Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

Global Health Metrics 1260 www.thelancet.com Vol 390 September 16, 2017 Global, regional, and national disability-adjusted life-years (DALYs) for 333 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016 GBD 2016 DALYs and HALE Collaborators* Summary Background Measurement of changes in health across locations is useful to compare and contrast changing epidemiological patterns against health system performance and identify specific needs for resource allocation in research, policy development, and programme decision making. Using the Global Burden of Diseases, Injuries, and Risk Factors Study 2016, we drew from two widely used summary measures to monitor such changes in population health: disability-adjusted life-years (DALYs) and healthy life expectancy (HALE). We used these measures to track trends and benchmark progress compared with expected trends on the basis of the Socio-demographic Index (SDI). Methods We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 for all-cause mortality, cause-specific mortality, and non-fatal disease burden to derive HALE and DALYs by sex for 195 countries and territories from 1990 to 2016. We calculated DALYs by summing years of life lost and years of life lived with disability for each location, age group, sex, and year. We estimated HALE using age-specific death rates and years of life lived with disability per capita. We explored how DALYs and HALE differed from expected trends when compared with the SDI: the geometric mean of income per person, educational attainment in the population older than age 15 years, and total fertility rate. Findings The highest globally observed HALE at birth for both women and men was in Singapore, at 75·2 years (95% uncertainty interval 71·9–78·6) for females and 72·0 years (68·8–75·1) for males. The lowest for females was in the Central African Republic (45·6 years [42·0–49·5]) and for males was in Lesotho (41·5 years [39·0–44·0]). From 1990 to 2016, global HALE increased by an average of 6·24 years (5·97–6·48) for both sexes combined. Global HALE increased by 6·04 years (5·74–6·27) for males and 6·49 years (6·08–6·77) for females, whereas HALE at age 65 years increased by 1·78 years (1·61–1·93) for males and 1·96 years (1·69–2·13) for females. Total global DALYs remained largely unchanged from 1990 to 2016 (–2·3% [–5·9 to 0·9]), with decreases in communicable, maternal, neonatal, and nutritional (CMNN) disease DALYs offset by increased DALYs due to non-communicable diseases (NCDs). The exemplars, calculated as the five lowest ratios of observed to expected age-standardised DALY rates in 2016, were Nicaragua, Costa Rica, the Maldives, Peru, and Israel. The leading three causes of DALYs globally were ischaemic heart disease, cerebrovascular disease, and lower respiratory infections, comprising 16·1% of all DALYs. Total DALYs and age-standardised DALY rates due to most CMNN causes decreased from 1990 to 2016. Conversely, the total DALY burden rose for most NCDs; however, age-standardised DALY rates due to NCDs declined globally. Interpretation At a global level, DALYs and HALE continue to show improvements. At the same time, we observe that many populations are facing growing functional health loss. Rising SDI was associated with increases in cumulative years of life lived with disability and decreases in CMNN DALYs offset by increased NCD DALYs. Relative compression of morbidity highlights the importance of continued health interventions, which has changed in most locations in pace with the gross domestic product per person, education, and family planning. The analysis of DALYs and HALE and their relationship to SDI represents a robust framework with which to benchmark location-specific health performance. Country-specific drivers of disease burden, particularly for causes with higher-than-expected DALYs, should inform health policies, health system improvement initiatives, targeted prevention efforts, and development assistance for health, including financial and research investments for all countries, regardless of their level of sociodemographic development. The presence of countries that substantially outperform others suggests the need for increased scrutiny for proven examples of best practices, which can help to extend gains, whereas the presence of underperforming countries suggests the need for devotion of extra attention to health systems that need more robust support. Funding Bill & Melinda Gates Foundation. Copyright © The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Lancet 2017; 390: 1260–344 *Collaborators listed at the end of the Article Correspondence to: Prof Simon Iain Hay, Institute for Health Metrics and Evaluation, Seattle, WA 98121, USA [email protected] Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1261 Introduction Objective measurement of population health is a fundamental requirement of good governance that allows international, regional, national, and local actors to frame evidence-based policy informed by past trends and current performance of health systems.1–4 Summary measures of population health include techniques that measure the overall burden of health loss due to fatal and non-fatal diseases, as well as measures of expected fatal and nonfatal disease burden based on Socio-demographic Index (SDI).5 The disability-adjusted life-year (DALY) measures health loss due to both fatal and non-fatal disease burden. DALYs are the sum of the years of life lost (YLLs) due to premature mortality and years of life lived with disability (YLDs).6 The YLL is based on remaining life expectancy when compared with a reference standard life table at age of death,7 and the YLD is calculated by multiplying the prevalence of a disease or injury and its main disabling outcomes by its weighted level of severity.6,8 One DALY represents 1 year of healthy life lost. Examination of levels and trends of DALYs facilitates quick comparison between different diseases and injuries. Conversely, healthy life expectancy (HALE), a metric based on methods by Sullivan,9 provides a single summary measure of population health across all causes combined by weighting years lived with a measure of functional health loss before death and is the most comprehensive among competing expectancy metrics.1–4 Together, DALYs and HALE enable comparisons of the magnitude of functional health loss across societies due to diseases, injuries, and risk factors, against which provisioning and performance of health systems can be calibrated.4 Research in context Evidence before this study The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) provided disability-adjusted life-year (DALY) estimates for 315 diseases and injuries for 195 countries and territories, including subnational assessments for 11 countries and, thus, for a total of 519 locations, from 1990 to 2015. GBD 2015 also introduced analyses of DALYs and healthy life expectancy (HALE) in relation to the Socio-demographic Index (SDI). Only the WHO Global Health Estimates has published updated estimates of DALYs, and these estimates were heavily reliant on GBD 2015 results. Added value of this study This study, the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016), updates and improves the first of the annual Global Burden of Disease iterations, GBD 2015. GBD 2016 is, to our knowledge, the only peerreviewed, Guidelines for Accurate and Transparent Health Estimates Reporting-compliant, comprehensive, and annual assessment of DALYs and HALE by age group, sex, cause, and location, analysed consistently from 1990 to 2016. The improved approaches to the analysis and refinements in data (gap fills, updates, and revisions), as well as the widening of scope by cause, location, age, and time are all relevant to this study. The summary population health metrics of DALYs and HALE synthesise the cumulative effect of all of these improvements, the most notable of which are as follows. First, we added substantial location-years of cause-specific mortality data and non-fatal data for GBD 2016. The added data progressively fill gaps in the period of estimation, most substantially for India. Second, many analytical methods have been improved, such as improvement of mortality to incidence ratios for cancers to better reflect lower survival than in GBD 2015 and for non-fatal tuberculosis to better reflect higher incidence in low-income and middle-income countries based on SDI. Third, we included new subnational assessments for Indonesia at the provincial level and further disaggregated subnational estimation in England to the local government area level. Fourth, we refined our estimation of age-specific outcomes for ages 80 years and older into 5 year groups extending to age 95 years and older to better account for disease burden in elderly populations than in GBD 2015. Fifth, we estimated DALYs for several additional causes for the first time. Sixth, we improved our analysis of the epidemiological transition as a function of SDI, which allowed for a more nuanced interpretation of global health trends against the sociodemographic development spectrum than in GBD 2015. Finally, we used these analyses to identify the exemplar countries that exceeded population health summary metric expectations relative to their SDI position alone. The GBD 2016 iteration supersedes all previous GBD studies of DALYs and HALE and re-estimates these measures for the complete time series from 1990 to 2016. We focus on new methods and approaches since GBD 2015 and highlight nations that overperformed or underperformed on the basis of what would be expected on the basis of their SDI. Implications of all the available evidence The epidemiological transition continues apace globally, with a shift from DALYs attributable to communicable, maternal, neonatal, and nutritional diseases to those attributable to non-communicable diseases. This progression is concomitant with improvements in SDI and thus improvements in education, fertility rates, and economic status. A more detailed analysis than in this study of the epidemiological changes that have occurred in countries that have consistently exceeded expectations could provide improved insights into good practice in public health policy, which might be emulated elsewhere. A similarly detailed appraisal of countries that are lagging in DALYs and HALE relative to expectations on the level of SDI alone will help identify countries in most need of domestic and international attention across the development continuum. Global Health Metrics 1262 www.thelancet.com Vol 390 September 16, 2017 As the second in a series7,8,10,11 of now annual updates, the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) is the most comprehensive and current source of summary health metrics. The Global Burden of Disease (GBD) is based on development of the largest available database of health outcomes, risk factor exposure, intervention coverage, and sociodemographic factors related to health. We applied analytical techniques to reduce data biases and support comparability, propagated the uncertainty in these estimates, and provided insights at the highest temporal and spatial resolution afforded by the data. The purpose of this study is to present the results of GBD 2016 for DALYs and HALE, building on updated estimates of mortality, causes of death, and non-fatal health loss7,10 to identify nations with notable variation in health performance from that expected on the basis of SDI. Approaches to the analysis have been previously described.2–4,12 GBD 2016 improvements include addition of newly available retrospective data, refined analytical methods (such as improvement to mortality to incidence ratios [MIRs] for cancers to better reflect lower survival in low-income and middle-income countries based on SDI), new subnational estimation for England and Indonesia, disaggregation of certain cause groupings to capture greater detail, and expansion of older age groups to enhance relevance for a wider range of health policy decisions.6 Methods Overview We used the results of GBD 2016 to evaluate trends in epidemiological patterns and health performance on a global, regional, national, and subnational scale using DALYs and HALE as summary measures of changes in health states. Greater detail than presented in this section for methods used to estimate DALYs and HALE, including analytic approaches for assessment of relative morbidity and mortality from individual diseases and injuries, is provided in related publications in this series8,10 and the appendix. This analysis follows the Guidelines for Accurate and Transparent Health Estimates Reporting,13,14 which include recommendations on documentation of data sources, estimation methods, and statistical analysis. We did analyses using Python version 2.7.12 and 2.7.3, Stata version 13.1, and R version 3.2.2. For more information on Guidelines for Accurate and Transparent Health Estimates Reporting compliance, please refer to the appendix (pp 13–15). Additionally, interactive online tools are available to explore GBD 2016 data sources in detail. Cause-specific estimation for GBD 2016 covers the years 1990–2016. For a subset of analyses, we focus on the last decade, from 2006 to 2016, to address current policy priorities. The GBD 2016 results for all years and by location can be explored further with dynamic data visualisations. Cause and location hierarchies In the GBD 2016 study, causes of mortality and morbidity are structured with use of a four-level classification hierarchy to produce levels that are mutually exclusive and collectively exhaustive. GBD 2016 estimates 333 causes of DALYs, 68 of which are a source of disability but not a cause of death (such as trachoma, hookworm, and low back and neck pain) and five of which are causes of death but not sources of morbidity (sudden infant death syndrome, aortic aneurysm, late maternal deaths, indirect maternal deaths, and maternal deaths aggravated by HIV/AIDS). Within each level of the hierarchy, the number of collectively exhaustive and mutually exclusive fatal and non-fatal causes for which the GBD study estimates is three at Level 1, 21 at Level 2, 168 at Level 3, and 276 at Level 4. The full GBD cause hierarchy, including corresponding International Classification of Diseases (ICD)-9 and ICD-10 codes, is detailed in GBD 2016 publications on cause-specific mortality10 and non-fatal health outcomes,8 with cause-specific methods detailed in the corresponding appendices. The GBD study is organised by a geographical hierarchy of seven super-regions containing 21 regions, with 195 countries and territories nested within those regions.12 GBD 2016 included new subnational assessments for Indonesia by province and for England by local government area. In this study, we present subnational data for the five countries with a population greater than 200 million people in 2016: Brazil, China, India, Indonesia, and the USA. Estimation of mortality and non-fatal health loss To estimate all-cause and cause-specific mortality, the GBD study first systematically addressed known data challenges—such as variation in coding of causes or age group reporting, misclassification of deaths from HIV/AIDS, or methods for incorporation of populationbased cancer registry data—using standardised methods described in detail in the GBD 2016 mortality7 and causes of death10 publications. As noted in other GBD publications, each death is attributed to a single underlying cause in accordance with the ICD. We take steps to standardise cause of death data to address the small fraction of deaths that are not assigned an age or sex, deaths assigned to broad age groups that are not 5 year age groups, and various revisions and national variants of the ICD. Additionally, we identify and redistribute deaths assigned to ICD codes that cannot be underlying causes of death, are intermediate causes of death rather than the underlying causes, or lack specificity in coding.10 We estimated cause-specific mortality using standardised modelling processes—most commonly, the Cause of Death Ensemble model, which uses covariate selection and out-of-sample validity analyses and generates estimates for each location-year, age, and sex.10 Additional detail, including model specifications and data availability for each cause-specific model, can be found in the See Online for appendix For the online tools see http://ghdx.healthdata.org For the data visualisations see https://vizhub.healthdata.org/ gbd-compare Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1263 appendix of the GBD 2016 mortality7 and causes of death10 publications. We used the all-cause mortality estimates to establish a reference life table from the lowest death rates for each age group among locations with total populations greater than 5 million.7 From this reference life table, we multiplied life expectancy at the age of death by causespecific deaths to calculate cause-specific YLLs. We then used the GBD world population age standard to calculate age-standardised rates for deaths and YLLs.7 The GBD world population age standard and the standard life expectancies are available in the appendix of the GBD 2016 mortality publication.7 Changes implemented since the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) for cause-specific mortality include incorporation of substantial sources of new mortality data; important model improvements for HIV, malaria, tuberculosis, injuries, diabetes, and cancers; disaggregation of specific causes into subgroupings to provide additional detail (the following were all estimated separately for the first time: alcoholic cardiomyopathy; urogenital, musculoskeletal, and digestive congenital anomalies; Zika virus disease; Guinea worm disease; self-harm by firearm; sexual violence; myocarditis; and the following types of tuberculosis: extensively drug-resistant tuberculosis, multidrugresistant tuberculosis without extensive drug resistance, drug-susceptible tuberculosis, extensively drug-resistant HIV/AIDS-tuberculosis, multidrug-resis tant HIV/AIDStuberculosis without extensive drug resistance, and drug-susceptible HIV/AIDS-tuberculosis); modelling of anti retroviral therapy (ART) coverage for each locationyear by CD4-positive cell count at initiation; breakdown of terminal age groups from 80 years and older to 80–84 years, 85–89 years, 90–94 years, and 95 years and older; expansion of the GBD location hierarchy; and changes in the calculation of SDI.10 The database for GBD 2016 now includes data for the 333 causes estimated for DALYs and new subnational units for Indonesia (n=34) and England (n=150). For GBD 2016, we included substantial amounts of additional data sources from new studies and our network of collaborators; details of the types of data added can be found in the GBD 2016 cause of death10 and nonfatal8 publications. Additionally, research teams did systematic reviews to incorporate literature data into fatal and non-fatal models. Further details on search strings are available in the GBD 2016 non-fatal8 and cause of death10 publication appendices. The Registrar General of India provided improved verbal autopsy data collected through their Sample Registration System, enabling a more detailed and thorough analysis of subnational data for India than in GBD 2015. The methods for constructing the SDI, initially developed for GBD 2015,15 were revised for GBD 2016 to account for expansion in the number of subnational estimates and the effect of a growing time period of estimation given fixed limits for index components.10 The components of SDI—total fertility rate (TFR), educational attainment in the population aged older than 15 years, and lag-distributed income (LDI)—are based on new systematic assessments of educational attainment, LDI, and fertility, and each component is scaled relative to maximum effect on health outcomes.10 In most cases, we estimated non-fatal health loss using the Bayesian meta-regression tool DisMod-MR 2.1 to synthesise variable data sources to produce internally consistent estimates of incidence, prevalence, remission, and excess mortality.16 Cause-specific data availability and epidemiological characteristics required additional analytical techniques in some cases (details are available in the appendix of the GBD 2016 non-fatal publication8); these causes include many neglected tropical diseases (NTDs) such as dengue, as well as injuries, malaria, and HIV/AIDS.17,18 We estimated each non-fatal sequela separately and assessed the occurrence of comorbidity in each age