submitted work. Y Béjot reports combination of the two. In addition to these general personal fees from AstraZeneca, Bristol Myers Squibb, Pfizer, and Medtronic, Merck Sharpe & Dohme, and Amgen; grants and personal limitations, there are many limitations for each specific fees from Boehringer-Ingelheim; personal fees and non-financial modelling exercise reported in this study. Appendix 1 support from Servier; and non-financial support from Biogen, outside of (sections 3.4 and 4.12) provides more insight into some of the submitted work. P S Briant reports personal fees from WHO, these issues. outside of the submitted work. H Christensen reports personal fees from Bristol Myers Squibb, Bayer, and Boehringer Ingelheim, outside of the submitted work. L Degenhardt reports grants from Indivior and Future directions Seqirus, outside of the submitted work. S J Dunachie reports grants Several method improvements signalled in previous from the Fleming Fund at the UK Department of Health and Social GBD publications have not yet been implemented but Care, during the conduct of the study. L M Haile reports personal fees from WHO, outside of the submitted work. S M S Islam reports grants remain a priority. For instance, DisMod-AT, a new version from National Heart Foundation of Australia and Deakin University, of our main non-fatal modelling tool that simultaneously during the conduct of the study. S L James reports grants from Sanofi solves for patterns over age and time, is still undergoing Pasteur and employment from Genentech, outside of the submitted testing before it can be implemented in GBD. Methods to work. P Jeemon reports and Clinical and Public Health intermediate fellowship (grant number IA/CPHI/14/1/501497) from the Wellcome make dependent comorbidity corrections computationally Trust—Department of Biotechnology, India Alliance (2015–2020). V Jha feasible, and imposing greater variation in severity reports grants from Baxter Healthcare, GlaxoSmithKline, Zydus Cadilla, distributions based on access to and quality of health NephroPlus, and Biocon, outside of the submitted work. J J Jozwiak care, are also still under development. More generally, reports personal fees from Amgen, ALAB Laboratoria, Teva, Synexus, and Boehringer Ingelheim, outside of the submitted work. imposing GBD principles and methods to the estimation S V Katikireddi reports grants from NRS Senior Clinical Fellowship, of access to health interventions and the effectiveness Scottish Government Chief Scientist Office, and the UK Medical thereof, and being able to link those estimates with our Research Council, during the conduct of the study. S Lewington reports grants from the UK Medical Research Council and the CDC Foundation future health scenario platform is a direction we are (with support from Amgen), outside of the submitted work. K J Looker keen to take. Developing this comprehensively is a large reports grants from WHO and GlaxoSmithKline, outside of the endeavour that will take many years to complete. As this submitted work. S Lorkowski reports personal fees from Akcea would greatly add value to the policy relevance of GBD, Therapeutics, Amedes, Amgen, Berlin-Chemie, Boehringer Ingelheim Pharma, Daiichi Sankyo, Merck Sharp & Dohme, Novo Nordisk, Sanofi- we will also aim to develop less comprehensive methods Aventis, Synlab, Unilever, and Upfield and non-financial support from that will nevertheless allow us to respond to policy makers Preventicus, outside of the submitted work. R A Lyons reports grants seeking information on major policy decisions in a more from Health Data Research UK, outside of the submitted work. timely fashion. J Massano reports personal fees from Abbvie, Bial, Merck Sharp & Dohme, and Zambon and other support from Boston Scientific, GE Healthcare, Medtronic, and Roche, outside of the submitted work. Conclusion W Mendoza is a Program Analyst in Population and Development at the Taking into account population growth and shifts in age UN Population Fund Country Office in Peru, an institution that does not structure, health continues to improve at the global level. necessarily endorse this study. M Moradi-Lakeh reports personal fees from Novartis, outside of the submitted work. J F Mosser reports grants The absolute burden of disease and its associated impact from the Bill & Melinda Gates Foundation, during the conduct of the on health systems, however, remain resolutely constant. study. S Nomura reports grants from the Japanese Ministry of Some diseases, such as diabetes, are increasing in Education, Culture, Sports, Science, and Technology. S B Patten reports burden, and more general all-cause DALY stagnation in funding from the Cuthbertson & Fischer Chair in Pediatric Mental 1218 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics Health, during the conduct of the study. T Pilgrim reports grants and Retirement Cohort was funded by the US National Institute on Aging personal fees from Biotronik and Boston Scientific, grants from Edwards (grant R01AG031716). The principal investigators are Luis Rosero-Bixby Lifesciences, and personal fees from HighLife SAS for his work as a and William H Dow and co-principal investigators are Xinia Fernández member of clinical event committee for a study sponsored by HighLife and Gilbert Brenes. The accuracy of the authors’ statistical analysis and SAS, outside of the submitted work. M J Postma reports grants and the findings they report are not the responsibility of ECDC. ECDC is not personal fees from Merck Sharp & Dohme, GlaxoSmithKline, Pfizer, responsible for conclusions or opinions drawn from the data provided. Boehringer Ingelheim, Novavax, Bristol Myers Squibb, AstraZeneca, ECDC is not responsible for the correctness of the data and for data Sanofi, IQVIA, and Seqirus; personal fees from Quintiles, Novartis, management, data merging and data collation after provision of the data. and Pharmerit; grants from Bayer, BioMerieux, WHO, EU, FIND, ECDC shall not be held liable for improper or incorrect use of the data. Antilope, DIKTI, LPDP, and Budi; and other support from Ingress The Health Behaviour in School-Aged Children (HBSC) study is an Health, PAG, and Asc Academics, outside of the submitted work. international study carried out in collaboration with WHO/EURO. E Pupillo reports grants from AIFA, outside of the submitted work. The international coordinator of the 1997–98, 2001–02, 2005–06, A E Schutte reports personal fees from Omron, Servier, Novartis, and 2009–10 surveys was Candace Currie and the databank manager for Takeda, and Abbott, outside of the submitted work. M G Shrime reports the 1997–98 survey was Bente Wold, whereas for the following surveys grants from Damon Runyon Cancer Research Foundation and Mercy Oddrun Samdal was the databank manager. A list of principal Ships, outside of the submitted work. J A Singh reports personal fees investigators in each country can be found on the HBSC website. For the HBSC website see from Crealta/Horizon, Medisys, Fidia, UBM, Trio Health, Medscape, Data used in this paper come from the 2009–10 Ghana Socioeconomic http://www.hbsc.org WebMD, Clinical Care Options, Clearview Healthcare Partners, Putnam Panel Study Survey, which is a nationally representative survey of more Associates, Spherix, Practice Point Communications, National Institutes than 5000 households in Ghana. The survey is a joint effort undertaken of Health, and the American College of Rheumatology; personal fees by the Institute of Statistical, Social and Economic Research (ISSER) at from Simply Speaking; other support from Amarin Pharmaceuticals and the University of Ghana and the Economic Growth Centre (EGC) at Yale Viking Pharmaceuticals; and non-financial support from the steering University. It was funded by EGC. ISSER and the EGC are not committee of OMERACT (an international organisation that develops responsible for the estimations reported by the analysts. The Palestinian measures for clinical trials and receives arm’s length funding from Central Bureau of Statistics granted the researchers access to relevant 12 pharmaceutical companies), US Food and Drug Administration, data in accordance with license number SLN2014-3-170, after subjecting Arthritis Advisory Committee, Veterans Affairs Rheumatology Field data to processing aiming to preserve the confidentiality of individual Advisory Committee, and the editor and director of the UAB Cochrane data in accordance with the General Statistics Law, 2000. The researchers Musculoskeletal Group Satellite Center on Network Meta-analysis, are solely responsible for the conclusions and inferences drawn upon outside of the submitted work. S T S Skou reports personal fees from available data. Data for this research was provided by MEASURE Journal of Orthopaedic & Sports Physical Therapy and Munksgaard and Evaluation, funded by USAID. The authors thank the Russia grants from The Lundbeck Foundation, outside of the submitted work; Longitudinal Monitoring Survey, conducted by the National Research and being co-founder of GLA:D. GLA:D is a non-profit initiative hosted University Higher School of Economics and ZAO Demoscope together at University of Southern Denmark aimed at implementing clinical with Carolina Population Center, University of North Carolina at guidelines for osteoarthritis in clinical practice. J D Stanaway reports Chapel Hill and the Institute of Sociology, Russia Academy of Sciences grants from Bill & Melinda Gates Foundation, during the conduct of the for making data available. This paper uses data from the Bhutan 2014 study. R Uddin reports travel and accommodation reimbursement from STEPS survey, implemented by the Ministry of Health with the support Deakin University Institute for Physical Activity and Nutrition, outside of WHO; the Kuwait 2006 and 2014 STEPS surveys, implemented by the of the submitted work. All other authors declare no competing interests. Ministry of Health with the support of WHO; the Libya 2009 STEPS survey, implemented by the Secretariat of Health and Environment with Data sharing the support of WHO; the Malawi 2009 STEPS survey, implemented by For the Global Health Data To download the data used in these analyses, please visit the Global Ministry of Health with the support of WHO; and the Moldova 2013 Exchange GBD 2019 website Health Data Exchange GBD 2019 website. STEPS survey, implemented by the Ministry of Health, the National see http://ghdx.healthdata.org/ Acknowledgments Bureau of Statistics, and the National Center of Public Health with the gbd−2019 Research reported in this publication was supported by the Bill & support of WHO. This paper uses data from Survey of Health, Ageing For more on SHARE see Melinda Gates Foundation; the University of Melbourne; Queensland and Retirement in Europe (SHARE) Waves 1 (DOI:10.6103/SHARE. http://www.share-project.org Department of Health, Australia; the National Health and Medical w1.700), 2 (10.6103/SHARE.w2.700), 3 (10.6103/SHARE.w3.700), Research Council, Australia; Public Health England; the Norwegian 4 (10.6103/SHARE.w4.700), 5 (10.6103/SHARE.w5.700), Institute of Public Health; St Jude Children’s Research Hospital; 6 (10.6103/SHARE.w6.700), and 7 (10.6103/SHARE.w7.700); the Cardiovascular Medical Research and Education Fund; the National see Börsch-Supan and colleagues (2013) for methodological details. Institute on Ageing of the National Institutes of Health (award The SHARE data collection has been funded by the European P30AG047845); and the National Institute of Mental Health of the Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: National Institutes of Health (award R01MH110163). The content is RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: solely the responsibility of the authors and does not necessarily CIT4-CT-2006-028812), FP7 (SHARE-PREP: GA N°211909, represent the official views of the funders. The authors alone are SHARE-LEAP: GA N°227822, SHARE M4: GA N°261982) and Horizon responsible for the views expressed in this Article and they do not 2020 (SHARE-DEV3: GA N°676536, SERISS: GA N°654221) and by necessarily represent the views, decisions, or policies of the institutions DG Employment, Social Affairs & Inclusion. Additional funding from with which they are affiliated, the National Health Service (NHS), the German Ministry of Education and Research, the Max Planck Society the National Institute for Health Research (NIHR), the UK Department for the Advancement of Science, the US National Institute on Aging of Health and Social Care, or Public Health England; the United States (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, Agency for International Development (USAID), the US Government, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064, or MEASURE Evaluation; or the European Centre for Disease HHSN271201300071C), and from various national funding sources is Prevention and Control (ECDC). This research used data from the Chile gratefully acknowledged. This study has been realised using the data National Health Survey 2003, 2009–10, and 2016–17. The authors are collected by the Swiss Household Panel, which is based at the Swiss grateful to the Ministry of Health, the survey copyright owner, for Centre of Expertise in the Social Sciences. The project is financed by the allowing them to have the database. All results of the study are those of Swiss National Science Foundation. The United States Aging, the authors and in no way committed to the Ministry. The Costa Rican Demographics, and Memory Study is a supplement to the Health and Longevity and Healthy Aging Study project is a longitudinal study by the Retirement Study (HRS), which is sponsored by the National Institute of University of Costa Rica’s Centro Centroamericano de Población and Aging (grant number NIA U01AG009740). It was conducted jointly by Instituto de Investigaciones en Salud, in collaboration with the Duke University and the University of Michigan. The HRS is sponsored University of California at Berkeley. The original pre-1945 cohort was by the National Institute on Aging (grant number NIA U01AG009740) funded by the Wellcome Trust (grant 072406), and the 1945–55 and is conducted by the University of Michigan. This paper uses data www.thelancet.com Vol 396 October 17, 2020 1219 Global Health Metrics from Add Health, a program project designed by J Richard Udry, Competitiveness Program (project ID P_40_382). P Hoogar acknowledges Peter S Bearman, and Kathleen Mullan Harris, and funded by a grant Centre for Bio Cultural Studies, Directorate of Research, Manipal P01-HD31921 from the Eunice Kennedy Shriver National Institute of Academy of Higher Education and Centre for Holistic Development and Child Health and Human Development, with cooperative funding from Research, Kalaghatagi. F N Hugo acknowledges the Visiting 17 other agencies. Special acknowledgment is due to Ronald R Rindfuss Professorship, PRINT Program, CAPES Foundation, Brazil. B-F Hwang and Barbara Entwisle for assistance in the original design. Information was supported by China Medical University (CMU107-Z-04), Taichung, For the Add Health website see on how to obtain the Add Health data files is available on the Add Health Taiwan. S M S Islam was funded by a National Heart Foundation Senior http://www.cpc.unc.edu/ website. No direct support was received from grant P01-HD31921 for this Research Fellowship and supported by Deakin University. R Q Ivers was addhealth analysis. The data reported here have been supplied by the United States supported by a research fellowship from the National Health and Medical Renal Data System. The interpretation and reporting of these data are Research Council of Australia. M Jakovljevic acknowledges the Serbian the responsibility of the authors and in no way should be seen as an part of this GBD-related contribution was co-funded through Grant official policy or interpretation of the US Government. Collection of data OI175014 of the Ministry of Education Science and Technological for the Mozambique National Survey on the Causes of Death 2007–08 was Development of the Republic of Serbia. P Jeemon was supported by a made possible by USAID under the terms of cooperative agreement Clinical and Public Health intermediate fellowship (grant number GPO-A-00-08-000_D3-00. This manuscript is based on data collected and IA/CPHI/14/1/501497) from the Wellcome Trust—Department of shared by the