group, sex, location, and year separately using a microsimulation framework. We distributed disability estimated for comorbid conditions to each contributing cause during the comorbidity estimation process. Although the distribution of sequelae—and therefore the severity and cumulative disability per case of a condition—can be different by age, sex, location, and year, previous studies have found that disability weights do not substantially vary across locations, income, or levels of educational attainment.19,20 In the GBD study, disability weights were based on population surveys with 60 890 respondents and held invariant between locations and over time.20 Additional details, including model specifications and data availability for each causespecific model and development of disability weights by cause and their use in the estimation of non-fatal health loss, are available in the appendix of the GBD 2016 nonfatal publication.8 For non-fatal estimation, several methodological changes were made for GBD 2016. New data for the main causes of YLDs were identified through our collaboration with the Indian Council of Medical Research and the Public Health Foundation of India. For particular risk factors and diseases, the volume of available data increased substantially, such as child growth failure (stunting, wasting, or underweight), anaemia, congenital anomalies, schistosomiasis, intestinal helminths, and lymphatic filariasis. We have improved our analysis of total admissions per person by country, year, age, and sex, which facilitated incorporation of additional hospital data sources that were previously excluded because of incomplete knowledge of catchment population size. We extended our analyses of linked USA medical claims data to impute age-specific and sex-specific ratios for multiple admissions per illness episode, ICD code appearance in the non-primary position, and inpatient versus outpatient use.8 We applied each of the three ratios sequentially to non-linked hospital inpatient data from elsewhere that only had a single ICD code per visit to adjust prevalence and incidence data. We have incorporated more predictive covariates into our non-fatal disease models to Global Health Metrics 1264 www.thelancet.com Vol 390 September 16, 2017 better predict variation in disease levels rather than measurement error as the source of variation, and we improved our analysis of the MIRs for cancers, resulting in considerably higher ratios in lower SDI quintiles and thus substantially lower YLD estimates for cancer. Estimation of DALYs, HALE, and corresponding uncertainty We calculated DALYs as the sum of YLLs and YLDs for each cause, location, age group, sex, and year.8,10 The same estimates of YLDs per person for each location, age, sex, and year from 1990 to 2016 are used to establish HALE by age group within abridged multiple-decrement life tables with use of methods developed by Sullivan.9 For all results, we report 95% uncertainty intervals (UIs) derived from 1000 draws from the posterior distribution of each step in the estimation process. Unlike confidence intervals, UIs capture uncertainty from multiple modelling steps, as well as from sources such as model estimation and model specification, rather than from sampling error alone. Uncertainty associated with estimation of mortality and YLLs reflects sample sizes of data sources, adjustment and standardisation methods applied to data, parameter uncertainty in model estimation, and uncertainty within all-cause and cause-specific mortality models. For estimation of prevalence, incidence, and YLDs, UIs incorporated variability from sample sizes within data sources, adjustments to data to account for non-reference definitions, parameter uncertainty in model estimation, and uncertainty associated with establishment of disability weights. Because direct information about the correlation between uncertainty in YLLs and YLDs was scarce, we assumed that uncertainty in age-specific YLDs was independent of age-specific YLLs or death rates. Epidemiological transition and relationship between DALYs, HALE, and SDI For GBD 2016, the composite indicator of SDI was again based on the geometric mean of three measures— LDI per person, average years of schooling among populations aged 15 years or older, and TFR—but the analysis was strengthened in three important ways.10 First, we substantially revised estimates of education, adding new data and improved methods for subnational locations. Second, instead of using estimates of TFR from the UN Population Division, we systematically reviewed, extracted, and analysed fertility data from all available locations to derive a time series of TFR for each national and subnational GBD location.7 Third, rather than rescaling SDI on the basis of the full range of observed values within the time series, we developed a fixed scale for GBD 2016; details on development of this fixed scale are available in the GBD 2016 mortality publication.7 We examined the average relationship between DALYs, HALE, and SDI using a Gaussian process regression model; we used these regressions to estimate expected values of these summary measures at each level of SDI. Additional detail on SDI calculation and location-specific SDI values are available in the appendix of the GBD 2016 mortality publication.7 Data sharing The statistical code used in the entire process is available through an online repository. Role of the funding source The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. Results Global levels of and trends for DALYs and HALE The total number of all-age DALYs in 2016 was 2·39 billion (95% UI 2·18 billion to 2·63 billion). Total all-age DALY counts for CMNN causes fell by 40·1% (37·4–42·7) from 1·11 billion (1·07 billion to 1·16 billion) in 1990 to 668 million (632 million to 708 million) in 2016, whereas total all-age DALY counts from NCDs increased by 36·6% from 1·07 billion (958 million to 1·20 billion) in 1990 to 1·47 billion (1·30 billion to 1·66 billion) in 2016 (table 1). Total DALYs from injuries decreased by 1·6% (–3·8 to 6·2) from 260 million (243 million to 277 million) in 1990 to 255 million (236 million to 281 million) in 2016. Age groups older than 80 years had 149 million (139 million to 159 million) all-age DALYs in 2016 compared with 75·1 million (71·1 million to 79·5 million) in 1990, with increases across all SDI quintiles. Of these, 87·8% were due to NCDs in 2016 compared with 86·8% in 1990. From 1990 to 2016, global HALE at birth increased from 56·9 years to 63·1 years, with 160 of 195 locations registering significant improvements. Global HALE increased by an average of 6·24 years (95% UI 5·97–6·48) for both sexes combined. Globally, HALE at birth increased from 55·38 years (53·27–57·31) in 1990 to 61·42 years (59·01–63·58) in 2016 for males and from 58·42 years (55·80–60·77) to 64·91 years (61·88–67·54) for females, rising 6·04 years (5·74–6·27) for males and 6·49 years (6·08–6·77) for females (tables 2 and 3). The total number of years of functional health lost (life expectancy minus HALE) increased from 1990 to 2016, from 8·22 years to 9·34 years. The gap between life expectancy at birth and HALE, which represents years of functional health lost, grew between 1990 and 2016 from 7·32 years (life expectancy 62·70 [62·42–62·99] vs HALE 55·38 [53·27–57·31]) to 8·37 years (69·79 [69·29–70·22] vs 61·42 [59·01–63·58]) for males and from 9·15 years (67·57 [67·33–67·77] vs 58·42 [55·80–60·77]) to 10·42 years (75·33 [74·95–75·64] vs 64·91 [61·88–67·54]) for females. Globally, in 2016, life expectancy at age 65 years was 18·57 years (18·37–18·72) for females and 15·72 years (15·61–15·83) for males, For the statistical code see https://github.com/ihmeuw/ ihme-modeling Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1265 whereas HALE was 13·88 years (12·57–15·02) for females and 11·87 years (10·83–12·80) for males. HALE increased by 1·96 years (1·69–2·13) from 11·92 (10·88–12·89) in 1990 for females and by 1·78 years (1·61–1·93) from 10·09 years (9·22–10·87) for males. Global trends for all-age DALYs and age-standardised DALY rates for Level 1 causes by SDI quintile are shown in figure 1. Trends in total DALYs, which show the absolute burden at each SDI quintile, are shown in figure 1A. The figure highlights the large burden and subsequent declines in low-middle-SDI (decreased by 44·5% [95% UI 41·3–47·6]) and middle-SDI (decreased by 56·2% [53·6–59·0]) locations for CMNN DALYs from 1990 to 2016, offset by large increases for NCD DALYs over the same time period in low-middle SDI (increased by 54·4% [47·9–60·8]) and middle-SDI (increased by 36·5% [39·9–32·8]) locations. Progress has been made in low-SDI countries for CMNN DALYs, which decreased by 20·7% (16·8–24·7) since 2006. At all levels of SDI, total NCD DALYs have increased since 1990. Trends in agestandardised rates—which account for both population size and age structure—emphasise the reduction in the contribution of CMNN causes to DALYs, both over time and with increasing SDI (figure 1B). These rapid decreases in age-standardised rates were fastest at low SDI, where age-standardised DALY rates from CMNN causes decreased by 51·5% (49·0–54·1) between 1990 and 2016 to be on par with age-standardised DALY rates for NCDs in 2016. At low-middle SDI, age-standardised CMNN DALY rates were more than double those for NCDs in 1990 (31·7 thousand [30·3 thousand to 33·3 thousand] per 100 000), but decreased to 14·1 thousand (13·2 thousand to 15·1 thousand) per 100 000 in 2016. At all other levels of SDI, agestandardised DALY rates for CMNN causes were lower than those for NCDs and lower than those of injuries in the high-SDI quintile. Reductions in age-standardised rates for NCDs occurred across all levels of SDI between 1990 and 2016, a trend that was also evident, albeit less strongly, for age-standardised DALY rates from injuries. Global causes of DALYs Age-standardised DALY rates for all causes decreased by 30·5% (95% UI 28·6–32·6) between 1990 and 2016 (appendix pp 49–62). In 2016, CMNN causes accounted for 28·0% (26·4–29·7) of global DALYs, NCDs contributed 61·4% (59·4–63·2), and injuries contributed 10·7% (10·1–11·3; appendix pp 35–48). From 2006 to 2016, CMNN causes decreased by 31·9% (29·7–34·2), with 48 Level 4 CMNN causes experiencing decreases in age-standardised DALY rates of greater than 20% (table 1). Decreases were greater than 70% for three infectious diseases: Guinea worm disease (decreased by 99·6% [99·5–99·7]), human African trypanosomiasis (decreased by 78·2% [68·8–84·6]), and measles (decreased by 73·6% [68·8–77·8]). By contrast with the overall trend of decreasing DALYs, a subset of Level 4 CMNN causes had increases in age-standardised DALY rates, including dengue (50·5% [24·7–97·7]) and cutaneous and mucocutaneous leishmaniasis (12·5% [1·7–26·1]). Overall, total all-age DALYs attributable to maternal disorders decreased by 23·9% (17·4–29·3) between 2006 and 2016 and by 30·4% (24·4–35·4) in terms of age-standardised DALY rates. As a cause group, neonatal disorders decreased by 22·8% (18·9–26·8) in all-age DALYs and 23·1% (19·2–27·0) in terms of agestandardised DALY rates over the same time period; however, this decrease was not significant for neonatal sepsis. Total DALYs from the London Declaration NTDs was 9·0 million (5·3 million to 14·5 million) in 2016. In 2016, the leading Level 3 causes of total DALYs among NCDs included ischaemic heart disease (175 million [95% UI 170 million to 180 million] DALYs), cerebrovascular disease (116 million [111 million to 121 million]), and low back and neck pain (87 million [61 million to 114 million]), comprising 16·1% (13·99–17·67) of all DALYs (table 1). Among chronic respiratory diseases, all causes, with the exception of interstitial lung disease, pulmonary sarcoidosis, and other chronic respiratory diseases, decreased in agestandardised DALY rates between 2006 and 2016, whereas total all-age DALY counts increased from 2006 to 2016 for all chronic respiratory diseases, with the exception of silicosis. Cirrhosis and other chronic liver diseases had a mean change of 7·6% (2·5–13·7) from 2006 to 2016 in total all-age DALY counts, but had a mean decrease in age-standardised DALY rates of 12·0% (7·2–16·1) over the same period. Age-standardised DALY rates of digestive diseases decreased from 2006 to 2016, with a mean percentage decrease of 13·6% (10·8–16·4); however, allage DALY counts for digestive diseases increased by 4·1% (0·4–7·7) over the same period. Total DALYs associated with most neurological disorders increased from 2006 to 2016, with Alzheimer’s disease and other dementias (increase of 37·5% [35·3–39·7]) and Parkinson’s disease (increase of 35·6% [32·9–38·2]) increasing by more than 30% each. Between 2006 and 2016, various NCDs significantly increased in terms of both total burden and age-standardised DALY rates. Several mental and substance use disorders followed this pattern, including eating disorders (age-standardised DALY rates increased by 8·9% [7·6–10·1]) and bipolar disorder (increased by 0·8% [0·2–1·4]). Diabetes (all-age DALY count increased by 24·4% [22·7–26·2]) and chronic kidney disease (increased by 20·0% [17·4–22·7]) both also increased in all-age DALY counts, as did musculoskeletal disorders (increased by 19·6% [18·5–20·8]). Percentage change in age-standardised DALY rates of unintentional injuries (decreased by 15·1% [95% UI 12·2–18·0]), road injuries (decreased by 14·7% [12·8–16·8]), and transport injuries (decreased by 14·3% [12·3–16·4]) each decreased substantially between 2006 and 2016 (table 1). Among unintentional injuries, drowning had the largest reduction in both all-age DALY burden (26·6% [20·1–30·1]) and age-standardised DALY rates (32·1% Global Health Metrics 1266 www.thelancet.com Vol 390 September 16, 2017 All-age DALYs (thousands) Age-standardised DALY rate (per 100 000) 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 All causes 2 448 430·5 (2 305 218·2 to 2 608 339·5) 2 490 698·9 (2 308 527·1 to 2 689 861·1) 2 391 258·0 (2 184 254·1 to 2 631 699·0) –2·3 (–5·9 to 0·9) –4·0 (–6·0 to –2·1)* 48 407·8 (45 385·4 to 51 762·0) 40 485·1 (37 556·0 to 43 679·3) 33 641·0 (30 808·7 to 36 924·3) –30·5 (–32·6 to –28·6)* –16·9 (–18·6 to –15·3)* Communicable, maternal, neonatal, and nutritional diseases 1 114 176·6 (1 073 948·8 to 1 156 050·2) 918 804·8 (885 242·1 to 959 452·3) 667 823·7 (632 212·4 to 708 405·1) –40·1 (–42·7 to –37·4)* –27·3 (–29·8 to –24·9)* 18 071·6 (17 386·0 to 18 790·5) 13 801·1 (13 286·8 to 14 406·5) 9396·8 (8894·5 to 9956·2) –48·0 (–50·1 to –45·8)* –31·9 (–34·2 to –29·7)* HIV/AIDS and tuberculosis 84 184·5 (79 728·6 to 89 558·0) 159 063·9 (152 851·5 to 165 139·0) 101 133·3 (97 487·1 to 105 092·9) 20·1 (13·0 to 26·7)* –36·4 (–37·9 to –34·7)* 1788·1 (1690·1 to 1918·3) 2439·5 (2345·8 to 2531·5) 1355·2 (1306·2 to 1407·5) –24·2 (–29·7 to –20·1)* –44·5 (–45·7 to –43·0)* Tuberculosis 68 029·7 (64 153·4 to 73 066·3) 56 881·5 (54 312·6 to 59 442·6) 43 557·9 (41 529·0 to 45 716·5) –36·0 (–41·3 to –32·4)* –23·4 (–26·1 to –20·6)* 1480·1 (1390·4 to 1613·6) 916·5 (874·7 to 956·7) 593·1 (565·5 to 621·7) –59·9 (–63·7 to –57·7)* –35·3 (–37·6 to –32·9)* Drug-susceptible tuberculosis 67 560·5 (63 730·5 to 72 611·6) 51 760·2 (49 194·0 to 54 289·2) 39 869·8 (38 054·8 to 41 916·2) –41·0 (–45·6 to –37·5)* –23·0 (–25·7 to –20·0)* 1469·5 (1380·7 to 1603·6) 834·1 (791·6 to 873·6) 543·0 (517·8 to 571·1) –63·0 (–66·4 to –60·8)* –34·9 (–37·2 to –32·5)* Multidrug-resistant tuberculosis without extensive drug resistance 469·2 (378·3 to 578·8) 4886·9 (4122·2 to 5829·2) 3319·4 (2787·6 to 3910·3) 607·5 (511·8 to 717·0)* –32·1 (–38·5 to –24·8)* 10·6 (8·5 to 13·1) 78·7 (66·3 to 93·9) 45·1 (37·9 to 53·2) 327·6 (267·7 to 394·1)* –42·7 (–48·1 to –36·5)* Extensively drug-resistant tuberculosis ·· 234·5 (194·6 to 279·1) 368·8 (301·1 to 444·5) ·· 57·3 (36·1 to 82·1)* ·· 3·8 (3·1 to 4·5) 5·0 (4·1 to 6·0) ·· 32·5 (14·8 to 53·2)* Latent tuberculosis infection ·· ·· ·· ·· ·· ·· ·· ·· ·· ·· HIV/AIDS 16 154·8 (14 497·1 to 18 106·5) 102 182·3 (96 751·1 to 107 544·2) 57 575·4 (54 618·5 to 60 967·9) 256·4 (220·1 to 293·5)* –43·6 (–45·4 to –41·6)* 308·1 (276·2 to 345·6) 1522·9 (1443·6 to 1601·3) 762·1 (723·6 to 806·2) 147·4 (121·5 to 173·2)* –50·0 (–51·5 to –48·2)* Drug-susceptible HIV/AIDSTuberculosis 4668·5 (3624·4 to 5760·2) 24 070·5 (16 708·0 to 31 379·1) 11 724·0 (8154·4 to 15 522·4) 151·1 (116·8 to 191·3)* –51·3 (–54·0 to –48·6)* 88·2 (68·0 to 109·1) 359·7 (249·9 to 468·7) 155·5 (108·2 to 205·9) 76·2 (52·6 to 103·8)* –56·8 (–59·2 to –54·4)* Multidrug-resistant HIV/AIDSTuberculosis without extensive drug resistance 25·9 (16·0 to 40·6) 2051·8 (1282·8 to 3070·2) 979·2 (597·7 to 1481·6) 3673·4 (2732·2 to 4952·8)* –52·3 (–61·7 to –41·2)* 0·5 (0·3 to 0·8) 30·7 (19·2 to 45·8) 13·0 (7·9 to 19·7) 2486·4 (1853·0 to 3358·6)* –57·6 (–66·1 to –47·8)* Extensively drugresistant HIV/AIDSTuberculosis ·· 39·9 (24·8 to 61·1) 57·3 (34·5 to 89·4) ·· 43·5 (25·5 to 65·4)* ·· 0·6 (0·4 to 0·9) 0·8 (0·5 to 1·2) ·· 26·8 (10·8 to 46·4)* HIV/AIDS resulting in other diseases 11 460·3 (9938·9 to 13 435·6) 76 020·0 (67 021·8 to 86 026·2) 44 814·9 (39 932·9 to 50 112·4) 291·1 (245·9 to 337·5)* –41·0 (–43·6 to –38·2)* 219·3 (189·8 to 257·4) 1131·9 (999·9 to 1280·9) 592·9 (528·6 to 663·0) 170·3 (138·2 to 203·1)* –47·6 (–49·9 to –45·1)* Diarrhoea, lower respiratory, and other common infectious diseases 557 388·0 (522 551·7 to 600 325·4) 337 062·8 (317 957·5 to 359 176·2) 229 961·4 (213 682·3 to 247 975·2) –58·7 (–61·9 to –55·2)* –31·8 (–35·3 to –27·8)* 8951·2 (8378·0 to 9601·7) 5152·7 (4858·8 to 5514·9) 3275·6 (3051·7 to 3531·8) –63·4 (–66·0 to –60·7)* –36·4 (–39·7 to –33·0)* Diarrhoeal diseases 175 168·6 (150 592·6 to 201 351·3) 113 944·8 (99 183·9 to 135 659·8) 74 414·6 (63 402·0 to 93 414·9) –57·5 (–62·8 to –50·1)* –34·7 (–41·0 to –28·1)* 2914·2 (2482·9 to 3469·2) 1768·5 (1526·0 to 2136·7) 1063·1 (907·5 to 1332·3) –63·5 (–67·6 to –58·3)* –39·9 (–45·2 to –34·3)* Intestinal infectious diseases 15 662·6 (8797·4 to 25 360·4) 12 822·7 (7207·6 to 20 879·4) 10 601·7 (6041·1 to 17 309·3) –32·3 (–43·5 to –21·8)* –17·3 (–25·1 to –10·8)* 249·7 (140·6 to 404·8) 184·6 (103·8 to 300·4) 144·3 (82·3 to 235·2) –42·2 (–51·5 to –33·7)* –21·9 (–29·4 to –15·6)* Typhoid fever 13 362·8 (7235·9 to 22 248·3) 10 793·8 (5876·4 to 17 717·0) 8843·0 (4901·5 to 14 436·1) –33·8 (–44·0 to –23·6)* –18·1 (–25·7 to –12·1)* 212·5 (115·0 to 353·9) 155·3 (84·8 to 254·0) 120·4 (66·6 to 196·8) –43·3 (–51·9 to –35·1)* –22·4 (–29·7 to –16·6)* Paratyphoid fever 1867·3 (850·2 to 3711·1) 1773·6 (826·8 to 3439·6) 1607·0 (759·0 to 3109·8) –13·9 (–27·1 to –1·5)* –9·4 (–17·9 to –1·8)* 30·6 (13·9 to 60·4) 25·6 (11·9 to 49·6) 21·7 (10·2 to 42·0) –29·0 (–39·4 to –19·2)* –15·1 (–22·9 to –8·0)* (Table 1 continues on next page) Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1267 All-age DALYs (thousands) Age-standardised DALY rate (per 100 000) 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 (Continued from previous page) Other intestinal infectious diseases 432·5 (107·2 to 1290·8) 255·4 (58·5 to 753·4) 151·7 (42·1 to 412·1) –64·9 (–90·6 to 42·1) –40·6 (–84·7 to 126·3) 6·7 (1·7 to 19·5) 3·8 (0·9 to 11·2) 2·2 (0·6 to 5·9) –67·5 (–91·3 to 31·5) –42·9 (–85·2 to 114·5) Lower respiratory infections 202 365·5 (182 794·4 to 220 607·6) 131 015·4 (121 489·8 to 139 228·6) 91 844·6 (84 674·4 to 98 252·6) –54·6 (–58·7 to –49·4)* –29·9 (–34·4 to –25·3)* 3237·1 (2942·1 to 3515·4) 2022·6 (1879·0 to 2147·6) 1326·7 (1221·8 to 1419·7) –59·0 (–62·5 to –54·6)* –34·4 (–38·6 to –30·2)* Upper respiratory infections 4868·6 (3012·8 to 7444·9) 5551·2 (3380·7 to 8488·2) 5991·2 (3621·1 to 9193·8) 23·1 (19·0 to 26·3)* 7·9 (6·2 to 9·4)* 88·2 (54·9 to 134·0) 83·0 (50·8 to 126·9) 81·0 (49·0 to 124·0) –8·3 (–10·5 to –6·9)* –2·5 (–3·8 to –1·5)* Otitis media 3111·7 (2057·2 to 4485·0) 3171·4 (2005·0 to 4675·3) 3187·5 (1993·2 to 4716·5) 2·4 (–3·8 to 7·0) 0·5 (–2·1 to 3·0) 53·4 (35·3 to 76·9) 46·7 (29·6 to 68·9) 43·3 (27·1 to 64·2) –18·9 (–23·7 to –15·1)* –7·3 (–9·8 to –4·9)* Meningitis 30 239·3 (23 939·3 to 34 552·6) 24 957·4 (21 655·0 to 28 764·2) 21 865·9 (18 204·6 to 28 280·5) –27·7 (–41·7 to 3·1) –12·4 (–23·9 to 7·8) 481·8 (385·6 to 549·0) 369·9 (321·3 to 426·1) 306·1 (254·0 to 398·0) –36·5 (–48·5 to –9·7)* –17·2 (–28·1 to 2·1) Pneumococcal meningitis 2187·5 (1808·2 to 2576·0) 1940·0 (1649·9 to 2287·4) 1902·8 (1569·5 to 2382·2) –13·0 (–26·3 to 10·0) –1·9 (–11·5 to 12·0) 37·2 (31·0 to 43·4) 29·1 (24·8 to 34·2) 26·2 (21·6 to 32·7) –29·6 (–39·6 to –11·3)* –9·9 (–18·7 to 3·2) Haemophilus influenzae type B meningitis 3330·3 (2606·2 to 3982·2) 2725·7 (2276·3 to 3165·4) 2426·0 (1967·2 to 3212·2) –27·1 (–41·5 to 3·7) –11·0 (–23·9 to 9·3) 52·4 (41·5 to 62·0) 40·3 (33·7 to 46·8) 34·1 (27·6 to 45·1) –34·8 (–47·4 to –7·4)* –15·3 (–27·6 to 3·9) Meningococcal meningitis 14 191·0 (11 094·1 to 16 492·1) 11 548·6 (9913·5 to 13 418·2) 8327·1 (6806·4 to 10 911·9) –41·3 (–53·1 to –16·3)* –27·9 (–37·7 to –12·2)* 224·2 (177·0 to 259·3) 170·8 (146·7 to 199·1) 116·6 (95·2 to 152·9) –48·0 (–58·4 to –26·0)* –31·7 (–41·1 to –16·8)* Other meningitis 10 530·5 (8030·6 to 12 434·2) 8743·1 (7350·6 to 10 148·8) 9210·0 (7559·7 to 12 250·5) –12·5 (–30·3 to 28·9) 5·3 (–9·4 to 33·0) 168·1 (129·1 to 197·5) 129·7 (109·2 to 150·4) 129·2 (105·5 to 173·3) –23·1 (–38·2 to 12·4) –0·4 (–14·4 to 26·1) Encephalitis 7918·4 (5206·9 to 10 751·2) 7380·9 (6422·5 to 9033·9) 6704·1 (5469·3 to 8574·2) –15·3 (–44·0 to 40·6) –9·2 (–24·4 to 10·8) 135·5 (91·8 to 180·2) 111·5 (97·1 to 136·5) 92·7 (75·7 to 118·4) –31·6 (–53·6 to 10·0) –16·9 (–30·8 to 1·2) Diphtheria 842·7 (611·3 to 1167·2) 263·8 (183·1 to 374·7) 86·9 (62·5 to 123·4) –89·7 (–93·2 to –84·0)* –67·0 (–78·6 to –47·7)* 12·9 (9·4 to 17·9) 3·9 (2·7 to 5·6) 1·2 (0·9 to 1·8) –90·5 (–93·7 to –85·3)* –68·6 (–79·8 to –49·7)* Whooping cough 14 651·2 (6598·0 to 28 290·2) 9778·0 (4727·9 to 17 764·8) 6249·9 (3360·7 to 10 754·7) –57·3 (–77·1 to –19·2)* –36·1 (–63·1 to 17·1) 219·5 (98·9 to 424·0) 144·6 (69·9 to 262·6) 89·4 (48·1 to 153·9) –59·3 (–78·1 to –22·8)* –38·1 (–64·3 to 13·3) Tetanus 24 893·6 (14 235·3 to 33 445·8) 6340·9 (3695·4 to 7940·5) 2366·6 (1446·0 to 3062·9) –90·5 (–92·7 to –87·7)* –62·7 (–68·8 to –55·4)* 385·3 (222·8 to 516·6) 93·3 (54·4 to 116·8) 33·6 (20·3 to 43·4) –91·3 (–93·2 to –88·9)* –64·0 (–70·0 to –57·1)* Measles 76 350·8 (31 267·6 to 147 358·9) 20 794·3 (8237·6 to 43 871·3) 5724·8 (2148·6 to 12 257·6) –92·5 (–94·4 to –90·5)* –72·5 (–76·9 to –67·7)* 1150·7 (471·2 to 2220·4) 307·9 (122·0 to 649·2) 81·3 (30·5 to 174·2) –92·9 (–94·7 to –91·0)* –73·6 (–77·8 to –69·0)* Varicella and herpes zoster 1314·9 (1138·7 to 1509·6) 1042·2 (909·6 to 1205·0) 923·5 (779·5 to 1098·7) –29·8 (–40·3 to –18·8)* –11·4 (–20·6 to –2·8)* 22·8 (19·8 to 25·9) 16·2 (14·1 to 18·9) 13·0 (11·0 to 15·4) –42·8 (–50·3 to –35·4)* –19·7 (–27·6 to –12·2)* Neglected tropical diseases and malaria 87 294·8 (71 756·4 to 103 455·7) 99 229·2 (85 820·3 to 113 978·1) 74 995·1 (63 114·8 to 86 650·7) –14·1 (–31·0 to 6·2) –24·4 (–37·6 to –8·6)* 1423·8 (1183·4 to 1676·5) 1478·5 (1280·4 to 1696·3) 1050·5 (882·7 to 1217·9) –26·2 (–40·4 to –9·3)* –28·9 (–41·5 to –14·0)* Malaria 60 389·3 (46 548·2 to 74 912·5) 77 253·7 (64 810·3 to 91 256·8) 56 201·2 (45 785·6 to 67 880·8) –6·9 (–30·5 to 26·5) –27·2 (–43·3 to –6·7)* 931·0 (722·1 to 1150·5) 1147·0 (963·0 to 1354·6) 794·7 (646·5 to 962·2) –14·6 (–36·1 to 15·2) –30·7 (–46·2 to –11·1)* Chagas disease 309·8 (286·3 to 334·8) 226·1 (204·9 to 251·3) 219·0 (194·6 to 250·7) –29·3 (–34·4 to –23·7)* –3·1 (–8·2 to 2·7) 7·7 (7·1 to 8·3) 4·0 (3·7 to 4·5) 3·1 (2·8 to 3·6) –59·3 (–62·2 to –56·0)* –22·6 (–26·7 to –17·8)* Leishmaniasis 2531·5 (1470·2 to 4203·0) 1897·2 (1151·9 to 3064·4) 981·0 (658·3 to 1480·6) –61·2 (–67·2 to –52·0)* –48·3 (–54·0 to –39·6)* 45·7 (27·0 to 75·4) 28·4 (17·2 to 45·7) 13·4 (9·0 to 20·3) –70·6 (–74·9 to –64·1)* –52·6 (–57·7 to –44·9)* (Table 1 continues on next page) Global Health Metrics 1268 www.thelancet.com Vol 390 September 16, 2017 All-age DALYs (thousands) Age-standardised DALY rate (per 100 000) 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 (Continued from previous page) Visceral leishmaniasis 2406·1 (1350·5 to 4080·8) 1684·8 (943·7 to 2878·0) 707·9 (400·1 to 1206·2) –70·6 (–74·4 to –66·4)* –58·0 (–61·9 to –53·9)* 43·1 (24·4 to 72·6) 25·1 (14·1 to 42·9) 9·8 (5·5 to 16·6) –77·4 (–79·9 to –74·6)* –61·1 (–64·7 to –57·2)* Cutaneous and mucocutaneous leishmaniasis 125·3 (67·7 to 217·2) 212·4 (131·6 to 329·4) 273·1 (177·2 to 398·9) 117·9 (67·5 to 215·8)* 28·6 (16·8 to 42·9)* 2·6 (1·4 to 4·4) 3·3 (2·0 to 5·0) 3·7 (2·4 to 5·4) 43·5 (11·8 to 103·5)* 12·5 (1·7 to 26·1)* African trypanosomiasis 1046·8 (559·1 to 1711·5) 539·0 (288·0 to 876·8) 128·4 (64·7 to 215·0) –87·7 (–91·3 to –82·2)* –76·2 (–83·2 to –65·9)* 19·2 (10·3 to 31·6) 7·8 (4·2 to 12·8) 1·7 (0·9 to 2·9) –91·1 (–93·6 to –87·2)* –78·2 (–84·6 to –68·8)* Schistosomiasis 2096·8 (1340·2 to 3410·1) 2464·8 (1447·7 to 4194·7) 1863·6 (1122·0 to 3175·2) –11·1 (–17·5 to –6·7)* –24·4 (–26·2 to –21·9)* 42·8 (27·5 to 69·2) 37·5 (22·2 to 63·5) 24·9 (15·0 to 42·3) –41·9 (–46·3 to –38·9)* –33·7 (–35·4 to –31·6)* Cysticercosis 489·0 (363·4 to 621·4) 500·8 (359·2 to 657·1) 468·1 (322·8 to 625·8) –4·3 (–13·5 to 3·9) –6·5 (–12·4 to –0·9)* 10·5 (7·8 to 13·4) 8·0 (5·7 to 10·5) 6·3 (4·4 to 8·4) –40·0 (–45·2 to –35·0)* –21·0 (–26·0 to –16·5)* Cystic echinococcosis 326·8 (237·4 to 449·1) 226·2 (161·3 to 313·8) 136·5 (95·3 to 193·7) –58·2 (–68·4 to –44·3)* –39·6 (–53·9 to –16·3)* 6·3 (4·5 to 8·7) 3·5 (2·5 to 4·8) 1·8 (1·3 to 2·6) –70·6 (–77·7 to –61·1)* –46·6 (–59·1 to –26·5)* Lymphatic filariasis 1595·7 (733·4 to 2983·5) 1897·7 (873·9 to 3542·8) 1189·0 (587·7 to 2114·9) –25·5 (–41·4 to –9·3)* –37·4 (–52·4 to –26·0)* 32·5 (14·9 to 60·7) 28·9 (13·3 to 54·0) 15·8 (7·8 to 28·1) –51·5 (–61·8 to –41·2)* –45·3 (–58·4 to –35·5)* Onchocerciasis 1420·4 (777·0 to 2254·9) 1266·4 (705·0 to 2003·3) 962·5 (452·3 to 1672·1) –32·2 (–47·6 to –16·8)* –24·0 (–41·6 to –6·5)* 28·4 (16·0 to 45·4) 19·1 (11·0 to 30·1) 12·9 (6·1 to 22·4) –54·6 (–65·7 to –43·4)* –32·6 (–48·5 to –17·5)* Trachoma 231·1 (156·9 to 324·3) 246·5 (166·2 to 348·4) 245·2 (162·4 to 353·6) 6·1 (–2·6 to 14·6) –0·5 (–6·5 to 5·2) 6·7 (4·5 to 9·4) 4·9 (3·3 to 6·9) 3·7 (2·5 to 5·3) –44·3 (–49·0 to –39·6)* –23·8 (–28·5 to –19·3)* Dengue 822·8 (308·1 to 1364·0) 1798·2 (789·6 to 2494·8) 2956·9 (1359·2 to 4146·9) 259·4 (104·2 to 683·3)* 64·4 (36·2 to 115·9)* 13·9 (5·2 to 23·2) 26·7 (11·7 to 37·1) 40·2 (18·6 to 56·3) 189·0 (65·5 to 523·4)* 50·5 (24·7 to 97·7)* Yellow fever 784·5 (170·6 to 2314·6) 424·5 (89·8 to 1247·2) 374·0 (80·8 to 1075·1) –52·3 (–61·3 to –41·2)* –11·9 (–26·9 to 7·4) 13·2 (2·9 to 39·0) 6·1 (1·3 to 18·0) 5·0 (1·1 to 14·5) –61·8 (–68·5 to –52·9)* –17·1 (–31·3 to 1·3) Rabies 2979·4 (1867·4 to 4076·4) 1451·6 (867·1 to 1868·0) 744·2 (383·8 to 1106·3) –75·0 (–82·7 to –61·4)* –48·7 (–58·9 to –34·1)* 51·3 (32·6 to 71·3) 21·5 (12·9 to 27·5) 10·1 (5·2 to 15·1) –80·3 (–86·5 to –69·9)* –52·9 (–62·4 to –39·5)* Intestinal nematode infections 7460·9 (4726·6 to 11 584·0) 4083·3 (2617·1 to 6154·1) 3331·2 (2076·2 to 5158·6) –55·4 (–57·9 to –52·6)* –18·4 (–22·9 to –14·3)* 132·8 (83·2 to 206·9) 60·8 (39·0 to 91·6) 45·0 (28·2 to 69·6) –66·1 (–68·0 to –64·0)* –25·9 (–30·0 to –22·2)* Ascariasis 4634·7 (2996·9 to 7119·9) 1902·0 (1325·4 to 2758·9) 1308·8 (883·2 to 1942·4) –71·8 (–74·6 to –68·4)* –31·2 (–37·6 to –24·8)* 80·3 (51·4 to 124·0) 28·3 (19·8 to 41·1) 17·9 (12·1 to 26·4) –77·8 (–79·9 to –74·9)* –37·0 (–42·8 to –31·0)* Trichuriasis 671·4 (364·9 to 1142·9) 421·5 (233·0 to 717·2) 337·0 (186·2 to 573·6) –49·8 (–55·2 to –44·3)* –20·0 (–27·9 to –11·6)* 12·5 (6·8 to 21·2) 6·3 (3·5 to 10·7) 4·5 (2·5 to 7·7) –63·7 (–67·8 to –59·7)* –27·8 (–34·9 to –20·2)* Hookworm disease 2154·8 (1278·7 to 3371·1) 1759·8 (1058·9 to 2739·1) 1685·4 (1001·5 to 2648·9) –21·8 (–26·7 to –16·6)* –4·2 (–9·5 to 1·3) 40·0 (23·7 to 62·8) 26·2 (15·7 to 40·7) 22·6 (13·5 to 35·5) –43·4 (–47·2 to –39·6)* –13·5 (–18·2 to –8·5)* Food-borne trematodiases 1425·0 (591·7 to 2937·7) 1659·6 (832·5 to 3083·5) 1771·2 (923·9 to 3158·4) 24·3 (–3·9 to 71·7) 6·7 (1·3 to 15·7)* 27·9 (12·0 to 56·7) 25·4 (12·9 to 46·8) 23·7 (12·2 to 42·0) –15·3 (–34·0 to 13·1) –7·0 (–11·4 to 0·5) Leprosy 23·0 (15·5 to 32·3) 31·3 (21·3 to 44·0) 31·6 (21·4 to 44·0) 37·5 (34·0 to 40·9)* 1·1 (–1·3 to 3·6) 0·6 (0·4 to 0·8) 0·5 (0·4 to 0·8) 0·4 (0·3 to 0·6) –20·7 (–22·7 to –18·8)* –18·1 (–20·0 to –16·2)* Ebola virus disease ·· ·· 0·3 (0·2 to 1·1) ·· ·· ·· ·· ·· ·· ·· (Table 1 continues on next page) Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1275 All-age DALYs (thousands) Age-standardised DALY rate (per 100 000) 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 (Continued from previous page) Gastritis and duodenitis 1952·5 (1571·5 to 2395·4) 2235·1 (1806·0 to 2810·5) 2689·8 (2104·0 to 3454·3) 37·8 (24·2 to 50·6)* 20·4 (13·5 to 26·7)* 45·9 (37·1 to 56·0) 38·4 (31·2 to 48·1) 37·7 (29·6 to 48·3) –17·8 (–25·0 to –10·7)* –1·7 (–7·2 to 3·2) Appendicitis 2200·3 (1726·6 to 2565·2) 2412·3 (2109·1 to 2744·1) 2150·0 (1928·1 to 2484·8) –2·3 (–16·9 to 19·1) –10·9 (–18·1 to –3·0)* 41·3 (32·8 to 47·4) 37·2 (32·3 to 42·2) 29·3 (26·3 to 33·9) –29·0 (–38·5 to –15·1)* –21·1 (–27·5 to –14·2)* Paralytic ileus and intestinal obstruction 6853·7 (5275·8 to 7778·5) 7379·6 (6085·8 to 7981·1) 7626·5 (6380·0 to 8328·6) 11·3 (–1·7 to 32·7) 3·4 (–2·8 to 10·6) 131·2 (101·5 to 146·5) 120·6 (99·3 to 130·0) 108·0 (90·5 to 117·7) –17·7 (–25·7 to –3·7)* –10·5 (–15·4 to –5·0)* Inguinal, femoral, and abdominal hernia 2405·0 (1863·4 to 3003·9) 2854·3 (2251·5 to 3551·6) 3106·1 (2401·1 to 3932·1) 29·1 (19·9 to 39·6)* 8·8 (5·5 to 11·4)* 54·2 (42·2 to 67·3) 48·2 (38·2 to 59·9) 43·5 (33·7 to 55·0) –19·7 (–26·4 to –12·2)* –9·7 (–12·8 to –7·6)* Inflammatory bowel disease 1217·7 (916·1 to 1735·5) 1606·7 (1305·0 to 2000·0) 1844·4 (1541·5 to 2195·5) 51·5 (14·2 to 81·3)* 14·8 (2·7 to 22·6)* 27·4 (21·5 to 36·3) 27·4 (22·3 to 33·5) 25·8 (21·6 to 30·7) –6·0 (–23·3 to 7·3) –5·9 (–14·8 to 0·3) Vascular intestinal disorders 1012·2 (904·2 to 1107·2) 1457·8 (1317·3 to 1656·0) 1702·6 (1568·9 to 1943·4) 68·2 (55·2 to 86·2)* 16·8 (10·4 to 23·5)* 28·1 (25·5 to 30·5) 28·1 (25·7 to 31·8) 25·5 (23·4 to 29·0) –9·3 (–15·3 to 0·2) –9·4 (–14·1 to –4·6)* Gallbladder and biliary diseases 1665·1 (1368·2 to 1821·1) 1851·7 (1729·6 to 2093·8) 2098·0 (1953·3 to 2401·6) 26·0 (14·4 to 56·3)* 13·3 (9·8 to 17·6)* 42·8 (35·5 to 47·3) 33·9 (31·8 to 38·2) 30·5 (28·4 to 35·0) –28·8 (–36·1 to –12·3)* –10·0 (–12·8 to –6·6)* Pancreatitis 2035·5 (1723·4 to 2341·3) 3046·0 (2707·5 to 3380·8) 3347·0 (2910·4 to 3722·1) 64·4 (44·7 to 82·4)* 9·9 (2·9 to 16·8)* 47·5 (40·4 to 54·7) 50·5 (44·9 to 56·2) 45·8 (39·8 to 50·9) –3·6 (–15·1 to 6·6) –9·4 (–15·1 to –3·9)* Other digestive diseases 2992·8 (2482·9 to 3546·7) 2468·6 (2189·1 to 2834·9) 2698·5 (2428·7 to 3008·7) –9·8 (–24·4 to 4·2) 9·3 (1·9 to 17·8)* 65·2 (56·4 to 75·0) 43·0 (38·7 to 48·8) 38·6 (34·8 to 43·0) –40·8 (–49·1 to –33·3)* –10·2 (–15·5 to –4·2)* Neurological disorders 64 973·1 (50 343·0 to 80 732·5) 87 251·9 (67 830·9 to 107 955·1) 103 580·0 (81 171·2 to 128 122·4) 59·4 (55·2 to 64·6)* 18·7 (17·0 to 20·7)* 1500·6 (1192·1 to 1832·8) 1491·2 (1182·9 to 1824·0) 1478·4 (1171·9 to 1813·0) –1·5 (–3·3 to 0·4) –0·9 (–2·1 to 0·3) Alzheimer’s disease and other dementias 13 024·5 (11 052·7 to 15 479·9) 20 912·2 (17 919·7 to 24 689·5) 28 764·1 (24 510·8 to 33 952·4) 120·8 (115·3 to 126·5)* 37·5 (35·3 to 39·7)* 460·9 (394·5 to 544·4) 469·6 (403·2 to 552·4) 470·6 (401·2 to 556·3) 2·1 (0·1 to 3·8)* 0·2 (–1·1 to 1·5) Parkinson’s disease 1304·3 (1024·7 to 1606·9) 2385·3 (1901·1 to 2910·3) 3234·5 (2563·6 to 4012·8) 148·0 (139·8 to 155·8)* 35·6 (32·9 to 38·2)* 42·0 (33·2 to 51·9) 49·9 (39·7 to 61·0) 51·3 (40·6 to 63·4) 22·1 (18·2 to 25·9)* 2·7 (1·0 to 4·5)* Epilepsy 12 420·8 (10 285·4 to 14 852·0) 13 435·2 (11 142·0 to 16 031·4) 13 492·2 (11 014·7 to 16 503·1) 8·6 (–2·9 to 23·7) 0·4 (–7·0 to 8·6) 226·5 (187·9 to 269·3) 201·6 (166·8 to 240·4) 182·6 (148·9 to 223·5) –19·4 (–27·6 to –9·0)* –9·4 (–16·1 to –2·2)* Multiple sclerosis 694·0 (586·0 to 807·1) 974·2 (827·1 to 1123·9) 1151·5 (968·6 to 1345·8) 65·9 (45·2 to 74·8)* 18·2 (12·8 to 21·9)* 16·3 (13·8 to 18·9) 16·2 (13·8 to 18·6) 15·6 (13·2 to 18·3) –4·2 (–16·4 to 0·8) –3·2 (–7·3 to –0·2)* Motor neuron disease 582·3 (527·8 to 651·4) 774·9 (745·5 to 817·5) 926·1 (881·6 to 961·8) 59·0 (44·5 to 71·6)* 19·5 (14·7 to 22·4)* 13·4 (12·5 to 14·6) 13·7 (13·2 to 14·3) 13·2 (12·5 to 13·7) –1·5 (–9·3 to 2·9) –3·6 (–7·3 to –1·3)* Migraine 29 843·4 (19 092·9 to 41 793·9) 39 485·3 (25 341·5 to 55 187·3) 45 121·9 (29 045·8 to 62 826·9) 51·2 (49·7 to 52·8)* 14·3 (13·7 to 14·9)* 599·9 (385·7 to 839·1) 597·8 (384·6 to 833·2) 598·6 (385·9 to 833·3) –0·2 (–0·8 to 0·4) 0·1 (–0·2 to 0·5) Tension-type headache 4700·9 (2968·1 to 6989·3) 6236·2 (3972·6 to 9204·2) 7195·1 (4614·6 to 10 499·9) 53·1 (47·5 to 58·4)* 15·4 (13·7 to 17·0)* 96·2 (61·1 to 142·5) 95·5 (61·2 to 139·9) 95·9 (61·5 to 140·0) –0·2 (–2·5 to 1·9) 0·4 (–0·5 to 1·4) Other neurological disorders 2402·9 (2004·0 to 2829·7) 3048·7 (2593·3 to 3555·3) 3694·5 (3114·1 to 4353·4) 53·8 (38·5 to 67·8)* 21·2 (14·0 to 28·5)* 45·5 38·7 to 52·8) 46·9 (40·2 to 54·4) 50·6 (42·7 to 59·5) 11·2 (2·1 to 20·0)* 7·9 (1·9 to 14·2)* Mental and substance use disorders 110 918·3 (83 056·1 to 141 228·0) 145 067·1 (108 650·3 to 183 887·6) 162 509·3 (121 886·4 to 206 517·4) 46·5 (44·9 to 48·7)* 12·0 (11·2 to 12·9)* 2240·8 (1681·3 to 2844·2) 2226·6 (1669·0 to 2821·8) 2172·7 (1629·4 to 2761·5) –3·0 (–4·1 to –1·8)* –2·4 (–3·2 to –1·7)* (Table 1 continues on next page) Global Health Metrics 1276 www.thelancet.com Vol 390 September 16, 2017 All-age DALYs (thousands) Age-standardised DALY rate (per 100 000) 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 (Continued from previous page) Schizophrenia 8447·6 (6181·9 to 10 521·3) 11 492·6 (8420·0 to 14 326·7) 13 414·3 (9858·7 to 16 714·0) 58·8 (55·8 to 62·0)* 16·7 (15·5 to 18·0)* 179·6 (132·2 to 222·3) 178·9 (132·0 to 222·3) 177·2 (130·5 to 220·3) –1·3 (–2·2 to –0·5)* –0·9 (–1·7 to –0·2)* Alcohol use disorders 11 264·3 (9057·6 to 14 013·8) 15 561·7 (12 607·6 to 19 100·6) 16 244·7 (13 003·1 to 19 955·3) 44·2 (37·4 to 51·0)* 4·4 (0·4 to 8·6)* 237·3 (192·3 to 292·7) 240·9 (196·4 to 293·9) 214·4 (172·0 to 262·9) –9·6 (–14·3 to –5·5)* –11·0 (–14·6 to –7·5)* Drug use disorders 14 247·5 (11 250·4 to 17 370·8) 18 009·0 (14 444·2 to 21 739·6) 20 394·2 (16 204·4 to 24 670·3) 43·1 (36·5 to 58·5)* 13·2 (9·8 to 16·9)* 278·6 (221·2 to 337·7) 270·5 (217·3 to 325·1) 268·4 (213·5 to 324·0) –3·6 (–8·1 to 6·7) –0·8 (–3·8 to 2·4) Opioid use disorders 10 261·8 (8052·7 to 12 572·0) 12 817·5 (10 018·1 to 15 694·7) 14 788·8 (11 380·5 to 18 259·7) 44·1 (37·5 to 56·0)* 15·4 (11·2 to 19·5)* 202·0 (159·3 to 247·4) 193·1 (151·2 to 236·4) 194·3 (149·8 to 239·6) –3·8 (–8·3 to 3·8) 0·7 (–3·0 to 4·2) Cocaine use disorders 790·1 (568·0 to 1051·1) 1060·8 (780·4 to 1375·9) 1154·2 (847·2 to 1512·1) 46·1 (38·4 to 65·1)* 8·8 (5·1 to 12·2)* 15·7 (11·3 to 20·8) 16·1 (11·8 to 20·8) 15·3 (11·2 to 20·0) –2·6 (–7·8 to 9·6) –4·9 (–8·3 to –1·7)* Amphetamine use disorders 660·3 (430·6 to 985·6) 834·2 (567·1 to 1190·4) 881·7 (599·5 to 1243·1) 33·5 (23·0 to 54·2)* 5·7 (1·8 to 10·2)* 11·8 (7·8 to 17·3) 12·0 (8·2 to 17·0) 11·5 (7·8 to 16·2) –1·9 (–8·8 to 13·2) –3·7 (–7·5 to 0·3) Cannabis use disorders 514·5 (321·8 to 757·2) 623·9 (389·2 to 905·4) 646·9 (400·9 to 945·5) 25·7 (21·7 to 29·8)* 3·7 (1·2 to 6·0)* 9·1 (5·7 to 13·3) 8·8 (5·5 to 12·9) 8·5 (5·2 to 12·4) –6·9 (–8·9 to –5·0)* –4·2 (–5·9 to –2·4)* Other drug use disorders 2020·8 (1550·8 to 2453·2) 2672·5 (2235·2 to 3177·1) 2922·5 (2425·1 to 3504·3) 44·6 (31·6 to 89·2)* 9·3 (3·8 to 14·5)* 40·0 (30·8 to 48·3) 40·5 (34·0 to 47·8) 38·8 (32·3 to 46·4) –3·0 (–12·0 to 26·0) –4·3 (–9·3 to 0·2) Depressive disorders 29 503·5 (20 318·2 to 40 109·0) 39 052·4 (26 930·5 to 52 535·2) 44 208·4 (30 573·2 to 59 878·5) 49·8 (46·9 to 53·1)* 13·2 (12·2 to 14·4)* 630·6 (438·2 to 852·5) 620·1 (430·6 to 831·7) 597·9 (414·5 to 806·2) –5·2 (–6·2 to –4·1)* –3·6 (–4·3 to –2·9)* Major depressive disorder 23 423·5 (16 150·4 to 31 868·1) 30 670·3 (21 102·1 to 41 462·3) 34 104·6 (23 469·5 to 46 039·4) 45·6 (42·4 to 49·2)* 11·2 (10·1 to 12·3)* 495·7 (341·2 to 671·8) 484·6 (334·7 to 655·2) 461·1 (317·9 to 622·5) –7·0 (–8·1 to –5·8)* –4·9 (–5·6 to –4·1)* Dysthymia 6080·0 (4134·8 to 8839·6) 8382·0 (5676·3 to 12 123·1) 10 103·8 (6860·6 to 14 611·5) 66·2 (62·6 to 69·8)* 20·5 (18·3 to 23·1)* 134·9 (91·8 to 195·5) 135·5 (91·6 to 196·3) 136·8 (93·0 to 197·6) 1·4 (0·5 to 2·3)* 1·0 (–0·5 to 2·7) Bipolar disorder 5873·0 (3645·7 to 8618·8) 7795·1 (4869·3 to 11 432·4) 8954·0 (5588·3 to 13 186·4) 52·5 (50·0 to 55·0)* 14·9 (13·8 to 16·0)* 118·2 (73·8 to 173·0) 118·3 (74·1 to 173·1) 119·3 (74·7 to 175·1) 0·9 (0·1 to 1·8)* 0·8 (0·2 to 1·4)* Anxiety disorders 17 893·2 (12 472·9 to 24 294·6) 23 364·5 (16 285·6 to 31 632·0) 26 417·4 (18 440·4 to 35 634·4) 47·6 (45·4 to 49·8)* 13·1 (11·9 to 14·3)* 357·0 (249·8 to 482·4) 356·6 (248·7 to 481·7) 354·2 (247·1 to 477·8) –0·8 (–1·8 to 0·2) –0·7 (–1·7 to 0·2) Eating disorders 1400·4 (920·5 to 2016·6) 1861·9 (1222·9 to 2684·6) 2180·1 (1420·8 to 3122·9) 55·7 (52·2 to 59·3)* 17·1 (15·2 to 18·8)* 24·6 (16·2 to 35·3) 26·2 (17·2 to 37·7) 28·6 (18·6 to 40·8) 16·1 (14·3 to 17·7)* 8·9 (7·6 to 10·1)* Anorexia nervosa 418·9 (263·0 to 626·4) 523·9 (329·8 to 776·8) 584·7 (367·2 to 859·0) 39·6 (36·1 to 43·4)* 11·6 (9·2 to 14·0)* 7·1 (4·5 to 10·6) 7·3 (4·6 to 10·8) 7·7 (4·8 to 11·3) 8·1 (5·6 to 10·5)* 5·4 (3·2 to 7·4)* Bulimia nervosa 981·5 (602·8 to 1471·9) 1338·0 (818·2 to 1997·1) 1595·4 (974·5 to 2394·5) 62·5 (58·5 to 66·9)* 19·2 (16·9 to 21·1)* 17·5 (10·6 to 26·3) 18·9 (11·5 to 28·4) 20·9 (12·7 to 31·2) 19·4 (17·5 to 21·2)* 10·2 (8·7 to 11·7)* Autistic spectrum disorders 6525·8 (4418·6 to 9180·8) 8104·7 (5491·2 to 11 393·0) 9025·7 (6119·0 to 12 681·1) 38·3 (37·3 to 39·4)* 11·4 (10·8 to 12·0)* 120·6 (81·9 to 169·6) 121·1 (82·1 to 170·2) 121·4 (82·3 to 170·6) 0·6 (0·0 to 1·2)* 0·3 (–0·2 to 0·8) Autism 3371·4 (2170·7 to 4891·2) 4178·3 (2693·8 to 6041·1) 4649·0 (2976·6 to 6699·0) 37·9 (36·3 to 39·6)* 11·3 (10·3 to 12·3)* 62·8 (40·4 to 90·9) 62·6 (40·3 to 90·4) 62·5 (40·0 to 90·2) –0·4 (–1·3 to 0·5) –0·1 (–0·9 to 0·8) Asperger syndrome and other autistic spectrum disorders 3154·3 (2078·1 to 4617·6) 3926·4 (2574·5 to 5735·4) 4376·7 (2864·1 to 6392·9) 38·8 (37·5 to 40·1)* 11·5 (10·9 to 12·1)* 57·8 (38·0 to 84·2) 58·5 (38·3 to 85·2) 58·9 (38·5 to 85·9) 1·8 (1·2 to 2·3)* 0·6 (0·1 to 1·1)* (Table 1 continues on next page) Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1277 All-age DALYs (thousands) Age-standardised DALY rate (per 100 000) 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 (Continued from previous page) Attention-deficit hyperactivity disorder 599·7 (359·0 to 952·7) 711·9 (426·0 to 1134·5) 755·2 (452·2 to 1196·6) 25·9 (23·5 to 28·3)* 6·1 (5·0 to 7·1)* 10·2 (6·1 to 16·1) 10·1 (6·0 to 16·1) 10·1 (6·0 to 15·9) –1·3 (–3·0 to 0·5) –0·5 (–1·4 to 0·4) Conduct disorder 5072·5 (3154·4 to 7682·5) 5820·5 (3615·8 to 8803·8) 5947·3 (3701·9 to 8998·5) 17·2 (15·6 to 18·7)* 2·2 (0·9 to 3·4)* 79·5 (49·4 to 120·5) 79·4 (49·4 to 120·6) 81·2 (50·5 to 122·9) 2·1 (0·7 to 3·4)* 2·3 (1·1 to 3·4)* Idiopathic developmental intellectual disability 3629·4 (1741·7 to 6242·4) 4500·4 (2160·1 to 7650·1) 4610·9 (2201·5 to 7932·1) 27·1 (21·7 to 30·9) 2·5 (–0·9 to 4·6) 66·2 (31·8 to 113·6) 66·1 (31·8 to 112·4) 61·6 (29·4 to 105·9) –7·0 (–11·0 to –4·1) –6·9 (–10·0 to –4·9) Other mental and substance use disorders 6461·6 (4417·8 to 9255·4) 8792·3 (5989·8 to 12 580·6) 10 357·1 (7059·0 to 14 807·2) 60·3 (59·0 to 61·9)* 17·8 (17·2 to 18·5)* 138·5 (94·5 to 197·7) 138·4 (94·3 to 197·8) 138·5 (94·4 to 197·7) 0·0 (–0·5 to 0·5) 0·1 (–0·4 to 0·5) Diabetes, urogenital, blood, and endocrine diseases 81 744·6 (71 204·2 to 94 535·9) 112 751·7 (98 101·9 to 131 367·5) 133 747·8 (115 976·8 to 155 676·1) 63·6 (59·4 to 68·0)* 18·6 (17·4 to 20·0)* 1882·2 (1642·5 to 2163·3) 1942·7 (1699·4 to 2247·3) 1887·6 (1641·9 to 2192·9) 0·3 (–1·8 to 2·3) –2·8 (–3·9 to –1·7)* Diabetes mellitus 27 475·9 (23 282·7 to 32 608·1) 45 989·7 (38 695·1 to 54 713·4) 57 233·7 (47 967·9 to 68 279·3) 108·3 (104·2 to 112·1)* 24·4 (22·7 to 26·2)* 708·6 (604·0 to 837·1) 827·9 (700·5 to 977·9) 814·2 (686·6 to 965·4) 14·9 (12·6 to 16·9)* –1·6 (–3·0 to –0·2)* Acute glomerulonephritis 560·5 (521·8 to 610·3) 337·8 (325·7 to 355·6) 325·6 (310·5 to 343·1) –41·9 (–46·6 to –37·0)* –3·6 (–8·4 to 1·5) 11·6 (10·8 to 12·5) 5·5 (5·3 to 5·8) 4·5 (4·3 to 4·8) –60·9 (–63·9 to –57·6)* –17·4 (–21·5 to –13·2)* Chronic kidney disease 21 597·2 (20 094·0 to 23 354·9) 29 187·2 (27 250·7 to 31 463·0) 35 032·4 (32 622·1 to 37 954·3) 62·2 (56·5 to 68·0)* 20·0 (17·4 to 22·7)* 521·4 (484·6 to 565·3) 515·4 (480·9 to 555·7) 500·1 (465·5 to 541·4) –4·1 (–7·5 to –1·2)* –3·0 (–5·0 to –1·0)* Due to diabetes mellitus 7860·2 (7048·8 to 8711·8) 11 731·4 (10 615·3 to 12 891·9) 14 660·6 (13 206·6 to 16 203·7) 86·5 (77·5 to 92·6)* 25·0 (21·9 to 27·6)* 202·9 (182·1 to 225·3) 212·1 (191·8 to 233·4) 209·2 (188·8 to 230·8) 3·1 (–1·4 to 6·1) –1·4 (–3·5 to 0·5) Due to hypertension 3481·1 (3030·8 to 3987·5) 5169·1 (4520·8 to 5845·6) 6606·7 (5760·2 to 7493·9) 89·8 (80·4 