International Vaccine Institute (IVI) from an original study Biotechnology, India Alliance (2015–20). O John is a recipient of UIPA IVI conducted. L G Abreu acknowledges support from Coordenação de scholarship from University of New South Wales, Sydney. S V Katikireddi Aperfeiçoamento de Pessoal de Nível Superior (Brazil; finance code 001) acknowledges funding from a NRS Senior Clinical Fellowship and Conselho Nacional de Desenvolvimento Científico e Tecnológico (SCAF/15/02), the Medical Research Council (MC_UU_12017/13, (CNPq, a Brazilian funding agency). I N Ackerman was supported by a MC_UU_12017/15), and the Scottish Government Chief Scientist Office Victorian Health and Medical Research Fellowship awarded by the (SPHSU13, SPHSU15). C Kieling is a CNPq researcher and a UK Victorian Government. O O Adetokunboh acknowledges the South Academy of Medical Sciences Newton Advanced Fellow. Y J Kim was African Department of Science and Innovation and the National Research supported by Research Management Office, Xiamen University Malaysia Foundation. A Agrawal acknowledges the Wellcome Trust DBT India (XMUMRF/2018-C2/ITCM/00010). K Krishan is supported by UGC Alliance Senior Fellowship. S M Aljunid acknowledges the Department of Centre of Advanced Study awarded to the Department of Anthropology, Health Policy and Management, Faculty of Public Health, Kuwait Panjab University, Chandigarh, India. M Kumar was supported by University and International Centre for Casemix and Clinical Coding, K43 TW 010716 FIC/NIMH. B Lacey acknowledges support from the Faculty of Medicine, National University of Malaysia for the approval and NIHR Oxford Biomedical Research Centre and the BHF Centre of support to participate in this research project. M Ausloos, C Herteliu, Research Excellence, Oxford. J V Lazarus was supported by a Spanish and A Pana acknowledge partial support by a grant of the Romanian Ministry of Science, Innovation and Universities Miguel Servet grant National Authority for Scientific Research and Innovation, (Instituto de Salud Carlos III [ISCIII]/ESF, the EU [CP18/00074]). CNDS-UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084. K J Looker thanks the NIHR Health Protection Research Unit in A Badawi is supported by the Public Health Agency of Canada. Evaluation of Interventions at the University of Bristol, in partnership D A Bennett was supported by the NIHR Oxford Biomedical Research with Public Health England, for research support. S Lorkowski was Centre. R Bourne acknowledges the Brien Holden Vision Institute, funded by the German Federal Ministry of Education and Research University of Heidelberg, Sightsavers, Fred Hollows Foundation, (nutriCARD, grant agreement number 01EA1808A). R A Lyons is and Thea Foundation. G B Britton and I Moreno Velásquez were supported by Health Data Research UK (HDR-9006), which is funded by supported by the Sistema Nacional de Investigación, SNI-SENACYT, the UK Medical Research Council, Engineering and Physical Sciences Panama. R Buchbinder was supported by an Australian National Health Research Council, Economic and Social Research Council, NIHR and Medical Research Council (NHMRC) Senior Principal Research (England), Chief Scientist Office of the Scottish Government Health and Fellowship. J J Carrero was supported by the Swedish Research Council Social Care Directorates, Health and Social Care Research and (2019-01059). F Carvalho acknowledges UID/MULTI/04378/2019 and Development Division (Welsh Government), Public Health Agency UID/QUI/50006/2019 support with funding from FCT/MCTES through (Northern Ireland), British Heart Foundation, and Wellcome Trust. national funds. A R Chang was supported by National Institutes of J J McGrath is supported by the Danish National Research Foundation Health/National Institute of Diabetes and Digestive and Kidney Diseases (Niels Bohr Professorship), and the Queensland Health Department grant K23 DK106515. V M Costa acknowledges the grant (via West Moreton HHS). P T N Memiah acknowledges support from SFRH/BHD/110001/2015, received by Portuguese national funds through CODESRIA. U O Mueller gratefully acknowledges funding by the Fundação para a Ciência e Tecnologia, IP, under the Norma Transitária German National Cohort Study BMBF grant number 01ER1801D. DL57/2016/CP1334/CT0006. A Douiri acknowledges support and funding S Nomura acknowledges the Ministry of Education, Culture, Sports, from the National Institute for Health Research Collaboration for Science, and Technology of Japan (18K10082). A Ortiz was supported by Leadership in Applied Health Research and Care South London at King’s ISCIII PI19/00815, DTS18/00032, ISCIII-RETIC REDinREN RD016/0009 College Hospital NHS Foundation Trust and the Royal College of Fondos FEDER, FRIAT, Comunidad de Madrid B2017/BMD-3686 Physicians, and support from the NIHR Biomedical Research Centre CIFRA2-CM. These funding sources had no role in the writing of the based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College manuscript or the decision to submit it for publication. S B Patten was London. B B Duncan acknowledges grants from the Foundation for the supported by the Cuthbertson & Fischer Chair in Pediatric Mental Health Support of Research of the State of Rio Grande do Sul (IATS and PrInt) at the University of Calgary. G C Patton was supported by an åNHMRC and the Brazilian Ministry of Health. H E Erskine is the recipient of an Senior Principal Research Fellowship. M R Phillips was supported in part Australian NHMRC Early Career Fellowship grant (APP1137969). by the National Natural Science Foundation of China (NSFC, number A J Ferrari was supported by a NHMRC Early Career Fellowship grant 81371502 and 81761128031). A Raggi, D Sattin, and S Schiavolin were (APP1121516). H E Erskine and A J Ferrari are employed by and supported by grants from the Italian Ministry of Health (Ricerca Corrente, A M Mantilla-Herrera and D F Santomauro affiliated with the Queensland Fondazione Istituto Neurologico C Besta, Linea 4—Outcome Research: Centre for Mental Health Research, which receives core funding from the dagli Indicatori alle Raccomandazioni Cliniche). P Rathi and Queensland Department of Health. M L Ferreira holds an NHMRC B Unnikrishnan acknowledge Kasturba Medical College, Mangalore, Research Fellowship. C Flohr was supported by the NIHR Biomedical Manipal Academy of Higher Education, Manipal. A L P Ribeiro was Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust. supported by Brazilian National Research Council, CNPq, and the M Freitas acknowledges financial support from the EU (European Minas Gerais State Research Agency, FAPEMIG. D C Ribeiro was Regional Development Fund [FEDER] funds through COMPETE supported by The Sir Charles Hercus Health Research Fellowship POCI-01-0145-FEDER-029248) and National Funds (Fundação para a (#18/111) Health Research Council of New Zealand. D Ribeiro Ciência e Tecnologia) through project PTDC/NAN-MAT/29248/2017. acknowledges financial support from the EU (FEDER funds through the A L S Guimaraes acknowledges support from CNPq. C Herteliu was Operational Competitiveness Program; POCI-01-0145-FEDER-029253). partially supported by a grant co-funded by FEDER through Operational P S Sachdev acknowledges funding from the NHMRC of Australia 1220 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics Program Grant. A M Samy was supported by a fellowship from the 14 Jones GH, Bruera E, Abdi S, Kantarjian HM. The opioid epidemic Egyptian Fulbright Mission Program. M M Santric-Milicevic acknowledges in the United States—overview, origins, and potential solutions. Obstet Gynecol Surv 2019; 74: 278. the Ministry of Education, Science and Technological Development of the 15 Lyden J, Binswanger IA. The United States opioid epidemic. Republic of Serbia (contract number 175087). R Sarmiento-Suárez received Semin Perinatol 2019; 43: 123–31. institutional support from Applied and Environmental Sciences University 16 National Institute on Drug Abuse. Opioid overdose crisis. (Bogotá, Colombia) and ISCIII (Madrid, Spain). A E Schutte received February, 2020. https://www.drugabuse.gov/drugs-abuse/opioids/ support from the South African National Research Foundation SARChI opioid-overdose-crisis (accessed Feb 21, 2020). Initiative (GUN 86895) and Medical Research Council. S T S Skou is 17 Public Health Agency of Canada. Apparent opioid-related deaths in currently funded by a grant from Region Zealand (Exercise First) and a Canada. December, 2019. https://health-infobase.canada.ca/ grant from the European Research Council under the EU’s Horizon 2020 substance-related-harms/opioids/ (accessed Dec 3, 2019). research and innovation program (grant agreement number 801790). 18 Soliman G, Elzalabany MS, Hassanein T, Miller FD. Mass screening J B Soriano is funded by Centro de Investigación en Red de Enfermedades for hepatitis B and C in Southern Upper Egypt. BMC Public Health Respiratorias, ISCIII. R Tabarés-Seisdedos was supported in part by the 2019; 19: 1326. national grant PI17/00719 from ISCIII–FEDER. N Taveira was partially 19 Abdel-Razek W, Hassany M, Kabil K, et al. The world’s largest supported by the European & Developing Countries Clinical Trials hepatitis C screening program in Egypt. 2019. https://www. 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Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Vos, Theo; Lim, Stephen S; Abbafati, Cristiana; Abbas, Kaja M; Abbasi, Mohammad; Abbasifard, Mitra; Abbasi-Kangevari, Mohsen; Abbastabar, Hedayat; Abd-Allah, Foad; Abdelalim, Ahmed; Abdollahi, Mohammad; Abdollahpour, Ibrahim; Abolhassani, Hassan; Aboyans, Victor; Abrams, Elissa M; Abreu, Lucas Guimarães; Abrigo, Michael R M; Abu-Raddad, Laith Jamal; Abushouk, Abdelrahman I; Acebedo, Alyssa; Ackerman, Ilana N; Adabi, Maryam; Adamu, Abdu A; Adebayo, Oladimeji M; Adekanmbi, Victor; Adelson, Jaimie D; Adetokunboh, Olatunji O; Adham, Davoud; Afshari, Mahdi; Afshin, Ashkan; Agardh, Emilie E; Agarwal, Gina; Agesa, Kareha M; Aghaali, Mohammad; Aghamir, Seyed Mohammad Kazem; Agrawal, Anurag; Ahmad, Tauseef; Ahmadi, Alireza; Ahmadi, Mehdi; Ahmadieh, Hamid; Ahmadpour, Ehsan; Akalu, Temesgen Yihunie; Akinyemi, Rufus Olusola; Akinyemiju, Tomi; Akombi, Blessing; Al-Aly, Ziyad; Alam, Khurshid; Alam, Noore; Alam, Samiah; Alam, Tahiya; Alanzi, Turki M; Albertson, Samuel B; Alcalde-Rabanal, Jacqueline Elizabeth; 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Abstract

Global Health Metrics Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019 GBD 2019 Diseases and Injuries Collaborators* Summary Lancet 2020; 396: 1204–22 Background In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries This online publication has been along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global corrected. The corrected version Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, first appeared at thelancet.com publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and on October 23, 2020 collectively exhaustive list of diseases and injuries. *For the list of Collaborators see Viewpoint Lancet 2020; Methods GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and 396: 1135–59 disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Correspondence to: Prof Christopher J L Murray, Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, Institute for Health Metrics and health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific Evaluation, University of death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian Washington, Seattle, WA 98195, process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD USA [email protected] population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and development investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. 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. 1204 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics Research in context Evidence before this study distributions used in DisMod-MR, the Bayesian meta-regression The Global Burden of Diseases, Injuries, and Risk Factors Study tool used to simultaneously estimate incidence, prevalence, (GBD) 2017 reported on incidence, prevalence, and mortality remission, excess mortality, and cause-specific mortality, from 359 diseases and injuries. Information on prevalence and were revised on the basis of simulation studies showing that mortality was also analysed in terms of summary measures: less informative priors helped to improve the coverage of years of life lost (YLLs), years lived with disability (YLDs), uncertainty intervals. (5) Redistribution algorithms for sepsis, disability-adjusted life-years (DALYs), and healthy life heart failure, pulmonary embolism, acute kidney injury, hepatic expectancy. GBD is the only comprehensive assessment failure, acute respiratory failure, pneumonitis, and five providing time trends for a mutually exclusive and collectively intermediate causes in the central nervous system were revised exhaustive list of diseases and injuries. For the first time, according to an analysis of 116 million deaths that were GBD 2017 also produced internally consistent estimates of attributed to multiple causes. (6) Processing of clinical population, fertility, mortality, and migration by age, sex, informatics data on hospital and clinic visits was revised to and year for 1950–2017. GBD 2017 also included subnational better take into account differential access across locations to assessments for 16 countries at administrative level 1 and for health-care facilities. (7) To enhance the stability of models in local authorities in England. the presence of the addition of subnational data in different GBD cycles, we adopted a set of standard locations for the Added value of this study estimation of covariate effects in models. (8) 7333 national and GBD 2019 updates and expands beyond GBD 2017 in ten ways. 24 657 subnational vital registration systems, 16 984 published (1) The number of countries for which subnational assessments studies, and 1654 household surveys were used in the analysis, have been undertaken was expanded to include Italy, Nigeria, including many newly available data sources. (9) Results are Pakistan, the Philippines, and Poland. (2) 12 new causes were presented so as to integrate causes of death, incidence, added to the GBD modelling framework, including pulmonary prevalence, YLDs, YLLs, and DALYs into a comprehensive arterial hypertension, nine new sites of cancer, and two new assessment of each disease and injury. (10) Closer technical sites of osteoarthritis (hand and other joints). (3) For each coordination with WHO has led to the addition of nine WHO disease, the preferred or reference case definition or member states to the analysis and revisions of the analytical measurement method was clearly defined and stored in a approach for select diseases. database. For both risks and diseases, the statistical relationship between the alternative and reference measurement method Implications of all the available evidence was analysed using network meta-regression using only data GBD 2019 provides the most up-to-date assessment of the where two different approaches were measured in the same descriptive epidemiology of a mutually exclusive and collectively location–time period. Although statistical cross-walking exhaustive list of diseases and injuries for 204 countries and between alternative and reference definitions and territories from 1990 to 2019. The comprehensive nature of the measurement methods has been a feature in all GBD studies, assessment provides policy-relevant information on the trends the approach in GBD 2019 was highly standardised and used of major causes of burden globally, regionally, and by country or improved methods across diseases and risks. (4) Some prior territory. Introduction been released, to our knowledge. In this study, we The Global Burden of Diseases, Injuries, and Risk Factors summarise GBD methods and present integrated results Study (GBD) provides a systematic scientific assess ment on fatal and non-fatal outcomes for the GBD disease of published, publicly available, and contributed data on and injury hierarchical cause list. GBD 2019 includes disease and injury incidence, prevalence, and mortality estimation of numerous different models for disease and for a mutually exclusive and collectively exhaustive list injury outcomes. This Article provides a high-level over- 1–3 of diseases and injuries. In an era of shifting global view of our findings. Results are presented both broadly agendas and expanded emphasis on non-communicable and in detail for a selection of diseases, injuries, and diseases and injuries along with communicable diseases, impairments in two-page summaries with a standard set sound and up-to-date evidence on trends—both progress of tables and figures. and adverse patterns—by cause at the national level is essential to reflect effects of public health policy and Methods 4–7 medical care delivery. Overview GBD 2019 provides an opportunity to incorporate The general approach to estimating causes of death and newly available datasets, enhance method performance disease incidence and prevalence for GBD 2019 is the 2,3 and standardisation, and reflect changes in scientific same as for GBD 2017. Appendix 1 provides details on See Online for appendix 1 1–3 understanding. Since GBD 2017, no comprehensive the methods used to model each disease and injury. update of descriptive epidemiology levels and trends has Here, we provide an overview of the methods, with an www.thelancet.com Vol 396 October 17, 2020 1205 Global Health Metrics emphasis on the main methodology changes since framework: pulmonary arterial hypertension, eye cancer, GBD 2017. soft tissue and other extraosseous sarcomas, malignant For each iteration of GBD, the estimates for the whole neoplasm of bone and articular cartilage, and neuro- time series are updated on the basis of addition of new blastoma and other peripheral nervous cell tumours at data and change in methods where appropriate. Thus, Level 3, and hepatoblastoma, Burkitt lymphoma, other the GBD 2019 results supersede those from previous non-Hodgkin lymphoma, retinoblastoma, other eye can- rounds of GBD. cers, and two sites of osteoarthritis (hand and other joints) GBD 2019 complies with the Guidelines for Accurate at Level 4. and Transparent Health Estimates Reporting (GATHER) statement (appendix 1 section 1.4). Analyses were com- Data pleted with Python version 3.6.2, Stata version 13, and The GBD estimation process is based on identifying For the statistical code see R version 3.5.0. Statistical code used for GBD estimation multiple relevant data sources for each disease or injury http://ghdx.healthdata.org/gbd- is publicly available online. including censuses, household surveys, civil registration 2019/code and vital statistics, disease registries, health service use, Geographical units, age groups, time periods, and cause air pollution monitors, satellite imaging, disease notifi - levels cations, and other sources. Each of these types of data are GBD 2019 estimated each epidemiological quantity of identified from systematic review of published studies, interest—incidence, prevalence, mortality, years lived searches of government and international organisation with disability (YLDs), years of life lost (YLLs), and websites, published reports, primary data sources such as disability-adjusted life-years (DALYs)—for 23 age the Demographic and Health Surveys, and contributions groups; males, females, and both sexes combined; and of datasets by GBD collaborators. 86 249 sources were 204 countries and territories that were grouped into used in this analysis, including 19 354 sources reporting 21 regions and seven super-regions. For GBD 2019, deaths, 31 499 reporting incidence, 19 773 reporting prev- nine countries and territories (Cook Islands, Monaco, alence, and 26 631 reporting other metrics. Each newly San Marino, Nauru, Niue, Palau, Saint Kitts and Nevis, identified and obtained data source is given a unique Tokelau, and Tuvalu) were added, such that the GBD identifier by a team of librarians and included in the For the GHDx see http://ghdx. location hierarchy now includes all WHO member Global Health Data Exchange (GHDx). The GHDx makes healthdata.org states. GBD 2019 includes subnational analyses for publicly available the metadata for each source included in Italy, Nigeria, Pakistan, the Philippines, and Poland, GBD as well as the data, where allowed by the data For the GHDx source tool see and 16 countries previously estimated at subnational provider. Readers can use the GHDx source tool to identify http://ghdx.healthdata.org/gbd- levels (Brazil, China, Ethiopia, India, Indonesia, Iran, which sources were used for estimating any disease or 2019/data-input-sources Japan, Kenya, Mexico, New Zealand, Norway, Russia, injury outcome in any given location. South Africa, Sweden, the UK, and the USA). All subnational analyses are at the first level of admin - Data processing istrative organisation within each country except for A crucial step in the GBD analytical process is correcting New Zealand (by Māori ethnicity), Sweden (by for known bias by redistributing deaths from unspecified Stockholm and non-Stockholm), the UK (by local gov- codes to more specific disease categories, and by adjusting ernment authorities), and the Philippines (by province). data with alternative case definitions or measurement In this publication, we present subnational estimates methods to the reference method. We highlight several for Brazil, India, Indonesia, Japan, Kenya, Mexico, major changes in data processing that in some cases have Sweden, the UK, and the USA; given space constraints, affected GBD results. See Online for appendix 2 these results are presented in appendix 2. At the most detailed spatial resolution, we generated estimates for Cause of death redistribution 990 locations. The GBD diseases and injuries analytical Vital registration with medical certification of cause of framework generated estimates for every year from death is a crucial resource for the GBD cause of death 1990 to 2019. analysis in many countries. Cause of death data obtained Diseases and injuries were organised into a levelled using various revisions of the International Classification cause hierarchy from the three broadest causes of death of Diseases and Injuries (ICD) were mapped to the and disability at Level 1 to the most specific causes at GBD cause list. Many deaths, however, are assigned to Level 4. Within the three Level 1 causes—communicable, causes that cannot be the underlying cause of death maternal, neonatal, and nutritional diseases; non-com mu- (eg, cardiopulmonary failure) or are inadequately speci- nicable diseases; and injuries—there are 22 Level 2 causes, fied (eg, injury from undetermined intent). These deaths 174 Level 3 causes, and 301 Level 4 causes (including were reassigned to the most probable underlying causes 131 Level 3 causes that are not further disaggregated at of death as part of the data processing for GBD. Level 4; see appendix 1 sections 3.4 and 4.12 for the full list Redistribution algorithms can be divided into three of causes). 364 total causes are non-fatal and 286 are fatal. categories: proportionate redistribution, fixed proportion For GBD 2019, 12 new causes were added to the modelling redistribution based on published studies or expert 1206 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics judgment, or statistical algorithms. For GBD 2019, data in the USA, Taiwan (province of China), New Zealand, for 116 million deaths attributed to multiple causes were and the Philippines, as well as USA Healthcare Cost and analysed to produce more empirical redistribution algo- Utilization Project inpatient data. Ratios of outpatient to rithms for sepsis, heart failure, pulmonary embolism, inpatient care for each cause were extracted from claims acute kidney injury, hepatic failure, acute respiratory data from the USA and Taiwan (province of China). failure, pneumonitis, and five intermediate causes The log of the ratios for each cause were modelled by (hydrocephalus, toxic encephalopathy, compres sion of age and sex using MR-BRT (Meta-Regression-Bayesian brain, encephalopathy, and cerebral oedema) in the Regularised Trimmed), the Bayesian meta-regression central nervous system. To redistribute unspecified tool. To account for the incomplete health-care access in injuries, we used a method similar to that of intermediate populations where not every person with a disease or cause redistribution, using the pattern of the nature of injury would be accounted for in administrative clinical injury codes in the causal chain where the ICD codes X59 records, we transformed the adjusted admission rates (“exposure to unspecified factor”) and Y34 (“unspecified using a scalar derived from the Healthcare Access and event, undetermined intent”) and GBD injury causes Quality Index. We used this approach to produce were the underlying cause of death. These new adjusted, standardised clinical data inputs. More details algorithms led to important changes in the causes to are provided in appendix 1 (section 4.3). which these intermediate outcomes were redistributed. Additionally, data on deaths from diabetes and stroke Modelling lack the detail on subtype in many countries; we ran For most diseases and injuries, processed data are regressions on vital registration data with at least 50% modelled using standardised tools to generate estimates of deaths coded specifically to type 1 or 2 diabetes and of each quantity of interest by age, sex, location, and year. ischaemic, haemorrhagic, or subarachnoid stroke to There are three main standardised tools: Cause of Death predict deaths by these subtypes when these were coded Ensemble model (CODEm), spatiotemporal Gaussian to unspecified diabetes or stroke. process regression (ST-GPR), and DisMod-MR. Previous 2,3,12 publications and the appendix provide more details on Correcting for non-reference case definitions or measurement these general GBD methods. Briefly, CODEm is a highly methods systematised tool to analyse cause of death data using an In previous cycles of GBD, data reported using alternative ensemble of different modelling methods for rates or case definitions or measurement methods were corrected cause fractions with varying choices of covariates that to the reference definition or measurement method perform best with out-of-sample predictive validity primarily as part of the Bayesian meta-regression models. testing. DisMod-MR is a Bayesian meta-regression tool For example, in DisMod-MR, the population data were that allows evaluation of all available data on incidence, simultaneously modelled as a function of country covar- prevalence, remission, and mortality for a disease, iates for variation in true rates and as a function of enforcing consistency between epidemiological para- indicator variables capturing alternative measurement meters. ST-GPR is a set of regression methods that methods. To enhance transparency and to standardise borrow strength between locations and over time for and improve methods in GBD 2019, we estimated single metrics of interest, such as risk factor exposure or correction factors for alternative case definitions or mortality rates. In addition, for select diseases, particularly measurement methods using network meta-regression, for rarer outcomes, alternative modelling strategies including only data where two methods were assessed in have been dev eloped, which are described in appendix 1 the same location–time period or in the exact same (section 3.2). population. This included validation studies where two In GBD 2019, we designated a set of standard locations methods had been compared in populations that were that included all countries and territories as well as not necessarily random samples of the general popu- the subnational locations for Brazil, China, India, and lation. Details on the correction factors from alternative the USA. Coefficients of covariates in the three main to reference measurement methods are provided in modelling tools were estimated for these standard appendix 1 (section 4.4.2). locations only—ie, we ignored data from subnational locations other than for Brazil, China, India, and the USA Clinical informatics (appendix 1 section 1.1). Using this set of standard Clinical informatics data include inpatient admissions, locations will prevent changes in regression coefficients outpatient (including general practitioner) visits, and from one GBD cycle to the next that are solely due to the health insurance claims. Several data processing steps addition of new subnational units in the analysis that were undertaken. Inpatient hospital data with a single might have lower quality data or small populations diagnosis only were adjusted to account for non- (appendix 1 section 1.1). Changes to CODEm for GBD 2019 primary diagnoses as well as outpatient care. For each included the addition of count models to the model GBD cause that used clinical data, ratios of non-primary ensemble for rarer causes. We also modified DisMod-MR to primary diagnosis rates were extracted from claims priors to effectively increase the out-of-sample coverage of www.thelancet.com Vol 396 October 17, 2020 1207 Age-standardised DALY rate Global Health Metrics uncertainty intervals (UIs) as assessed in simulation some parts of the world would have obscured the trends testing (appendix 1 section 4.5). in all other causes; we also subtracted out DALY rates For the cause Alzheimer’s disease and other dementias, from natural disasters and war and conflict to avoid we changed the method of addressing large variations trends in disease burden in some countries being between locations and over time in the assignment of dominated by these sudden and dramatic changes. As a dementia as the underlying cause of death. Based on measure of the epidemiological transition, we present a systematic review of published cohort studies, we the ratio of YLDs due to non-communicable diseases estimated the relative risk of death in individuals with and injuries, and due to total burden in DALYs. We dementia. We identified the proportion of excess deaths present 95% UIs for every metric based on the 25th and in patients with dementia where dementia is the under- 975th ordered values of 1000 draws of the posterior lying cause of death as opposed to a correlated risk factor distribution. (appendix 1 section 2.6.2). We changed the strategy of modelling deaths for acute hepatitis A, B, C, and E from Role of the funding source a natural history model relying on inpatient case fatality The funders of this study had no role in study design, rates to CODEm models after predicting type-specific data collection, data analysis, data interpretation, or the acute hepatitis deaths from vital registration data with writing of the report. The corresponding author had full specified hepatitis type. access to the data in the study and final responsibility for DisMod-MR was used to estimate deaths from three the decision to submit for publication. outcomes (dementia, Parkinson’s, and atrial fibrillation), and to determine the proportions of deaths by underlying Results aetiologies of cirrhosis, liver cancer, and chronic kidney Global trends disease deaths. Between 1990 and 2019, the number of global DALYs remained almost constant, but once the effects of Socio-demographic Index, annual rate of change, and population growth and ageing were removed by con- data presentation verting counts to age-standardised rates, there were clear The Socio-demographic Index (SDI) is a composite improvements in overall health (figure 1). Over the past indicator of a country’s lag-distributed income per decade, the pace of decline in global age-standardised capita, average years of schooling, and the fertility rate in DALY rates accelerated in age groups younger than females under the age of 25 years (appendix 1 section 6). 