to 96·3)* 27·8 (24·7 to 30·6)* 97·3 (84·5 to 110·8) 99·2 (87·0 to 112·3) 98·2 (85·5 to 111·1) 0·9 (–3·9 to 4·0) –1·0 (–3·3 to 0·9) Due to glomerulonephritis 4609·2 (4053·1 to 5203·8) 5467·8 (4843·2 to 6157·1) 5932·9 (5226·4 to 6746·0) 28·7 (22·3 to 36·4)* 8·5 (5·5 to 11·8)* 96·8 (85·7 to 109·7) 89·0 (79·1 to 100·4) 82·2 (72·8 to 93·3) –15·1 (–18·2 to –11·3)* –7·7 (–9·8 to –5·4)* Due to other causes 5646·7 (5024·1 to 6347·1) 6819·0 (6061·0 to 7661·1) 7832·1 (6915·6 to 8848·7) 38·7 (31·1 to 47·3)* 14·9 (11·6 to 18·4)* 124·5 (110·6 to 141·4) 115·1 (102·5 to 129·8) 110·6 (97·5 to 125·5) –11·2 (–14·7 to –7·3)* –4·0 (–6·2 to –1·4)* Urinary diseases and male infertility 6451·7 (5595·1 to 7476·6) 8383·8 (7248·0 to 9800·2) 9965·5 (8532·5 to 11 725·6) 54·5 (43·5 to 61·3)* 18·9 (14·9 to 21·6)* 152·8 (131·3 to 179·4) 148·8 (128·2 to 175·0) 143·5 (122·7 to 169·2) –6·1 (–11·5 to –2·8)* –3·6 (–6·5 to –1·5)* Interstitial nephritis and urinary tract infections 2443·8 (2111·8 to 2752·8) 3457·4 (3137·6 to 3688·9) 4269·2 (4005·5 to 4529·9) 74·7 (54·0 to 95·1)* 23·5 (15·5 to 30·3)* 55·6 (48·9 to 61·3) 60·7 (55·4 to 64·5) 61·9 (58·2 to 65·6) 11·3 (–0·3 to 22·0) 1·9 (–4·2 to 7·3) Urolithiasis 496·9 (352·4 to 588·4) 566·9 (491·0 to 658·3) 622·5 (523·5 to 778·9) 25·3 (7·1 to 86·6)* 9·8 (2·6 to 25·5)* 11·9 (8·6 to 14·1) 9·7 (8·4 to 11·2) 8·8 (7·4 to 11·0) –26·5 (–36·9 to 8·6) –9·8 (–15·6 to 3·0) Benign prostatic hyperplasia 1818·5 (1164·2 to 2622·2) 2625·2 (1692·9 to 3763·4) 3383·9 (2178·0 to 4835·1) 86·1 (82·2 to 90·8)* 28·9 (27·6 to 30·3)* 50·4 (32·4 to 72·4) 49·9 (32·2 to 72·1) 49·2 (31·8 to 71·0) –2·3 (–4·2 to –0·1)* –1·3 (–2·2 to –0·3)* Male infertility 104·7 (42·2 to 207·7) 138·3 (55·3 to 275·1) 165·1 (66·1 to 327·1) 57·7 (52·9 to 62·6)* 19·3 (15·7 to 22·6)* 2·0 (0·8 to 3·9) 2·0 (0·8 to 4·0) 2·1 (0·9 to 4·3) 9·5 (6·8 to 12·1)* 7·9 (4·9 to 10·6)* Other urinary diseases 1587·8 (1231·6 to 1910·8) 1596·0 (1346·0 to 1852·1) 1524·9 (1353·8 to 1722·1) –4·0 (–17·5 to 18·2) –4·5 (–12·0 to 7·6) 32·9 (25·7 to 39·6) 26·5 (22·4 to 30·7) 21·4 (19·1 to 24·2) –34·7 (–44·6 to –20·4)* –19·0 (–25·5 to –9·1)* Gynaecological diseases 7068·6 (4854·9 to 10 047·8) 9365·7 (6439·4 to 13 315·0) 10 460·2 (7184·1 to 14 928·1) 48·0 (45·8 to 50·2)* 11·7 (10·4 to 13·1)* 145·2 (99·4 to 206·1) 140·4 (96·1 to 200·0) 136·8 (94·0 to 195·1) –5·8 (–7·2 to –4·3)* –2·5 (–3·5 to –1·4)* (Table 1 continues on next page) Global Health Metrics 1278 www.thelancet.com Vol 390 September 16, 2017 All-age DALYs (thousands) Age-standardised DALY rate (per 100 000) 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 (Continued from previous page) Uterine fibroids 882·6 (552·9 to 1378·7) 1237·0 (765·6 to 1961·3) 1462·5 (897·4 to 2340·2) 65·7 (59·0 to 71·2)* 18·2 (16·3 to 19·8)* 20·1 (12·6 to 31·5) 19·3 (12·0 to 30·7) 19·2 (11·8 to 30·7) –4·6 (–8·6 to –1·5)* –0·6 (–2·2 to 0·7) Polycystic ovarian syndrome 98·7 (53·5 to 166·2) 108·9 (57·8 to 188·7) 112·4 (59·4 to 198·0) 14·0 (–4·7 to 29·3) 3·3 (–4·0 to 8·3) 1·9 (1·1 to 3·3) 1·6 (0·8 to 2·7) 1·5 (0·8 to 2·6) –24·7 (–39·1 to –12·8)* –7·3 (–14·5 to –2·3)* Female infertility 103·8 (37·9 to 234·0) 129·8 (47·2 to 288·4) 179·8 (66·2 to 399·0) 73·2 (58·8 to 91·0)* 38·5 (29·4 to 49·5)* 1·9 (0·7 to 4·3) 1·9 (0·7 to 4·2) 2·3 (0·9 to 5·2) 23·2 (13·8 to 35·5)* 25·3 (17·7 to 34·8)* Endometriosis 233·8 (156·3 to 325·3) 310·3 (209·4 to 429·1) 334·9 (226·3 to 461·8) 43·2 (36·4 to 50·6)* 7·9 (5·3 to 10·9)* 4·4 (3·0 to 6·2) 4·5 (3·0 to 6·2) 4·3 (2·9 to 6·0) –2·3 (–5·9 to 1·4) –3·8 (–6·1 to –1·4)* Genital prolapse 586·6 (297·3 to 1051·1) 694·7 (348·9 to 1246·0) 792·6 (394·6 to 1424·8) 35·1 (31·0 to 38·0)* 14·1 (12·5 to 15·4)* 14·9 (7·5 to 26·6) 12·3 (6·2 to 22·0) 11·1 (5·5 to 19·8) –25·8 (–27·9 to –24·3)* –10·0 (–11·2 to –9·0)* Premenstrual syndrome 2632·6 (1628·3 to 4036·0) 3470·7 (2146·2 to 5295·9) 3791·1 (2344·6 to 5812·9) 44·0 (42·4 to 45·5)* 9·2 (7·7 to 10·6)* 50·7 (31·3 to 77·2) 50·3 (31·1 to 76·7) 49·3 (30·5 to 75·6) –2·7 (–3·7 to –1·9)* –2·0 (–3·3 to –0·8)* Other gynaecological diseases 2530·6 (1722·7 to 3505·2) 3414·2 (2325·2 to 4737·6) 3786·8 (2576·9 to 5232·9) 49·6 (44·5 to 55·1)* 10·9 (8·6 to 13·8)* 51·2 (34·8 to 71·6) 50·5 (34·4 to 70·1) 49·2 (33·5 to 67·9) –4·1 (–6·9 to –0·6)* –2·7 (–4·7 to –0·1)* Haemoglobinopathies and haemolytic anaemias 12 160·7 (9789·5 to 14 856·4) 12 033·2 (9847·5 to 14 691·3) 12 321·6 (9916·9 to 15 372·2) 1·3 (–8·3 to 15·8) 2·4 (–2·6 to 8·3) 213·4 (174·0 to 258·9) 181·7 (148·9 to 221·9) 169·7 (136·9 to 211·4) –20·5 (–27·2 to –10·5)* –6·6 (–10·9 to –1·3)* Thalassaemias 1432·0 (961·6 to 1835·3) 771·2 (639·7 to 918·5) 516·0 (443·5 to 633·5) –64·0 (–70·0 to –40·5)* –33·1 (–39·9 to –18·8)* 21·7 (14·6 to 27·8) 11·2 (9·3 to 13·4) 7·2 (6·2 to 8·9) –66·6 (–72·1 to –45·0)* –35·5 (–42·1 to –21·6)* Thalassaemias trait 2703·8 (1770·0 to 3974·9) 2957·1 (1959·0 to 4290·6) 3280·4 (2156·8 to 4823·8) 21·3 (17·3 to 25·1)* 10·9 (8·0 to 13·9)* 49·7 (32·6 to 73·1) 44·9 (29·7 to 65·2) 44·6 (29·3 to 65·5) –10·2 (–12·1 to –8·4)* –0·6 (–3·1 to 2·0) Sickle cell disorders 4349·9 (3163·6 to 5209·6) 4224·4 (3414·2 to 4951·4) 4117·9 (3587·8 to 4842·0) –5·3 (–20·7 to 19·9) –2·5 (–11·6 to 10·5) 68·7 (50·4 to 81·8) 60·8 (49·0 to 71·5) 56·6 (49·1 to 66·5) –17·7 (–30·6 to 3·8) –6·9 (–15·9 to 6·1) Sickle cell trait 1119·1 (726·0 to 1651·0) 1331·4 (871·9 to 1959·4) 1555·0 (1014·1 to 2282·9) 39·0 (36·2 to 41·7)* 16·8 (14·8 to 18·8)* 19·5 (12·8 to 28·8) 19·7 (12·9 to 28·9) 21·1 (13·8 to 31·0) 8·1 (6·2 to 9·7)* 7·2 (5·4 to 9·1)* G6PD deficiency 577·8 (488·8 to 691·1) 702·7 (606·1 to 834·8) 737·8 (637·7 to 875·0) 27·7 (18·3 to 42·4)* 5·0 (1·2 to 10·1)* 10·9 (9·2 to 13·1) 10·7 (9·3 to 12·7) 9·9 (8·6 to 11·8) –8·8 (–14·7 to 1·2) –7·3 (–10·7 to –2·8)* G6PD trait 0·4 (0·3 to 0·6) 0·5 (0·3 to 0·7) 0·6 (0·4 to 0·8) 37·0 (30·4 to 43·2)* 17·6 (14·9 to 20·7)* ·· ·· ·· –3·0 (–6·5 to 0·1) 4·9 (2·6 to 7·5)* Other haemoglobinopathies and haemolytic anaemias 1977·7 (1591·8 to 2467·5) 2046·0 (1610·7 to 2612·6) 2113·9 (1640·0 to 2730·4) 6·9 (0·9 to 13·2)* 3·3 (0·6 to 5·8)* 42·9 (35·4 to 52·5) 34·4 (27·5 to 43·3) 30·2 (23·6 to 38·9) –29·4 (–34·6 to –23·9)* –12·1 (–15·2 to –9·4)* Endocrine, metabolic, blood, and immune disorders 6430·0 (5399·9 to 7454·1) 7454·3 (6326·0 to 8750·0) 8408·8 (7025·4 to 9979·2) 30·8 (20·9 to 40·9)* 12·8 (9·0 to 16·5)* 129·3 (108·6 to 151·1) 123·0 (104·1 to 144·5) 118·7 (99·1 to 140·9) –8·2 (–13·4 to –3·2)* –3·5 (–6·4 to –0·8)* Musculoskeletal disorders 86 655·4 (63 137·9 to 112 703·8) 117 031·2 (85 442·0 to 152 359·0) 140 030·6 (102 331·5 to 181 687·7) 61·6 (59·1 to 63·9)* 19·6 (18·5 to 20·8)* 2014·2 (1474·2 to 2609·4) 1944·7 (1422·3 to 2518·0) 1918·7 (1404·4 to 2493·3) –4·7 (–5·7 to –3·9)* –1·3 (–2·0 to –0·6)* Rheumatoid arthritis 3329·7 (2458·2 to 4257·7) 4443·0 (3238·4 to 5693·1) 5563·4 (3985·9 to 7171·2) 67·1 (60·8 to 72·7)* 25·2 (22·4 to 27·6)* 82·8 (61·6 to 104·8) 77·7 (56·9 to 99·3) 78·0 (55·8 to 100·1) –5·7 (–9·4 to –2·9)* 0·4 (–1·8 to 2·2) Osteoarthritis 7947·9 (5593·8 to 10 856·2) 12 385·2 (8735·1 to 16 806·2) 16 282·9 (11 486·0 to 22 047·2) 104·9 (102·9 to 107·2)* 31·5 (30·8 to 32·2)* 213·4 (150·2 to 291·8) 226·6 (159·7 to 308·0) 232·1 (163·7 to 313·9) 8·8 (7·6 to 10·2)* 2·4 (1·9 to 3·0)* (Table 1 continues on next page) Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1279 All-age DALYs (thousands) Age-standardised DALY rate (per 100 000) 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 (Continued from previous page) Low back and neck pain 55 941·4 (39 562·5 to 73 187·3) 72 599·3 (51 532·0 to 94 807·4) 86 584·5 (61 335·4 to 113 628·5) 54·8 (51·8 to 57·4)* 19·3 (17·7 to 20·7)* 1294·3 (914·6 to 1702·0) 1198·9 (855·0 to 1573·7) 1182·7 (837·4 to 1550·6) –8·6 (–9·6 to –7·7)* –1·4 (–2·4 to –0·5)* Low back pain 39 129·9 (27 512·7 to 51 797·6) 48 853·4 (34 564·0 to 64 073·6) 57 648·2 (40 820·5 to 75 877·0) 47·3 (44·4 to 50·1)* 18·0 (16·0 to 20·1)* 897·3 (637·6 to 1178·5) 805·3 (574·2 to 1053·7) 788·9 (558·7 to 1034·6) –12·1 (–12·9 to –11·3)* –2·0 (–3·6 to –0·9)* Neck pain 16 811·5 (11 404·8 to 23 792·2) 23 745·9 (16 214·4 to 33 438·2) 28 936·3 (19 578·5 to 40 543·1) 72·1 (68·5 to 76·2)* 21·9 (19·9 to 24·0)* 397·0 (268·6 to 556·8) 393·6 (269·1 to 549·8) 393·8 (267·8 to 550·2) –0·8 (–2·2 to 0·7) 0·1 (–1·3 to 1·4) Gout 596·8 (413·3 to 810·6) 848·9 (589·9 to 1155·4) 1071·2 (742·4 to 1455·0) 79·5 (76·2 to 82·9)* 26·2 (24·3 to 27·9)* 15·4 (10·6 to 21·1) 15·2 (10·4 to 20·6) 15·2 (10·4 to 20·6) –1·8 (–3·3 to –0·2)* –0·1 (–1·3 to 1·1) Other musculoskeletal disorders 18 839·6 (13 162·2 to 26 102·7) 26 754·8 (18 664·4 to 36 839·1) 30 528·6 (21 196·7 to 42 458·6) 62·0 (57·4 to 67·0)* 14·1 (11·4 to 16·8)* 408·3 (285·4 to 564·2) 426·3 (298·3 to 586·2) 410·6 (285·6 to 568·6) 0·6 (–1·3 to 2·1) –3·7 (–5·7 to –1·8)* Other non-communicable diseases 164 624·2 (127 834·6 to 209 136·6) 183 247·3 (141 607·2 to 236 700·6) 196 036·1 (148 295·9 to 258 150·6) 19·1 (6·3 to 31·0)* 7·0 (2·4 to 10·8)* 3107·0 (2367·7 to 4005·6) 2912·8 (2241·2 to 3781·4) 2758·6 (2096·2 to 3616·8) –11·2 (–17·7 to –5·3)* –5·3 (–8·4 to –2·6)* Congenital birth defects 68 393·0 (53 592·7 to 84 283·8) 58 286·5 (48 759·3 to 66 905·7) 50 429·8 (44 114·0 to 56 200·2) –26·3 (–38·1 to –8·0)* –13·5 (–20·8 to –5·2)* 1042·0 (821·5 to 1280·7) 853·9 (714·0 to 980·0) 716·0 (626·4 to 798·7) –31·3 (–42·1 to –15·1)* –16·1 (–23·1 to –8·3)* Neural tube defects 10 315·0 (7239·9 to 14 462·5) 6150·2 (4476·4 to 8763·9) 5130·9 (3867·1 to 7016·1) –50·3 (–58·8 to –39·9)* –16·6 (–27·1 to –5·4)* 156·9 (111·4 to 218·6) 90·3 (65·8 to 128·5) 72·6 (54·5 to 99·5) –53·8 (–61·3 to –44·5)* –19·6 (–29·7 to –9·2)* Congenital heart anomalies 27 581·3 (21 339·9 to 34 673·0) 22 936·6 (19 833·8 to 26 150·9) 18 563·8 (16 539·0 to 21 464·8) –32·7 (–44·5 to –7·0)* –19·1 (–26·5 to –6·9)* 413·5 (320·8 to 519·5) 334·6 (289·1 to 381·8) 264·7 (235·7 to 305·9) –36·0 (–47·0 to –11·7)* –20·9 (–28·1 to –9·0)* Orofacial clefts 480·8 (242·6 to 688·3) 320·2 (200·3 to 468·4) 238·7 (148·0 to 369·6) –50·4 (–67·5 to –18·4)* –25·5 (–41·3 to –8·5)* 7·2 (3·7 to 10·2) 4·7 (2·9 to 6·8) 3·4 (2·1 to 5·3) –52·4 (–68·8 to –22·9)* –27·0 (–42·6 to –10·0)* Down’s syndrome 1325·4 (767·0 to 3069·7) 1240·1 (945·4 to 2039·8) 1166·0 (1005·7 to 1482·8) –12·0 (–54·8 to 41·0) –6·0 (–30·4 to 12·8) 20·7 (12·4 to 46·5) 18·3 (14·0 to 30·1) 16·3 (14·0 to 20·7) –21·4 (–58·2 to 22·5) –11·2 (–33·7 to 6·2) Turner syndrome 37·8 (18·1 to 60·9) 44·2 (21·1 to 71·5) 47·3 (22·1 to 76·5) 25·2 (20·6 to 30·2)* 6·9 (3·2 to 10·4)* 0·7 (0·3 to 1·1) 0·6 (0·3 to 1·0) 0·6 (0·3 to 1·0) –3·4 (–6·7 to –0·1)* –1·0 (–4·3 to 2·1) Klinefelter syndrome 13·0 (6·3 to 24·4) 15·7 (7·6 to 29·6) 17·1 (8·2 to 32·3) 32·1 (28·5 to 35·8)* 9·0 (6·2 to 11·7)* 0·2 (0·1 to 0·4) 0·2 (0·1 to 0·4) 0·2 (0·1 to 0·4) –0·3 (–2·6 to 2·3) –0·1 (–2·5 to 2·5) Other chromosomal abnormalities 1668·6 (1053·4 to 3061·2) 1851·6 (1371·3 to 2805·1) 1952·2 (1542·5 to 2587·9) 17·0 (–18·1 to 54·2) 5·4 (–10·1 to 18·4) 24·9 (15·8 to 45·4) 26·8 (19·9 to 40·8) 27·9 (22·0 to 37·1) 12·2 (–20·7 to 47·3) 4·0 (–11·4 to 16·7) Congenital musculoskeletal and limb anomalies 2120·7 (1465·8 to 3497·7) 2234·9 (1621·0 to 3167·4) 2258·4 (1661·2 to 2988·8) 6·5 (–21·7 to 26·8) 1·1 (–12·2 to 9·4) 36·6 (25·7 to 57·3) 33·6 (24·4 to 47·1) 31·2 (23·0 to 41·5) –14·6 (–32·7 to –2·5)* –7·0 (–17·7 to –0·0)* Urogenital congenital anomalies 1277·1 (845·2 to 1639·7) 1202·0 (877·2 to 1462·4) 1085·9 (858·1 to 1305·3) –15·0 (–35·6 to 10·8) –9·7 (–19·8 to 3·1) 19·4 (12·9 to 24·9) 17·6 (12·8 to 21·4) 15·4 (12·2 to 18·6) –20·4 (–39·5 to 2·8) –12·1 (–21·9 to 0·3) Digestive congenital anomalies 4666·6 (3171·1 to 8658·0) 3973·4 (3024·5 to 6520·3) 3343·0 (2674·6 to 4964·7) –28·4 (–49·5 to –3·2)* –15·9 (–27·9 to –1·6)* 69·9 (47·8 to 128·5) 58·2 (44·3 to 95·3) 48·0 (38·3 to 71·5) –31·3 (–51·3 to –8·1)* –17·5 (–29·3 to –3·8)* Other congenital birth defects 18 906·7 (10 942·1 to 30 995·8) 18 317·5 (12 204·8 to 26 209·6) 16 626·6 (12 154·3 to 21 604·2) –12·1 (–33·0 to 18·4) –9·2 (–20·5 to 3·7) 291·9 (171·4 to 473·1) 268·9 (179·1 to 384·4) 235·6 (172·4 to 307·3) –19·3 (–37·5 to 5·5) –12·4 (–22·8 to –0·2)* Skin and subcutaneous diseases 41 366·2 (28 152·7 to 59 346·6) 51 550·5 (35 306·4 to 73 530·7) 57 394·0 (39 334·2 to 81 653·4) 38·8 (37·0 to 41·0)* 11·3 (10·6 to 12·4)* 756·7 (518·9 to 1081·9) 770·3 (529·3 to 1097·1) 781·3 (535·8 to 1110·4) 3·2 (2·5 to 4·4)* 1·4 (0·8 to 2·2)* (Table 1 continues on next page) Global Health Metrics 1280 www.thelancet.com Vol 390 September 16, 2017 All-age DALYs (thousands) Age-standardised DALY rate (per 100 000) 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 (Continued from previous page) Dermatitis 8427·2 (5021·7 to 13 797·8) 10 043·3 (6010·8 to 16 327·0) 11 210·2 (6714·5 to 18 218·1) 33·0 (31·2 to 35·0)* 11·6 (10·8 to 12·4)* 151·8 (91·0 to 246·4) 151·3 (90·6 to 245·3) 153·0 (91·6 to 248·4) 0·7 (–0·3 to 1·8) 1·1 (0·2 to 1·8)* Psoriasis 3321·0 (2384·1 to 4347·4) 4638·9 (3323·7 to 6084·4) 5643·4 (4039·7 to 7377·2) 69·9 (68·4 to 71·5)* 21·7 (20·8 to 22·6)* 69·7 (50·0 to 91·2) 73·5 (52·6 to 96·1) 76·6 (54·9 to 100·0) 9·8 (9·1 to 10·6)* 4·2 (3·5 to 5·0)* Cellulitis 254·1 (179·0 to 320·6) 451·3 (288·9 to 558·2) 607·6 (398·4 to 739·6) 139·1 (103·8 to 168·2)* 34·6 (25·1 to 48·9)* 5·5 (3·9 to 6·9) 7·6 (4·8 to 9·4) 8·6 (5·6 to 10·4) 56·3 (32·3 to 74·2)* 13·1 (5·5 to 24·8)* Pyoderma 1094·9 (615·2 to 1411·5) 1577·2 (930·0 to 1948·5) 1944·8 (1249·8 to 2603·1) 77·6 (42·6 to 123·0)* 23·3 (7·9 to 40·5)* 22·1 (12·9 to 27·9) 26·0 (15·4 to 32·2) 27·7 (17·8 to 37·0) 25·1 (3·6 to 51·2)* 6·5 (–6·4 to 20·8) Scabies 3332·5 (1844·4 to 5364·2) 3670·6 (2034·7 to 5849·4) 3787·8 (2103·6 to 6029·0) 13·7 (10·8 to 16·7)* 3·2 (1·8 to 4·6)* 58·6 (32·4 to 93·0) 53·9 (29·8 to 85·6) 51·0 (28·3 to 81·0) –13·0 (–13·9 to –12·2)* –5·4 (–6·2 to –4·8)* Fungal skin diseases 2267·4 (900·2 to 4720·9) 2973·4 (1184·9 to 6172·3) 3508·8 (1403·0 to 7271·0) 54·8 (51·8 to 57·7)* 18·0 (17·0 to 19·1)* 46·1 (18·3 to 95·7) 47·7 (18·9 to 98·8) 48·9 (19·5 to 101·4) 6·0 (5·3 to 6·8)* 2·5 (2·2 to 2·9)* Viral skin diseases 4543·4 (2823·8 to 6780·1) 5425·8 (3369·5 to 8082·0) 5915·2 (3674·3 to 8828·1) 30·2 (29·2 to 31·2)* 9·0 (8·6 to 9·5)* 80·2 (49·9 to 119·5) 80·0 (49·8 to 119·1) 79·9 (49·6 to 119·3) –0·4 (–0·8 to 0·0) –0·1 (–0·5 to 0·2) Acne vulgaris 12 086·8 (8150·7 to 17 552·7) 15 067·6 (10 169·9 to 21 752·0) 15 836·0 (10 643·5 to 22 842·6) 31·0 (29·7 to 32·4)* 5·1 (4·3 to 5·8)* 202·3 (136·5 to 292·9) 207·8 (140·1 to 300·1) 212·1 (143·0 to 306·4) 4·9 (4·2 to 5·6)* 2·1 (1·5 to 2·6)* Alopecia areata 350·9 (224·8 to 530·5) 447·0 (286·2 to 675·8) 504·2 (322·7 to 760·0) 43·7 (42·1 to 45·3)* 12·8 (11·8 to 13·9)* 6·9 (4·4 to 10·4) 6·8 (4·3 to 10·2) 6·7 (4·3 to 10·1) –3·5 (–4·4 to –2·6)* –1·3 (–2·2 to –0·5)* Pruritus 452·1 (211·1 to 825·5) 599·8 (278·6 to 1087·3) 709·1 (329·7 to 1298·7) 56·8 (52·5 to 61·0)* 18·2 (16·7 to 19·8)* 9·4 (4·4 to 17·2) 9·6 (4·5 to 17·5) 9·7 (4·5 to 17·8) 2·9 (2·4 to 3·5)* 1·3 (0·8 to 1·8)* Urticaria 3155·2 (2020·3 to 4556·7) 3684·4 (2348·7 to 5265·8) 4029·9 (2575·9 to 5745·3) 27·7 (24·9 to 31·0)* 9·4 (8·3 to 10·5)* 55·1 (35·3 to 78·7) 55·0 (35·1 to 78·7) 54·9 (35·1 to 78·6) –0·5 (–1·1 to 0·1) –0·3 (–0·8 to 0·3) Decubitus ulcer 377·1 (291·5 to 475·3) 553·3 (411·4 to 680·3) 670·4 (513·0 to 836·1) 77·8 (65·5 to 86·8)* 21·2 (17·1 to 27·4)* 10·7 (8·2 to 13·6) 10·8 (8·0 to 13·3) 10·2 (7·8 to 12·6) –5·1 (–11·6 to 0·4) –5·7 (–9·0 to –0·4)* Other skin and subcutaneous diseases 1703·5 (851·6 to 3091·8) 2417·9 (1203·9 to 4377·1) 3026·6 (1513·0 to 5474·6) 77·7 (76·2 to 79·3)* 25·2 (24·5 to 25·9)* 38·1 (19·1 to 69·0) 40·4 (20·2 to 72·9) 42·2 (21·1 to 76·3) 10·8 (10·1 to 11·7)* 4·5 (4·1 to 5·1)* Sense organ diseases 39 443·2 (27 342·1 to 54 552·7) 54 826·6 (38 158·5 to 76 040·8) 66 701·9 (46 534·4 to 92 391·9) 69·1 (67·3 to 71·0)* 21·7 (20·7 to 22·5)* 977·4 (686·1 to 1356·7) 977·9 (685·8 to 1355·9) 959·3 (670·2 to 1331·0) –1·9 (–2·5 to –1·2)* –1·9 (–2·6 to –1·3)* Glaucoma 211·5 (142·7 to 292·9) 339·7 (230·3 to 471·3) 461·1 (311·4 to 642·1) 118·0 (114·5 to 121·8)* 35·7 (33·7 to 38·0)* 6·3 (4·3 to 8·7) 6·9 (4·7 to 9·5) 7·0 (4·8 to 9·8) 12·1 (10·4 to 14·0)* 2·1 (0·8 to 3·6)* Cataract 2698·3 (1924·0 to 3682·2) 4418·1 (3157·8 to 5992·5) 5789·0 (4134·6 to 7915·3) 114·5 (111·4 to 117·9)* 31·0 (29·4 to 32·6)* 80·1 (57·2 to 108·9) 88·3 (63·4 to 119·6) 88·3 (63·3 to 120·5) 10·3 (9·0 to 11·7)* 0·0 (–1·1 to 1·1) Macular degeneration 174·5 (118·9 to 238·7) 297·1 (202·1 to 406·9) 408·6 (277·9 to 555·6) 134·1 (129·3 to 139·0)* 37·5 (34·7 to 40·4)* 5·3 (3·6 to 7·3) 6·1 (4·2 to 8·4) 6·3 (4·3 to 8·6) 17·8 (15·3 to 20·4)* 2·7 (0·7 to 4·7)* Refraction and accommodation disorders 10 172·5 (6357·4 to 15 852·4) 13 035·5 (8163·5 to 20 302·0) 14 972·5 (9340·6 to 23 361·5) 47·2 (45·2 to 49·0)* 14·9 (13·7 to 15·9)* 230·6 (144·4 to 359·2) 220·0 (137·9 to 342·1) 209·1 (130·4 to 326·0) –9·3 (–10·0 to –8·7)* –5·0 (–5·8 to –4·3)* Age-related and other hearing loss 21 193·7 (14 943·8 to 29 581·7) 29 673·0 (20 837·2 to 41 494·7) 36 287·5 (25 341·8 to 50 893·6) 71·2 (67·0 to 75·3)* 22·3 (20·4 to 24·0)* 534·7 (379·0 to 745·0) 533·4 (375·3 to 743·4) 524·3 (368·0 to 734·0) –1·9 (–3·5 to –0·7)* –1·7 (–2·9 to –0·6)* (Table 1 continues on next page) Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1281 All-age DALYs (thousands) Age-standardised DALY rate (per 100 000) 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 (Continued from previous page) Other vision loss 1205·2 (853·2 to 1628·8) 1776·9 (1256·9 to 2396·0) 2241·2 (1578·3 to 3016·4) 86·0 (80·7 to 91·0)* 26·1 (23·9 to 28·1)* 30·1 (21·3 to 40·6) 31·8 (22·4 to 42·9) 32·0 (22·5 to 43·1) 6·3 (4·6 to 8·0)* 0·7 (–0·8 to 2·0) Other sense organ diseases 3787·5 (2395·1 to 5704·0) 5286·3 (3336·8 to 7935·4) 6542·0 (4132·0 to 9828·4) 72·7 (71·6 to 73·9)* 23·8 (23·1 to 24·4)* 90·3 (57·2 to 136·3) 91·4 (57·9 to 137·9) 92·3 (58·4 to 139·0) 2·1 (1·7 to 2·5)* 0·9 (0·6 to 1·3)* Oral disorders 11 294·7 (6848·2 to 17 556·0) 15 552·8 (9435·9 to 24 158·8) 19 006·2 (11 586·8 to 29 629·5) 68·3 (65·8 to 70·6)* 22·2 (21·2 to 23·3)* 270·1 (165·4 to 420·1) 266·3 (162·9 to 413·0) 265·7 (162·8 to 413·4) –1·6 (–2·3 to –0·9)* –0·2 (–0·7 to 0·2) Caries of deciduous teeth 120·6 (53·2 to 233·0) 119·1 (52·5 to 231·1) 127·2 (55·9 to 249·1) 5·5 (2·8 to 7·3)* 6·8 (4·5 to 8·5)* 1·8 (0·8 to 3·5) 1·8 (0·8 to 3·4) 1·8 (0·8 to 3·4) –3·7 (–5·9 to –2·1)* 0·2 (–1·9 to 1·8) Caries of permanent teeth 1295·7 (581·5 to 2521·8) 1571·3 (699·0 to 3047·3) 1707·6 (760·1 to 3324·9) 31·8 (29·7 to 33·9)* 8·7 (8·0 to 9·4)* 25·7 (11·4 to 50·0) 24·0 (10·7 to 46·5) 23·0 (10·2 to 44·6) –10·6 (–11·9 to –9·3)* –4·2 (–4·8 to –3·6)* Periodontal diseases 2676·0 (1065·8 to 5539·9) 3893·0 (1550·5 to 8078·4) 4898·0 (1946·8 to 10 208·7) 83·0 (80·9 to 84·8)* 25·8 (24·7 to 26·8)* 64·2 (25·6 to 134·6) 65·1 (25·9 to 136·6) 66·6 (26·6 to 139·4) 3·8 (3·0 to 4·4)* 2·3 (1·8 to 2·9)* Edentulism and severe tooth loss 4603·5 (3028·6 to 6528·4) 6553·4 (4335·5 to 9234·5) 8338·4 (5467·4 to 11 760·4) 81·1 (79·7 to 82·5)* 27·2 (26·0 to 28·4)* 125·7 (82·1 to 177·4) 122·8 (80·3 to 172·5) 121·7 (79·6 to 171·1) –3·2 (–3·6 to –2·8)* –0·9 (–1·6 to –0·3)* Other oral disorders 2598·9 (1602·8 to 3894·8) 3416·0 (2109·2 to 5128·9) 3935·0 (2427·2 to 5907·9) 51·4 (50·0 to 52·8) 15·2 (14·6 to 15·8) 52·7 (32·6 to 79·0) 52·7 (32·5 to 79·2) 52·7 (32·5 to 79·2) –0·0 (–0·4 to 0·3) 0·0 (–0·3 to 0·3) Sudden infant death syndrome 4127·1 (3107·6 to 6179·4) 3031·0 (2487·9 to 3993·4) 2504·2 (2015·2 to 3003·1) –39·3 (–57·8 to –20·1)* –17·4 (–33·2 to –1·1)* 60·8 (45·8 to 91·0) 44·4 (36·4 to 58·4) 36·4 (29·3 to 43·6) –40·1 (–58·4 to –21·2)* –18·0 (–33·7 to –1·9)* Injuries 259 714·5 (242 647·1 to 276 817·9) 259 792·1 (242 234·6 to 280 976·8) 255 434·3 (236 089·5 to 280 689·1) –1·6 (–6·2 to 3·8) –1·7 (–4·7 to 1·4) 4875·4 (4544·8 to 5239·0) 3976·8 (3692·0 to 4319·2) 3457·3 (3192·1 to 3803·2) –29·1 (–32·0 to –25·9)* –13·1 (–15·6 to –10·6)* Transport injuries 70 430·3 (67 012·4 to 74 545·4) 80 802·1 (76 835·3 to 85 315·6) 78 051·8 (73 391·8 to 83 700·8) 10·8 (6·4 to 15·9)* –3·4 (–5·8 to –0·9)* 1320·1 (1253·5 to 1399·1) 1218·5 (1155·7 to 1292·0) 1044·0 (980·2 to 1120·6) –20·9 (–23·9 to –17·8)* –14·3 (–16·4 to –12·3)* Road injuries 64 788·4 (61 744·3 to 68 397·9) 74 299·1 (71 021·9 to 78 088·0) 71 395·0 (67 520·8 to 76 128·0) 10·2 (5·5 to 15·2)* –3·9 (–6·3 to –1·6)* 1210·6 (1151·7 to 1279·9) 1119·1 (1066·5 to 1179·6) 954·5 (901·8 to 1019·0) –21·1 (–24·1 to –18·0)* –14·7 (–16·8 to –12·8)* Pedestrian road injuries 24 717·6 (22 795·5 to 28 090·8) 26 131·7 (24 588·0 to 28 158·5) 24 001·5 (22 518·2 to 25 750·8) –2·9 (–15·1 to 4·8) –8·2 (–13·1 to –5·0)* 459·3 (424·8 to 520·6) 398·7 (375·0 to 429·0) 323·3 (303·6 to 346·5) –29·6 (–38·3 to –24·6)* –18·9 (–23·2 to –16·2)* Cyclist road