50 years compared with the 1990–2010 time period For changes over time, we present annualised rates of (table). The annualised rate of decline was greatest in the change as the difference in the natural log of the values 0–9-year age group. In the population aged 50 years and at the start and end of the time interval divided by the older, the rate of change was slower from 2010 to 2019 number of years in the interval. We examine the compared with the earlier time period. relationship between SDI and the annualised rate of These general trends are made up of complex trends for change in age-standardised DALY rates for all causes, specific diseases and injuries. Overall trends in the number apart from HIV/AIDS, natural disasters, and war and of DALYs across the different age groups between conflict, by country or territory, for the time periods 1990 and 2019 are driven by some key diseases and injuries 1990–2010 and 2010–19. We deliberately subtracted out (figure 2). The ten most important drivers of increasing DALYs due to HIV/AIDS because their magnitude in burden (ie, the causes that had the largest absolute increases in number of DALYs between 1990 and 2019) include six causes that largely affect older adults (ischaemic 3000 60 000 heart disease, diabetes, stroke, chronic kidney disease, lung cancer, and age-related hearing loss), whereas the 50 000 other four causes (HIV/AIDS, other musculoskeletal disorders, low back pain, and depressive disorders) are 2000 40 000 common from teenage years into old age (figure 2). Despite these ten conditions contributing the largest 30 000 number of additional DALYs over the 30-year period, only HIV/AIDS, other musculoskeletal disorders, and diabetes 1000 20 000 saw large increases in age-standardised DALY rates, with 10 000 an increase of 58·5% (95% UI 37·1–89·2) for HIV/AIDS, Age-standardised DALY rate DALY count 30·7% (27·6–34·3) for other musculoskeletal disorders, 0 0 and 24·4% (18·5–29·7) for diabetes. The burden of 1990 2000 2010 2019 Year HIV/AIDS, however, peaked in 2004 and has dropped substantially after the global scale-up of antiretroviral Figure 1: Global DALYs and age-standardised DALY rates, 1990–2019 treatment (ART). The changes in age-standardised rates Shaded sections indicate 95% uncertainty intervals. DALY=disability-adjusted life-year. for chronic kidney disease, age-related hearing loss, and 1208 www.thelancet.com Vol 396 October 17, 2020 DALY count (millions) Global Health Metrics DALYs 2019 Annualised rate of change, 1990–2010 Annualised rate of change, 2010–19 Count Age-standardised rate DALYs Age-standardised rate DALYs Age-standardised rate (millions) (per 100 000) 0–9 years 531 (458 to 621) 19 125·7 (16 495·1 to 22 382·5) −2·3% (−2·5 to −2·2) −2·5% (−2·6 to −2·3) −3·7% (−4·4 to −2·9) −4·0% (−4·7 to −3·2) 10–24 years 229 (194 to 270) 12 313·0 (10 399·9 to 14 478·3) 0·2% (0·1 to 0·2) −0·7% (−0·8 to −0·6) −1·1% (−1·4 to −0·9) −1·3% (−1·5 to −1·1) 25–49 years 616 (533 to 709) 22 691·2 (19 613·7 to 26 116·3) 1·4% (1·4 to 1·5) −0·4% (−0·4 to −0·3) −0·0% (−0·2 to 0·1) −1·2% (−1·4 to −1·0) 50–74 years 832 (752 to 919) 28 263·2 (25 527·6 to 31 213·4) 1·3% (1·2 to 1·3) −1·0% (−1·0 to −0·9) 2·0% (1·8 to 2·1) −0·9% (−1·1 to −0·8) ≥75 years 329 (308 to 351) 77 320·5 (72 372·5 to 82 440·3) 2·2% (2·2 to 2·2) −0·9% (−0·9 to −0·9) 2·3% (2·3 to 2·4) −0·8% (−0·9 to −0·8) All ages 2540 (2290 to 2810) 32 801·7 (29 535·1 to 36 319·5) −0·0% (−0·1 to 0·0) −1·4% (−1·5 to −1·3) −0·2% (−0·4 to 0·0) −1·3% (−1·5 to −1·1) DALY=disability-adjusted life-year. Table: Global DALYs in 2019 and annualised rate of change in DALYs and age-standardised DALY rates over 1990–2010 and 2010–19, by age group and for all ages depressive disorders were small (figure 2). Substantial accounted for by congenital syphilis in this age group; declines in age-standardised rates were seen in ischaemic tenth). Congenital birth defects (ranked fourth) as well heart disease (28·6%, 95% UI 24·2–33·3), stroke (35·2%, as two nutritional disorders—dietary iron deficiency 30·5–40·5), and lung cancer (16·1%, 8·2–24·0). (seventh) and protein-energy malnutrition (eighth)— The ten most important contri butors to declining completed the top ten. The percentage change in age- burden (ie, the causes that had the largest absolute standardised DALY rates for eight of the ten leading decreases in number of DALYs between 1990 and 2019) causes was large, ranging from a 35·4% (23·8–44·8) include nine that predominantly affect children (lower decline for neonatal disorders to 78·3% (69·9–85·5) respir atory infections, diarrhoeal diseases, neonatal dis- decline for protein-energy malnutrition over the study orders, measles, protein-energy malnutrition, congenital period. The decreases for the remaining two top-ten birth defects, drowning, tetanus, and malaria), as well causes, sexually transmitted infections and dietary iron as tuberculosis, which largely affects adults. All of deficiency, were much more modest. Sub-Saharan Africa these causes with declining burden also had substantial experienced nearly half of the total DALYs (49·9% decreases in age-standardised DALY rates, ranging [47·6–52·3]) for this age group in 2019. from 32·6% (21·2–42·1) decline for neonatal disorders The change in disease burden in adolescents aged to 90·4% (87·5–92·8) decline for measles, not just 10–24 years was much more modest (figure 2). DALYs decreases in the absolute number of DALYs due to declined by 6·2% (95% UI 2·1–10·5) overall between demographic changes (figure 2A). Although most of the 1990 and 2019. DALYs for non-communicable diseases ten leading Level 3 causes of DALYs were the same for increased by 13·1% (9·5–16·3), whereas injuries declined both sexes in 2019, road injuries (ranked fourth for by 24·8% (19·7–29·3) and infectious diseases by 18·7% males), cirrhosis (ninth), and lung cancer (tenth) were in (13·4–24·0). Three injury causes were among the top ten the top ten for males only, and were replaced by low back causes of global DALYs in this age group in 2019: road pain (ranked sixth for females), gynaecological diseases injuries (ranked first), self-harm (third), and interpersonal (ninth), and headache disorders (tenth) for females violence (fifth; figure 2). Headache disor ders, two mental (appendix 2 figure S5 and tables S2–5, S7, S8, S12, S13, disorders (depression and anxiety), low back pain, dietary S16). Congenital defects were ranked tenth for both sexes iron deficiency, HIV/AIDS, and diarrhoeal disease were combined in 2019 but did not make the top ten for either the other causes in the top ten for adolescents. Among sex separately. the top ten causes in this age group, age-standardised The burden for children younger than 10 years declined DALY rates for road injuries, self-harm, and diarrhoeal profoundly between 1990 and 2019, by 57·5% (95% UI diseases decreased by more than a third each between 50·3–63·1). Key drivers of this progress included large 1990 and 2019. As in the 0–9-year age group, the large reductions in major infectious diseases affecting increase in burden due to HIV/AIDS in the 10–24-year age children—namely, lower respiratory infections, diarrhoeal group reflects a rapid increase in the first half of the study diseases, and meningitis, each of which declined by period followed by a decline after the global scale-up of more than 60% between 1990 and 2019 (figure 2). In 2019, ART; despite declining in recent years, the HIV/AIDs neonatal disorders were the leading cause of burden in burden has not yet returned to 1990 levels. The other this age group, accounting for 32·4% (30·7–34·1) of the causes in the top ten showed small or insignificant change group’s global DALYs, increasing from 23·0% (22·0–24·1) (figure 2). The sex differences in the top ten rankings are in 1990. Six infectious diseases were also among the striking. The three previously mentioned injuries were top ten causes of burden in children: lower respiratory the top-ranked causes of DALYs among male adolescents infections (ranked second), diarrhoeal diseases (third), (appendix 2 figure S9), whereas headaches, depressive malaria (fifth), meningitis (sixth), whooping cough (ninth), disorders, and anxiety disorders were the top three causes and sexually transmitted infections (which were fully of DALYs among females (appendix 2 figure S10). www.thelancet.com Vol 396 October 17, 2020 1209 Global Health Metrics A All ages Leading causes 1990 Percentage of DALYs Leading causes 2019 Percentage of DALYs Percentage change in Percentage change in 1990 2019 number of DALYs, age-standardised DALY 1990–2019 rate, 1990–2019 1 Neonatal disorders 10·6 (9·9 to 11·4) 1 Neonatal disorders 7·3 (6·4 to 8·4) –32·3 (–41·7 to –20·8) –32·6 (–42·1 to –21·2) 2 Lower respiratory infections 8·7 (7·6 to 10·0) 2 Ischaemic heart disease 7·2 (6·5 to 7·9) 50·4 (39·9 to 60·2) –28·6 (–33·3 to –24·2) 3 Diarrhoeal diseases 7·3 (5·9 to 8·8) 3 Stroke5·7 (5·1 to 6·2) 32·4 (22·0 to 42·2) –35·2 (–40·5 to –30·5) 4 Ischaemic heart disease 4·7 (4·4 to 5·0) 4 Lower respiratory infections 3·8 (3·3 to 4·3) –56·7 (–64·2 to –47·5) –62·5 (–69·0 to –54·9) 5 Stroke 4·2 (3·9 to 4·5)5 Diarrhoeal diseases 3·2 (2·6 to 4·0) –57·5 (–66·2 to –44·7) –64·6 (–71·7 to –54·2) 6 Congenital birth defects 3·2 (2·3 to 4·8) 6 COPD 2·9 (2·6 to 3·2) 25·6 (15·1 to 46·0) –39·8 (–44·9 to –30·2) 7 Tuberculosis 3·1 (2·8 to 3·4) 7 Road injuries 2·9 (2·6 to 3·0) 2·4 (–6·9 to 10·8) –31·0 (–37·1 to –25·4) 8 Road injuries 2·7 (2·6 to 3·0) 8 Diabetes 2·8 (2·5 to 3·1) 147·9 (135·9 to 158·9) 24·4 (18·5 to 29·7) 9 Measles 2·7 (0·9 to 5·6) 9 Low back pain 2·5 (1·9 to 3·1) 46·9 (43·3 to 50·5) –16·3 (–17·1 to –15·5) 10 Malaria 2·5 (1·4 to 4·1) 10 Congenital birth defects 2·1 (1·7 to 2·6) –37·3 (–50·6 to –12·8) –40·0 (–52·7 to –17·1) 11 COPD 2·3 (1·9 to 2·5) 11 HIV/AIDS1·9 (1·6 to 2·2) 127·7 (97·3 to 171·7) 58·5 (37·1 to 89·2) 12 Protein-energy malnutrition 2·0 (1·6 to 2·7) 12 Tuberculosis 1·9 (1·7 to 2·0) –41·0 (–47·2 to –33·5) –62·8 (–66·6 to –58·0) 13 Low back pain 1·7 (1·2 to 2·1) 13 Depressive disorders 1·8 (1·4 to 2·4) 61·1 (56·9 to 65·0) –1·8 (–2·9 to –0·8) 14 Self-harm 1·4 (1·2 to 1·5) 14 Malaria 1·8 (0·9 to 3·1) –29·4 (–56·9 to 6·6) –37·8 (–61·9 to –6·2) 15 Cirrhosis 1·3 (1·2 to 1·5) 15 Headache disorders 1·8 (0·4 to 3·8) 56·7 (52·4 to 62·1) 1·1 (–4·2 to 2·9) 16 Meningitis 1·3 (1·1 to 1·5) 16 Cirrhosis 1·8 (1·6 to 2·0) 33·0 (22·4 to 48·2) –26·8 (–32·5 to –19·0) 17 Drowning 1·3 (1·1 to 1·4) 17 Lung cancer 1·8 (1·6 to 2·0) 69·1 (53·1 to 85·4) –16·2 (–24·0 to –8·2) 18 Headache disorders 1·1 (0·2 to 2·4) 18 Chronic kidney disease 1·6 (1·5 to 1·8) 93·2 (81·6 to 105·0) 6·3 (0·2 to 12·4) 19 Depressive disorders 1·1 (0·8 to 1·5) 19 Other musculoskeletal 1·6 (1·2 to 2·1) 128·9 (122·0 to 136·3) 30·7 (27·6 to 34·3) 20 Diabetes 1·1 (1·0 to 1·2) 20 Age-related hearing loss 1·6 (1·2 to 2·1) 82·8 (75·2 to 88·9) –1·8 (–3·7 to –0·1) 21 Lung cancer 1·0 (1·0 to 1·1) 21 Falls 1·5 (1·4 to 1·7) 47·1 (31·5 to 61·0) –14·5 (–22·5 to –7·4) 22 Falls 1·0 (0·9 to 1·2) 22 Self-harm 1·3 (1·2 to 1·5) –5·6 (–14·2 to 3·7) –38·9 (–44·3 to –33·0) 23 Dietary iron deficiency 1·0 (0·7 to 1·3) 23 Gynaecological diseases 1·2 (0·9 to 1·5) 48·7 (45·8 to 51·8) –6·8 (–8·7 to –4·9) 24 Interpersonal violence 0·9 (0·9 to 1·0) 24 Anxiety disorders 1·1 (0·8 to 1·5) 53·7 (48·8 to 59·1) –0·1 (–1·0 to 0·7) 25 Whooping cough 0·9 (0·4 to 1·7) 25 Dietary iron deficiency 1·1 (0·8 to 1·5) 13·8 (10·5 to 17·2) –16·4 (–18·7 to –14·0) 27 Age-related hearing loss 0·8 (0·6 to 1·1) 26 Interpersonal violence 1·1 (1·0 to 1·2) 10·2 (3·2 to 19·2) –23·8 (–28·6 to –17·8) 29 Chronic kidney disease 0·8 (0·8 to 0·9) 40 Meningitis 0·6 (0·5 to 0·8) –51·3 (–59·4 to –42·0) –57·2 (–64·4 to –48·6) 30 HIV/AIDS 0·8 (0·6 to 1·0) 41 Protein-energy malnutrition 0·6 (0·5 to 0·7) –71·1 (–79·6 to –59·7) –74·5 (–82·0 to –64·5) 32 Gynaecological diseases 0·8 (0·6 to 1·0) 46 Drowning 0·5 (0·5 to 0·6) –60·6 (–65·2 to –53·6) –68·2 (–71·9 to –62·8) 34 Anxiety disorders 0·7 (0·5 to 1·0) 55 Whooping cough 0·4 (0·2 to 0·7) –54·5 (–74·6 to –16·9) –56·3 (–75·6 to –20·3) 35 Other musculoskeletal 0·7 (0·5 to 1·0) 71 Measles 0·3 (0·1 to 0·6) –89·8 (–92·3 to –86·8) –90·4 (–92·8 to –87·5) B 0–9 years 1 Neonatal disorders 23·0 (22·0 to 24·1) 1 Neonatal disorders 32·4 (30·7 to 34·1) –36·2 (–45·4 to –24·7) –35·4 (–44·8 to –23·8) 2 Lower respiratory infections 17·0 (14·9 to 19·7) 2 Lower respiratory infections 11·6 (10·5 to 12·6) –69·1 (–75·9 to –60·9) –69·6 (–76·3 to –61·6) 3 Diarrhoeal diseases 13·1 (10·7 to 15·1) 3 Diarrhoeal diseases 9·3 (7·9 to 10·8) –67·8 (–75·3 to –57·2) –68·5 (–75·9 to –58·4) 4 Congenital birth defects 6·6 (4·6 to 10·0) 4 Congenital birth defects 8·6 (7·4 to 10·7) –41·6 (–54·6 to –17·4) –40·1 (–55·1 to –17·9) 5 Measles 5·7 (2·0 to 11·8) 5 Malaria 6·4 (3·3 to 10·8) –36·9 (–61·4 to –2·2) –38·5 (–63·1 to –6·5) 6 Malaria 4·6 (2·5 to 7·5) 6 Meningitis 2·1 (1·8 to 2·5) –59·7 (–68·1 to –49·3) –61·0 (–69·2 to –51·1) 7 Protein-energy malnutrition 4·1 (3·1 to 5·5) 7 Dietary iron deficiency 2·0 (1·3 to 2·9) –0·8 (–5·3 to 3·6) –8·2 (–12·3 to –4·1) 8 Meningitis 2·3 (2·0 to 2·7) 8 Protein-energy malnutrition 2·0 (1·7 to 2·3) –78·1 (–85·0 to –68·9) –78·3 (–85·5 to –69·9) 9 Whooping cough 1·9 (0·8 to 3·8) 9 Whooping cough 1·9 (0·9 to 3·3) –54·7 (–74·7 to –17·3) –53·2 (–75·6 to –20·4) 10 Drowning 1·8 (1·5 to 2·1) 10 STIs 1·4 (0·5 to 2·8) –16·3 (–30·7 to 1·7) –14·9 (–30·1 to 2·5) 11 Tuberculosis 1·8 (1·5 to 2·1) 11 Measles 1·3 (0·4 to 2·7) –90·0 (–92·6 to –86·9) –90·5 (–92·9 to –87·6) 12 Tetanus 1·7 (1·4 to 1·9) 12 Road injuries 1·1 (1·0 to 1·4) –61·5 (–68·7 to –45·0) –63·7 (–70·8 to –48·8) 13 Road injuries 1·3 (1·1 to 1·5) 13 Tuberculosis 1·0 (0·9 to 1·2) –74·5 (–79·8 to –67·8) –75·5 (–80·6 to –69·2) 14 Dietary iron deficiency 0·9 (0·6 to 1·3) 14 HIV/AIDS1·0 (0·9 to 1·2) –18·6 (–35·6 to 3·6) –25·0 (–35·3 to –13·6) 15 STIs 0·7 (0·2 to 1·5) 15 iNTS 1·0 (0·6 to 1·5) 68·3 (27·4 to 121·2) 61·4 (20·6 to 109·3) 16 Typhoid and paratyphoid 0·7 (0·3 to 1·3) 16 Drowning 0·9 (0·8 to 1·1) –77·6 (–81·3 to –70·1) –79·0 (–82·6 to –72·2) 17 Foreign body 0·6 (0·5 to 0·7) 17 Haemoglobinopathies 0·9 (0·7 to 1·0) –10·3 (–30·3 to 22·5) –13·7 (–34·3 to 14·7) 18 HIV/AIDS 0·6 (0·5 to 0·7) 18 Typhoid and paratyphoid 0·8 (0·4 to 1·5) –46·7 (–59·1 to –31·1) –50·7 (–62·5 to –36·9) 19 Encephalitis 0·5 (0·4 to 0·7) 19 Asthma 0·5 (0·4 to 0·8) –32·2 (–46·2 to –14·5) –37·5 (–50·0 to –21·5) 20 Acute hepatitis 0·5 (0·4 to 0·5) 20 Foreign body 0·5 (0·4 to 0·5) –62·9 (–69·6 to –56·2) –63·6 (–70·2 to –57·1) 21 Haemoglobinopathies 0·4 (0·3 to 0·6) 21 EMBID 0·5 (0·4 to 0·6) –18·9 (–33·3 to –0·9) –22·1 (–36·1 to –6·0) 22 Leukaemia 0·4 (0·3 to 0·6) 22 Sudden infant death 0·5 (0·2 to 1·0) –50·6 (–61·6 to –29·8) –46·9 (–61·7 to –30·0) 23 Sudden infant death 0·4 (0·2 to 0·9) 23 Idiopathic epilepsy 0·5 (0·3 to 0·6) –30·7 (–45·8 to 3·6) –34·0 (–49·1 to –3·8) 24 Asthma 0·4 (0·3 to 0·5) 24 Other unspecified infectious 0·4 (0·3 to 0·6) –28·4 (–48·3 to 7·8) –29·3 (–50·3 to 3·3) 25 Falls 0·4 (0·3 to 0·5)25 Dermatitis 0·4 (0·2 to 0·7) 2·7 (1·7 to 3·7) –6·0 (–6·9 to –5·1) 28 Idiopathic epilepsy 0·3 (0·2 to 0·4) 26 Leukaemia 0·4 (0·4 to 0·5) –54·8 (–67·7 to –32·9) –55·3 (–69·5 to –37·0) 30 Other unspecified infectious 0·3 (0·2 to 0·4) 27 Falls 0·4 (0·3 to 0·5) –48·3 (–68·7 to –22·6) –47·2 (–67·0 to –18·0) 33 iNTS 0·3 (0·1 to 0·4) 28 Encephalitis 0·4 (0·3 to 0·5) –67·6 (–76·7 to –47·6) –68·5 (–77·9 to –50·2) 34 EMBID 0·3 (0·2 to 0·3) 32 Tetanus 0·3 (0·3 to 0·5) –91·3 (–93·8 to –85·6) –91·2 (–93·8 to –85·6) 44 Dermatitis 0·2 (0·1 to 0·3) 39 Acute hepatitis 0·3 (0·2 to 0·3) –73·1 (–81·7 to –59·1) –74·1 (–82·6 to –61·1) Communicable, maternal, neonatal, and nutritional diseases Non-communicable diseases Injuries (Figure 2 continues on next page) 1210 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics Maternal disorders, gynaecological disorders, and dietary pain (fourth), headache disorders (fifth), and depressive iron deficiency were also in the top ten causes for females disorders (sixth; figure 2). Tuberculosis and four non- in this relatively young age group (appendix 2 figure S10). communicable causes—ischaemic heart disease, gynae- Five causes that were in the top ten for ages 10–24 cological disorders, other musculoskeletal disorders, and in 2019 were also in the top ten in the 25–49 age group: stroke—completed the top ten rankings. There were road injuries (ranked first), HIV/AIDS (second), low back substantial improvements since 1990 in DALY rates of C 10–24 years Leading causes 1990 Percentage of DALYs Leading causes 2019 Percentage of DALYs Percentage change in Percentage change in 1990 2019 number of DALYs, age-standardised DALY 1990–2019 rate, 1990–2019 1 Road injuries7·8 (6·9 to 8·8) 1 Road injuries6·6 (5·6 to 7·7) –20·1 (–28·3 to –12·9) –33·6 (–40·4 to –27·7) 24·6 (20·6 to 27·1) 3·3 (0·2 to 5·6) 2 Self-harm 4·9 (4·1 to 5·6) 2 Headache disorders 5·0 (0·6 to 10·9) 3 Headache disorders 3·8 (0·4 to 8·2) 3 Self-harm 3·7 (3·1 to 4·5) –28·4 (–36·3 to –18·9) –40·5 (–47·2 to –32·8) 4 Tuberculosis 3·6 (3·1 to 4·1) 4 Depressive disorders3·7 (2·6 to 5·0) 20·7 (17·4 to 23·5) 0·0 (–2·8 to 2·4) 5 Diarrhoeal diseases 3·2 (2·1 to 4·9) 5 Interpersonal violence 3·5 (2·9 to 4·1) 2·1 (–5·0 to 11·1) –15·4 (–21·3 to –7·9) 6 Interpersonal violence 3·2 (2·8 to 3·6) 6 Anxiety disorders3·3 (2·3 to 4·4) 17·9 (15·7 to 20·3) –2·0 (–3·8 to –0·1) 7 Maternal disorders 3·0 (2·6 to 3·4) 7 Low back pain 3·2 (2·2 to 4·3) 6·0 (4·4 to 7·6) –12·0 (–13·3 to –10·6) 8 Depressive disorders2·8 (2·0 to 3·9) 8 Dietary iron deficiency 2·6 (1·9 to 3·4) 15·9 (8·6 to 22·4) –3·5 (–9·5 to 2·0) 9 Low back pain 2·8 (1·9 to 3·8) 9 HIV/AIDS 2·6 (1·9 to 3·5) 159·0 (115·4 to 211·1) 112·8 (84·3 to 141·9) 10 Drowning 2·7 (2·3 to 3·2) 10 Diarrhoeal diseases 2·6 (1·9 to 3·6) –25·7 (–40·1 to –0·3) –37·0 (–50·2 to –17·0) 11 Typhoid and paratyphoid 2·6 (1·2 to 4·9) 11 Neonatal disorders 2·3 (1·8 to 2·8) 143·6 (114·3 to 174·6) 103·6 (78·4 to 128·5) 12 Anxiety disorders2·6 (1·8 to 3·5) 12 Tuberculosis 2·1 (1·8 to 2·5) –44·3 (–50·7 to –36·9) –53·8 (–59·1 to –47·7) 13 Dietary iron deficiency 2·1 (1·6 to 2·8) 13 Gynaecological diseases 1·9 (1·4 to 2·6) 19·1 (15·8 to 22·0) –1·4 (–4·2 to 1·0) 14 Malaria 2·1 (1·3 to 3·3) 14 Typhoid and paratyphoid 1·8 (0·8 to 3·3) –35·5 (–46·0 to –26·4) –46·2 (–54·9 to –38·5) 15 Lower respiratory infections 1·7 (1·4 to 2·0) 15 Maternal disorders 1·8 (1·5 to 2·2) –42·7 (–51·9 to –33·8) –52·5 (–60·2 to –45·3) 16 Conflict and terrorism 1·5 (1·3 to 1·9) 16 Malaria 1·8 (1·0 to 3·0) –19·4 (–50·8 to 15·8) –31·9 (–59·0 to –3·6) 17 Gynaecological diseases 1·5 (1·1 to 2·1) 17 Conduct disorder 1·8 (1·1 to 2·6) 24·7 (22·2 to 27·0) 4·4 (2·3 to 6·3) 18 Falls 1·5 (1·3 to 1·6) 18 Drug use disorders1·6 (1·3 to 2·1) 21·8 (15·2 to 28·7) 0·6 (–4·8 to 6·2) 19 Congenital birth defects 1·5 (1·3 to 1·7) 19 Acne vulgaris 1·6 (1·0 to 2·4) 41·5 (39·8 to 43·2) 18·1 (16·7 to 19·5) 20 Idiopathic epilepsy1·4 (1·1 to 1·8) 20 Idiopathic epilepsy1·6 (1·2 to 2·1) 6·5 (–7·1 to 25·7) –11·4 (–22·8 to 4·6) 21 Conduct disorder 1·3 (0·8 to 2·0) 21 Congenital birth defects 1·5 (1·3 to 1·7) –5·6 (–15·6 to 7·4) –21·2 (–29·7 to –10·5) 22 Drug use disorders1·3 (1·0 to 1·6) 22 Falls 1·4 (1·3 to 1·6) –8·4 (–16·9 to 0·4) –23·9 (–30·9 to –16·7) 23 Asthma 1·2 (1·0 to 1·6) 23 Drowning 1·4 (1·2 to 1·7) –50·7 (–55·9 to –44·7) –58·8 (–63·2 to –53·9) 24 Stroke 1·2 (1·0 to 1·3) 24 Lower respiratory infections1·4 (1·2 to 1·7) –20·9 (–29·9 to –10·5) –34·1 (–41·6 to –25·5) 25 Meningitis 1·1 (1·0 to 1·3) 25 Age-related hearing loss 1·3 (0·9 to 1·8) 18·6 (13·4 to 24·2) –1·2 (–5·7 to 3·2) 27 Acne vulgaris 1·1 (0·7 to 1·6) 27 Asthma 1·3 (1·0 to 1·8) –1·1 (–8·3 to 5·1) –18·0 (–23·8 to –12·4) 28 Age-related hearing loss 1·1 (0·7 to 1·5) 30 Stroke 1·1 (0·9 to 1·3) –12·8 (–21·5 to –2·9) –27·6 (–34·8 to –19·4) 33 HIV/AIDS 0·9 (0·6 to 1·5) 34 Meningitis 0·9 (0·7 to 1·1) –26·0 (–34·0 to –16·4) –38·3 (–45·0 to –30·4) 35 Neonatal disorders 0·9 (0·7 to 1·1) 46 Conflict and terrorism 0·6 (0·5 to 0·8) –62·1 (–65·7 to –57·9) –68·5 (–71·6 to –65·1) D 25–49 years 1 Road injuries 5·6 (5·1 to 6·1) 1 Road injuries5·1 (4·6 to 5·7) 23·2 (11·1 to 33·2) –22·5 (–30·1 to –16·2) 2 Tuberculosis 5·5 (4·8 to 6·2) 2 HIV/AIDS 4·8 (4·0 to 5·9) 176·2 (131·1 to 244·3) 72·2 (52·4 to 91·9) 3 Ischaemic heart disease 4·4 (3·8 to 4·9) 3 Ischaemic heart disease 4·7 (4·0 to 5·4) 42·7 (28·4 to 57·3) –18·5 (–26·7 to –10·1) 4 Low back pain 3·9 (2·9 to 5·1) 4 Low back pain3·9 (2·9 to 5·0) 33·0 (29·2 to 36·9) –19·2 (–20·5 to –18·0) 5 Self-harm 3·8 (3·3 to 4·4) 5 Headache disorders 3·7 (0·8 to 7·7) 61·2 (56·5 to 64·5) 0·2 (–3·7 to 2·3) 6 Stroke 3·5 (3·1 to 3·9) 6 Depressive disorders3·5 (2·5 to 4·5) 53·2 (49·3 to 56·8) –4·9 (–6·4 to –3·4) 7 Headache disorders 3·1 (0·7 to 6·4) 7 Gynaecological diseases 3·3 (2·5 to 4·2) 52·7 (49·7 to 56·0) –4·5 (–6·3 to –2·5) 8 Depressive disorders 3·0 (2·2 to 3·9) 8 Other musculoskeletal 3·2 (2·3 to 4·2) 107·1 (101·0 to 114·3) 26·7 (23·4 to 30·5) 9 Cirrhosis 2·8 (2·5 to 3·2) 9 Stroke 3·2 (2·8 to 3·6) 19·9 (8·0 to 31·1) –31·0 (–37·9 to –24·6) 10 Gynaecological diseases 2·8 (2·2 to 3·7) 10 Tuberculosis 3·0 (2·6 to 3·4) –27·0 (–34·7 to –18·7) –55·5 (–60·2 to –50·5) 11 Maternal disorders 2·6 (2·3 to 2·9) 11 Self-harm 2·9 (2·4 to 3·4) –0·9 (–10·3 to 9·1) –37·2 (–43·2 to –30·9) 12 Interpersonal violence 2·5 (2·3 to 2·8) 12 Cirrhosis 2·8 (2·4 to 3·2) 29·6 (19·0 to 44·5) –23·8 (–30·1 to –15·1) 13 HIV/AIDS 2·3 (1·6 to 3·2) 13 Interpersonal violence 2·3 (2·0 to 2·6) 18·1 (10·7 to 26·5) –24·4 (–29·0 to –19·0) 14 Other musculoskeletal 2·0 (1·5 to 2·8) 14 Diabetes 2·2 (1·9 to 2·5) 123·9 (110·1 to 135·3) 29·2 (21·1 to 36·0) 15 Diarrhoeal diseases 2·0 (1·3 to 3·1) 15 Anxiety disorders 2·0 (1·4 to 2·7) 61·6 (57·5 to 65·4) 1·1 (0·0 to 2·1) 16 Falls 1·8 (1·6 to 2·0) 16 Drug use disorders 1·9 (1·5 to 2·2) 92·0 (82·7 to 102·5) 25·4 (19·3 to 31·6) 17 Anxiety disorders 1·7 (1·2 to 2·2) 17 Falls 1·8 (1·6 to 2·0) 34·4 (25·8 to 41·7) –18·0 (–23·4 to –13·5) 18 Alcohol use disorders 1·7 (1·4 to 2·0) 18 Chronic kidney disease 1·6 (1·4 to 1·8) 67·3 (53·9 to 80·3) 0·7 (–7·3 to 8·4) 19 Neck pain 1·3 (0·9 to 2·0) 19 Neck pain1·6 (1·1 to 2·4) 60·2 (52·4 to 67·9) –3·6 (–6·0 to –1·5) 20 Diabetes 1·3 (1·2 to 1·5) 20 Alcohol use disorders 1·6 (1·3 to 1·9) 28·2 (22·9 to 33·2) –20·9 (–24·2 to –17·9) 21 Chronic kidney disease 1·3 (1·2 to 1·4) 21 Age-related hearing loss 1·5 (1·1 to 2·1) 64·3 (58·7 to 69·1) –0·5 (–3·1 to 1·9) 22 Drug use disorders 1·3 (1·0 to 1·6) 22 Schizophrenia 1·5 (1·1 to 1·9) 59·6 (57·5 to 61·9) –0·9 (–2·0 to 0·2) 23 Schizophrenia 1·3 (0·9 to 1·6) 23 Maternal disorders 1·4 (1·2 to 1·6) –28·9 (–39·6 to –19·2) –53·4 (–60·5 to –47·2) 24 Age-related hearing loss 1·3 (0·9 to 1·7) 24 Diarrhoeal diseases 1·3 (1·0 to 1·9) –13·5 (–32·6 to 15·5) –46·2 (–59·0 to –29·6) 25 Lower respiratory infections1·2 (1·1 to 1·4) 25 Oral disorders 1·2 (0·7 to 2·1) 70·7 (66·4 to 74·1) 2·8 (0·5 to 5·1) –23·1 (–30·2 to –16·0) 32 Oral disorders 1·0 (0·5 to 1·6) 27 Lower respiratory infections 1·2 (1·0 to 1·4) 26·8 (15·2 to 38·5) Communicable, maternal, neonatal, and nutritional diseases Non-communicable diseases Injuries (Figure 2 continues on next page) www.thelancet.com Vol 396 October 17, 2020 1211 Global Health Metrics E 50–74 years Leading causes 1990 Percentage of DALYs Leading causes 2019 Percentage of DALYs Percentage change in Percentage change in 1990 2019 number of DALYs, age-standardised DALY 1990–2019 rate, 1990–2019 1 Ischaemic heart disease 12·5 (11·6 to 13·4) 1 Ischaemic heart disease 11·8 (10·7 to 12·9) 46·1 (35·6 to 56·4) –29·1 (–34·2 to –24·1) 2 Stroke 10·9 (10·0 to 11·8) 2 Stroke9·3 (8·5 to 10·1) 31·5 (19·5 to 42·9) –36·3 (–42·1 to –30·8) 3 COPD 6·5 (5·5 to 7·1) 3 Diabetes 5·1 (4·6 to 5·7) 156·1 (143·4 to 167·9) 24·5 (18·5 to 30·4) 4 Tuberculosis 4·0 (3·6 to 4·4) 4 COPD 4·7 (4·2 to 5·2) 12·0 (0·9 to 32·3) –45·9 (–51·4 to –36·2) 5 Lung cancer 3·6 (3·3 to 3·9) 5 Lung cancer 3·9 (3·4 to 4·3) 64·3 (48·8 to 80·2) –19·8 (–27·3 to –12·1) 72·1 (70·0 to 74·3) 6 Diabetes 3·1 (2·8 to 3·4) 6 Low back pain 3·1 (2·3 to 4·0) –15·9 (–16·9 to –14·9) 7 Cirrhosis 2·8 (2·6 to 3·1) 7 Cirrhosis 2·7 (2·4 to 3·0) 44·6 (33·2 to 57·1) –29·1 (–34·7 to –23·0) 8 Low back pain 2·8 (2·1 to 3·7) 8 Chronic kidney disease 2·3 (2·1 to 2·5) 130·2 (113·0 to 145·6) 12·1 (3·7 to 19·5) –2·6 (–4·9 to –0·5) 9 Diarrhoeal diseases 2·6 (1·6 to 4·0) 9 Age-related hearing loss 2·2 (1·5 to 3·0) 100·8 (96·0 to 104·9) 10 Stomach cancer 2·4 (2·2 to 2·6) 10 Road injuries 2·1 (1·9 to 2·3) 72·9 (56·5 to 83·9) –15·2 (–23·2 to –9·9) 11 Road injuries 1·9 (1·8 to 2·0) 11 Other musculoskeletal 1·9 (1·4 to 2·6) 172·0 (160·6 to 187·4) 33·6 (280 to 40·2) –64·7 (–68·9 to –59·4) 12 Lower respiratory infections 1·8 (1·6 to 2·0) 12 Tuberculosis 1·9 (1·7 to 2·1) –27·8 (–36·2 to –16·9) 13 Age-related hearing loss 1·7 (1·2 to 2·3) 13 Lower respiratory infections 1·8 (1·6 to 1·9) 49·8 (37·9 to 62·4) –27·5 (–33·3 to –21·5) 14 Chronic kidney disease 1·6 (1·4 to 1·7) 14 Depressive disorders 1·7 (1·3 to 2·3) 107·3 (104·7 to 110·1) 1·5 (0·2 to 2·9) 15 Asthma 1·5 (1·2 to 1·9) 15 Colorectal cancer 1·7 (1·6 to 1·9) 95·1 (80v8 to 108·2) –5·1 (–12·1 to 1·2) 16 Hypertensive heart disease 16 Falls 1·7 (1·5 to 2·0) 88·3 (76·5 to 100·0) –8·4 (–14·1 to –2·6) 1·5 (1·2 to 1·7) 17 Falls 1·4 (1·3 to 1·6) 17 Stomach cancer 1·7 (1·5 to 1·9) 6·3 (–5·0 to 18·9) –48·1 (–536 to –42·0) 18 Colorectal cancer 1·4 (1·3 to 1·5) 18 Osteoarthritis 1·5 (0·8 to 2·9) 113·6 (110·9 to 116·4) 4·1 (28 to 5·4) 19 Depressive disorders 1·3 (0·9 to 1·7) 19 Blindness and vision loss 1·4 (1·1 to 2·0) 88·8 (81·9 to 95·8) –8·6 (–12·0 to –5·0) 20 Blindness and vision loss 1·2 (0·9 to 1·6) 20 Breast cancer 1·4 (1·3 to 1·5) 85·0 (69·9 to 99·4) –9·5 (–16·9 to –2·5) 21 Liver cancer 1·2 (1·0 to 1·3) 21 Diarrhoeal diseases 1·4 (0·9 to 2·1) –21·0 (–42·4 to 11·9) –61·0 (–72·1 to –45·8) 22 Breast cancer 1·2 (1·1 to 1·2) 22 Hypertensive heart disease 36·7 (20·8 to 58·8) 1·3 (1·0 to 1·5) –33·8 (–41·7 to –23·4) 23 Oesophageal cancer 1·1 (0·9 to 1·2) 23 Headache disorders 1·2 (0·4 to 2·5) 102·5 (88·7 to 108·2) –1·2 (–7·4 to 2·3) 90·5 (86·0 to 94·7) 24 Osteoarthritis 1·1 (0·6 to 2·2) 24 Oral disorders 1·2 (0·8 to 1·8) –7·4 (–9·6 to –5·1) 1·1 (0·7 to 1·7) 115·9 (110·5 to 122·2) 25 Self-harm 1·1 (1·0 to 1·2) 25 Neck pain 5·7 (3·0 to 8·5) 26 Other musculoskeletal 1·1 (0·7 to 1·5) 27 Oesophageal cancer 1·0 (0·9 to 1·1) 38·2 (18·9 to 71·9) –32·1 (–41·9 to –16·1) –1·3 (–14·3 to 11·2) 28 Oral disorders 1·0 (0·6 to 1·5) 28 Asthma 1·0 (0·8 to 1·1) –51·8 (–58·3 to –46·0) 29 Headache disorders 0·9 (0·3 to 1·9) 29 Liver cancer 0·9 (0·8 to 1·0) 22·2 (5·2 to 44·0) –39·9 (–48·5 to –29·5) 32 Neck pain 0·8 (0·5 to 1·2) 31 Self-harm 0·9 (0·8 to 1·0) 20·4 (11·3 to 33·7) –41·0 (–45·5 to –34·5) F 75 years and older 1 Ischaemic heart disease 18·6 (17·1 to 19·7) 1 Ischaemic heart disease 16·2 (14·6 to 17·6) 66·6 (57·7 to 74·2) –32·4 (–35·8 to –29·4) 2 Stroke 15·5 (14·3 to 16·7) 2 Stroke 13·0 (11·7 to 14·0) 60·5 (48·7 to 72·5) –33·4 (–38·3 to –28·5) 8·5 (7·5 to 9·2) 63·6 (49·1 to 86·1) 3 COPD 9·9 (8·6 to 10·7) 3 COPD –31·0 (–37·1 to –21·9) 4 Alzheimer's disease 3·8 (1·7 to 8·6) 4 Alzheimer's disease 5·6 (2·6 to 12·2) 180·0 (168·0 to 194·7) 2·6 (–2·1 to 6·6) 5 Lower respiratory infections 3·3 (3·0 to 3·6) 5 Diabetes 4·0 (3·6 to 4·3) 190·7 (179·4 to 201·0) 23·1 (18·6 to 27·5) 6 Diarrhoeal diseases 3·1 (2·0 to 4·5) 6 Lower respiratory infections3·3 (2·9 to 3·6) 87·4 (76·2 to 99·6) –25·3 (–29·3 to –20·4) 2·6 (2·4 to 2·9) 164·3 (143·6 to 183·8) 7 Diabetes 7 Lung cancer 2·6 (2·3 to 2·8) 16·4 (7·4 to 24·9) 8 Hypertensive heart disease 8 Falls 2·6 (2·2 to 2·9) 166·4 (151·1 to 183·4) 2·3 (1·9 to 2·5) 6·4 (0·4 to 13·3) 9 Age-related hearing loss 2·0 (1·5 to 2·7) 9 Chronic kidney disease 2·5 (2·3 to 2·7) 196·0 (173·9 to 211·1) 21·6 (12·6 to 27·4) 10 Lung cancer 1·9 (1·8 to 2·0) 10 Age-related hearing loss 2·5 (1·9 to 3·3) 137·8 (132·0 to 143·9) –2·2 (–4·3 to –0·2) 2·4 (1·8 to 2·7) 11 Falls 1·8 (1·6 to 2·1) 11 Hypertensive heart disease 106·0 (68·5 to 131·7) –15·1 (–31·5 to –5·0) 1·8 (1·6 to 2·1) 1·9 (1·2 to 3·0) 15·1 (–16·8 to 65·3) 12 Tuberculosis 12 Diarrhoeal diseases –51·0 (–64·9 to –30·4) 13 Low back pain 1·7 (1·2 to 2·3) 13 Low back pain 1·8 (1·3 to 2·4) 105·7 (100·2 to 111·4) –12·5 (–13·8 to –11·3) 14 Chronic kidney disease 1·6 (1·5 to 1·8) 14 Colorectal cancer 1·7 (1·5 to 1·8) 126·9 (113·4 to 138·3) –4·5 (–9·7 to 0·1) 15 Stomach cancer 1·6 (1·4 to 1·7) 15 Blindness and vision loss 1·7 (1·3 to 2·2) 124·7 (119·3 to 130·7) –7·4 (–9·9 to –4·8) 1·4 (1·1 to 1·8) 16 Blindness and vision loss 16 Atrial fibrillation 1·3 (1·1 to 1·5) 148·6 (134·8 to 161·9) –1·8 (–6·9 to 2·5) 17 Colorectal cancer 1·4 (1·3 to 1·5) 17 Stomach cancer 1·3 (1·1 to 1·4) 55·0 (43·8 to 66·6) –32·9 (–37·5 to –28·0) 18 Asthma 1·2 (1·0 to 1·7) 18 Prostate cancer 1·1 (1·0 to 1·4) 117·0 (102·1 to 142·3) –8·5 (–14·6 to 2·1) 19 Cirrhosis 1·2 (1·0 to 1·3) 19 Cirrhosis 1·1 (1·0 to 1·2) –21·3 (–30·2 to –13·5) 82·3 (62·1 to 100·9) 20 Prostate cancer 1·0 (0·8 to 1·2) 20 Parkinson's disease 1·1 (1·0 to 1·2) 153·7 (138·7 to 166·6) 6·0 (0·0 to 11·1) 1·0 (0·8 to 1·2) 1·1 (06 to 21) 21 Atrial fibrillation 21 Osteoarthritis 139·5 (136·5 to 142·6) 0·8 (–0·4 to 2·1) 22 Osteoarthritis 0·9 (0·5 to 1·7) 22 Oral disorders 0·9 (0·6 to 1·3) 112·0 (106·4 to 117·6) –10·9 (–12·9 to –8·8) 23 Oral disorders 0·8 (0·6 to 1·2) 23 Tuberculosis 0·9 (0·8 to 1·0) –6·3 (–16·9 to 14·6) –59·2 (–64·0 to –50·3) 24 Parkinson's disease 0·8 (0·8 to 0·9) 24 Asthma 0·8 (0·7 to 1·0) 25·2 (3·2 to 41·2) –46·2 (–55·9 to –39·8) 0·8 (0·7 to 0·9) –9·3 (–13·5 to –5·9) 25 Upper digestive diseases 0·8 (0·7 to 0·9) 25 Road injuries 110·0 (99·8 to 118·1) 26 Road injuries 0·7 (0·6 to 0·8) 32 Upper digestive diseases0·6 (0·5 to 0·6) 34·0 (22·8 to 46·2) –43·8 (–48·4 to –38·7) Communicable, maternal, neonatal, and nutritional diseases Non-communicable diseases Injuries Figure 2: Leading 25 Level 3 causes of global DALYs and percentage of total DALYs (1990 and 2019), and percentage change in number of DALYs and age-standardised DALY rates from 1990 to 2019 for both sexes combined for all ages (A), children younger than 10 years (B), and ages 10–24 years (C), 25–49 years (D), 50–74 years (E), and 75 years and older (F) Causes are connected by lines between time periods; solid lines are increases in rank and dashed lines are decreases. Age-related hearing loss=age-related and other hearing loss. Alzheimer’s disease=Alzheimer’s disease and other dementias. Atrial fibrillation=atrial fibrillation and flutter. Cirrhosis=cirrhosis and other chronic liver diseases. COPD=chronic obstructive pulmonary disease. EMBID=endocrine, metabolic, blood, and immune disorders. DALY=disability-adjusted life-year. iNTS=invasive non-typhoidal salmonella. Haemoglobinopathies=haemoglobinopathies and haemolytic anaemias. Lung cancer=tracheal, bronchus, and lung cancer. Other musculoskeletal=other musculoskeletal disorders. Other unspecified infectious=other unspecified infectious diseases. Sudden infant death=sudden infant death syndrome. STI=sexually transmitted infections excluding HIV. 1212 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics tuberculosis, road injuries, stroke, and, to a lesser extent, epidemiological transition ranged from 8·4% (95% UI low back pain and ischaemic heart disease. For similar 6·2–10·9) in Chad to 56·9% (48·7–64·3) in Qatar. reasons as in the previous age group, HIV/AIDS The values in 1990 ranged from 3·5% (2·6–4·7) in Niger DALY rates increased substantially. The increase in the to 47·5% (37·6–56·0) in Andorra. In 2019, non-commu- residual “other musculoskeletal disorder” category is nicable and injury YLDs contributed to more than half of more difficult to interpret, as it is a collection of several all disease burden in 11 countries. All but two countries, individual diseases. HIV/AIDS, ischaemic heart disease, Ukraine and Lesotho, had higher ratios in 2019 compared stroke, and head ache disorders appeared in the top-ten with 1990. rankings for DALYs for both males and females in 2019. When comparing the annualised rate of change in age- Three injury causes (road injuries, self-harm, and inter- standardised DALY rates for all causes except HIV/AIDS, personal violence) and cirrhosis ranked prominently natural disasters, and war and conflict between the time among males but not females. Among females, gynae- periods 1990–2010 and 2010–19 for each country and cological disorders, depressive disorders, other musculo- territory, the rate, as shown by a simple linear regression skeletal disorders, maternal disorders, and anxiety line, is steeper in the latter time period, suggesting that disorders were top ten causes (appendix 2 figures S9, S10). change has accelerated over the last decade in countries In 2019, the ten leading causes of DALYs in age groups and territories at the lower end of the SDI range (figure 4). 