injuries 3390·5 (2919·1 to 3889·2) 4747·7 (4149·8 to 5488·4) 4968·2 (4229·6 to 5946·9) 46·5 (30·4 to 67·8)* 4·6 (–0·2 to 10·0) 68·7 (58·8 to 79·8) 74·3 (64·8 to 86·2) 67·2 (57·1 to 80·7) –2·2 (–12·4 to 11·4) –9·5 (–13·4 to –4·7)* Motorcyclist road injuries 11 467·4 (10 510·3 to 12 944·1) 15 105·0 (13 758·6 to 16 396·3) 14 937·5 (13 499·8 to 16 289·9) 30·3 (18·2 to 40·1)* –1·1 (–5·4 to 2·6) 211·8 (194·4 to 239·6) 222·6 (202·8 to 242·2) 197·3 (178·6 to 215·4) –6·8 (–15·5 to 0·2) –11·3 (–15·1 to –8·2)* Motor vehicle road injuries 24 319·9 (20 644·9 to 27 442·2) 27 217·7 (24 983·6 to 30 653·8) 26 211·8 (24 325·0 to 29 404·9) 7·8 (0·7 to 25·4)* –3·7 (–7·2 to 2·4) 452·8 (386·1 to 509·4) 406·3 (372·9 to 455·6) 349·4 (324·1 to 391·5) –22·9 (–27·6 to –11·0)* –14·0 (–17·0 to –8·9)* Other road injuries 893·0 (714·6 to 1103·6) 1097·0 (919·3 to 1343·6) 1275·9 (1026·6 to 1626·3) 42·9 (23·2 to 73·5)* 16·3 (9·2 to 23·7)* 17·9 (14·3 to 22·2) 17·2 (14·3 to 21·1) 17·3 (13·9 to 22·1) –3·3 (–15·0 to 15·3) 0·8 (–4·9 to 6·7) Other transport injuries 5641·9 (4761·1 to 6609·6) 6503·0 (5751·7 to 7477·8) 6656·8 (5797·7 to 7707·3) 18·0 (5·2 to 38·4)* 2·4 (–3·0 to 8·9) 109·5 (92·6 to 128·2) 99·4 (87·3 to 114·9) 89·5 (77·8 to 103·9) –18·3 (–26·4 to –5·1)* –10·0 (–14·5 to –4·4)* Unintentional injuries 121 695·5 (109 006·9 to 134 119·5) 111 226·3 (98 814·1 to 125 226·5) 107 423·5 (94 550·6 to 124 015·0) –11·7 (–18·4 to –4·3)* –3·4 (–7·1 to 0·1) 2282·2 (2056·3 to 2530·5) 1750·0 (1549·2 to 1975·9) 1486·2 (1306·5 to 1716·5) –34·9 (–39·0 to –30·6)* –15·1 (–18·0 to –12·2)* (Table 1 continues on next page) Global Health Metrics 1282 www.thelancet.com Vol 390 September 16, 2017 All-age DALYs (thousands) Age-standardised DALY rate (per 100 000) 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 (Continued from previous page) Falls 26 607·6 (22 592·9 to 31 276·8) 31 258·7 (26 220·2 to 37 276·0) 35 773·8 (29 777·6 to 43 175·7) 34·5 (22·3 to 44·8)* 14·4 (9·0 to 18·1)* 589·1 (498·2 to 700·1) 530·8 (444·1 to 630·7) 506·5 (422·1 to 610·0) –14·0 (–20·6 to –9·2)* –4·6 (–8·8 to –1·8)* Drowning 34 779·0 (29 934·2 to 37 903·4) 22 886·7 (20 014·2 to 24 143·1) 16 809·0 (15 206·1 to 18 047·4) –51·7 (–55·6 to –43·2)* –26·6 (–30·1 to –20·1)* 567·0 (491·4 to 614·9) 337·6 (295·4 to 356·2) 229·2 (207·1 to 246·3) –59·6 (–62·8 to –53·0)* –32·1 (–35·4 to –26·1)* Fire, heat, and hot substances 10 059·5 (7875·0 to 11 492·7) 8822·8 (7294·7 to 10 004·2) 8150·8 (6763·6 to 9476·2) –19·0 (–28·2 to –5·5)* –7·6 (–13·1 to –0·1)* 186·1 (150·6 to 212·3) 135·9 (113·3 to 154·4) 111·4 (92·4 to 129·4) –40·2 (–46·0 to –31·7)* –18·1 (–22·7 to –11·8)* Poisonings 5471·1 (3951·1 to 6579·7) 3747·9 (2956·8 to 4213·0) 3149·4 (2379·7 to 3574·1) –42·4 (–53·8 to –21·9)* –16·0 (–25·9 to –2·9)* 95·1 (69·4 to 112·2) 56·3 (44·5 to 63·3) 43·0 (32·5 to 48·9) –54·8 (–63·8 to –39·4)* –23·6 (–32·8 to –11·8)* Exposure to mechanical forces 12 931·3 (11 440·5 to 14 960·9) 12 360·8 (10 580·5 to 14 181·8) 11 921·2 (9836·7 to 14 174·8) –7·8 (–25·2 to 2·4) –3·6 (–8·0 to 1·0) 239·6 (211·3 to 277·2) 189·7 (162·2 to 219·0) 162·2 (133·8 to 193·1) –32·3 (–44·1 to –26·6)* –14·4 (–18·0 to –11·0)* Unintentional firearm injuries 1681·2 (1345·7 to 1839·8) 1415·5 (1182·3 to 1575·2) 1351·5 (1104·3 to 1513·3) –19·6 (–26·2 to –12·4)* –4·5 (–10·9 to 0·7) 30·9 (24·8 to 34·1) 21·1 (17·6 to 23·5) 18·1 (14·8 to 20·3) –41·5 (–46·1 to –36·4)* –14·3 (–19·9 to –9·9)* Unintentional suffocation 3239·4 (2677·8 to 4135·3) 2101·3 (1713·6 to 2425·9) 1757·1 (1410·8 to 2041·2) –45·8 (–59·1 to –34·4)* –16·4 (–23·6 to –8·8)* 50·6 (42·1 to 63·9) 31·2 (25·4 to 36·0) 24·7 (19·8 to 28·7) –51·2 (–63·0 to –41·5)* –20·8 (–27·6 to –13·7)* Other exposure to mechanical forces 8010·6 (6937·0 to 9470·1) 8844·1 (7355·1 to 10 402·4) 8812·6 (7062·9 to 10 749·0) 10·0 (–10·2 to 20·3) –0·4 (–5·0 to 4·2) 158·1 (135·5 to 187·8) 137·4 (113·9 to 162·8) 119·5 (96·0 to 145·9) –24·4 (–37·5 to –19·1)* –13·1 (–16·6 to –9·6)* Adverse effects of medical treatment 5352·0 (3741·7 to 6317·4) 5077·1 (4014·2 to 5746·0) 4991·7 (4229·9 to 5575·1) –6·7 (–17·7 to 15·4) –1·7 (–8·0 to 7·5) 99·5 (73·7 to 114·5) 80·2 (64·1 to 90·4) 69·7 (59·2 to 77·9) –30·0 (–36·6 to –18·4)* –13·2 (–18·2 to –6·2)* Animal contact 6346·7 (4715·4 to 7403·2) 6241·9 (4797·7 to 7155·5) 5936·0 (4650·5 to 6820·3) –6·5 (–17·4 to 13·4) –4·9 (–10·6 to 3·5) 118·2 (89·0 to 138·0) 95·1 (73·2 to 109·3) 80·7 (63·2 to 92·7) –31·7 (–38·8 to –18·1)* –15·1 (–20·1 to –7·7)* Venomous animal contact 5129·9 (3648·8 to 6063·6) 5170·5 (3898·3 to 5979·9) 4865·9 (3689·6 to 5640·6) –5·2 (–16·8 to 17·5) –5·9 (–12·1 to 3·0) 94·9 (68·3 to 111·9) 78·4 (59·1 to 90·7) 66·0 (50·0 to 76·6) –30·4 (–37·9 to –14·7)* –15·7 (–21·1 to –7·9)* Non-venomous animal contact 1216·9 (949·1 to 1693·5) 1071·3 (835·4 to 1440·4) 1070·1 (837·8 to 1396·1) –12·1 (–25·2 to –0·4)* –0·1 (–7·7 to 7·1) 23·3 (18·2 to 31·6) 16·7 (13·0 to 22·4) 14·7 (11·5 to 19·1) –37·1 (–44·5 to –30·2)* –12·3 (–18·3 to –6·4)* Foreign body 7078·1 (5242·3 to 9108·8) 6599·7 (5467·6 to 7941·9) 6742·8 (5709·7 to 7921·3) –4·7 (–20·0 to 18·0) 2·2 (–6·6 to 11·3) 127·2 (98·0 to 159·1) 102·2 (84·9 to 122·5) 94·0 (79·8 to 110·3) –26·1 (–35·1 to –12·1)* –8·0 (–15·0 to –0·3)* Pulmonary aspiration and foreign body in airway 5386·2 (4058·7 to 7061·1) 5309·7 (4303·9 to 6377·5) 5327·3 (4587·3 to 6136·5) –1·1 (–17·2 to 20·3) 0·3 (–8·8 to 11·6) 96·0 (74·8 to 121·0) 82·1 (66·9 to 98·0) 74·6 (64·3 to 86·0) –22·3 (–31·9 to –8·3)* –9·1 (–16·7 to 0·6) Foreign body in eyes 124·2 (63·7 to 213·3) 146·6 (78·2 to 243·6) 185·8 (100·2 to 310·1) 49·6 (41·7 to 57·3)* 26·8 (24·1 to 30·1)* 2·6 (1·4 to 4·3) 2·4 (1·3 to 3·9) 2·5 (1·4 to 4·2) –2·5 (–4·3 to –0·8)* 8·2 (6·3 to 10·3)* Foreign body in other body part 1567·7 (948·1 to 2028·4) 1143·5 (869·3 to 1474·9) 1229·7 (929·1 to 1602·4) –21·6 (–36·7 to 11·6) 7·5 (0·6 to 13·4)* 28·6 (18·2 to 36·8) 17·7 (13·4 to 22·9) 16·8 (12·7 to 21·9) –41·3 (–50·8 to –21·5)* –5·2 (–10·6 to –0·8)* Environmental heat and cold exposure 4613·3 (3492·7 to 5757·7) 4712·3 (3611·7 to 5881·4) 4676·7 (3520·0 to 5993·7) 1·4 (–9·2 to 11·5) –0·8 (–7·9 to 6·3) 96·6 (74·1 to 121·0) 75·7 (58·1 to 94·5) 64·1 (48·3 to 82·0) –33·6 (–40·8 to –27·6)* –15·3 (–21·8 to –9·7)* Other unintentional injuries 8456·9 (7319·9 to 10 038·8) 9518·6 (8199·6 to 11 247·0) 9272·1 (7666·7 to 11 405·2) 9·6 (–6·2 to 20·4) –2·6 (–7·2 to 1·8) 163·8 (140·6 to 195·7) 146·4 (124·8 to 174·5) 125·5 (103·7 to 154·8) –23·4 (–33·6 to –17·0)* –14·3 (–18·0 to –10·8)* Self-harm and interpersonal violence 57 011·9 (52 622·2 to 60 351·8) 61 630·8 (56 736·9 to 64 509·4) 58 717·9 (53 866·4 to 62 809·6) 3·0 (–3·5 to 11·0) –4·7 (–8·4 to –0·1)* 1091·2 (1007·5 to 1153·3) 919·1 (845·6 to 962·8) 776·8 (712·6 to 830·8) –28·8 (–33·2 to –23·4)* –15·5 (–18·7 to –11·4)* (Table 1 continues on next page) Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1283 [26·1–35·4]) from 2006 to 2016. Age-standardised DALY rates from self-harm (decreased by 17·7% [12·9–21·2]) and interpersonal violence (12·0% [7·0–16·1]) have both decreased by more than 10% since 2006. Age-standardised DALY rates resulting from conflict and terrorism increased by 97·4% (25·7–240·9) from 2006 to 2016; this rise was primarily driven by ongoing conflicts in north Africa, the Middle East, and sub-Saharan Africa. This result represents an increase in all-age DALYs of 114·0% (36·2–271·3) from 2006 to 2016. Global DALY counts in 2016 for causes that were estimated separately for the first time are as follows: alcoholic cardiomyopathy 2·59 million (95% UI 2·06 million to 3·24 million), urogenital congenital anomalies 1·09 million (0·86 million to 1·31 million), congenital musculoskeletal and limb anomalies 2·26 million (1·66 million to 2·99 million), digestive congenital anomalies 3·34 million (2·67 million to 4·96 million), Zika virus disease 5100 (3400–8000), Guinea worm disease 0·87 (0·5–1·35), self-harm by firearm 2·85 million (2·39 million to 3·59 million), sexual violence 1·37 million (0·92 million to 1·95 million), myocarditis 1·37 million (1·12 million to 1·51 million), drug-susceptible tuberculosis 39·9 million (38·1 million to 41·9 million), multidrug-resistant tuberculosis without extensive drug resistance 3·32 million (2·79 million to All-age DALYs (thousands) Age-standardised DALY rate (per 100 000) 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 1990 2006 2016 Percentage change, 1990–2016 Percentage change, 2006–16 (Continued from previous page) Self-harm 36 069·6 (33 180·8 to 38 321·5) 37 517·7 (34 901·0 to 39 462·7) 35 149·6 (32 845·1 to 37 938·3) –2·5 (–8·5 to 6·3) –6·3 (–10·4 to –0·9)* 705·3 (650·2 to 753·4) 565·6 (526·2 to 594·0) 465·7 (434·7 to 502·5) –34·0 (–37·9 to –28·2)* –17·7 (–21·2 to –12·9)* Self-harm by firearm 2951·5 (2377·2 to 3829·1) 2875·2 (2348·6 to 3710·6) 2853·2 (2386·0 to 3589·2) –3·3 (–13·1 to 13·8) –0·8 (–7·5 to 9·7) 57·9 (47·4 to 75·0) 43·2 (35·6 to 55·7) 37·8 (31·6 to 47·6) –34·8 (–41·0 to –24·0)* –12·6 (–18·4 to –3·9)* Self-harm by other specified means 33 118·1 (30 211·8 to 35 109·1) 34 642·5 (32 074·6 to 36 526·4) 32 296·4 (30 216·7 to 34 924·8) –2·5 (–8·5 to 6·3) –6·8 (–11·0 to –1·3)* 647·4 (590·9 to 686·4) 522·4 (481·9 to 549·9) 427·9 (399·9 to 462·6) –33·9 (–37·9 to –28·1)* –18·1 (–21·8 to –13·3)* Interpersonal violence 20 942·3 (17 360·5 to 23 608·2) 24 113·2 (19 792·0 to 26 579·9) 23 568·3 (19 646·6 to 26 479·2) 12·5 (3·6 to 22·4)* –2·3 (–6·9 to 3·5) 385·9 (318·6 to 436·9) 353·5 (290·4 to 391·3) 311·1 (259·7 to 349·8) –19·4 (–25·8 to –12·5)* –12·0 (–16·1 to –7·0)* Physical violence by firearm 6529·3 (4689·4 to 7918·7) 8381·5 (5616·3 to 9307·9) 8720·1 (5844·7 to 9853·4) 33·5 (15·0 to 48·5)* 4·0 (–0·7 to 9·0) 119·1 (84·7 to 144·7) 120·5 (80·8 to 133·8) 114·3 (76·6 to 129·1) –4·1 (–17·5 to 6·7) –5·2 (–9·6 to –0·6)* Physical violence by sharp object 5070·8 (3833·9 to 6262·5) 5886·3 (4626·0 to 7279·7) 5288·2 (4290·5 to 6922·3) 4·3 (–8·9 to 25·1) –10·2 (–16·5 to –0·5)* 94·5 (71·8 to 117·5) 86·4 (67·6 to 106·7) 69·5 (56·4 to 90·9) –26·5 (–36·0 to –12·1)* –19·6 (–25·1 to –10·7)* Sexual violence 1162·6 (774·9 to 1647·4) 1319·4 (884·1 to 1879·0) 1365·8 (917·1 to 1946·0) 17·5 (14·4 to 20·6)* 3·5 (2·2 to 4·8)* 21·2 (14·2 to 30·0) 19·1 (12·9 to 27·3) 18·1 (12·2 to 25·8) –14·5 (–15·6 to –13·3)* –5·5 (–6·2 to –4·9)* Physical violence by other means 8179·6 (6388·8 to 9519·2) 8526·0 (7045·4 to 10 132·6) 8194·1 (7011·8 to 10 058·4) 0·2 (–15·5 to 20·3) –3·9 (–12·3 to 4·9) 151·1 (120·0 to 176·2) 127·5 (105·6 to 151·6) 109·3 (93·5 to 133·9) –27·7 (–38·2 to –14·0)* –14·3 (–21·6 to –6·5)* Forces of nature, conflict and terrorism, and executions and police conflict 10 576·7 (8362·5 to 12 866·6) 6132·8 (4005·8 to 8352·3) 11 241·1 (8003·1 to 14 753·8) 6·3 (–25·0 to 46·5) 83·3 (24·2 to 183·3)* 181·9 (143·2 to 221·0) 89·3 (58·2 to 121·9) 150·3 (107·0 to 197·3) –17·4 (–41·4 to 14·1) 68·3 (14·3 to 159·6)* Exposure to forces of nature 3090·2 (1185·9 to 4943·3) 946·7 (636·1 to 1276·6) 617·1 (369·5 to 992·6) –80·0 (–90·2 to –49·1)* –34·8 (–50·5 to –15·8)* 53·5 (20·8 to 85·5) 14·0 (9·4 to 19·0) 8·3 (5·0 to 13·4) –84·5 (–92·3 to –60·9)* –40·9 (–54·9 to –24·4)* Conflict and terrorism 6889·0 (5852·1 to 7909·2) 4824·1 (2855·5 to 6799·0) 10 326·0 (7176·9 to 13 627·5) 49·9 (5·2 to 100·3)* 114·0 (36·2 to 271·3)* 117·4 (99·2 to 135·3) 69·9 (41·6 to 98·8) 138·1 (95·9 to 182·2) 17·6 (–17·4 to 57·1) 97·4 (25·7 to 240·9)* Executions and police conflict 733·5 (292·1 to 958·6) 418·5 (263·0 to 654·0) 355·7 (221·1 to 576·5) –51·5 (–65·3 to –12·8)* –15·0 (–22·4 to –7·5)* 13·4 (5·4 to 17·6) 6·2 (3·9 to 9·9) 4·7 (2·9 to 7·7) –64·8 (–74·6 to –37·4)* –24·2 (–30·2 to –17·8)* Data in parentheses are 95% uncertainty intervals. To download the data in this table, please visit the Global Health Data Exchange. ··=not defined (used for asymptomatic causes, epidemics, and outbreaks). DALYs=disability-adjusted life-years. G6PD=glucose-6-phosphate dehydrogenase. *Percentage changes that are statistically significant. Table 1: Global all-age DALYs and age-standardised DALY rates in 1990, 2006, and 2016 with mean percentage changes between 1990 and 2016, 2006 and 2016, and 1990 and 2016 for all causes For the Global Health Data Exchange see http://ghdx. healthdata.org/node/311214 Global Health Metrics 1284 www.thelancet.com Vol 390 September 16, 2017 1990, at birth 2006, at birth 2016, at birth Females Males Females Males Females Males Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Global 67·57 (67·33– 67·77) 58·42 (55·80– 60·77) 62·70 (62·42– 62·99) 55·38 (53·27– 57·31) 71·44 (71·25– 71·65) 61·73 (58·91– 64·21) 66·35 (66·05– 66·63) 58·51 (56·27– 60·55) 75·33 (74·95– 75·64) 64·91 (61·88– 67·54) 69·79 (69·29– 70·22) 61·42 (59·01– 63·58) High SDI 79·15 (79·05– 79·24) 68·23 (65·08– 71·02) 72·32 (72·21– 72·44) 63·82 (61·37– 66·00) 82·13 (82·06– 82·21) 70·61 (67·28– 73·57) 76·32 (76·23– 76·41) 66·98 (64·32– 69·32) 83·37 (83·18– 83·57) 71·49 (68·03– 74·53) 78·06 (77·81– 78·29) 68·33 (65·60– 70·82) High-middle SDI 73·54 (73·16– 73·92) 63·64 (60·78– 66·23) 66·44 (65·99– 66·90) 58·73 (56·43– 60·76) 76·76 (76·39– 77·11) 66·53 (63·49– 69·12) 69·36 (68·83– 69·82) 61·45 (59·13– 63·51) 79·86 (78·65– 80·80) 68·96 (65·69– 71·84) 73·12 (72·09– 74·05) 64·57 (62·03– 67·04) Middle SDI 68·75 (68·41– 69·09) 60·16 (57·68– 62·37) 64·15 (63·79– 64·50) 57·36 (55·31– 59·11) 73·67 (73·45– 73·88) 64·32 (61·62– 66·73) 68·32 (68·06– 68·58) 60·95 (58·80– 62·90) 77·28 (76·99– 77·57) 67·19 (64·26– 69·70) 71·06 (70·68– 71·40) 63·21 (60·91– 65·16) Low-middle SDI 60·39 (59·94– 60·81) 51·69 (49·11– 53·94) 58·41 (57·92– 58·90) 51·03 (48·92– 52·98) 65·55 (65·17– 65·94) 56·12 (53·36– 58·47) 62·39 (61·93– 62·85) 54·51 (52·29– 56·57) 70·26 (69·74– 70·74) 60·11 (57·16– 62·70) 66·25 (65·64– 66·79) 57·90 (55·43– 60·03) Low SDI 53·71 (53·24– 54·20) 46·18 (43·95– 48·11) 51·14 (50·52– 51·75) 44·45 (42·48– 46·30) 57·62 (57·12– 58·15) 49·75 (47·41– 51·86) 55·98 (55·32– 56·59) 48·81 (46·70– 50·76) 64·10 (63·33– 64·82) 55·44 (52·83– 57·79) 61·63 (60·68– 62·52) 53·86 (51·50– 56·11) High income 79·34 (79·24– 79·43) 68·40 (65·23– 71·19) 72·64 (72·54– 72·75) 64·15 (61·70– 66·33) 82·31 (82·23– 82·38) 70·76 (67·43– 73·73) 76·62 (76·52– 76·70) 67·29 (64·63– 69·64) 83·48 (83·28– 83·67) 71·61 (68·12– 74·66) 78·27 (78·04– 78·49) 68·58 (65·82– 71·06) High-income North America 79·04 (78·94– 79·13) 67·67 (64·44– 70·61) 72·15 (72·03– 72·26) 63·25 (60·75– 65·49) 80·67 (80·58– 80·76) 68·83 (65·45– 71·90) 75·63 (75·54– 75·74) 65·87 (63·11– 68·29) 81·50 (81·28– 81·72) 69·34 (65·86– 72·43) 76·79 (76·54– 77·04) 66·71 (63·87– 69·21) Canada 80·64 (80·33– 80·93) 69·83 (66·72– 72·67) 74·23 (73·91– 74·55) 65·75 (63·26– 67·95) 82·75 (82·46– 83·01) 71·51 (68·28– 74·46) 78·10 (77·84– 78·42) 68·86 (66·17– 71·23) 83·89 (83·49– 84·30) 72·30 (69·00– 75·27) 79·76 (79·30– 80·22) 70·04 (67·23– 72·61) Greenland 66·09 (64·72– 67·48) 57·28 (54·54– 59·83) 57·74 (56·27– 59·11) 51·39 (49·21– 53·48) 68·78 (67·50– 70·02) 59·52 (56·78– 62·08) 65·08 (63·83– 66·20) 57·69 (55·47– 59·79) 72·82 (70·37– 75·59) 62·90 (59·41– 66·12) 67·80 (64·69– 70·64) 60·03 (56·88– 63·34) USA 78·87 (78·77– 78·98) 67·45 (64·20– 70·42) 71·93 (71·81– 72·05) 62·99 (60·48– 65·23) 80·45 (80·35– 80·54) 68·54 (65·16– 71·62) 75·37 (75·26– 75·48) 65·54 (62·79– 67·99) 81·23 (80·99– 81·46) 69·01 (65·50– 72·12) 76·45 (76·19– 76·73) 66·34 (63·49– 68·86) Australasia 79·85 (79·63– 80·08) 68·90 (65·74– 71·70) 73·76 (73·52– 74·00) 64·95 (62·38– 67·23) 83·32 (83·13– 83·52) 71·69 (68·27– 74·72) 78·76 (78·55– 78·99) 69·05 (66·22– 71·56) 84·39 (83·80– 84·99) 72·55 (69·07– 75·61) 80·32 (79·63– 81·02) 70·28 (67·38– 72·83) Australia 80·11 (79·87– 80·39) 69·11 (65·92– 71·94) 73·97 (73·69– 74·25) 65·10 (62·51– 67·40) 83·58 (83·35– 83·80) 71·88 (68·45– 74·96) 78·93 (78·68– 79·19) 69·17 (66·31– 71·70) 84·58 (83·84– 85·27) 72·70 (69·18– 75·75) 80·48 (79·67– 81·21) 70·38 (67·51– 72·95) New Zealand 78·55 (78·18– 78·89) 67·90 (64·78– 70·66) 72·73 (72·38– 73·08) 64·24 (61·75– 66·50) 82·05 (81·73– 82·34) 70·75 (67·40– 73·65) 77·93 (77·60– 78·25) 68·50 (65·75– 70·98) 83·40 (82·25– 84·62) 71·83 (68·56– 74·89) 79·55 (78·27– 80·83) 69·78 (66·84– 72·44) High-income Asia Pacific 80·68 (80·38– 80·95) 70·30 (67·29– 73·05) 74·10 (73·68– 74·48) 65·82 (63·48– 67·98) 84·95 (84·70– 85·18) 73·64 (70·34– 76·57) 78·06 (77·75– 78·37) 68·88 (66·28– 71·30) 86·42 (85·62– 87·09) 74·70 (71·14– 77·75) 80·07 (79·10– 80·94) 70·47 (67·66– 73·06) Brunei 75·39 (73·88– 76·73) 65·61 (62·70– 68·40) 71·68 (70·69– 72·82) 63·24 (60·71– 65·65) 78·90 (77·93– 79·65) 68·72 (65·74– 71·51) 74·64 (73·89– 75·45) 65·84 (63·47– 68·18) 79·48 (77·79– 81·73) 69·34 (66·13– 72·26) 74·56 (72·51– 77·34) 65·84 (62·82– 68·69) Japan 81·81 (81·76– 81·85) 71·31 (68·26– 74·00) 75·91 (75·86– 75·96) 67·45 (65·01– 69·60) 85·57 (85·51– 85·64) 74·20 (70·86– 77·10) 78·76 (78·71– 78·81) 69·51 (66·85– 71·84) 86·94 (86·73– 87·16) 75·10 (71·59– 78·08) 80·83 (80·57– 81·08) 71·11 (68·36– 73·60) Singapore 78·10 (76·47– 79·73) 68·51 (65·44– 71·31) 72·86 (71·33– 74·39) 65·11 (62·52– 67·52) 83·71 (82·39– 84·98) 73·34 (70·20– 76·23) 78·55 (77·18– 79·92) 69·99 (67·44– 72·54) 86·08 (83·92– 88·42) 75·16 (71·85– 78·57) 81·26 (78·75– 83·67) 72·01 (68·76– 75·05) South Korea 76·33 (74·99– 77·68) 66·56 (63·56– 69·48) 67·74 (66·18– 69·33) 60·30 (57·86– 62·70) 82·11 (81·07– 83·18) 71·18 (67·93– 74·18) 75·48 (74·23– 76·96) 66·63 (64·01– 69·40) 84·22 (81·22– 87·13) 72·97 (68·98– 76·74) 77·67 (74·32– 81·52) 68·49 (64·84– 72·03) (Table 2 continues on next page) Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1291 1990, at birth 2006, at birth 2016, at birth Females Males Females Males Females Males Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE (Continued from previous page) Philippines 70·92 (70·24– 71·61) 61·69 (58·96– 64·14) 63·45 (62·60– 64·36) 55·80 (53·48– 57·96) 72·07 (71·24– 72·76) 62·73 (59·95– 65·22) 64·58 (63·69– 65·46) 57·07 (54·88– 59·12) 73·87 (72·05– 75·85) 64·47 (61·52– 67·57) 66·64 (64·58– 68·74) 59·07 (56·50– 61·71) Sri Lanka 75·38 (74·55– 76·26) 65·51 (62·68– 68·15) 67·77 (66·67– 68·89) 59·80 (57·30– 62·16) 78·31 (77·17– 79·27) 68·15 (65·22– 70·82) 70·46 (69·17– 71·65) 62·27 (59·85– 64·54) 81·09 (78·67– 83·77) 70·47 (66·87– 74·07) 73·72 (70·53– 76·90) 65·07 (61·57– 68·32) Seychelles 74·15 (73·50– 74·82) 65·14 (62·54– 67·54) 64·48 (63·41– 65·58) 57·71 (55·61– 59·61) 76·20 (75·56– 77·25) 66·77 (63·92– 69·39) 68·64 (67·74– 69·55) 61·10 (58·91– 63·25) 77·40 (76·02– 78·87) 67·72 (64·76– 70·58) 70·25 (68·17– 72·03) 62·41 (59·83– 64·93) Thailand 74·08 (73·42– 74·68) 64·41 (61·59– 66·96) 67·20 (66·41– 68·01) 59·48 (57·24– 61·59) 77·81 (77·23– 78·38) 67·73 (64·85– 70·31) 71·48 (70·70– 72·29) 63·15 (60·71– 65·36) 80·91 (79·63– 82·00) 70·24 (67·11– 73·26) 74·59 (72·93– 76·22) 65·71 (62·91– 68·38) Timor-Leste 57·61 (54·99– 60·60) 50·23 (47·11– 53·26) 58·39 (56·06– 60·79) 50·62 (47·62– 53·51) 69·06 (66·95– 71·25) 60·09 (57·11– 63·16) 67·57 (65·41– 69·81) 58·60 (55·53– 61·71) 73·68 (70·57– 76·59) 64·27 (60·67– 67·86) 71·69 (68·65– 74·73) 62·39 (58·56– 65·67) Vietnam 71·11 (69·45– 73·05) 62·16 (59·16– 65·03) 65·27 (63·23– 67·12) 58·12 (55·61– 60·67) 75·49 (73·94– 77·73) 66·09 (63·17– 69·01) 68·53 (66·58– 70·74) 61·21 (58·75– 63·77) 78·10 (76·59– 79·12) 68·44 (65·57– 71·08) 70·87 (69·14– 72·67) 63·30 (60·72– 65·77) Oceania 60·31 (58·44– 62·02) 52·36 (49·47– 54·96) 56·79 (55·08– 58·47) 50·27 (47·85– 52·56) 61·11 (58·80– 63·18) 53·01 (49·94– 55·71) 58·35 (56·00– 60·49) 51·58 (48·75– 54·22) 63·76 (61·41– 66·00) 55·16 (51·88– 58·03) 60·72 (58·24– 62·92) 53·57 (50·64– 56·19) American Samoa 75·02 (73·75– 76·44) 64·48 (61·29– 67·60) 66·93 (65·44– 68·49) 58·64 (56·07– 61·15) 74·38 (72·61– 75·88) 63·93 (60·72– 67·22) 69·25 (67·72– 70·70) 60·50 (57·64– 63·08) 74·43 (71·97– 77·03) 63·94 (60·47– 67·29) 70·37 (67·82– 72·73) 61·39 (58·12– 64·51) Federated States of Micronesia 65·60 (63·23– 67·73) 57·09 (53·99– 59·87) 61·03 (58·77– 63·16) 54·13 (51·41– 56·65) 67·23 (64·39– 69·83) 58·56 (55·40– 61·67) 64·01 (61·74– 66·18) 56·67 (53·87– 59·30) 67·58 (64·54– 70·55) 58·84 (55·44– 62·17) 63·57 (60·73– 66·27) 56·28 (53·25– 59·28) Fiji 68·04 (63·85– 72·17) 58·99 (54·86– 62·98) 62·73 (59·19– 66·20) 55·32 (51·72– 58·77) 67·12 (65·31– 69·36) 58·07 (54·97– 61·03) 62·61 (60·57– 64·97) 55·23 (52·53– 57·84) 67·67 (63·49– 71·89) 58·63 (54·64– 62·73) 63·27 (59·17– 67·99) 56·00 (52·16– 60·22) Guam 76·44 (75·25– 77·71) 66·30 (63·15– 69·05) 70·34 (68·94– 71·64) 62·61 (60·08– 64·88) 76·69 (75·63– 77·75) 66·43 (63·41– 69·36) 70·14 (68·50– 71·30) 62·27 (59·67– 64·55) 76·09 (74·28– 78·08) 65·75 (62·62– 68·84) 69·05 (66·86– 71·35) 61·20 (58·42– 63·78) Kiribati 60·87 (58·96– 62·74) 52·77 (49·98– 55·51) 55·53 (53·76– 57·42) 49·42 (47·04– 51·64) 63·92 (61·83– 65·92) 55·29 (52·20– 58·18) 56·63 (54·56– 58·59) 50·15 (47·47– 52·68) 65·59 (62·39– 68·39) 56·70 (53·26– 60·06) 58·17 (55·44– 61·12) 51·49 (48·39– 54·51) Marshall Islands 67·99 (66·53– 69·39) 58·54 (55·60– 61·38) 61·47 (59·95– 62·84) 54·23 (51·81– 56·48) 65·11 (63·20– 67·11) 56·14 (53·04– 59·02) 61·00 (59·18– 62·90) 53·70 (51·07– 56·26) 67·26 (64·36– 70·12) 57·87 (54·25– 61·23) 62·75 (60·08– 65·45) 55·07 (52·11– 58·20) Northern Mariana Islands 74·74 (72·01– 77·56) 64·95 (61·34– 68·42) 72·67 (70·19– 75·43) 64·25 (61·24– 67·37) 77·62 (75·38– 79·86) 67·24 (63·57– 70·35) 74·91 (72·81– 76·87) 65·99 (62·86– 68·76) 77·37 (74·86– 79·95) 66·90 (63·25– 70·24) 73·88 (71·52– 76·20) 65·02 (62·01– 67·86) Papua New Guinea 57·55 (55·23– 59·80) 49·98 (47·04– 52·65) 54·45 (52·17– 56·65) 48·22 (45·54– 50·61) 58·93 (55·93– 61·58) 51·16 (47·90– 54·17) 56·57 (53·58– 59·29) 50·03 (46·91– 52·97) 62·17 (59·37– 64·96) 53·81 (50·34– 56·94) 59·50 (56·53– 62·33) 52·50 (49·24– 55·40) Samoa 72·32 (70·28– 74·30) 62·82 (59·73– 65·93) 65·84 (63·47– 67·86) 58·20 (55·27– 60·85) 73·62 (71·80– 75·57) 63·79 (60·62– 66·81) 68·98 (67·00– 70·99) 60·94 (58·20– 63·63) 74·00 (71·99– 76·12) 64·09 (60·85– 67·24) 69·90 (67·94– 72·24) 61·73 (58·95– 64·39) Solomon Islands 61·76 (59·54– 64·33) 53·80 (50·92– 56·89) 58·55 (56·42– 61·01) 52·15 (49·75– 54·77) 62·15 (59·76– 64·69) 54·07 (51·07– 56·93) 60·06 (57·82– 62·35) 53·36 (50·66– 55·92) 64·24 (61·49– 66·86) 55·79 (52·73– 58·68) 61·91 (59·31– 64·32) 54·85 (52·12– 57·46) Tonga 70·12 (67·56– 72·67) 60·50 (57·17– 63·85) 65·71 (63·10– 68·33) 58·15 (55·13– 61·11) 72·43 (70·40– 74·34) 62·32 (58·91– 65·47) 66·85 (64·60– 69·05) 59·07 (56·15– 61·55) 73·33 (70·85– 75·64) 63·03 (59·33– 66·31) 67·50 (65·23– 70·07) 59·46 (56·61– 62·26) Vanuatu 63·86 (61·77– 66·01) 55·35 (52·25– 58·43) 59·86 (57·86– 61·86) 53·15 (50·44– 55·83) 63·97 (61·41– 66·27) 55·50 (52·38– 58·48) 60·75 (58·57– 62·89) 54·04 (51·41– 56·65) 65·68 (62·77– 68·20) 56·81 (53·52– 60·12) 62·22 (59·66– 64·66) 55·23 (52·22– 58·05) (Table 2 continues on next page) Global Health Metrics 1292 www.thelancet.com Vol 390 September 16, 2017 1990, at birth 2006, at birth 2016, at birth Females Males Females Males Females Males Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE (Continued from previous page) North Africa and Middle East 68·93 (68·42– 69·38) 57·92 (54·77– 60·69) 65·01 (64·43– 65·60) 56·14 (53·49– 58·51) 73·19 (72·67– 73·67) 61·72 (58·38– 64·77) 69·13 (68·46– 69·80) 59·90 (57·22– 62·30) 75·59 (74·89– 76·29) 63·66 (60·17– 66·71) 70·90 (70·05– 71·78) 61·41 (58·64– 63·99) Afghanistan 51·35 (49·83– 52·92) 42·97 (40·24– 45·55) 51·87 (50·14– 53·90) 44·20 (41·28– 46·73) 54·51 (53·00– 55·97) 46·03 (43·31– 48·59) 53·80 (51·93– 55·70) 46·34 (43·59– 49·01) 59·16 (57·45– 60·78) 49·95 (46·99– 52·73) 56·83 (54·45– 59·11) 49·11 (46·21– 51·87) Algeria 72·56 (71·34– 73·76) 61·07 (57·66– 64·19) 69·44 (68·19– 70·72) 60·04 (57·20– 62·71) 76·85 (75·62– 77·68) 64·85 (61·39– 68·08) 74·42 (73·29– 75·58) 64·42 (61·43– 67·15) 78·41 (77·46– 79·34) 66·11 (62·60– 69·36) 76·43 (75·06– 77·66) 66·04 (62·82– 68·90) Bahrain 70·99 (69·28– 72·59) 59·71 (56·49– 63·00) 68·54 (66·94– 70·18) 59·52 (56·73– 62·36) 74·94 (73·50– 76·48) 62·73 (59·17– 66·14) 72·76 (71·18– 74·39) 62·85 (59·79– 65·88) 77·81 (75·72– 80·15) 65·01 (61·16– 68·83) 75·99 (73·45– 78·51) 65·52 (61·84– 68·99) Egypt 67·29 (66·32– 68·21) 56·84 (53·88– 59·56) 63·32 (62·42– 64·29) 55·04 (52·44– 57·31) 72·89 (72·08– 73·70) 61·63 (58·41– 64·61) 67·66 (66·77– 68·54) 59·00 (56·40– 61·49) 74·98 (73·15– 76·99) 63·54 (60·05– 66·80) 69·44 (67·60– 71·52) 60·64 (57·89– 63·40) Iran 71·07 (68·91– 73·37) 59·86 (56·10– 63·19) 65·24 (62·58– 68·12) 56·37 (53·08– 59·74) 75·20 (72·96– 77·42) 63·59 (59·94– 66·96) 70·75 (68·00– 73·35) 61·37 (57·97– 64·66) 78·38 (76·58– 80·58) 66·03 (62·27– 69·57) 73·78 (71·23– 76·22) 63·72 (60·16– 66·85) Iraq 67·58 (65·61– 69·71) 56·63 (53·29– 59·79) 64·75 (62·05– 67·37) 55·19 (51·77– 58·30) 66·88 (64·23– 69·42) 56·47 (52·82– 59·86) 60·90 (57·38– 65·04) 52·54 (48·76– 56·43) 70·48 (67·37– 73·54) 59·26 (55·41– 62·99) 64·83 (61·47– 69·19) 55·76 (52·01– 59·74) Jordan 72·58 (70·91– 74·84) 60·94 (57·41– 64·21) 71·03 (69·24– 72·57) 61·11 (58·15– 63·85) 74·23 (71·88– 76·82) 62·75 (59·00– 66·31) 73·34 (70·65– 75·64) 63·51 (60·19– 66·80) 77·99 (74·85– 81·02) 65·70 (61·49– 69·46) 74·71 (71·39– 78·25) 64·59 (60·97– 68·43) Kuwait 76·70 (74·82– 78·73) 65·00 (61·29– 68·33) 74·54 (72·38– 76·73) 64·65 (61·43– 67·77) 77·12 (75·72– 78·44) 65·38 (61·84– 68·56) 76·93 (75·01– 78·65) 66·54 (63·23– 69·57) 79·47 (76·63– 82·70) 67·37 (63·50– 71·31) 79·96 (76·38– 83·60) 69·01 (65·03– 73·34) Lebanon 71·92 (70·05– 74·03) 60·87 (57·53– 64·06) 66·38 (64·58– 68·79) 57·36 (54·50– 60·39) 78·72 (77·82– 79·96) 66·52 (62·98– 69·83) 77·22 (75·61– 78·85) 66·63 (63·32– 69·56) 81·42 (79·91– 82·87) 68·66 (64·94– 72·23) 78·79 (77·11– 80·32) 67·94 (64·59– 70·98) Libya 74·68 (73·33– 75·82) 62·95 (59·53– 66·07) 71·59 (70·34– 72·98) 61·97 (59·12– 64·75) 77·01 (75·27– 78·13) 65·03 (61·54– 68·16) 73·87 (72·63– 75·29) 64·02 (60·99– 66·73) 77·59 (76·21– 78·74) 65·47 (61·78– 68·74) 72·62 (71·01– 74·65) 63·00 (59·92– 65·82) Morocco 68·70 (67·60– 69·73) 57·76 (54·46– 60·75) 65·69 (64·85– 66·57) 56·74 (54·04– 59·15) 73·80 (72·47– 74·96) 62·22 (58·80– 65·42) 71·16 (70·12– 72·08) 61·48 (58·51– 64·21) 76·44 (75·14– 77·71) 64·38 (60·74– 67·77) 73·55 (72·48– 74·54) 63·58 (60·60– 66·20) Palestine 71·78 (69·66– 73·66) 61·01 (57·61– 64·21) 70·13 (68·34– 72·03) 60·77 (57·75– 63·60) 74·19 (73·77– 74·60) 63·12 (59·87– 65·98) 69·54 (69·07– 69·99) 60·50 (57·83– 62·89) 73·48 (72·75– 74·22) 62·56 (59·49– 65·38) 70·23 (69·47– 71·02) 61·05 (58·38– 63·44) Oman 71·89 (70·46– 73·25) 60·59 (57·11– 63·71) 69·50 (68·12– 70·81) 59·95 (56·92– 62·74) 78·21 (77·27– 79·33) 66·25 (62·72– 69·47) 74·05 (72·92– 75·15) 64·33 (61·45– 67·07) 79·93 (79·21– 80·83) 67·64 (63·95– 70·96) 75·28 (74·48– 76·27) 65·29 (62·36– 68·01) Qatar 75·25 (73·06– 77·85) 63·35 (59·66– 67·15) 74·82 (72·23– 77·11) 64·23 (60·77– 67·49) 78·98 (77·27– 80·91) 66·20 (62·32– 69·81) 75·74 (73·25– 78·54) 65·28 (61·77– 68·76) 81·82 (78·89– 85·16) 68·54 (64·22– 72·79) 79·00 (75·41– 82·65) 67·94 (63·77– 72·07) Saudi Arabia 73·52 (72·07– 74·90) 61·62 (58·14– 64·84) 70·62 (69·03– 72·20) 61·15 (58·17– 63·96) 74·85 (74·23– 75·41) 63·49 (60·33– 66·49) 73·41 (72·66– 74·13) 64·07 (61·36– 66·59) 78·67 (77·83– 79·62) 66·61 (63·09– 69·87) 75·97 (74·95– 77·00) 66·21 (63·34– 68·81) Sudan 59·53 (58·32– 60·64) 50·03 (47·25– 52·75) 57·60 (56·49– 58·74) 49·47 (46·80– 51·85) 66·70 (65·69– 67·70) 56·23 (53·08– 58·98) 63·67 (62·69– 64·72) 54·90 (52·20– 57·27) 70·29 (68·93– 71·52) 59·15 (55·70– 62·35) 66·37 (64·87– 67·67) 57·23 (54·39– 60·00) Syria 72·81 (71·69– 73·94) 61·54 (58·19– 64·52) 69·12 (68·12– 70·07) 60·03 (57·31– 62·60) 77·06 (76·30– 77·70) 65·44 (61·99– 68·51) 73·22 (72·43– 73·99) 63·91 (61·04– 66·43) 73·62 (69·06– 78·25) 62·58 (57·77– 67·20) 63·29 (56·34– 71·29) 55·64 (49·19– 62·44) Tunisia 74·12 (73·09– 75·18) 62·92 (59·62– 66·06) 69·36 (68·15– 70·56) 60·38 (57·62– 62·91) 78·82 (77·62– 80·64) 67·00 (63·42– 70·58) 73·67 (71·98– 75·57) 64·07 (60·96– 67·00) 80·46 (78·87– 82·70) 68·32 (64·48– 72·01) 74·58 (72·07– 77·24) 65·02 (61·75– 68·47) (Table 2 continues on next page) Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1293 1990, at birth 2006, at birth 2016, at birth Females Males Females Males Females Males Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE (Continued from previous page) Turkey 73·48 (72·41– 74·51) 61·46 (58·05– 64·58) 66·84 (65·71– 68·03) 57·77 (55·03– 60·35) 80·56 (79·58– 81·47) 67·67 (63·94– 71·19) 73·90 (73·02– 74·83) 64·15 (61·44– 66·84) 82·33 (80·42– 84·35) 69·20 (65·37– 72·84) 75·84 (73·66– 78·13) 65·94 (62·76– 69·10) United Arab Emirates 73·80 (71·06– 76·69) 62·23 (58·32– 65·78) 71·73 (68·48– 74·76) 61·87 (57·92– 65·75) 78·57 (77·31– 79·40) 66·33 (62·78– 69·58) 75·06 (73·81– 76·25) 64·89 (61·82– 67·73) 78·64 (76·18– 80·63) 66·53 (62·95– 70·18) 74·52 (72·10– 76·83) 64·62 (61·09– 67·66) Yemen 58·84 (57·63– 60·16) 48·28 (45·15– 51·30) 58·57 (57·40– 59·81) 49·39 (46·58– 51·99) 65·68 (64·61– 66·84) 54·20 (50·83– 57·27) 65·37 (64·18– 66·55) 55·43 (52·41– 58·18) 67·94 (66·60– 69·16) 56·24 (52·77– 59·33) 65·51 (63·70– 67·23) 55·81 (52·76– 58·59) South Asia 59·80 (59·24– 60·35) 50·89 (48·19– 53·20) 58·46 (57·94– 58·97) 50·96 (48·82– 52·90) 66·30 (65·82– 66·79) 56·41 (53·59– 58·90) 63·48 (62·93– 64·03) 55·24 (52·96– 57·29) 70·59 (70·08– 71·12) 60·00 (56·96– 62·75) 67·11 (66·46– 67·67) 58·43 (55·97– 60·54) Bangladesh 58·61 (57·32– 59·87) 49·95 (47·23– 52·37) 57·61 (56·57– 58·73) 50·10 (47·67– 52·26) 70·37 (69·36– 71·27) 59·79 (56·76– 62·56) 66·64 (65·79– 67·55) 58·08 (55·57– 60·33) 75·10 (73·49– 76·44) 64·04 (60·88– 67·11) 70·47 (68·82– 72·04) 61·69 (58·94– 64·30) Bhutan 59·12 (57·08– 61·38) 50·11 (47·16– 53·12) 60·68 (58·50– 62·85) 52·49 (49·55– 55·02) 71·21 (69·51– 72·79) 60·17 (56·70– 63·28) 69·12 (67·36– 70·85) 59·86 (57·04– 62·76) 75·91 (73·78– 77·46) 64·06 (60·43– 67·44) 72·23 (70·06– 74·38) 62·56 (59·32– 65·53) India 59·69 (59·02– 60·35) 50·75 (48·01– 53·17) 58·29 (57·68– 58·87) 50·81 (48·69– 52·79) 66·01 (65·38– 66·59) 56·09 (53·27– 58·68) 63·22 (62·59– 63·80) 54·96 (52·71– 57·10) 70·33 (69·59– 71·03) 59·67 (56·62– 62·44) 66·93 (66·24– 67·56) 58·18 (55·72– 60·37) Nepal 57·71 (56·74– 58·64) 49·63 (47·28– 51·88) 57·69 (56·67– 58·72) 50·14 (47·90– 52·23) 67·17 (66·20– 68·21) 57·74 (54·95– 60·24) 65·78 (64·76– 66·74) 57·35 (54·84– 59·72) 71·91 (71·07– 72·81) 61·80 (58·81– 64·42) 69·74 (68·49– 71·04) 60·92 (58·30– 63·39) Pakistan 62·48 (61·49– 63·45) 53·50 (50·73– 55·99) 62·41 (61·38– 63·42) 54·57 (52·08– 56·85) 64·74 (63·33– 66·23) 55·57 (52·77– 58·33) 63·35 (61·99– 64·79) 55·58 (53·21– 57·94) 68·89 (66·86– 71·19) 59·09 (55·86– 62·20) 66·44 (64·27– 68·45) 58·23 (55·39– 60·97) Sub-Saharan Africa 55·39 (54·92– 55·80) 47·87 (45·54– 49·89) 52·04 (51·48– 52·68) 45·35 (43·39– 47·24) 56·38 (55·83– 56·89) 48·88 (46·67– 50·96) 54·12 (53·55– 54·65) 47·33 (45·35– 49·21) 64·59 (63·91– 65·26) 56·05 (53·55– 58·32) 61·18 (60·30– 62·02) 53·57 (51·23– 55·82) Southern sub-Saharan Africa 67·35 (66·60– 68·16) 58·18 (55·61– 60·63) 60·25 (59·31– 61·19) 52·67 (50·49– 54·85) 51·29 (50·07– 52·53) 44·50 (42·44– 46·63) 48·31 (47·41– 49·22) 42·47 (40·68– 44·15) 64·86 (63·79– 65·96) 55·75 (53·05– 58·36) 58·36 (57·38– 59·33) 50·90 (48·68– 53·04) Botswana 67·60 (65·60– 69·84) 58·83 (55·88– 61·79) 61·23 (58·34– 64·21) 53·70 (50·51– 56·85) 51·04 (45·54– 56·94) 44·50 (39·85– 49·30) 47·99 (44·54– 52·03) 42·16 (38·85– 45·79) 69·21 (64·51– 78·50) 59·74 (54·95– 66·73) 61·69 (58·43– 67·01) 53·88 (50·27– 58·28) Lesotho 64·20 (62·27– 66·35) 55·55 (52·72– 58·36) 56·59 (54·64– 58·76) 49·82 (47·38– 52·48) 45·45 (41·73– 49·33) 39·46 (36·03– 42·72) 41·01 (38·66– 43·40) 36·16 (33·65– 38·50) 53·67 (49·88– 58·25) 46·37 (42·72– 50·26) 47·13 (44·61– 49·71) 41·46 (38·97– 43·98) Namibia 64·69 (63·56– 65·91) 56·44 (53·69– 58·78) 58·32 (57·03– 59·62) 51·36 (49·11– 53·52) 54·71 (50·75– 58·90) 47·83 (44·03– 51·74) 50·34 (47·92– 52·74) 44·40 (41·90– 46·92) 69·31 (65·48– 75·19) 60·14 (55·93– 65·17) 60·39 (57·83– 63·37) 53·00 (50·09– 56·01) South Africa 68·05 (67·23– 68·92) 58·59 (55·83– 61·09) 60·72 (59·85– 61·59) 52·98 (50·78– 55·06) 52·28 (50·85– 53·65) 45·25 (42·99– 47·42) 49·72 (48·70– 50·77) 43·60 (41·64– 45·51) 65·51 (64·17– 66·75) 56·09 (53·35– 58·88) 59·24 (57·90– 60·31) 51·47 (49·14– 53·72) Swaziland 65·42 (63·35– 67·49) 56·99 (54·16– 59·75) 59·06 (56·88– 61·39) 51·93 (49·38– 54·50) 44·30 (40·61– 48·56) 38·50 (35·00– 42·26) 41·59 (38·83– 44·65) 36·49 (33·84– 39·30) 62·16 (57·33– 68·37) 53·38 (48·49– 58·49) 53·25 (50·19– 57·12) 46·38 (43·19– 49·67) Zimbabwe 64·36 (62·06– 67·28) 56·31 (53·45– 59·60) 59·02 (55·45– 63·66) 51·98 (48·42– 56·28) 47·07 (43·92– 50·60) 41·38 (38·39– 44·54) 44·34 (42·25– 46·58) 39·39 (37·15– 41·71) 61·87 (59·24– 65·19) 54·11 (51·15– 57·35) 56·66 (54·38– 59·28) 50·19 (47·52– 53·02) Western sub-Saharan Africa 55·57 (54·69– 56·34) 47·67 (45·11– 49·96) 53·32 (52·28– 54·31) 46·21 (44·00– 48·31) 58·25 (57·26– 59·21) 50·20 (47·85– 52·54) 56·00 (55·03– 56·96) 48·79 (46·59– 50·96) 64·77 (63·72– 65·90) 55·98 (53·26– 58·55) 61·89 (60·59– 63·06) 54·12 (51·54– 56·44) Benin 57·60 (56·50– 58·70) 48·94 (46·09– 51·36) 53·32 (52·04– 54·64) 45·99 (43·44– 48·17) 61·95 (60·46– 63·47) 53·19 (50·16– 55·83) 57·76 (56·51– 59·08) 50·39 (48·08– 52·79) 66·34 (64·76– 68·10) 57·19 (54·17– 60·02) 62·53 (60·91– 64·06) 54·72 (52·08– 57·28) (Table 2 continues on next page) Global Health Metrics 1294 www.thelancet.com Vol 390 September 16, 2017 1990, at birth 2006, at birth 2016, at birth Females Males Females Males Females Males Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE (Continued from previous page) Burkina Faso 52·09 (51·11– 53·19) 44·26 (41·75– 46·61) 49·12 (47·86– 50·52) 41·95 (39·58– 44·21) 56·81 (55·85– 57·76) 48·86 (46·30– 51·19) 54·41 (53·23– 55·54) 47·20 (44·77– 49·52) 62·06 (61·04– 63·05) 53·68 (51·08– 56·24) 59·51 (57·95– 60·84) 52·17 (49·79– 54·55) Cameroon 59·63 (58·32– 61·10) 51·05 (48·24– 53·59) 57·28 (55·81– 58·81) 49·57 (47·05– 51·91) 56·56 (54·54– 58·87) 48·79 (46·01– 51·46) 54·00 (52·22– 55·70) 47·10 (44·57– 49·48) 62·02 (59·34– 65·18) 53·81 (50·69– 57·12) 58·34 (56·01– 60·59) 51·26 (48·34– 54·11) Cape Verde 71·55 (70·49– 72·79) 61·66 (58·62– 64·50) 64·09 (62·76– 65·53) 56·21 (53·78– 58·70) 76·48 (74·84– 78·29) 65·95 (62·49– 69·20) 66·27 (64·07– 69·43) 58·27 (55·46– 61·31) 78·50 (77·22– 80·25) 67·83 (64·48– 71·09) 68·79 (66·61– 71·11) 60·61 (57·69– 63·48) Chad 54·43 (53·27– 55·67) 46·62 (44·13– 48·84) 52·10 (50·71– 53·32) 45·17 (42·76– 47·24) 55·55 (53·89– 57·37) 47·63 (45·00– 50·25) 52·59 (50·96– 54·22) 45·68 (43·28– 48·08) 61·36 (59·50– 63·20) 52·71 (49·91– 55·50) 58·32 (56·39– 60·28) 50·74 (48·15– 53·29) Côte d’Ivoire 57·68 (56·58– 58·79) 49·17 (46·50– 51·82) 52·39 (51·01– 53·58) 45·37 (42·99– 47·51) 56·04 (54·36– 57·85) 48·52 (45·85– 50·93) 52·09 (50·45– 53·67) 45·65 (43·25– 47·87) 62·29 (60·54– 64·01) 53·92 (51·07– 56·57) 57·72 (55·83– 59·48) 50·60 (48·00– 53·01) The Gambia 62·30 (60·68– 64·02) 53·36 (50·28– 56·14) 60·88 (59·15– 62·62) 53·05 (50·22– 55·72) 66·26 (64·59– 68·06) 56·75 (53·72– 59·73) 62·80 (61·35– 64·29) 54·78 (51·92– 57·28) 69·22 (67·62– 71·33) 59·42 (56·30– 62·46) 65·46 (63·97– 66·94) 57·15 (54·20– 59·62) Ghana 59·84 (58·36– 61·44) 51·82 (49·16– 54·38) 57·78 (56·18– 59·37) 50·59 (48·06– 53·14) 61·92 (60·12– 63·95) 54·01 (51·22– 56·79) 59·01 (57·79– 60·31) 52·03 (49·63– 54·16) 67·53 (65·97– 69·68) 58·98 (56·19– 61·86) 64·49 (62·75– 66·08) 56·90 (54·34– 59·35) Guinea 51·91 (50·53– 53·30) 44·55 (42·05– 46·92) 52·11 (50·71– 53·57) 45·26 (42·89– 47·43) 57·25 (55·74– 58·97) 49·53 (47·03– 51·96) 55·58 (53·83– 57·33) 48·76 (46·21– 51·09) 61·62 (59·52– 64·00) 53·40 (50·48– 56·36) 59·76 (57·32– 62·13) 52·48 (49·61– 55·35) Guinea-Bissau 51·89 (50·31– 53·78) 44·89 (42·43– 47·26) 46·57 (44·71– 48·57) 40·83 (38·59– 43·17) 56·06 (54·03– 58·01) 48·66 (45·95– 51·27) 51·56 (50·00– 53·30) 45·36 (43·07– 47·54) 61·37 (59·44– 63·33) 53·32 (50·43– 56·03) 56·30 (54·53– 58·20) 49·60 (47·09– 52·04) Liberia 51·76 (50·48– 53·07) 43·77 (41·14– 46·17) 47·99 (46·63– 49·49) 40·93 (38·51– 43·17) 58·82 (57·58– 60·31) 49·98 (47·20– 52·57) 58·52 (57·29– 59·75) 49·98 (47·27– 52·50) 64·82 (63·59– 66·07) 55·40 (52·50– 58·00) 64·00 (62·76– 65·16) 55·19 (52·43– 57·82) Mali 49·39 (48·14– 50·66) 42·49 (40·11– 44·73) 49·41 (48·04– 50·70) 42·56 (40·19– 44·83) 57·72 (56·20– 59·37) 49·75 (47·12– 52·25) 56·46 (54·86– 58·11) 48·81 (46·21– 51·40) 62·74 (60·01– 65·49) 54·29 (51·16– 57·53) 61·02 (58·09– 63·77) 53·18 (49·98– 56·44) Mauritania 60·41 (59·05– 61·76) 52·16 (49·49– 54·62) 59·50 (58·11– 61·07) 51·87 (49·30– 54·28) 65·99 (64·25– 68·11) 57·15 (54·23– 59·95) 66·83 (64·96– 68·67) 58·11 (55·20– 60·91) 70·21 (67·56– 73·27) 60·85 (57·43– 64·22) 70·29 (67·75– 73·20) 61·28 (57·96– 64·65) Niger 48·07 (46·57– 49·48) 41·61 (39·47– 43·84) 46·19 (44·61– 47·75) 40·45 (38·31– 42·43) 57·00 (55·24– 58·62) 49·47 (46·71– 51·86) 55·43 (53·62– 57·32) 48·68 (46·27– 51·07) 62·83 (60·16– 65·50) 54·70 (51·47– 57·94) 60·60 (58·25– 62·87) 53·42 (50·58– 56·30) Nigeria 55·80 (53·93– 57·43) 47·83 (45·05– 50·36) 53·99 (51·98– 55·95) 46·72 (44·09– 49·35) 58·29 (56·34– 60·30) 50·06 (47·39– 52·97) 56·69 (54·87– 58·62) 49·22 (46·60– 51·93) 66·41 (64·30– 69·14) 57·17 (53·86– 60·50) 63·69 (61·16– 66·21) 55·46 (52·16– 58·36) São Tomé and Príncipe 64·55 (62·95– 66·14) 56·13 (53·44– 58·78) 61·94 (60·46– 63·55) 54·06 (51·51– 56·55) 68·04 (66·93– 69·14) 59·38 (56·77– 61·81) 65·63 (64·35– 66·88) 57·37 (54·72– 59·76) 72·09 (70·52– 73·93) 62·86 (59·84– 65·70) 69·04 (66·71– 71·46) 60·40 (57·08– 63·43) Senegal 59·13 (57·93– 60·29) 51·16 (48·64– 53·57) 55·97 (54·99– 56·95) 49·06 (46·73– 51·08) 63·73 (62·67– 64·93) 55·34 (52·58– 57·81) 60·56 (59·49– 61·69) 53·17 (50·79– 55·42) 67·78 (66·59– 68·83) 58·95 (56·18– 61·49) 64·64 (63·32– 65·97) 56·81 (54·17– 59·31) Sierra Leone 52·78 (51·41– 54·15) 45·44 (42·94– 47·75) 48·43 (46·82– 50·02) 42·07 (39·83– 44·17) 54·14 (52·72– 55·61) 46·80 (44·24– 49·05) 51·62 (50·37– 52·85) 44·94 (42·80– 47·02) 60·19 (58·38– 62·14) 52·30 (49·54– 55·01) 58·11 (56·42– 59·82) 50·98 (48·48– 53·42) Togo 58·64 (57·47– 59·92) 50·53 (47·88– 52·99) 56·13 (54·90– 57·39) 49·04 (46·54– 51·27) 59·12 (57·12– 61·53) 51·10 (48·41– 53·75) 54·86 (53·29– 56·46) 48·25 (45·93– 50·52) 65·08 (63·58– 66·82) 56·63 (53·68– 59·26) 60·11 (58·22– 61·70) 53·13 (50·60– 55·49) Eastern sub-Saharan Africa 52·92 (52·39– 53·47) 46·24 (44·24– 48·02) 49·46 (48·81– 50·17) 43·47 (41·58– 45·13) 56·64 (55·91– 57·36) 49·56 (47·39– 51·57) 54·57 (53·86– 55·19) 48·01 (46·07– 49·81) 65·08 (64·13– 66·09) 56·97 (54·43– 59·30) 61·72 (60·63– 62·80) 54·35 (52·04– 56·58) (Table 2 continues on next page) Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1295 1990, at birth 2006, at birth 2016, at birth Females Males Females Males Females Males Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE (Continued from previous page) Burundi 49·28 (47·21– 51·62) 43·59 (41·19– 46·03) 46·69 (44·34– 49·16) 41·49 (39·09– 43·99) 55·18 (53·18– 57·01) 48·71 (46·26– 51·06) 53·46 (51·54– 55·47) 47·33 (44·70– 49·70) 61·51 (58·70– 64·10) 54·51 (51·44– 57·29) 59·20 (56·03– 62·28) 52·57 (49·11– 55·76) Comoros 59·46 (57·78– 61·52) 51·92 (49·20– 54·61) 57·80 (55·88– 59·73) 50·47 (47·88– 52·97) 65·30 (63·79– 67·10) 57·14 (54·36– 59·97) 63·78 (62·30– 65·32) 55·98 (53·42– 58·63) 68·52 (66·78– 70·86) 60·05 (57·27– 63·16) 66·61 (64·38– 68·77) 58·57 (55·52– 61·31) Djibouti 64·10 (62·77– 65·15) 55·99 (53·27– 58·39) 58·43 (57·21– 59·67) 51·61 (49·41– 53·77) 64·36 (61·78– 67·15) 56·34 (53·15– 59·62) 59·51 (57·39– 61·57) 52·59 (49·86– 55·17) 68·80 (66·11– 71·85) 60·24 (56·93– 63·65) 64·67 (62·50– 66·70) 57·03 (54·20– 59·80) Eritrea 53·74 (52·30– 55·22) 47·10 (44·78– 49·34) 50·96 (49·44– 52·49) 45·01 (42·76– 47·10) 61·51 (59·71– 63·42) 54·15 (51·29– 56·75) 60·05 (58·00– 62·07) 52·98 (50·39– 55·63) 64·53 (62·75– 66·62) 56·84 (54·01– 59·55) 62·88 (60·62– 65·07) 55·54 (52·73– 58·26) Ethiopia 48·89 (47·64– 50·20) 43·00 (40·98– 44·98) 44·37 (43·16– 45·76) 39·31 (37·54– 41·04) 57·30 (55·68– 59·03) 50·56 (48·24– 52·87) 56·81 (55·05– 58·31) 50·17 (47·86– 52·43) 66·53 (64·21– 68·96) 58·67 (55·72– 61·51) 64·74 (62·10– 67·59) 57·20 (54·14– 60·45) Kenya 62·63 (61·99– 63·30) 55·07 (52·71– 57·15) 60·25 (59·41– 61·07) 53·14 (50·93– 55·15) 59·91 (59·18– 60·60) 52·60 (50·41– 54·67) 57·04 (56·37– 57·71) 50·40 (48·34– 52·22) 69·03 (68·24– 69·88) 60·30 (57·58– 62·64) 64·72 (63·89– 65·56) 56·94 (54·49– 59·05) Madagascar 56·76 (55·42– 58·23) 49·55 (47·22– 51·91) 54·43 (53·13– 55·73) 47·74 (45·56– 49·87) 61·40 (59·57– 63·44) 53·79 (50·81– 56·51) 59·41 (57·47– 61·41) 52·19 (49·62– 54·76) 63·88 (60·78– 67·84) 56·22 (52·80– 59·92) 61·51 (58·34– 64·83) 54·29 (51·18– 57·82) Malawi 49·89 (48·02– 51·91) 43·53 (41·08– 46·03) 47·56 (44·19– 51·16) 41·45 (38·16– 44·72) 49·75 (46·64– 53·37) 43·49 (40·30– 46·97) 47·18 (44·96– 49·62) 41·39 (39·05– 44·13) 62·59 (59·81– 65·90) 54·63 (51·37– 57·95) 57·90 (55·27– 60·52) 50·94 (48·22– 53·98) Mozambique 52·80 (51·72– 53·94) 45·66 (43·25– 47·89) 48·51 (47·27– 49·70) 42·25 (40·17– 44·09) 54·89 (51·81– 58·00) 47·39 (44·02– 50·59) 50·33 (48·12– 52·56) 43·91 (41·18– 46·35) 62·88 (60·39– 65·36) 54·32 (51·22– 57·53) 57·05 (54·88– 59·18) 49·78 (46·92– 52·53) Rwanda 50·78 (49·17– 52·31) 44·72 (42·38– 46·96) 47·51 (45·55– 49·50) 42·13 (39·82– 44·32) 61·04 (59·22– 62·93) 53·38 (50·75– 55·88) 57·93 (56·51– 59·53) 50·93 (48·55– 53·13) 69·32 (67·21– 71·54) 60·74 (57·78– 63·78) 65·98 (63·86– 67·95) 58·02 (55·04– 60·77) Somalia 51·72 (50·09– 53·38) 45·34 (42·86– 