50–74 years and 75 years and older largely overlapped. Improvements have started to stagnate, or even reverse, Ischaemic heart disease and stroke were ranked first in countries with higher SDI, as is the case in Dominica, and second, respectively, in both age groups. Chronic the Dominican Republic, Guam, Jamaica, Saint Lucia, obstructive pulmonary disease (COPD), diabetes, lung Saint Vincent and the Grenadines, Ukraine, the USA, and cancer, chronic kidney disease, and age-related hearing Venezuela. Countries with greater than 2% annual reduc- loss appeared in the top ten in both age groups. For ages tions in age-standardised DALY rates over both time 50–74 years, low back pain, cirrhosis, and road injuries periods were Ethiopia, Angola, Burundi, Malawi, Sudan, were the remaining top-ten-ranking causes of DALYs, Myanmar, Laos, and Bangladesh. Four countries from the whereas Alzheimer’s disease and other dementias, lower former Soviet Union—Russia, Belarus, Kazakhstan, and respiratory infections, and falls appeared in the top ten for Uzbekistan—experienced increases in age-standardised those aged 75 years and older. The most notable changes DALY rates between 1990 and 2010, but recovered in in top ten causes in these two age groups between the following decade; Russia, Kazakhstan, and Belarus 1990 and 2019 were large declines in age-standardised experienced an estimated annual decline of 2% or greater DALY rates for ischaemic heart disease, stroke, COPD, between 2010 and 2019, and Uzbekistan experienced an cirrhosis, and road injuries, but increases in DALY rates estimated 1·5% annual decline. Another former Soviet for diabetes and chronic kidney disease. There was a Union republic, Ukraine, saw modest decline in the decline in age-standardised lung cancer rates for ages 1990 to 2010 period, but a worsening trend in the decade 50–74 years, but an increase in the oldest age category. The after. ten leading causes for DALYs by sex in both of these older age groups largely overlapped in 2019. Among 50–74-year- Cause-specific trends olds, breast cancer, other musculoskeletal disorders, and Two-page cause-specific summaries provide detailed depressive disorders appeared in the top ten for females results on mortality, prev alence, incidence, YLLs, YLDs, only, while road injuries, cirrhosis, and tuberculosis made and DALYs for a selection of diseases, injuries, and it into the top ten for males. For the oldest age group, falls impairments in the GBD cause hierarchy. These sum- and hypertensive heart disease ranked in the top ten maries include 2019 counts, age-standardised rates, and among females, but not males; lung cancer and prostate rankings; the fraction of DALYs attributed to risk factors; cancer ranked among the top ten in males (appendix 2 patterns over time and age; and the relationship between figures S9, S10). SDI and DALY rates by country or territory. They were written to increase the accessibility to and transparency National trends of GBD estimates for each cause. Summaries for select Countries and territories vary widely in their stages of the causes are highlighted in print (pp S2–213); summaries For all two-page summaries see https://www.thelancet.com/ epidemiological transition. With increasing SDI, we for all diseases, injuries, and impairments can be found gbd/summaries expect to see a shift in the burden of disease from commu- online. nicable, maternal, neonatal, and nutritional diseases towards non-communicable causes. We also expect to Discussion see a shift towards a larger fraction of the burden due to Main findings YLDs compared with YLLs. These two major trends can Global health has steadily improved over the past be summarised by the percentage of all-cause DALYs 30 years, as measured by changes in age-standardised made up of non-communicable disease and injury YLDs. DALY rates. While health has improved, after accounting Figure 3 shows this proportion across 204 countries and for population growth and ageing, the absolute number territories in 1990 and 2019. In 2019, this measure of the of DALYs has remained stable. The shift to a much www.thelancet.com Vol 396 October 17, 2020 1213 Global Health Metrics A 1990 YLD proportion of DALYs <10% 30% to 34% 10% to 14% 35% to 39% 15% to 19% 40% to 44% 20% to 24% 45% to 49% 25% to 29% ≥50% Eastern Caribbean and central America Persian Gulf Balkan Peninsula Southeast Asia West Africa Mediterranean Northern Europe B 2019 Eastern Caribbean and central America Persian Gulf Balkan Peninsula Southeast Asia West Africa Mediterranean Northern Europe Figure 3: Proportion of total DALYs contributed by injury and non-communicable disease YLDs, by country or territory, 2019 Proportions were rounded to the nearest whole number. DALY=disability-adjusted life-year. YLD=year lived with disability. 1214 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics GBD super-region Central Europe, eastern Europe, and central Asia High income Latin America and Caribbean North Africa and Middle East South Asia Sub-Saharan Africa AB 1990–2010 2010–19 0·01 UZB ZWE UKR LSO GUM DOM LCA NRU DMA SWZ VCT USA BLR RUS JAM KAZ VEN –0·01 SDN YEM IND MRT CMR ARE MOZ SGP TGO OMN –0·02 ERI AFG GTM CHN KGZ SDN NGA TUR GNB GHA BDI ZAF SOM BLR MWI AGO CIV ZMB KOR RUS KHM BOL MMR LAO MWI NER PER SWZ SLE MDA TLS NPL ETH BGD MMR LAO KAZ BGD –0·03 AGO LBR BTN COD ETH RWA MDV GNQ –0·04 0 50 100 0 50 100 SDI SDI Figure 4: Annualised rate of change in age-standardised DALY rates for all causes excluding HIV/AIDS, natural disasters, and war and conflict, and SDI by country or territory, for 1990–2010 (A) and 2010–19 (B) A simple linear regression line is shown in each figure for the relationship between annualised rate of change and the average SDI value of each country and territory for each time period. AFG=Afghanistan. AGO=Angola. ARE=United Arab Emirates. BDI=Burundi. BGD=Bangladesh. BLR=Belarus. BOL=Bolivia. BTN=Bhutan. CHN=China. CIV=Côte d’Ivoire. CMR=Cameroon. COD=Democratic Republic of the Congo. DALY=disability-adjusted life-year. DMA=Dominica. DOM=Dominican Republic. ERI=Eritrea. ETH=Ethiopia. GHA=Ghana. GNB=Guinea-Bissau. GNQ=Equatorial Guinea. GTM=Guatemala. GUM=Guam. IND=India. JAM=Jamaica. KAZ=Kazakhstan. KHM=Cambodia. KOR=South Korea. KNA=Saint Kitts and Nevis. LAO=Laos. LBR=Liberia. LCA=Saint Lucia. LSO=Lesotho. MDA=Moldova. MDV=Maldives. MMR=Myanmar. MOZ=Mozambique. MRT=Mauritania. MWI=Malawi. NER=Niger. NGA=Nigeria. NPL=Nepal. NRU=Nauru. OMN=Oman. PER=Peru. RUS=Russia. RWA=Rwanda. SDN=Sudan. SGP=Singapore. SLE=Sierra Leone. SOM=Somalia. SWZ=eSwatini. TGO=Togo. TLS=Timor-Leste. TUR=Turkey. UKR=Ukraine. UZB=Uzbekistan. VCT=Saint Vincent and the Grenadines. VEN=Venezuela. YEM=Yemen. ZAF=South Africa. ZWE=Zimbabwe. SDI=Socio-demographic Index. greater number of DALYs occurring at older ages, despite national screening programme for the whole population reductions in age-standardised DALY rates, illustrates aged 12 years and older was established in 2019. Egypt the importance of understanding how ageing shapes had the highest prevalence of chronic hepatitis C in the future health needs. Policy makers should remain aware world, ascribed to iatrogenic infection during treatment 20–22 that the number of DALYs represents the burden of campaigns for schistosomiasis in the 1960s and 1970s. disease that the world’s health systems must manage. The sharp decline in chronic infections in Egypt is Although most diseases showed a pattern of stable or expected to be reflected in a large decline in deaths slowly changing rates of death and disability over the from cirrhosis and liver cancer in coming years. Unlike study period, there are some notable exceptions. Deaths hepatitis B vaccination in children, where the effect of due to drug use disorders have risen sharply over the intervention cannot be expected until several decades past decade. In 2019, more than half of all global overdose later, removal of hepatitis C virus in the adult population deaths occurred in the USA. Liberal prescribing of high- leads to more immediate health impact. dose opioids, inadequate provision of opioid substitution In children younger than 10 years, the decline in therapy, and the lacing of street drugs with highly neonatal disorders was slower than for the major potent opioids such as fentanyl are considered major infectious diseases, thus increasing neonatal disorders’ 14–17 contributors to this public health crisis. By contrast, a share of total DALYs. Among injuries in this age group, positive, rapid change in disease rates has taken place in drowning saw the largest decline in DALYs. The position Egypt, where close to 80% of the population aged 12 years of congenital syphilis among the top ten causes of and older has been screened for hepatitis C, and those DALYs in children is indicative of health system failure. with detectable virus are treated with a low-cost treatment With testing and treatment in the second trimester of 18,19 23 regimen. We estimated that the number of cases of pregnancy, this cause could be eliminated. The main chronic hepatitis C has dropped by 65·9% (95% UI reasons for failure are limited access to health services, 51·1–79·7) since screening and treatment were initiated the low use of rapid diagnostic tests, the failure of through regular health services in 2014 and an enhanced antenatal clinics to screen or treat when a woman is www.thelancet.com Vol 396 October 17, 2020 1215 Annualised rate of change in DALY rate Global Health Metrics tested positive, and the recent global shortage of is mitigating the pathway to reducing the burden of 24 37,38 benzathine penicillin, the treatment of choice. Despite cardiovascular diseases. In the 25–49-year age group, the large health gains among children younger than tuberculosis that is not associated with HIV infection 10 years, considerable burdens still remain in sub- ranked among the top ten causes in 2019. There are Saharan Africa. Sustaining the global pace of progress similar worries about sustained global funding of will become more challenging as an ever-increasing tuberculosis control as mentioned for HIV/AIDS, let proportion of the global birth cohort is born in sub- alone having the additional resources and research Saharan Africa, with the highest rates of burden in development effort that would be required to reach these age groups. It is encouraging, however, that the WHO’s goals to reduce the 2015 levels of tuberculosis largest decreases in DALY rates globally have occurred in deaths and incidence by 90% and 80%, respectively, by 39–41 sub-Saharan African countries, such as Ethiopia, Angola, the year 2030. Rwanda, and Malawi, although there are others that The prominent rankings of COPD and lung cancer have seen much less progress. in the 50–74-year and 75-years-and-older age groups Among the top ten causes of DALYs in adolescents emphasise the continuing need for tobacco-control aged 10–24 years, self-harm had the largest decline measures and attention to reducing exposure to indoor (28·4% [95% UI 18·9–36·3]) over the study period. The and outdoor air pollution. Already, low-income and prevalence of depressive disorders and other mental middle-income countries account for 62·6% of the disorders, which are major underlying causes of self- global burden of COPD and lung cancer, and this share harm, did not change, sug gesting that the decline in is likely to increase sharply over coming decades due to self-harm deaths was largely due to other factors such as ageing populations and less successful tobacco and air better access to mental health services, urbanisation, and pollution control. The finding that lung cancer DALY 27–30 a reduction in access to more lethal means of suicide. rates are declining in the 50–74-year age group but not in The increase in DALY rates of neonatal disorders in this those aged 75 years and older is probably due to a cohort age group is a downside to the large improvements in effect; this could be encouraging if it reflects a greater neonatal survival, causing a greater proportion of the response to tobacco control in younger generations that surviving babies to have long-term neurological and will drive further declines in coming years. Chronic sensory deficits. kidney disease is strongly linked to cardiovascular In the 25–49-year age group, HIV/AIDS was the second diseases and diabetes, and shares common risks and leading cause of DALYs in 2019 despite a drop since 2005, intervention approaches. Given its prominent position when ART became more widely available. To be on in the top ten rankings of DALYs in older age groups and course to end HIV/AIDS as a public health threat the costs associated with end-stage kidney disease by 2030, UNAIDS estimates that a substantial increase in treatments, screening and low-cost treatments at earlier global funding would be required, whereas high-income stages of chronic kidney disease should be more widely 32 43 countries have reduced their funding. The prominent implemented. Cirrhosis ranked seventh among those position of headache disorders in the DALY rankings in aged 50–74 years in 2019. With low-cost treatments the 10–24-year and 25–49-year age groups has received available to low-income and middle-income countries, little attention in global health policy debates. While there is an opportunity to eradicate hepatitis C as an there is no cure for these disorders, there are effective underlying cause—a strategy that Egypt is well on the 33 19 symptomatic and preventive treatments available. way to achieving in coming years. Child hood vac- Ischaemic heart disease, stroke, and diabetes were not cinations for hepatitis B will eventually also reduce among the 25 leading causes in the two younger age cirrhosis (and liver cancer) outcomes, but the full effect groups, but emerged as major contributors to burden in will probably not be apparent for years. Alcohol is the the 25–49-year age group and, more prominently, in the third modifiable cause of cirrhosis; there is strong older age groups that follow. These diseases share many evidence that taxation and regulations can reduce alcohol common risk factors and treatment approaches. The use to less harmful levels. Age-related hearing loss is a burden in high-income countries has been rapidly top ten cause of DALYs in the two older age groups. declining since the 1980s, but a more recent downturn in While some reduction in burden can be achieved by this decline over the past 5 years has been noted as an control of loud noises during leisure or occupational important explanation for the slowdown in life expectancy activities, most of the burden cannot be prevented gains. Low-income and middle-income countries still through currently known strategies. For a large propor- have ample opportunity to make greater use of known tion of the elderly, hearing aids can relieve some of the effective intervention strategies (tobacco control, blood symptoms and associated social isolation. The quality of pressure-lowering and cholesterol-lowering treatments, hearing aids has improved over the past decade, but low- and emergency response and treatment for acute events) cost appliances are not readily available in low-income 35 45 that have been so effective in high-income countries. and middle-income countries. However, the rising prevalence of diabetes, linked to the Alzheimer’s disease and other dementias, and falls are almost ubiquitous increase in body-mass index globally, two causes that appear in the top ten ranking of DALYs 1216 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics only for those aged 75 years and older. The ability to violence in Latin American countries, in addition to intervene by prevention or treatment for dementia is still the decelerated decline of cardiovascular mortality, are limited despite a large research and development effort driving the patterns in these locations. The mix of to identify drugs, but efforts continue. There is good universal and more geographically specific influences on evidence that a range of modifiable risks (tobacco, health reinforces the need for regular, detailed reporting physical inactivity, metabolic risks, and hearing loss) on population health by underlying cause to help decision 47,48 contribute to the development of dementia, but little makers to identify success stories of disease control, as evidence of the effectiveness of interventions addressing well as opportunities to improve and emulate countries 47,49 these risk factors. Falls in the elderly are common and that are performing well. linked to psychotropic and cardiovascular medications, 51,52 cognitive impairment, depression, and general frailty. Limitations There is evidence for the effectiveness of multifactorial The major limitation of the GBD analysis of the burden interventions combining education, exercise, and home of diseases and injuries is the availability of primary data. safety modification interventions. Where data are not available, the results depend on the The trend towards disability as an increasing share of out-of-sample predictive validity of the modelling efforts. overall burden has continued. In 11 countries, more than While improvements to data processing and modelling half the burden was from YLDs of NCDs and injuries in can lead to incremental improvements in the accuracy of 2019. To some extent, the absence of a discernible trend our estimates, fundamental improvements require more in disability might be an artifact of the poor availability of and better primary data collection. Even when data are data on severity, and, therefore, an inability to quantify available, they might not have been obtained using the effect of health service interventions that modulate the preferred case definition or measurement method. severity. The larger issue, however, is that most of the The more explicit identification of the preferred and focus of global public health has been on life-saving alternative measurement method for each outcome, and 7,54,55 interventions directed at the main causes of death. the bias mapping from alternative to reference method The large contributors to disability, such as muscu- undertaken as part of GBD 2019, have led to greater loskeletal conditions and mental disorders, are associated stability in data adjustments. These improvements will with few deaths. As disability becomes an increasingly also aid in identifying priorities for data collection and large component of disease burden and, as importantly, in determining preferred case definitions and study a larger component of health expenditure, a greater methods. Moving to use of standard locations for research development investment is needed to identify estimating fixed effects in the models will aid in cycle-to- 56–58 new, and more effective, intervention strategies. With cycle stability of models. Through the use of standard a rapidly ageing global population, the demands on locations, the addition of more subnational units in a health services to deal with disabling outcomes, which given GBD cycle should not shift the regression model increase with age, will require policy makers to anticipate predictions as much as they previously would have. these changes. GBD provides key information on the Nevertheless, collinearity between covariates in some of changes in types of health services in terms of facilities these models might contribute to some instability in and adequately trained personnel that will be needed. fixed effects between cycles. Future work on ensemble The finding that health gains in countries at the lower models might help to solve the collinearity problem. Of end of the SDI scale have, on average, accelerated over the note, because the cause of death models developed using past decade compared with the two decades before CODEm are an ensemble of all high-performing possible indicates the potential for low-income countries to make models, they avoid the instability due to collinearity. a real difference by investing in health. Progress, however, Although our statistical modelling is designed to capture has been uneven. The more recent downturn in reduc- uncertainty from stochastic variation in input data, age tions in DALY rates in countries and territories with and sex splitting of data, corrections for alternative case higher SDI is striking and near universal, although an definitions or uninformative cause of death codes, other actual reversal into increases of age-standardised DALY data manipulations, and model choice, it remains a rates has only happened in a small number of countries challenge to fully represent the UIs around estimates, in the Caribbean and the USA. Plausible drivers of this particularly in locations with sparse or absent data. This change include obesity, diminishing potential for further will remain a major focus of GBD by tapping into existing reductions in smoking, and improvements in coverage of knowledge in other estimation fields as well as our own treatments for high blood pressure and cholesterol to development of methods. maintain the past declines in cardiovascular mortality. The shift to adjusting dementia deaths to reflect Inequalities in access to preventive and curative services only those with end-stage disease is conceptually more by lower socioeconomic groups might be a further appealing than the past crude adjustment for the large obstacle to continued improvements in cardiovascular variation in coding practices. We will, however, need to mortality. The large increase in drug overdose deaths in replicate the methods of determining the share of excess the USA and the increasing number of deaths from mortality in people with dementia who are in the last www.thelancet.com Vol 396 October 17, 2020 1217 Global Health Metrics stages of the disease and for whom an assignment of some high SDI countries points out that further gains dementia as the underlying cause of death is therefore are not inevitable. Close monitoring of health trends and justified. A greater focus in future rounds of GBD will careful policy evaluation of the options to counteract need to be directed to identifying data of treatment effects adverse trends is required. Leading causes of DALYs, as on severity distributions of the large contributors to well as solutions, differ substantially across age groups, YLDs, such as mental, neurological, and musculoskeletal highlighting the need to formulate policy for different disorders, for which we currently do not distinguish phases of the life course. geographical variation in severity. This is of particular Contributors Please see appendix 1 for more detailed information about individual importance as these conditions represent an increasing authors’ contributions to the research, divided into the following share of total burden. Our effort to improve the consis - categories: managing the estimation process; writing the first draft of tency between mortality rates, prevalence, and incidence the manuscript; providing data or critical feedback on data sources; for selected conditions by providing more explicit developing methods or computational machinery; applying analytical methods to produce estimates; providing critical feedback on methods or guidance on excess mortality rates in DisMod-MR has results; drafting the work or revising it critically for important revealed that more attention will be required in future intellectual content; extracting, cleaning, or cataloguing data; designing rounds of GBD. After imposing a pattern of excess or coding figures and tables; and managing the overall research mortality that follows an expected pattern of lower rates enterprise. in countries with better health systems, the models might Declaration of interests predict prevalence or incidence estimates that are far I N Ackerman reports grants from Victorian Government, outside of the submitted work. C A T Antonio reports personal fees from Johnson & removed from observed data. The challenge is then to Johnson (Philippines), outside of the submitted work. E Beghi reports identify whether the inconsistency is due to error in the grants from Italian Ministry of Health and SOBI and personal fees from cause of death estimates, the non-fatal data sources, or a Arvelle Therapeutics, outside of the submitted work. Y Béjot reports combination of the two. In addition to these general personal fees from AstraZeneca, Bristol Myers Squibb, Pfizer, and Medtronic, Merck Sharpe & Dohme, and Amgen; grants and personal limitations, there are many limitations for each specific fees from Boehringer-Ingelheim; personal fees and non-financial modelling exercise reported in this study. Appendix 1 support from Servier; and non-financial support from Biogen, outside of (sections 3.4 and 4.12) provides more insight into some of the submitted work. P S Briant reports personal fees from WHO, these issues. outside of the submitted work. H Christensen reports personal fees from Bristol Myers Squibb, Bayer, and Boehringer Ingelheim, outside of the submitted work. L Degenhardt reports grants from Indivior and Future directions Seqirus, outside of the submitted work. S J Dunachie reports grants Several method improvements signalled in previous from the Fleming Fund at the UK Department of Health and Social GBD publications have not yet been implemented but Care, during the conduct of the study. L M Haile reports personal fees from WHO, outside of the submitted work. S M S Islam reports grants remain a priority. For instance, DisMod-AT, a new version from National Heart Foundation of Australia and Deakin University, of our main non-fatal modelling tool that simultaneously during the conduct of the study. S L James reports grants from Sanofi solves for patterns over age and time, is still undergoing Pasteur and employment from Genentech, outside of the submitted testing before it can be implemented in GBD. Methods to work. P Jeemon reports and Clinical and Public Health intermediate fellowship (grant number IA/CPHI/14/1/501497) from the Wellcome make dependent comorbidity corrections computationally Trust—Department of Biotechnology, India Alliance (2015–2020). V Jha feasible, and imposing greater variation in severity reports grants from Baxter Healthcare, GlaxoSmithKline, Zydus Cadilla, distributions based on access to and quality of health NephroPlus, and Biocon, outside of the submitted work. J J Jozwiak care, are also still under development. More generally, reports personal fees from Amgen, ALAB Laboratoria, Teva, Synexus, and Boehringer Ingelheim, outside of the submitted work. imposing GBD principles and methods to the estimation S V Katikireddi reports grants from NRS Senior Clinical Fellowship, of access to health interventions and the effectiveness Scottish Government Chief Scientist Office, and the UK Medical thereof, and being able to link those estimates with our Research Council, during the conduct of the study. S Lewington reports grants from the UK Medical Research Council and the CDC Foundation future health scenario platform is a direction we are (with support from Amgen), outside of the submitted work. K J Looker keen to take. Developing this comprehensively is a large reports grants from WHO and GlaxoSmithKline, outside of the endeavour that will take many years to complete. As this submitted work. S Lorkowski reports personal fees from Akcea would greatly add value to the policy relevance of GBD, Therapeutics, Amedes, Amgen, Berlin-Chemie, Boehringer Ingelheim Pharma, Daiichi Sankyo, Merck Sharp & Dohme, Novo Nordisk, Sanofi- we will also aim to develop less comprehensive methods Aventis, Synlab, Unilever, and Upfield and non-financial support from that will nevertheless allow us to respond to policy makers Preventicus, outside of the submitted work. R A Lyons reports grants seeking information on major policy decisions in a more from Health Data Research UK, outside of the submitted work. timely fashion. J Massano reports personal fees from Abbvie, Bial, Merck Sharp & Dohme, and Zambon and other support from Boston Scientific, GE Healthcare, Medtronic, and Roche, outside of the submitted work. Conclusion W Mendoza is a Program Analyst in Population and Development at the Taking into account population growth and shifts in age UN Population Fund Country Office in Peru, an institution that does not structure, health continues to improve at the global level. necessarily endorse this study. M Moradi-Lakeh reports personal fees from Novartis, outside of the submitted work. J F Mosser reports grants The absolute burden of disease and its associated impact from the Bill & Melinda Gates Foundation, during the conduct of the on health systems, however, remain resolutely constant. study. S Nomura reports grants from the Japanese Ministry of Some diseases, such as diabetes, are increasing in Education, Culture, Sports, Science, and Technology. S B Patten reports burden, and more general all-cause DALY stagnation in funding from the Cuthbertson & Fischer Chair in Pediatric Mental 1218 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics Health, during the conduct of the study. T Pilgrim reports grants and Retirement Cohort was funded by the US National Institute on Aging personal fees from Biotronik and Boston Scientific, grants from Edwards (grant R01AG031716). The principal investigators are Luis Rosero-Bixby Lifesciences, and personal fees from HighLife SAS for his work as a and William H Dow and co-principal investigators are Xinia Fernández member of clinical event committee for a study sponsored by HighLife and Gilbert Brenes. The accuracy of the authors’ statistical analysis and SAS, outside of the submitted work. M J Postma reports grants and the findings they report are not the responsibility of ECDC. ECDC is not personal fees from Merck Sharp & Dohme, GlaxoSmithKline, Pfizer, responsible for conclusions or opinions drawn from the data provided. Boehringer Ingelheim, Novavax, Bristol Myers Squibb, AstraZeneca, ECDC is not responsible for the correctness of the data and for data Sanofi, IQVIA, and Seqirus; personal fees from Quintiles, Novartis, management, data merging and data collation after provision of the data. and Pharmerit; grants from Bayer, BioMerieux, WHO, EU, FIND, ECDC shall not be held liable for improper or incorrect use of the data. Antilope, DIKTI, LPDP, and Budi; and other support from Ingress The Health Behaviour in School-Aged Children (HBSC) study is an Health, PAG, and Asc Academics, outside of the submitted work. international study carried out in collaboration with WHO/EURO. E Pupillo reports grants from AIFA, outside of the submitted work. The international coordinator of the 1997–98, 2001–02, 2005–06, A E Schutte reports personal fees from Omron, Servier, Novartis, and 2009–10 surveys was Candace Currie and the databank manager for Takeda, and Abbott, outside of the submitted work. M G Shrime reports the 1997–98 survey was Bente Wold, whereas for the following surveys grants from Damon Runyon Cancer Research Foundation and Mercy Oddrun Samdal was the databank manager. A list of principal Ships, outside of the submitted work. J A Singh reports personal fees investigators in each country can be found on the HBSC website. For the HBSC website see from Crealta/Horizon, Medisys, Fidia, UBM, Trio Health, Medscape, Data used in this paper come from the 2009–10 Ghana Socioeconomic http://www.hbsc.org WebMD, Clinical Care Options, Clearview Healthcare Partners, Putnam Panel Study Survey, which is a nationally representative survey of more Associates, Spherix, Practice Point Communications, National Institutes than 5000 households in Ghana. The survey is a joint effort undertaken of Health, and the American College of Rheumatology; personal fees by the Institute of Statistical, Social and Economic Research (ISSER) at from Simply Speaking; other support from Amarin Pharmaceuticals and the University of Ghana and the Economic Growth Centre (EGC) at Yale Viking Pharmaceuticals; and non-financial support from the steering University. It was funded by EGC. ISSER and the EGC are not committee of OMERACT (an international organisation that develops responsible for the estimations reported by the analysts. The Palestinian measures for clinical trials and receives arm’s length funding from Central Bureau of Statistics granted the researchers access to relevant 12 pharmaceutical companies), US Food and Drug Administration, data in accordance with license number SLN2014-3-170, after subjecting Arthritis Advisory Committee, Veterans Affairs Rheumatology Field data to processing aiming to preserve the confidentiality of individual Advisory Committee, and the editor and director of the UAB Cochrane data in accordance with the General Statistics Law, 2000. The researchers Musculoskeletal Group Satellite Center on Network Meta-analysis, are solely responsible for the conclusions and inferences drawn upon outside of the submitted work. S T S Skou reports personal fees from available data. Data for this research was provided by MEASURE Journal of Orthopaedic & Sports Physical Therapy and Munksgaard and Evaluation, funded by USAID. The authors thank the Russia grants from The Lundbeck Foundation, outside of the submitted work; Longitudinal Monitoring Survey, conducted by the National Research and being co-founder of GLA:D. GLA:D is a non-profit initiative hosted University Higher School of Economics and ZAO Demoscope together at University of Southern Denmark aimed at implementing clinical with Carolina Population Center, University of North Carolina at guidelines for osteoarthritis in clinical practice. J D Stanaway reports Chapel Hill and the Institute of Sociology, Russia Academy of Sciences grants from Bill & Melinda Gates Foundation, during the conduct of the for making data available. This paper uses data from the Bhutan 2014 study. R Uddin reports travel and accommodation reimbursement from STEPS survey, implemented by the Ministry of Health with the support Deakin University Institute for Physical Activity and Nutrition, outside of WHO; the Kuwait 2006 and 2014 STEPS surveys, implemented by the of the submitted work. All other authors declare no competing interests. Ministry of Health with the support of WHO; the Libya 2009 STEPS survey, implemented by the Secretariat of Health and Environment with Data sharing the support of WHO; the Malawi 2009 STEPS survey, implemented by For the Global Health Data To download the data used in these analyses, please visit the Global Ministry of Health with the support of WHO; and the Moldova 2013 Exchange GBD 2019 website Health Data Exchange GBD 2019 website. STEPS survey, implemented by the Ministry of Health, the National see http://ghdx.healthdata.org/ Acknowledgments Bureau of Statistics, and the National Center of Public Health with the gbd−2019 Research reported in this publication was supported by the Bill & support of WHO. This paper uses data from Survey of Health, Ageing For more on SHARE see Melinda Gates Foundation; the University of Melbourne; Queensland and Retirement in Europe (SHARE) Waves 1 (DOI:10.6103/SHARE. http://www.share-project.org Department of Health, Australia; the National Health and Medical w1.700), 2 (10.6103/SHARE.w2.700), 3 (10.6103/SHARE.w3.700), Research Council, Australia; Public Health England; the Norwegian 4 (10.6103/SHARE.w4.700), 5 (10.6103/SHARE.w5.700), Institute of Public Health; St Jude Children’s Research Hospital; 6 (10.6103/SHARE.w6.700), and 7 (10.6103/SHARE.w7.700); the Cardiovascular Medical Research and Education Fund; the National see Börsch-Supan and colleagues (2013) for methodological details. Institute on Ageing of the National Institutes of Health (award The SHARE data collection has been funded by the European P30AG047845); and the National Institute of Mental Health of the Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: National Institutes of Health (award R01MH110163). The content is RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: solely the responsibility of the authors and does not necessarily CIT4-CT-2006-028812), FP7 (SHARE-PREP: GA N°211909, represent the official views of the funders. The authors alone are SHARE-LEAP: GA N°227822, SHARE M4: GA N°261982) and Horizon responsible for the views expressed in this Article and they do not 2020 (SHARE-DEV3: GA N°676536, SERISS: GA N°654221) and by necessarily represent the views, decisions, or policies of the institutions DG Employment, Social Affairs & Inclusion. Additional funding from with which they are affiliated, the National Health Service (NHS), the German Ministry of Education and Research, the Max Planck Society the National Institute for Health Research (NIHR), the UK Department for the Advancement of Science, the US National Institute on Aging of Health and Social Care, or Public Health England; the United States (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, Agency for International Development (USAID), the US Government, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064, or MEASURE Evaluation; or the European Centre for Disease HHSN271201300071C), and from various national funding sources is Prevention and Control (ECDC). This research used data from the Chile gratefully acknowledged. This study has been realised using the data National Health Survey 2003, 2009–10, and 2016–17. The authors are collected by the Swiss Household Panel, which is based at the Swiss grateful to the Ministry of Health, the survey copyright owner, for Centre of Expertise in the Social Sciences. The project is financed by the allowing them to have the database. All results of the study are those of Swiss National Science Foundation. The United States Aging, the authors and in no way committed to the Ministry. The Costa Rican Demographics, and Memory Study is a supplement to the Health and Longevity and Healthy Aging Study project is a longitudinal study by the Retirement Study (HRS), which is sponsored by the National Institute of University of Costa Rica’s Centro Centroamericano de Población and Aging (grant number NIA U01AG009740). It was conducted jointly by Instituto de Investigaciones en Salud, in collaboration with the Duke University and the University of Michigan. The HRS is sponsored University of California at Berkeley. The original pre-1945 cohort was by the National Institute on Aging (grant number NIA U01AG009740) funded by the Wellcome Trust (grant 072406), and the 1945–55 and is conducted by the University of Michigan. This paper uses data www.thelancet.com Vol 396 October 17, 2020 1219 Global Health Metrics from Add Health, a program project designed by J Richard Udry, Competitiveness Program (project ID P_40_382). P Hoogar acknowledges Peter S Bearman, and Kathleen Mullan Harris, and funded by a grant Centre for Bio Cultural Studies, Directorate of Research, Manipal P01-HD31921 from the Eunice Kennedy Shriver National Institute of Academy of Higher Education and Centre for Holistic Development and Child Health and Human Development, with cooperative funding from Research, Kalaghatagi. F N Hugo acknowledges the Visiting 17 other agencies. Special acknowledgment is due to Ronald R Rindfuss Professorship, PRINT Program, CAPES Foundation, Brazil. B-F Hwang and Barbara Entwisle for assistance in the original design. Information was supported by China Medical University (CMU107-Z-04), Taichung, For the Add Health website see on how to obtain the Add Health data files is available on the Add Health Taiwan. S M S Islam was funded by a National Heart Foundation Senior http://www.cpc.unc.edu/ website. No direct support was received from grant P01-HD31921 for this Research Fellowship and supported by Deakin University. R Q Ivers was addhealth analysis. The data reported here have been supplied by the United States supported by a research fellowship from the National Health and Medical Renal Data System. The interpretation and reporting of these data are Research Council of Australia. M Jakovljevic acknowledges the Serbian the responsibility of the authors and in no way should be seen as an part of this GBD-related contribution was co-funded through Grant official policy or interpretation of the US Government. Collection of data OI175014 of the Ministry of Education Science and Technological for the Mozambique National Survey on the Causes of Death 2007–08 was Development of the Republic of Serbia. P Jeemon was supported by a made possible by USAID under the terms of cooperative agreement Clinical and Public Health intermediate fellowship (grant number GPO-A-00-08-000_D3-00. This manuscript is based on data collected and IA/CPHI/14/1/501497) from the Wellcome Trust—Department of shared by the International Vaccine Institute (IVI) from an original study Biotechnology, India Alliance (2015–20). O John is a recipient of UIPA IVI conducted. L G Abreu acknowledges support from Coordenação de scholarship from University of New South Wales, Sydney. S V Katikireddi Aperfeiçoamento de Pessoal de Nível Superior (Brazil; finance code 001) acknowledges funding from a NRS Senior Clinical Fellowship and Conselho Nacional de Desenvolvimento Científico e Tecnológico (SCAF/15/02), the Medical Research Council (MC_UU_12017/13, (CNPq, a Brazilian funding agency). I N Ackerman was supported by a MC_UU_12017/15), and the Scottish Government Chief Scientist Office Victorian Health and Medical Research Fellowship awarded by the (SPHSU13, SPHSU15). C Kieling is a CNPq researcher and a UK Victorian Government. O O Adetokunboh acknowledges the South Academy of Medical Sciences Newton Advanced Fellow. Y J Kim was African Department of Science and Innovation and the National Research supported by Research Management Office, Xiamen University Malaysia Foundation. A Agrawal acknowledges the Wellcome Trust DBT India (XMUMRF/2018-C2/ITCM/00010). K Krishan is supported by UGC Alliance Senior Fellowship. S M Aljunid acknowledges the Department of Centre of Advanced Study awarded to the Department of Anthropology, Health Policy and Management, Faculty of Public Health, Kuwait Panjab University, Chandigarh, India. M Kumar was supported by University and International Centre for Casemix and Clinical Coding, K43 TW 010716 FIC/NIMH. B Lacey acknowledges support from the Faculty of Medicine, National University of Malaysia for the approval and NIHR Oxford Biomedical Research Centre and the BHF Centre of support to participate in this research project. M Ausloos, C Herteliu, Research Excellence, Oxford. J V Lazarus was supported by a Spanish and A Pana acknowledge partial support by a grant of the Romanian Ministry of Science, Innovation and Universities Miguel Servet grant National Authority for Scientific Research and Innovation, (Instituto de Salud Carlos III [ISCIII]/ESF, the EU [CP18/00074]). CNDS-UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084. K J Looker thanks the NIHR Health Protection Research Unit in A Badawi is supported by the Public Health Agency of Canada. Evaluation of Interventions at the University of Bristol, in partnership D A Bennett was supported by the NIHR Oxford Biomedical Research with Public Health England, for research support. S Lorkowski was Centre. R Bourne acknowledges the Brien Holden Vision Institute, funded by the German Federal Ministry of Education and Research University of Heidelberg, Sightsavers, Fred Hollows Foundation, (nutriCARD, grant agreement number 01EA1808A). R A Lyons is and Thea Foundation. G B Britton and I Moreno Velásquez were supported by Health Data Research UK (HDR-9006), which is funded by supported by the Sistema Nacional de Investigación, SNI-SENACYT, the UK Medical Research Council, Engineering and Physical Sciences Panama. R Buchbinder was supported by an Australian National Health Research Council, Economic and Social Research Council, NIHR and Medical Research Council (NHMRC) Senior Principal Research (England), Chief Scientist Office of the Scottish Government Health and Fellowship. J J Carrero was supported by the Swedish Research Council Social Care Directorates, Health and Social Care Research and (2019-01059). F Carvalho acknowledges UID/MULTI/04378/2019 and Development Division (Welsh Government), Public Health Agency UID/QUI/50006/2019 support with funding from FCT/MCTES through (Northern Ireland), British Heart Foundation, and Wellcome Trust. national funds. A R Chang was supported by National Institutes of J J McGrath is supported by the Danish National Research Foundation Health/National Institute of Diabetes and Digestive and Kidney Diseases (Niels Bohr Professorship), and the Queensland Health Department grant K23 DK106515. V M Costa acknowledges the grant (via West Moreton HHS). P T N Memiah acknowledges support from SFRH/BHD/110001/2015, received by Portuguese national funds through CODESRIA. U O Mueller gratefully acknowledges funding by the Fundação para a Ciência e Tecnologia, IP, under the Norma Transitária German National Cohort Study BMBF grant number 01ER1801D. DL57/2016/CP1334/CT0006. A Douiri acknowledges support and funding S Nomura acknowledges the Ministry of Education, Culture, Sports, from the National Institute for Health Research Collaboration for Science, and Technology of Japan (18K10082). A Ortiz was supported by Leadership in Applied Health Research and Care South London at King’s ISCIII PI19/00815, DTS18/00032, ISCIII-RETIC REDinREN RD016/0009 College Hospital NHS Foundation Trust and the Royal College of Fondos FEDER, FRIAT, Comunidad de Madrid B2017/BMD-3686 Physicians, and support from the NIHR Biomedical Research Centre CIFRA2-CM. These funding sources had no role in the writing of the based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College manuscript or the decision to submit it for publication. S B Patten was London. B B Duncan acknowledges grants from the Foundation for the supported by the Cuthbertson & Fischer Chair in Pediatric Mental Health Support of Research of the State of Rio Grande do Sul (IATS and PrInt) at the University of Calgary. G C Patton was supported by an åNHMRC and the Brazilian Ministry of Health. H E Erskine is the recipient of an Senior Principal Research Fellowship. M R Phillips was supported in part Australian NHMRC Early Career Fellowship grant (APP1137969). by the National Natural Science Foundation of China (NSFC, number A J Ferrari was supported by a NHMRC Early Career Fellowship grant 81371502 and 81761128031). A Raggi, D Sattin, and S Schiavolin were (APP1121516). H E Erskine and A J Ferrari are employed by and supported by grants from the Italian Ministry of Health (Ricerca Corrente, A M Mantilla-Herrera and D F Santomauro affiliated with the Queensland Fondazione Istituto Neurologico C Besta, Linea 4—Outcome Research: Centre for Mental Health Research, which receives core funding from the dagli Indicatori alle Raccomandazioni Cliniche). P Rathi and Queensland Department of Health. M L Ferreira holds an NHMRC B Unnikrishnan acknowledge Kasturba Medical College, Mangalore, Research Fellowship. C Flohr was supported by the NIHR Biomedical Manipal Academy of Higher Education, Manipal. A L P Ribeiro was Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust. supported by Brazilian National Research Council, CNPq, and the M Freitas acknowledges financial support from the EU (European Minas Gerais State Research Agency, FAPEMIG. D C Ribeiro was Regional Development Fund [FEDER] funds through COMPETE supported by The Sir Charles Hercus Health Research Fellowship POCI-01-0145-FEDER-029248) and National Funds (Fundação para a (#18/111) Health Research Council of New Zealand. D Ribeiro Ciência e Tecnologia) through project PTDC/NAN-MAT/29248/2017. acknowledges financial support from the EU (FEDER funds through the A L S Guimaraes acknowledges support from CNPq. C Herteliu was Operational Competitiveness Program; POCI-01-0145-FEDER-029253). partially supported by a grant co-funded by FEDER through Operational P S Sachdev acknowledges funding from the NHMRC of Australia 1220 www.thelancet.com Vol 396 October 17, 2020 Global Health Metrics Program Grant. A M Samy was supported by a fellowship from the 14 Jones GH, Bruera E, Abdi S, Kantarjian HM. The opioid epidemic Egyptian Fulbright Mission Program. M M Santric-Milicevic acknowledges in the United States—overview, origins, and potential solutions. Obstet Gynecol Surv 2019; 74: 278. the Ministry of Education, Science and Technological Development of the 15 Lyden J, Binswanger IA. The United States opioid epidemic. Republic of Serbia (contract number 175087). R Sarmiento-Suárez received Semin Perinatol 2019; 43: 123–31. institutional support from Applied and Environmental Sciences University 16 National Institute on Drug Abuse. Opioid overdose crisis. (Bogotá, Colombia) and ISCIII (Madrid, Spain). A E Schutte received February, 2020. https://www.drugabuse.gov/drugs-abuse/opioids/ support from the South African National Research Foundation SARChI opioid-overdose-crisis (accessed Feb 21, 2020). Initiative (GUN 86895) and Medical Research Council. S T S Skou is 17 Public Health Agency of Canada. Apparent opioid-related deaths in currently funded by a grant from Region Zealand (Exercise First) and a Canada. December, 2019. https://health-infobase.canada.ca/ grant from the European Research Council under the EU’s Horizon 2020 substance-related-harms/opioids/ (accessed Dec 3, 2019). research and innovation program (grant agreement number 801790). 18 Soliman G, Elzalabany MS, Hassanein T, Miller FD. Mass screening J B Soriano is funded by Centro de Investigación en Red de Enfermedades for hepatitis B and C in Southern Upper Egypt. BMC Public Health Respiratorias, ISCIII. R Tabarés-Seisdedos was supported in part by the 2019; 19: 1326. national grant PI17/00719 from ISCIII–FEDER. N Taveira was partially 19 Abdel-Razek W, Hassany M, Kabil K, et al. The world’s largest supported by the European & Developing Countries Clinical Trials hepatitis C screening program in Egypt. 2019. https://www. 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