47·64) 49·60 (47·49– 51·75) 43·81 (41·26– 46·38) 54·55 (52·80– 56·31) 47·93 (45·51– 50·47) 53·54 (51·26– 55·80) 47·33 (44·72– 50·09) 57·74 (55·75– 59·45) 50·75 (48·08– 53·18) 56·64 (54·11– 59·07) 50·10 (47·03– 52·87) South Sudan 53·28 (51·20– 55·51) 44·83 (41·62– 47·86) 49·85 (47·40– 52·59) 42·34 (39·34– 45·54) 57·77 (54·98– 60·99) 49·35 (45·93– 52·82) 55·76 (53·13– 58·60) 47·94 (44·83– 51·03) 60·72 (57·90– 63·93) 52·40 (49·04– 55·82) 58·71 (56·05– 61·53) 50·96 (47·77– 54·05) Tanzania 55·98 (54·76– 57·26) 48·69 (46·28– 51·04) 53·74 (52·07– 55·35) 47·08 (44·67– 49·47) 56·72 (54·77– 59·54) 49·54 (46·90– 52·22) 55·30 (53·82– 56·98) 48·68 (46·36– 51·06) 66·05 (64·28– 68·33) 57·79 (54·94– 60·76) 62·59 (60·50– 64·45) 55·33 (52·61– 57·81) Uganda 52·07 (50·38– 53·44) 44·88 (42·39– 47·10) 46·34 (43·41– 48·89) 40·17 (37·23– 43·06) 55·59 (53·98– 57·23) 48·16 (45·69– 50·66) 51·84 (50·37– 53·29) 45·29 (42·89– 47·31) 64·75 (62·93– 67·01) 56·33 (53·55– 59·24) 59·77 (57·99– 61·40) 52·45 (50·10– 54·90) Zambia 52·33 (50·38– 54·59) 45·94 (43·52– 48·33) 52·21 (49·34– 54·78) 45·87 (42·88– 48·71) 48·21 (45·39– 51·35) 42·34 (39·62– 45·29) 45·23 (43·18– 47·31) 40·12 (37·88– 42·23) 61·95 (58·22– 66·84) 54·14 (50·45– 58·71) 55·58 (52·54– 59·34) 49·10 (46·12– 52·44) Central subSaharan Africa 54·41 (53·25– 55·53) 46·46 (43·99– 48·74) 50·83 (49·71– 51·99) 43·71 (41·49– 45·86) 56·35 (55·20– 57·49) 48·42 (45·79– 50·67) 54·80 (53·60– 55·95) 47·29 (44·88– 49·53) 62·80 (61·19– 64·43) 54·20 (51·34– 56·76) 60·62 (58·87– 62·28) 52·58 (49·77– 55·26) Angola 52·16 (49·57– 55·16) 45·20 (42·37– 48·22) 48·27 (45·63– 51·05) 42·20 (39·52– 45·23) 57·13 (54·31– 60·11) 49·77 (46·75– 52·71) 56·77 (53·90– 59·62) 49·56 (46·60– 52·46) 65·37 (61·78– 68·70) 56·85 (53·28– 60·26) 63·94 (60·34– 67·08) 55·76 (52·30– 59·13) Central African Republic 51·04 (48·84– 53·26) 43·80 (40·92– 46·39) 45·38 (43·16– 47·67) 39·24 (36·70– 41·72) 47·44 (44·14– 51·09) 41·04 (37·74– 44·37) 43·21 (40·31– 46·22) 37·65 (34·73– 40·63) 52·59 (48·76– 56·64) 45·60 (41·97– 49·45) 47·91 (44·46– 51·58) 41·91 (38·62– 45·46) Congo (Brazzaville) 55·96 (53·78– 58·10) 48·26 (45·37– 51·06) 52·19 (50·02– 54·61) 45·47 (42·70– 48·24) 56·61 (54·64– 58·58) 48·95 (46·20– 51·56) 58·33 (56·48– 60·14) 50·60 (47·97– 53·23) 62·35 (59·54– 65·61) 53·99 (50·40– 57·77) 63·62 (60·10– 67·33) 55·47 (51·93– 59·12) (Table 2 continues on next page) Global Health Metrics 1296 www.thelancet.com Vol 390 September 16, 2017 3·91 million), extensively drug-resistant tuberculosis 369 000 (301 000–445 000), drug-susceptible HIV/AIDStuberculosis 11·7 million (8·15 million to 15·5 million), multidrug-resistant HIV/AIDS-tuberculosis without extensive drug resistance 0·98 million (0·60 million to 1·48 million), and extensively drug-resistant HIV/AIDStuberculosis 57 300 (34 500–89 400; table 1). Changes in leading causes of disease burden over time and SDI quintiles The leading Level 3 causes of global DALYs in 1990 were lower respiratory infections, diarrhoeal diseases, and ischaemic heart disease (figure 2A). By 2016, lower respiratory infections had decreased in relative rank to the third-leading cause, whereas diarrhoeal diseases were fifth and ischaemic heart disease rose to first. The leading 30 causes of DALYs by SDI quintile varied considerably (figure 2B–F). CMNN causes were dominant in the lowSDI quintile, occupying 11 of the top 15 causes in 2016. NCDs were more commonly among the leading causes of DALYs with successively higher levels of SDI, occupying 12 of the top 15 causes in the high-SDI quintile in 2016. For low SDI, only two NCD causes (congenital birth defects and ischaemic heart disease) ranked in the top ten in 2016, compared with all of the top ten for high SDI. Of the leading 30 causes from 2006 to 2016, HIV/AIDS had the largest decrease in age-standardised DALY rates for low, low-middle, and middle SDI. For high-middle SDI, the largest decrease was for lower respiratory infection, and for high SDI, the largest decrease was for road injuries. At low-middle SDI, 14 of the leading 30 causes in 2016 were NCDs, whereas 13 were CMNN causes and three were injuries, compared with middle SDI, where seven were CMNN causes, 19 were NCDs, and four were injuries. The leading seven causes for low, low-middle, and middle SDI quintiles all decreased in age-standardised DALY rates from 2006 to 2016. At high-middle and high SDI, the leading three causes of DALYs remained the same from 2006 to 2016 (ischaemic heart disease, low back and neck pain, and cerebrovascular disease). Two of the leading 30 causes for high-middle SDI were CMNN causes in 2016, whereas 24 were NCDs and four were injuries, compared with high SDI, where one was a CMNN cause, 26 were NCDs, and three were injuries. The leading five causes for high-middle SDI decreased in age-standardised DALY rates from both 1990 to 2006 and 2006 to 2016, although this pattern did not hold for high SDI, where just the leading cause decreased over both intervals. Road injuries and falls are the only injuries that appear across all five SDI quintiles; self-harm appears in all quintiles except for low SDI. Ischaemic heart disease and cerebrovascular disease appear in the leading 13 causes across all SDI quintiles. HIV/AIDS appears in the leading ten causes for all quintiles except for high and highmiddle SDI, where it did not appear in the leading 30. Regional and country-specific HALE and DALYs in 2016 In 2016, HALE at birth was highest in Singapore for both females (75·2 years [95% UI 71·9–78·6]) and males (72·0 years [68·8–75·1]) and lowest for females in the Central African Republic (45·6 years [42·0–49·5]) and for males in Lesotho (41·5 [39·0–44·0]; table 2 and 3). In 2016, HALE at birth was greater than 70 years in only 12 locations for males and was greater than this threshold in 49 locations for females. HALE at birth was less than 50 years for both sexes in three locations: the Central African Republic, Lesotho, and Afghanistan. Between 1990 and 2016, HALE at birth increased for males in 160 locations and for females in 167 locations. For males, the largest increase occurred in Ethiopia, rising from 39·3 years (37·5–41·0) in 1990 to 57·2 years (54·1–60·45) in 2016. For females, the largest increase occurred in the Maldives, rising from 53·6 (51·1–56·1) in 1990 to 70·3 (66·3–73·7) in 2016. Over the same time period, HALE at birth decreased in just 1990, at birth 2006, at birth 2016, at birth Females Males Females Males Females Males Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE (Continued from previous page) Democratic Republic of the Congo 55·32 (53·74– 56·81) 46·96 (44·23– 49·57) 52·33 (50·71– 53·96) 44·65 (42·09– 47·12) 56·75 (55·28– 58·22) 48·53 (45·70– 51·08) 55·03 (53·48– 56·45) 47·24 (44·61– 49·74) 62·72 (60·80– 64·53) 53·99 (50·96– 56·80) 60·44 (58·33– 62·42) 52·26 (49·34– 55·04) Equatorial Guinea 49·82 (47·16– 52·85) 43·11 (40·10– 46·00) 46·54 (43·66– 49·72) 40·62 (37·58– 43·52) 59·26 (54·70– 64·65) 51·26 (47·01– 55·97) 58·66 (54·71– 63·17) 50·91 (46·94– 55·13) 66·55 (62·28– 71·43) 57·44 (53·04– 61·96) 64·49 (60·25– 69·11) 55·97 (51·95– 60·33) Gabon 62·90 (61·23– 64·49) 54·14 (51·30– 56·75) 56·02 (54·28– 57·83) 49·01 (46·57– 51·43) 61·32 (59·50– 63·25) 52·87 (50·05– 55·57) 59·71 (57·34– 61·77) 52·16 (49·40– 54·83) 68·31 (65·83– 71·18) 58·73 (55·38– 62·22) 64·90 (61·35– 68·37) 56·59 (52·95– 60·04) Data in parentheses are 95% uncertainty intervals. To download the data in this table, please visit the Global Health Data Exchange. GBD=Global Burden of Disease. HALE=healthy life expectancy. SDI=Socio-demographic index. Table 2: Global, regional, and GBD location-specific life expectancy and HALE at birth, by sex, in 1990, 2006, and 2016 Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1297 1990, at age 65 years 2006, at age 65 years 2016, at age 65 years Females Males Females Males Females Males Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Global 15·90 (15·82– 15·98) 11·92 (10·88– 12·89) 13·33 (13·26– 13·39) 10·09 (9·22– 10·87) 17·27 (17·19– 17·35) 12·93 (11·74– 13·98) 14·69 (14·62– 14·75) 11·10 (10·13– 11·97) 18·57 (18·37– 18·72) 13·88 (12·57– 15·02) 15·72 (15·61– 15·83) 11·87 (10·83– 12·80) High SDI 18·57 (18·50– 18·63) 14·06 (12·85– 15·16) 14·79 (14·73– 14·84) 11·22 (10·27– 12·08) 20·72 (20·67– 20·78) 15·69 (14·31– 16·93) 17·22 (17·17– 17·28) 13·00 (11·86– 14·03) 21·67 (21·53– 21·81) 16·38 (14·91– 17·68) 18·37 (18·22– 18·50) 13·86 (12·65– 14·98) High-middle SDI 16·31 (16·07– 16·54) 12·15 (11·02– 13·22) 13·35 (13·16– 13·54) 10·09 (9·20– 10·92) 17·39 (17·15– 17·62) 13·00 (11·78– 14·08) 14·27 (14·06– 14·45) 10·81 (9·86– 11·68) 19·19 (18·36– 19·89) 14·32 (12·85– 15·71) 15·84 (15·39– 16·28) 12·01 (10·90– 13·06) Middle SDI 14·81 (14·63– 14·98) 11·25 (10·32– 12·13) 12·64 (12·50– 12·78) 9·78 (9·00– 10·50) 16·55 (16·43– 16·67) 12·48 (11·38– 13·50) 14·09 (13·98– 14·21) 10·84 (9·96– 11·66) 18·19 (18·02– 18·35) 13·68 (12·48– 14·77) 15·01 (14·86– 15·17) 11·50 (10·55– 12·33) Low-middle SDI 13·08 (12·90– 13·28) 9·47 (8·51– 10·37) 12·48 (12·32– 12·64) 9·15 (8·28– 9·96) 14·35 (14·16– 14·54) 10·40 (9·36– 11·34) 13·24 (13·10– 13·39) 9·74 (8·83– 10·58) 15·50 (15·26– 15·73) 11·24 (10·13– 12·27) 13·95 (13·75– 14·14) 10·27 (9·28– 11·13) Low SDI 11·65 (11·45– 11·86) 8·54 (7·67– 9·29) 11·75 (11·52– 11·97) 8·59 (7·72– 9·42) 12·29 (12·02– 12·58) 9·08 (8·19– 9·91) 12·43 (12·20– 12·67) 9·17 (8·24– 9·97) 13·32 (13·00– 13·64) 9·90 (8·94– 10·78) 13·22 (12·89– 13·54) 9·80 (8·82– 10·69) High income 18·77 (18·71– 18·84) 14·28 (13·06– 15·39) 14·96 (14·90– 15·01) 11·40 (10·45– 12·27) 20·89 (20·83– 20·95) 15·88 (14·51– 17·12) 17·35 (17·30– 17·40) 13·16 (12·02– 14·18) 21·80 (21·66– 21·94) 16·55 (15·09– 17·85) 18·47 (18·32– 18·60) 14·00 (12·79– 15·11) High-income North America 19·09 (19·03– 19·16) 14·49 (13·27– 15·62) 15·17 (15·11– 15·24) 11·46 (10·48– 12·36) 20·05 (19·99– 20·12) 15·06 (13·71– 16·29) 17·33 (17·28– 17·40) 12·91 (11·73– 13·98) 20·74 (20·59– 20·89) 15·46 (14·05– 16·73) 18·19 (18·05– 18·34) 13·48 (12·23– 14·63) Canada 19·69 (19·47– 19·89) 15·30 (14·07– 16·43) 15·50 (15·31– 15·70) 12·03 (11·03– 12·93) 21·08 (20·86– 21·27) 16·28 (15·00– 17·49) 17·92 (17·75– 18·13) 13·82 (12·68– 14·86) 21·96 (21·66– 22·27) 16·91 (15·50– 18·21) 19·12 (18·82– 19·44) 14·71 (13·46– 15·88) Greenland 11·30 (10·69– 11·92) 8·66 (7·82– 9·51) 8·95 (8·51– 9·40) 6·83 (6·17– 7·45) 11·88 (11·27– 12·47) 9·10 (8·25– 9·92) 10·70 (10·19– 11·17) 8·20 (7·47– 8·92) 14·20 (12·77– 15·95) 10·89 (9·51– 12·36) 11·89 (10·54– 13·29) 9·14 (7·93– 10·46) USA 19·04 (18·97– 19·11) 14·41 (13·19– 15·55) 15·14 (15·07– 15·20) 11·41 (10·43– 12·30) 19·94 (19·88– 20·01) 14·93 (13·57– 16·16) 17·27 (17·21– 17·33) 12·80 (11·62– 13·88) 20·60 (20·44– 20·76) 15·30 (13·89– 16·59) 18·09 (17·93– 18·25) 13·34 (12·08– 14·49) Australasia 18·95 (18·79– 19·12) 14·66 (13·46– 15·73) 15·10 (14·95– 15·25) 11·65 (10·69– 12·53) 21·35 (21·21– 21·49) 16·52 (15·14– 17·76) 18·31 (18·17– 18·46) 14·11 (12·94– 15·15) 22·10 (21·65– 22·56) 17·07 (15·62– 18·38) 19·37 (18·91– 19·84) 14·92 (13·66– 16·08) Australia 19·05 (18·88– 19·25) 14·73 (13·54– 15·81) 15·18 (15·01– 15·35) 11·71 (10·75– 12·59) 21·51 (21·34– 21·68) 16·63 (15·26– 17·91) 18·40 (18·24– 18·58) 14·17 (12·98– 15·23) 22·22 (21·67– 22·74) 17·16 (15·72– 18·49) 19·47 (18·93– 19·96) 14·98 (13·69– 16·17) New Zealand 18·43 (18·18– 18·66) 14·29 (13·11– 15·34) 14·71 (14·50– 14·92) 11·36 (10·40– 12·22) 20·52 (20·29– 20·73) 15·95 (14·61– 17·11) 17·83 (17·62– 18·03) 13·81 (12·64– 14·85) 21·48 (20·65– 22·40) 16·64 (15·19– 18·03) 18·90 (18·08– 19·77) 14·60 (13·30– 15·87) High-income Asia Pacific 19·46 (19·31– 19·61) 15·03 (13·80– 16·15) 15·65 (15·51– 15·78) 11·91 (10·90– 12·82) 22·73 (22·57– 22·88) 17·47 (15·99– 18·77) 17·93 (17·80– 18·07) 13·50 (12·30– 14·59) 23·79 (23·27– 24·24) 18·30 (16·64– 19·74) 19·20 (18·75– 19·61) 14·51 (13·22– 15·73) Brunei 16·84 (15·98– 17·54) 12·75 (11·48– 13·92) 14·52 (14·31– 14·78) 10·70 (9·68– 11·64) 18·67 (18·25– 19·03) 14·26 (13·01– 15·41) 15·85 (15·56– 16·19) 11·78 (10·69– 12·79) 18·88 (18·18– 20·19) 14·50 (13·16– 15·79) 15·88 (15·05– 17·40) 11·88 (10·61– 13·13) Japan 19·96 (19·92– 20·00) 15·43 (14·15– 16·55) 16·22 (16·19– 16·25) 12·36 (11·32– 13·29) 23·23 (23·17– 23·28) 17·89 (16·38– 19·22) 18·24 (18·21– 18·28) 13·75 (12·53– 14·83) 24·24 (24·07– 24·40) 18·67 (17·07– 20·03) 19·53 (19·35– 19·70) 14·78 (13·50– 15·95) Singapore 17·64 (16·49– 18·82) 13·74 (12·40– 15·01) 14·65 (13·72– 15·61) 11·26 (10·13– 12·42) 21·50 (20·48– 22·49) 16·78 (15·30– 18·19) 17·71 (16·77– 18·66) 13·67 (12·45– 14·94) 23·33 (21·62– 25·20) 18·16 (16·31– 20·02) 19·70 (17·89– 21·49) 15·13 (13·37– 16·82) South Korea 16·70 (15·82– 17·56) 12·82 (11·56– 14·06) 12·36 (11·66– 13·12) 9·33 (8·35– 10·31) 20·05 (19·28– 20·86) 15·26 (13·84– 16·68) 16·20 (15·45– 17·11) 12·11 (10·94– 13·41) 21·67 (19·44– 23·94) 16·62 (14·54– 18·80) 17·52 (15·45– 20·08) 13·20 (11·32– 15·13) (Table 3 continues on next page) Global Health Metrics 1298 www.thelancet.com Vol 390 September 16, 2017 1990, at age 65 years 2006, at age 65 years 2016, at age 65 years Females Males Females Males Females Males Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE (Continued from previous page) Western Europe 18·48 (18·37– 18·59) 13·98 (12·75– 15·11) 14·70 (14·61– 14·79) 11·25 (10·32– 12·10) 20·81 (20·71– 20·91) 15·80 (14·41– 17·04) 17·26 (17·17– 17·36) 13·23 (12·12– 14·24) 21·76 (21·56– 21·96) 16·54 (15·08– 17·87) 18·49 (18·29– 18·69) 14·21 (13·00– 15·28) Andorra 20·84 (19·08– 22·79) 15·59 (13·63– 17·52) 16·54 (15·62– 17·99) 12·54 (11·23– 13·98) 23·19 (21·49– 24·44) 17·38 (15·50– 19·13) 18·33 (17·43– 19·29) 13·94 (12·51– 15·26) 23·06 (20·90– 24·50) 17·31 (15·33– 19·16) 18·46 (17·43– 20·09) 14·08 (12·52– 15·58) Austria 17·88 (17·71– 18·06) 13·60 (12·43– 14·67) 14·36 (14·20– 14·51) 10·97 (9·99– 11·81) 20·48 (20·31– 20·63) 15·61 (14·25– 16·81) 17·16 (17·00– 17·32) 13·07 (11·92– 14·08) 21·45 (20·98– 21·95) 16·35 (14·86– 17·69) 18·41 (18·01– 18·90) 14·08 (12·82– 15·30) Belgium 18·47 (18·16– 18·74) 13·84 (12·59– 14·99) 14·29 (14·00– 14·59) 10·86 (9·84– 11·73) 20·49 (20·24– 20·77) 15·22 (13·79– 16·51) 16·87 (16·60– 17·15) 12·70 (11·54– 13·74) 21·38 (20·63– 22·22) 16·00 (14·41– 17·50) 17·99 (17·22– 18·72) 13·62 (12·27– 14·90) Cyprus 15·67 (15·34– 15·99) 11·88 (10·80– 12·88) 14·03 (13·70– 14·36) 10·77 (9·81– 11·61) 18·99 (18·64– 19·34) 14·41 (13·09– 15·59) 16·52 (16·19– 16·84) 12·64 (11·53– 13·62) 20·17 (19·74– 20·58) 15·38 (14·04– 16·67) 17·44 (17·01– 17·92) 13·39 (12·22– 14·49) Denmark 17·97 (17·41– 18·50) 13·58 (12·33– 14·73) 14·27 (13·81– 14·80) 10·99 (10·00– 11·87) 19·09 (18·53– 19·65) 14·47 (13·09– 15·70) 16·33 (15·86– 16·81) 12·50 (11·38– 13·56) 20·76 (19·77– 21·83) 15·65 (14·08– 17·10) 18·01 (17·04– 18·92) 13·77 (12·45– 15·05) Finland 17·74 (17·50– 17·98) 13·32 (12·13– 14·43) 13·76 (13·56– 13·97) 10·37 (9·43– 11·23) 20·74 (20·51– 20·94) 15·37 (13·87– 16·74) 16·73 (16·52– 16·95) 12·42 (11·26– 13·46) 21·99 (21·44– 22·62) 16·49 (14·87– 18·00) 18·39 (17·82– 18·97) 13·86 (12·61– 15·07) France 20·15 (19·94– 20·34) 15·31 (13·98– 16·52) 15·72 (15·52– 15·93) 12·08 (11·05– 13·01) 22·25 (22·04– 22·44) 17·06 (15·60– 18·34) 17·91 (17·71– 18·15) 13·85 (12·66– 14·89) 23·22 (22·83– 23·66) 17·74 (16·17– 19·19) 19·24 (18·87– 19·63) 14·87 (13·56– 16·01) Germany 17·68 (17·28– 18·06) 13·32 (12·06– 14·47) 14·03 (13·65– 14·41) 10·67 (9·69– 11·56) 20·21 (19·87– 20·57) 15·31 (13·94– 16·58) 16·75 (16·41– 17·13) 12·79 (11·69– 13·86) 21·10 (20·46– 21·82) 15·98 (14·47– 17·42) 17·93 (17·27– 18·62) 13·69 (12·43– 14·90) Greece 18·02 (17·79– 18·26) 13·71 (12·55– 14·78) 15·58 (15·35– 15·83) 11·98 (10·98– 12·88) 20·24 (20·03– 20·46) 15·40 (14·10– 16·59) 17·42 (17·19– 17·64) 13·37 (12·24– 14·38) 20·98 (20·42– 21·55) 16·07 (14·70– 17·39) 18·34 (17·72– 18·99) 14·12 (12·91– 15·28) Iceland 19·13 (18·70– 19·55) 14·48 (13·19– 15·67) 16·16 (15·82– 16·53) 12·37 (11·30– 13·32) 20·76 (20·29– 21·27) 15·73 (14·34– 16·95) 18·31 (17·99– 18·66) 14·02 (12·79– 15·10) 21·35 (20·53– 22·14) 16·24 (14·78– 17·62) 18·98 (18·37– 19·56) 14·60 (13·28– 15·89) Ireland 16·89 (16·46– 17·33) 12·86 (11·69– 13·94) 13·36 (13·00– 13·78) 10·28 (9·37– 11·12) 19·70 (19·27– 20·16) 14·96 (13·66– 16·21) 16·66 (16·26– 17·04) 12·79 (11·69– 13·85) 21·03 (19·99– 22·10) 16·02 (14·42– 17·48) 18·13 (17·14– 19·09) 13·96 (12·61– 15·28) Israel 16·81 (15·99– 17·61) 12·74 (11·45– 13·98) 15·08 (14·39– 15·87) 11·53 (10·39– 12·59) 20·00 (19·21– 20·82) 15·12 (13·63– 16·54) 17·63 (16·86– 18·41) 13·46 (12·15– 14·68) 21·54 (20·02– 22·96) 16·31 (14·53– 17·97) 18·93 (17·36– 20·55) 14·48 (12·89– 16·15) Italy 18·91 (18·73– 19·10) 14·31 (13·04– 15·46) 15·08 (14·91– 15·25) 11·53 (10·55– 12·45) 21·36 (21·18– 21·52) 16·26 (14·82– 17·57) 17·57 (17·39– 17·76) 13·50 (12·38– 14·54) 21·94 (21·44– 22·46) 16·77 (15·29– 18·16) 18·60 (18·05– 19·13) 14·34 (13·08– 15·50) Luxembourg 17·75 (17·43– 18·09) 13·25 (12·00– 14·38) 14·01 (13·75– 14·27) 10·51 (9·54– 11·40) 20·82 (20·44– 21·15) 15·60 (14·14– 16·86) 17·44 (17·14– 17·75) 13·13 (11·92– 14·24) 21·49 (20·63– 22·52) 16·16 (14·62– 17·61) 18·95 (18·21– 19·74) 14·32 (12·93– 15·58) Malta 16·55 (15·73– 17·38) 12·60 (11·35– 13·85) 14·28 (13·61– 14·99) 10·99 (9·95– 12·06) 19·57 (18·79– 20·34) 14·93 (13·49– 16·22) 16·55 (15·89– 17·23) 12·76 (11·64– 13·80) 21·34 (19·94– 22·91) 16·28 (14·58– 18·01) 18·03 (16·76– 19·40) 13·92 (12·45– 15·45) Netherlands 18·96 (18·64– 19·33) 14·26 (12·98– 15·45) 14·43 (14·11– 14·73) 10·98 (10·03– 11·85) 20·17 (19·85– 20·47) 15·21 (13·82– 16·48) 16·73 (16·45– 17·05) 12·72 (11·62– 13·74) 21·38 (20·65– 22·06) 16·12 (14·61– 17·54) 18·11 (17·36– 18·78) 13·79 (12·53– 15·00) Norway 18·81 (18·47– 19·16) 14·30 (13·04– 15·45) 14·80 (14·50– 15·15) 11·24 (10·21– 12·17) 20·62 (20·28– 20·92) 15·74 (14·38– 16·96) 17·42 (17·12– 17·70) 13·20 (12·02– 14·27) 21·57 (20·67– 22·52) 16·49 (14·89– 17·88) 18·68 (17·94– 19·52) 14·21 (12·90– 15·60) Portugal 17·57 (17·36– 17·79) 13·32 (12·17– 14·41) 14·26 (14·04– 14·47) 10·96 (9·99– 11·76) 20·21 (19·95– 20·46) 15·36 (13·97– 16·55) 16·50 (16·25– 16·75) 12·76 (11·70– 13·73) 21·52 (21·01– 22·08) 16·43 (14·99– 17·78) 17·82 (17·32– 18·37) 13·82 (12·60– 14·92) (Table 3 continues on next page) Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1299 1990, at age 65 years 2006, at age 65 years 2016, at age 65 years Females Males Females Males Females Males Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE (Continued from previous page) Spain 19·26 (19·12– 19·40) 14·71 (13·40– 15·84) 15·59 (15·44– 15·74) 12·07 (11·09– 12·98) 21·58 (21·44– 21·70) 16·53 (15·08– 17·79) 17·57 (17·43– 17·71) 13·65 (12·54– 14·65) 22·82 (22·47– 23·17) 17·57 (16·08– 18·94) 19·17 (18·76– 19·56) 14·99 (13·74– 16·09) Sweden 19·05 (18·78– 19·30) 14·38 (13·12– 15·57) 15·34 (15·10– 15·60) 11·77 (10·74– 12·66) 20·58 (20·36– 20·83) 15·53 (14·13– 16·77) 17·58 (17·35– 17·81) 13·36 (12·20– 14·42) 21·37 (20·36– 22·32) 16·05 (14·52– 17·62) 18·64 (17·74– 19·51) 14·12 (12·67– 15·40) Switzerland 19·57 (19·09– 20·04) 14·64 (13·24– 15·95) 15·38 (14·93– 15·84) 11·70 (10·61– 12·72) 21·55 (21·10– 21·98) 16·14 (14·60– 17·55) 18·22 (17·74– 18·69) 13·82 (12·60– 15·03) 22·51 (20·50– 24·40) 17·02 (14·97– 18·88) 19·47 (17·46– 21·31) 14·90 (12·94– 16·63) UK 17·80 (17·71– 17·89) 13·43 (12·25– 14·50) 13·98 (13·90– 14·05) 10·69 (9·78– 11·49) 19·88 (19·79– 19·97) 15·01 (13·68– 16·19) 17·10 (17·02– 17·18) 13·08 (11·97– 14·10) 20·88 (20·73– 21·04) 15·77 (14·37– 17·07) 18·27 (18·13– 18·40) 14·00 (12·83– 15·05) England 17·94 (17·87– 18·00) 13·51 (12·31– 14·61) 14·11 (14·05– 14·17) 10·78 (9·86– 11·60) 20·03 (19·97– 20·10) 15·09 (13·74– 16·30) 17·24 (17·18– 17·30) 13·17 (12·07– 14·18) 21·05 (20·94– 21·16) 15·87 (14·44– 17·17) 18·44 (18·34– 18·54) 14·11 (12·91– 15·18) Northern Ireland 17·39 (16·77– 18·11) 13·25 (12·04– 14·43) 13·52 (12·95– 14·16) 10·41 (9·47– 11·31) 19·69 (19·11– 20·35) 15·00 (13·66– 16·35) 16·70 (16·11– 17·30) 12·86 (11·72– 13·93) 20·59 (19·45– 21·77) 15·71 (14·13– 17·25) 17·75 (16·64– 18·93) 13·68 (12·32– 15·06) Scotland 16·75 (16·13– 17·38) 12·77 (11·63– 13·89) 12·99 (12·46– 13·53) 10·02 (9·10– 10·89) 18·69 (18·06– 19·34) 14·30 (13·06– 15·50) 16·00 (15·44– 16·53) 12·38 (11·31– 13·40) 19·71 (18·66– 20·75) 15·02 (13·60– 16·58) 17·16 (16·23– 18·09) 13·24 (11·94– 14·55) Wales 17·67 (17·07– 18·28) 13·40 (12·18– 14·59) 13·68 (13·14– 14·18) 10·51 (9·55– 11·40) 19·67 (19·19– 20·26) 15·00 (13·70– 16·27) 16·92 (16·34– 17·45) 13·03 (11·95– 14·11) 20·43 (19·42– 21·41) 15·58 (14·14– 17·10) 17·70 (16·71– 18·66) 13·65 (12·32– 14·97) Southern Latin America 17·74 (17·50– 17·99) 13·79 (12·63– 14·82) 14·03 (13·79– 14·27) 10·84 (9·94– 11·67) 19·34 (19·11– 19·60) 15·03 (13·83– 16·15) 15·30 (15·05– 15·55) 11·83 (10·86– 12·74) 20·24 (19·54– 20·92) 15·72 (14·32– 16·97) 16·14 (15·49– 16·77) 12·50 (11·38– 13·58) Argentina 17·76 (17·46– 18·08) 13·83 (12·70– 14·89) 13·90 (13·61– 14·19) 10·75 (9·84– 11·59) 19·01 (18·70– 19·33) 14·82 (13·65– 15·91) 14·81 (14·50– 15·12) 11·47 (10·52– 12·36) 19·73 (19·11– 20·39) 15·37 (14·05– 16·56) 15·55 (14·94– 16·19) 12·06 (11·04– 13·05) Chile 17·69 (17·18– 18·26) 13·64 (12·46– 14·77) 14·65 (14·15– 15·19) 11·25 (10·19– 12·25) 20·40 (19·90– 20·91) 15·72 (14·40– 16·95) 17·08 (16·61– 17·62) 13·14 (11·99– 14·23) 21·63 (19·39– 23·86) 16·71 (14·58– 18·88) 17·91 (15·85– 20·06) 13·84 (11·93– 15·86) Uruguay 17·70 (17·44– 17·95) 13·78 (12·64– 14·77) 13·69 (13·46– 13·92) 10·65 (9·75– 11·44) 19·50 (19·24– 19·74) 15·17 (13·95– 16·22) 14·76 (14·54– 15·01) 11·46 (10·49– 12·30) 20·44 (19·85– 21·02) 15·87 (14·53– 17·10) 15·54 (15·03– 16·08) 12·09 (11·04– 13·07) Central Europe, eastern Europe, and central Asia 15·81 (15·50– 16·12) 11·59 (10·43– 12·70) 12·43 (12·17– 12·66) 9·08 (8·14– 9·92) 16·22 (15·89– 16·52) 11·99 (10·77– 13·05) 12·41 (12·12– 12·70) 9·12 (8·21– 9·96) 17·80 (16·51– 18·92) 13·16 (11·60– 14·68) 14·07 (13·21– 14·96) 10·33 (9·14– 11·60) Eastern Europe 15·85 (15·36– 16·35) 11·64 (10·50– 12·82) 12·20 (11·76– 12·66) 8·92 (8·01– 9·79) 15·81 (15·30– 16·30) 11·74 (10·53– 12·79) 11·57 (11·10– 12·05) 8·53 (7·66– 9·39) 17·37 (15·46– 19·28) 12·90 (10·99– 14·77) 13·26 (11·85– 14·92) 9·79 (8·39– 11·34) Belarus 16·52 (16·15– 16·86) 12·23 (11·04– 13·34) 12·94 (12·58– 13·28) 9·56 (8·60– 10·48) 16·27 (15·89– 16·65) 12·10 (10·89– 13·16) 11·53 (11·20– 11·88) 8·54 (7·67– 9·35) 18·15 (16·70– 19·71) 13·45 (11·83– 15·17) 13·25 (12·01– 14·60) 9·79 (8·48– 11·19) Estonia 16·11 (15·79– 16·44) 11·79 (10·58– 12·88) 12·21 (11·91– 12·52) 8·91 (7·98– 9·77) 18·25 (17·93– 18·60) 13·55 (12·24– 14·75) 13·28 (12·96– 13·57) 9·78 (8·80– 10·71) 20·14 (19·43– 21·38) 14·90 (13·28– 16·52) 15·65 (14·85– 16·60) 11·50 (10·22– 12·74) Latvia 15·93 (15·55– 16·30) 11·58 (10·34– 12·70) 12·23 (11·83– 12·64) 8·87 (7·93– 9·75) 16·87 (16·49– 17·26) 12·49 (11·28– 13·60) 12·38 (12·03– 12·74) 9·09 (8·16– 9·96) 19·04 (18·02– 20·10) 14·02 (12·56– 15·56) 14·24 (13·32– 15·35) 10·44 (9·21– 11·68) Lithuania 17·10 (16·81– 17·39) 12·49 (11·23– 13·64) 13·47 (13·18– 13·78) 9·82 (8·81– 10·74) 17·97 (17·68– 18·24) 13·14 (11·83– 14·34) 13·26 (12·99– 13·56) 9·69 (8·69– 10·62) 19·63 (19·02– 20·27) 14·42 (12·96– 15·79) 14·70 (14·11– 15·31) 10·68 (9·43– 11·81) Moldova 14·43 (13·82– 15·08) 10·67 (9·61– 11·75) 12·09 (11·51– 12·71) 8·94 (7·98– 9·83) 14·59 (13·99– 15·27) 11·00 (9·86– 12·05) 11·89 (11·28– 12·52) 8·95 (8·01– 9·88) 16·35 (15·32– 17·49) 12·29 (10·95– 13·68) 13·35 (12·40– 14·45) 10·01 (8·85– 11·26) (Table 3 continues on next page) Global Health Metrics 1300 www.thelancet.com Vol 390 September 16, 2017 1990, at age 65 years 2006, at age 65 years 2016, at age 65 years Females Males Females Males Females Males Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE (Continued from previous page) Russia 15·82 (15·10– 16·62) 11·60 (10·43– 12·83) 12·01 (11·35– 12·78) 8·74 (7·77– 9·73) 15·80 (15·04– 16·52) 11·70 (10·45– 12·88) 11·46 (10·77– 12·20) 8·41 (7·47– 9·38) 17·27 (14·52– 20·14) 12·84 (10·56– 15·28) 13·18 (11·27– 15·67) 9·72 (7·95– 11·73) Ukraine 15·79 (15·35– 16·24) 11·65 (10·50– 12·72) 12·41 (12·02– 12·80) 9·15 (8·19– 10·04) 15·60 (15·19– 16·00) 11·65 (10·49– 12·71) 11·64 (11·27– 12·04) 8·66 (7·81– 9·49) 17·23 (15·13– 19·48) 12·93 (11·04– 15·00) 13·26 (11·61– 15·48) 9·92 (8·44– 11·63) Central Europe 15·72 (15·58– 15·85) 11·42 (10·24– 12·46) 12·64 (12·52– 12·77) 9·16 (8·18– 10·03) 17·49 (17·36– 17·64) 12·79 (11·50– 13·94) 14·03 (13·90– 14·16) 10·19 (9·12– 11·12) 18·92 (18·59– 19·25) 13·84 (12·43– 15·11) 15·38 (15·08– 15·69) 11·16 (10·02– 12·19) Albania 18·27 (18·06– 18·51) 13·70 (12·40– 14·86) 14·91 (14·71– 15·13) 11·14 (10·05– 12·10) 17·66 (17·41– 17·91) 13·33 (12·10– 14·47) 14·96 (14·76– 15·17) 11·26 (10·18– 12·19) 19·59 (18·61– 20·63) 14·70 (13·25– 16·22) 16·11 (14·93– 17·27) 12·07 (10·68– 13·40) Bosnia and Herzegovina 15·47 (14·56– 16·56) 11·53 (10·22– 12·78) 12·83 (12·00– 13·72) 9·57 (8·51– 10·72) 17·32 (16·38– 18·35) 12·84 (11·52– 14·23) 14·66 (13·73– 15·57) 10·78 (9·57– 11·90) 18·56 (17·17– 19·95) 13·69 (12·11– 15·33) 15·58 (14·44– 16·83) 11·39 (10·05– 12·76) Bulgaria 15·20 (14·94– 15·45) 11·24 (10·16– 12·23) 12·81 (12·58– 13·03) 9·34 (8·39– 10·22) 16·22 (15·99– 16·46) 12·07 (10·92– 13·12) 13·17 (12·93– 13·41) 9·71 (8·75– 10·59) 17·60 (16·23– 18·93) 13·07 (11·52– 14·58) 14·28 (13·12– 15·58) 10·50 (9·26– 11·91) Croatia 15·96 (15·35– 16·56) 11·68 (10·40– 12·87) 12·72 (12·18– 13·36) 9·26 (8·25– 10·23) 17·50 (16·97– 18·04) 12·88 (11·53– 14·10) 13·90 (13·41– 14·44) 10·18 (9·10– 11·21) 18·57 (17·49– 19·78) 13·66 (12·08– 15·13) 15·15 (14·20– 16·27) 11·04 (9·76– 12·45) Czech Republic 15·33 (15·16– 15·49) 10·84 (9·61– 11·93) 11·84 (11·68– 12·01) 8·44 (7·49– 9·26) 18·09 (17·89– 18·26) 12·92 (11·51– 14·19) 14·69 (14·51– 14·89) 10·43 (9·25– 11·46) 19·66 (19·21– 20·11) 14·10 (12·46– 15·53) 16·34 (15·88– 16·79) 11·62 (10·26– 12·83) Hungary 15·43 (15·02– 15·87) 10·85 (9·57– 12·01) 12·08 (11·72– 12·46) 8·58 (7·59– 9·51) 17·48 (17·05– 17·92) 12·56 (11·22– 13·85) 13·54 (13·12– 13·98) 9·70 (8·60– 10·69) 18·34 (17·45– 19·21) 13·25 (11·82– 14·70) 14·74 (13·88– 15·67) 10·61 (9·36– 11·91) Macedonia 14·55 (14·03– 15·08) 10·86 (9·76– 11·88) 12·61 (12·18– 13·04) 9·36 (8·42– 10·25) 14·93 (14·49– 15·43) 11·14 (10·05– 12·19) 12·71 (12·32– 13·13) 9·44 (8·50– 10·33) 16·26 (15·79– 16·82) 12·08 (10·86– 13·19) 13·67 (13·22– 14·15) 10·13 (9·08– 11·06) Montenegro 18·05 (17·25– 18·70) 13·38 (12·05– 14·73) 15·39 (14·78– 16·01) 11·28 (10·01– 12·38) 17·80 (17·45– 18·18) 13·23 (11·83– 14·45) 14·76 (14·34– 15·23) 10·88 (9·78– 11·93) 18·50 (17·59– 19·69) 13·73 (12·17– 15·34) 15·72 (14·87– 16·56) 11·56 (10·30– 12·82) Poland 16·07 (15·76– 16·37) 11·70 (10·48– 12·78) 12·40 (12·15– 12·70) 9·02 (8·08– 9·89) 18·64 (18·37– 18·94) 13·56 (12·18– 14·84) 14·52 (14·22– 14·83) 10·51 (9·40– 11·53) 20·10 (19·32– 20·89) 14·69 (13·06– 16·19) 16·03 (15·32– 16·80) 11·61 (10·31– 12·85) Romania 15·37 (15·00– 15·76) 11·32 (10·16– 12·37) 13·17 (12·78– 13·52) 9·57 (8·57– 10·52) 16·86 (16·48– 17·27) 12·44 (11·23– 13·61) 13·89 (13·54– 14·25) 10·13 (9·06– 11·13) 18·11 (17·30– 18·94) 13·40 (12·02– 14·73) 14·78 (13·93– 15·63) 10·79 (9·53– 12·02) Serbia 16·12 (15·50– 16·90) 11·85 (10·61– 13·13) 13·48 (12·93– 14·25) 9·88 (8·82– 10·96) 15·81 (15·34– 16·26) 11·67 (10·49– 12·76) 13·42 (13·05– 13·85) 9·84 (8·80– 10·81) 17·87 (17·46– 18·22) 13·12 (11·77– 14·30) 14·95 (14·58– 15·45) 10·92 (9·77– 11·97) Slovakia 15·65 (15·33– 15·99) 11·39 (10·22– 12·44) 12·14 (11·83– 12·46) 8·74 (7·76– 9·58) 17·30 (16·98– 17·65) 12·75 (11·47– 13·88) 13·46 (13·14– 13·76) 9·76 (8·70– 10·71) 18·79 (17·84– 19·81) 13·84 (12·34– 15·36) 15·07 (14·16– 16·02) 10·90 (9·55– 12·23) Slovenia 16·98 (16·32– 17·66) 12·11 (10·74– 13·47) 13·24 (12·67– 13·90) 9·42 (8·27– 10·46) 19·67 (19·06– 20·38) 14·01 (12·47– 15·47) 15·53 (14·86– 16·18) 10·97 (9·67– 12·25) 21·28 (20·30– 22·41) 15·12 (13·28– 16·79) 17·51 (16·48– 18·62) 12·44 (10·88– 14·05) Central Asia 15·86 (15·55– 16·14) 11·82 (10·69– 12·85) 12·81 (12·53– 13·07) 9·64 (8·74– 10·45) 15·03 (14·74– 15·32) 11·29 (10·24– 12·21) 12·04 (11·76– 12·30) 9·13 (8·31– 9·91) 16·82 (16·31– 17·33) 12·58 (11·34– 13·68) 13·51 (13·06– 14·02) 10·21 (9·17– 11·16) Armenia 16·36 (15·90– 16·84) 12·21 (11·01– 13·31) 13·96 (13·48– 14·47) 10·43 (9·38– 11·39) 16·46 (15·89– 16·98) 12·35 (11·12– 13·46) 13·65 (13·18– 14·16) 10·26 (9·26– 11·21) 18·31 (17·42– 19·33) 13·70 (12·15– 15·07) 14·76 (14·01– 15·62) 11·09 (9·93– 12·20) Azerbaijan 15·87 (15·41– 16·47) 11·96 (10·82– 13·07) 12·45 (12·04– 12·87) 9·48 (8·59– 10·34) 14·90 (14·14– 15·75) 11·31 (10·09– 12·42) 12·22 (11·59– 12·93) 9·37 (8·51– 10·26) 17·26 (15·65– 18·62) 12·98 (11·35– 14·41) 13·75 (12·53– 15·42) 10·48 (9·13– 12·11) (Table 3 continues on next page) Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1307 1990, at age 65 years 2006, at age 65 years 2016, at age 65 years Females Males Females Males Females Males Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE (Continued from previous page) Burkina Faso 12·40 (11·91– 13·17) 8·98 (7·91– 10·05) 12·23 (11·74– 13·15) 8·77 (7·71– 9·88) 12·97 (12·44– 13·46) 9·62 (8·60– 10·58) 12·76 (12·43– 13·05) 9·39 (8·42– 10·30) 13·30 (12·88– 13·77) 9·97 (9·00– 10·91) 13·17 (12·89– 13·50) 9·83 (8·88– 10·73) Cameroon 13·39 (12·57– 14·32) 9·84 (8·63– 10·89) 13·20 (12·62– 14·04) 9·66 (8·58– 10·75) 13·02 (11·61– 14·67) 9·67 (8·30– 11·14) 12·33 (11·78– 12·90) 9·08 (8·12– 9·98) 14·03 (12·41– 16·13) 10·55 (8·94– 12·39) 12·92 (12·13– 13·78) 9·61 (8·58– 10·65) Cape Verde 16·82 (16·36– 17·50) 12·57 (11·34– 13·76) 13·64 (13·30– 14·20) 10·22 (9·26– 11·18) 18·52 (17·54– 19·66) 13·89 (12·35– 15·40) 13·37 (12·54– 15·40) 10·05 (8·97– 11·50) 18·81 (18·11– 20·06) 14·20 (12·74– 15·70) 13·99 (13·16– 15·03) 10·55 (9·38– 11·70) Chad 13·10 (12·56– 13·70) 9·58 (8·49– 10·56) 12·80 (12·43– 13·23) 9·33 (8·30– 10·25) 13·59 (12·63– 14·50) 9·91 (8·71– 11·12) 12·52 (11·98– 13·13) 9·12 (8·10– 10·07) 14·31 (13·21– 15·24) 10·53 (9·28– 11·82) 13·14 (12·60– 13·96) 9·64 (8·60– 10·67) Côte d’Ivoire 12·50 (11·87– 13·22) 8·95 (7·86– 10·06) 11·99 (11·62– 12·35) 8·59 (7·60– 9·49) 12·16 (11·28– 13·45) 9·02 (7·95– 10·20) 11·80 (11·06– 12·26) 8·66 (7·65– 9·57) 12·99 (12·23– 14·00) 9·68 (8·59– 10·82) 12·58 (11·69– 13·06) 9·29 (8·14– 10·21) The Gambia 13·49 (12·43– 14·56) 9·92 (8·68– 11·15) 13·98 (13·17– 14·71) 10·38 (9·17– 11·49) 14·45 (13·37– 15·71) 10·63 (9·35– 12·04) 13·74 (13·29– 14·36) 10·19 (9·10– 11·18) 14·90 (14·03– 16·33) 11·07 (9·80– 12·57) 14·50 (13·75– 15·08) 10·81 (9·61– 11·81) Ghana 12·72 (11·81– 13·84) 9·50 (8·43– 10·65) 13·03 (12·47– 13·83) 9·68 (8·64– 10·76) 13·55 (12·53– 15·05) 10·28 (9·00– 11·72) 12·93 (12·69– 13·35) 9·70 (8·75– 10·56) 14·31 (13·63– 15·75) 10·88 (9·79– 12·28) 13·93 (13·19– 14·59) 10·44 (9·34– 11·45) Guinea 12·55 (11·79– 13·20) 9·24 (8·22– 10·27) 13·69 (13·08– 14·38) 10·07 (8·99– 11·13) 12·77 (11·85– 13·85) 9·53 (8·39– 10·57) 12·78 (12·21– 13·55) 9·54 (8·47– 10·54) 13·00 (12·02– 14·44) 9·77 (8·61– 11·03) 13·10 (12·52– 13·93) 9·80 (8·72– 10·84) Guinea-Bissau 11·17 (10·40– 12·25) 8·28 (7·27– 9·32) 10·81 (10·12– 11·46) 7·95 (7·04– 8·81) 11·67 (10·81– 13·00) 8·68 (7·64– 9·85) 10·83 (10·08– 11·85) 8·02 (7·10– 9·02) 12·43 (11·57– 13·29) 9·32 (8·21– 10·33) 11·07 (10·36– 12·17) 8·24 (7·28– 9·25) Liberia 12·87 (12·19– 13·55) 9·19 (8·12– 10·24) 13·32 (12·76– 13·88) 9·44 (8·35– 10·51) 12·85 (11·97– 14·00) 9·26 (8·08– 10·42) 13·39 (12·82– 14·01) 9·62 (8·49– 10·70) 13·39 (12·92– 14·05) 9·80 (8·69– 10·83) 13·87 (13·47– 14·35) 10·11 (9·03– 11·10) Mali 11·41 (10·78– 12·08) 8·45 (7·48– 9·39) 12·64 (12·18– 13·24) 9·22 (8·21– 10·21) 13·50 (12·59– 14·36) 10·03 (8·88– 11·14) 14·23 (13·49– 14·98) 10·45 (9·31– 11·53) 14·28 (12·68– 15·69) 10·70 (9·24– 12·11) 14·69 (13·35– 16·01) 10·91 (9·54– 12·34) Mauritania 12·41 (11·70– 13·18) 9·15 (8·13– 10·19) 13·14 (12·66– 13·85) 9·59 (8·50– 10·66) 14·11 (13·03– 15·46) 10·45 (9·16– 11·71) 15·56 (14·62– 16·62) 11·41 (10·07– 12·76) 15·12 (13·82– 17·09) 11·31 (9·75– 13·01) 16·12 (14·81– 17·86) 11·90 (10·35– 13·61) Niger 13·13 (12·55– 13·58) 9·80 (8·78– 10·74) 12·73 (12·10– 13·48) 9·45 (8·40– 10·48) 13·86 (12·92– 14·49) 10·38 (9·23– 11·42) 13·34 (12·71– 14·10) 9·97 (8·96– 10·96) 14·14 (12·56– 15·46) 10·68 (9·22– 12·03) 13·47 (12·69– 14·49) 10·11 (9·00– 11·35) Nigeria 13·88 (12·99– 14·54) 10·20 (9·05– 11·27) 14·32 (13·34– 15·44) 10·51 (9·27– 11·90) 15·00 (14·02– 16·49) 11·09 (9·84– 12·49) 14·95 (13·99– 16·27) 11·05 (9·81– 12·48) 16·78 (15·59– 18·56) 12·49 (10·97– 14·15) 16·32 (14·96– 17·83) 12·14 (10·60– 13·64) São Tomé and Príncipe 14·67 (13·71– 15·41) 11·04 (9·88– 12·18) 14·05 (13·33– 14·80) 10·41 (9·27– 11·53) 13·91 (13·52– 14·37) 10·51 (9·54– 11·41) 13·97 (13·51– 14·51) 10·38 (9·32– 11·34) 15·21 (14·52– 16·23) 11·55 (10·38– 12·75) 14·59 (13·22– 15·99) 10·89 (9·37– 12·43) Senegal 13·25 (12·57– 13·91) 9·89 (8·85– 10·95) 12·63 (12·28– 13·06) 9·37 (8·38– 10·26) 13·28 (12·72– 14·02) 9·96 (8·93– 11·01) 13·02 (12·82– 13·40) 9·69 (8·68– 10·58) 13·86 (13·33– 14·26) 10·42 (9·40– 11·37) 13·62 (13·00– 14·31) 10·17 (9·08– 11·22) Sierra Leone 13·64 (13·11– 14·12) 10·11 (9·03– 11·09) 12·33 (11·78– 12·96) 9·01 (7·96– 9·99) 12·39 (11·59– 13·22) 9·17 (8·13– 10·23) 12·03 (11·70– 12·50) 8·79 (7·85– 9·68) 12·93 (11·99– 14·20) 9·70 (8·58– 10·91) 12·86 (12·42– 13·49) 9·52 (8·46– 10·54) Togo 13·00 (12·26– 13·76) 9·63 (8·59– 10·69) 12·81 (12·34– 13·30) 9·47 (8·41– 10·42) 13·60 (12·35– 15·28) 10·15 (8·83– 11·60) 12·22 (11·58– 12·65) 9·10 (8·16– 10·03) 13·81 (13·12– 15·13) 10·44 (9·27– 11·72) 12·82 (11·89– 13·45) 9·64 (8·58– 10·61) Eastern sub-Saharan Africa 11·12 (10·88– 11·37) 8·41 (7·63– 9·10) 11·42 (11·16– 11·72) 8·54 (7·69– 9·31) 12·35 (11·95– 12·78) 9·37 (8·48– 10·18) 12·29 (12·03– 12·52) 9·23 (8·34– 10·02) 13·67 (13·20– 14·20) 10·43 (9·47– 11·34) 13·18 (12·71– 13·64) 9·94 (8·96– 10·81) (Table 3 continues on next page) Global Health Metrics 1308 www.thelancet.com Vol 390 September 16, 2017 1990, at age 65 years 2006, at age 65 years 2016, at age 65 years Females Males Females Males Females Males Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE (Continued from previous page) Burundi 9·43 (8·73– 10·38) 7·25 (6·51– 8·09) 9·51 (8·67– 10·67) 7·21 (6·37– 8·26) 11·02 (10·25– 11·74) 8·49 (7·62– 9·38) 11·46 (10·48– 12·32) 8·73 (7·60– 9·77) 12·09 (11·16– 13·02) 9·39 (8·35– 10·40) 12·29 (11·00– 13·34) 9·42 (8·29– 10·56) Comoros 12·25 (11·44– 13·47) 9·26 (8·20– 10·46) 12·63 (12·08– 13·35) 9·35 (8·37– 10·29) 13·82 (12·96– 14·96) 10·53 (9·39– 11·83) 13·71 (13·27– 14·51) 10·31 (9·29– 11·38) 14·31 (13·57– 15·90) 10·96 (9·83– 12·39) 14·28 (13·44– 15·15) 10·79 (9·64– 11·90) Djibouti 14·87 (14·23– 15·29) 11·27 (10·14– 12·31) 12·89 (12·58– 13·36) 9·75 (8·81– 10·66) 14·72 (12·98– 16·44) 11·21 (9·45– 12·88) 13·05 (12·38– 13·73) 9·86 (8·78– 10·82) 15·34 (13·92– 17·48) 11·73 (10·11– 13·71) 13·91 (12·86– 15·01) 10·52 (9·34– 11·78) Eritrea 10·29 (9·80– 10·84) 7·80 (7·00– 8·61) 10·64 (9·97– 11·34) 7·99 (7·13– 8·88) 12·03 (11·31– 12·92) 9·19 (8·18– 10·21) 12·61 (11·58– 13·31) 9·50 (8·41– 10·50) 12·49 (11·73– 13·45) 9·61 (8·63– 10·69) 12·75 (11·68– 13·73) 9·67 (8·49– 10·76) Ethiopia 9·54 (9·06– 10·09) 7·25 (6·56– 7·97) 9·78 (9·24– 10·50) 7·35 (6·56– 8·14) 11·41 (10·61– 12·18) 8·73 (7·79– 9·69) 12·47 (11·67– 12·84) 9·43 (8·46– 10·31) 13·26 (12·20– 14·32) 10·18 (9·02– 11·38) 13·32 (12·03– 14·75) 10·12 (8·80– 11·52) Kenya 13·41 (13·09– 13·74) 10·31 (9·40– 11·15) 13·74 (13·38– 14·10) 10·44 (9·47– 11·32) 14·05 (13·65– 14·44) 10·79 (9·84– 11·66) 13·14 (12·83– 13·42) 9·97 (9·05– 10·78) 15·37 (14·93– 15·85) 11·79 (10·73– 12·79) 14·14 (13·81– 14·51) 10·69 (9·71– 11·58) Madagascar 11·92 (11·31– 12·61) 9·05 (8·19– 10·01) 12·13 (11·79– 12·43) 9·11 (8·22– 9·93) 12·57 (11·72– 13·83) 9·61 (8·49– 10·80) 12·78 (12·19– 13·40) 9·66 (8·70– 10·60) 13·10 (11·71– 15·54) 10·11 (8·76– 12·00) 13·05 (11·51– 14·31) 9·93 (8·66– 11·13) Malawi 12·06 (10·88– 13·26) 9·11 (7·97– 10·40) 13·32 (11·80– 16·19) 9·89 (8·35– 12·03) 12·63 (10·45– 14·93) 9·63 (7·73– 11·73) 11·51 (10·27– 13·33) 8·57 (7·39– 10·06) 13·97 (12·43– 16·42) 10·67 (9·21– 12·67) 12·90 (11·40– 14·06) 9·70 (8·43– 10·99) Mozambique 12·71 (12·07– 13·26) 9·51 (8·52– 10·49) 11·86 (11·50– 12·31) 8·76 (7·87– 9·60) 14·52 (12·48– 16·02) 10·81 (8·97– 12·42) 11·94 (10·91– 12·86) 8·76 (7·56– 9·86) 14·76 (13·22– 16·48) 11·05 (9·57– 12·65) 13·01 (11·80– 14·04) 9·57 (8·27– 10·72) Rwanda 10·17 (9·56– 10·82) 7·74 (6·92– 8·55) 10·69 (9·78– 11·65) 8·06 (7·08– 9·05) 14·05 (12·83– 15·21) 10·69 (9·41– 11·91) 13·03 (12·64– 13·77) 9·82 (8·87– 10·78) 15·00 (13·91– 16·44) 11·48 (10·13– 12·98) 14·27 (13·24– 15·18) 10·77 (9·57– 11·89) Somalia 10·21 (9·54– 10·86) 7·76 (6·89– 8·58) 11·58 (10·94– 12·19) 8·72 (7·78– 9·63) 10·34 (9·69– 11·08) 7·87 (7·03– 8·86) 11·59 (10·64– 12·32) 8·77 (7·76– 9·78) 10·85 (10·08– 11·56) 8·29 (7·35– 9·17) 11·93 (10·81– 12·75) 9·03 (7·87– 10·03) South Sudan 12·45 (11·38– 13·56) 8·77 (7·43– 10·01) 12·70 (11·77– 13·88) 8·89 (7·68– 10·13) 12·72 (11·39– 14·54) 9·21 (7·81– 10·90) 12·72 (11·88– 13·89) 9·13 (7·99– 10·42) 12·86 (11·61– 14·69) 9·50 (8·22– 11·12) 12·94 (12·02– 13·95) 9·44 (8·33– 10·58) Tanzania 12·46 (11·77– 13·34) 9·43 (8·40– 10·53) 12·65 (12·07– 13·54) 9·49 (8·47– 10·54) 13·15 (11·96– 15·19) 10·01 (8·70– 11·77) 12·80 (12·36– 13·65) 9·66 (8·66– 10·64) 14·10 (13·34– 15·98) 10·80 (9·62– 12·29) 13·64 (12·43– 14·45) 10·38 (9·17– 11·42) Uganda 10·95 (10·45– 11·64) 8·14 (7·30– 9·03) 11·31 (10·58– 12·11) 8·27 (7·27– 9·25) 12·01 (11·33– 12·82) 9·01 (8·03– 10·01) 11·81 (10·83– 12·44) 8·76 (7·74– 9·66) 13·61 (12·73– 15·05) 10·35 (9·21– 11·69) 12·79 (11·72– 13·52) 9·58 (8·43– 10·61) Zambia 11·59 (10·67– 13·24) 8·88 (7·83– 10·24) 14·29 (13·12– 15·19) 10·74 (9·49– 11·99) 10·41 (9·17– 12·23) 7·93 (6·83– 9·42) 9·47 (8·91– 10·43) 7·12 (6·34– 7·99) 13·01 (11·11– 16·03) 9·95 (8·29– 12·45) 10·99 (9·92– 12·90) 8·27 (7·19– 9·81) Central sub-Saharan Africa 11·66 (11·23– 12·15) 8·47 (7·56– 9·35) 11·32 (11·00– 11·66) 8·14 (7·24– 8·98) 11·65 (11·24– 12·11) 8·56 (7·67– 9·40) 12·10 (11·81– 12·39) 8·80 (7·89– 9·69) 12·76 (12·32– 13·22) 9·46 (8·51– 10·35) 12·88 (12·42– 13·25) 9·46 (8·45– 10·39) Angola 11·07 (9·98– 12·74) 8·23 (7·17– 9·58) 10·78 (9·56– 11·90) 7·98 (6·87– 9·08) 11·57 (10·28– 13·28) 8·67 (7·46– 10·05) 12·53 (11·28– 13·71) 9·30 (8·12– 10·51) 13·33 (11·70– 15·06) 9·99 (8·59– 11·47) 13·62 (11·94– 15·00) 10·11 (8·69– 11·47) Central African Republic 10·33 (9·69– 11·07) 7·60 (6·71– 8·45) 9·17 (8·60– 9·84) 6·70 (5·91– 7·52) 10·17 (8·73– 12·21) 7·56 (6·31– 9·11) 8·94 (8·24– 10·36) 6·58 (5·71– 7·76) 10·65 (9·27– 12·61) 7·97 (6·73– 9·62) 9·38 (8·61– 10·77) 6·95 (6·09– 8·16) Congo (Brazzaville) 10·23 (9·47– 11·12) 7·54 (6·62– 8·50) 9·93 (9·32– 10·88) 7·27 (6·36– 8·22) 11·08 (10·24– 12·10) 8·21 (7·19– 9·20) 12·97 (12·28– 13·52) 9·54 (8·50– 10·51) 12·36 (11·03– 13·94) 9·22 (7·89– 10·70) 13·89 (12·35– 15·53) 10·32 (8·97– 11·90) (Table 3 continues on next page) Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1309 two countries for females (Lesotho and South Africa) and three for males (Lesotho, South Africa, and Swaziland). In 2016, HALE at age 65 years was highest in Singapore for males (15·1 years [13·4–16·8]) and Japan for females (18·7 years [17·1–20·0]), whereas it was lowest for males in Lesotho (6·9 years [6·0–7·8]) and females in Afghanistan (7·4 years [6·5–8·3]). The leading Level 3 causes of DALYs in 2016 varied by country, but with regional commonalities, described on the basis of GBD regions12 in figure 3. Across the 46 countries within sub-Saharan Africa, the leading Level 3 cause of DALYs for females was either HIV/AIDS (17 countries), malaria (13 countries), or diarrhoeal diseases (nine countries). Cerebrovascular disease, ischaemic heart disease, and low back and neck pain were the most common leading Level 3 causes of all-age DALYs for females in all GBD regions except for Oceania, Andean Latin America and central Latin America, and all regions of sub-Saharan Africa. Among the 34 countries in the high-income super-region, low back and neck pain was the leading cause of DALYs for females in all but three countries. Exceptions within the region were Greece and the USA, for which ischaemic heart disease was the leading cause of DALYs, and Greenland, where self-harm was the highest cause of DALYs for females (and for males). For males, these regional commonalities also occurred, but with some notable differences in the leading causes by location compared with females. Leading causes of DALYs for males in sub-Saharan Africa were broadly similar to those for females: HIV/AIDS (16 countries), malaria (11 countries), or lower respiratory infections (nine countries). Cerebrovascular disease or ischaemic heart disease was the leading cause of all-age DALYs for males in almost all GBD regions except for Andean Latin America, central Latin America, tropical Latin America, high-income Asia Pacific, and all regions of sub-Saharan Africa. Exceptions within the regions included Greenland, where self-harm was the leading cause of DALYs for males in 2016, Peru, where lower respiratory infection was the leading cause, and Nicaragua, where chronic kidney disease was the leading cause. The leading cause of DALYs for both males and females in Syria and Iraq was conflict and terrorism, reflecting the ongoing conflict. Epidemiological transition The shift in the global burden of DALYs from CMNN to NCDs is a clear indicator of the epidemiological transition.21 The ratios of DALYs from NCDs to those from CMNN diseases in 1990, 1995, 2000, 2006, 2010, and 2016 are shown by SDI and at the global, GBD regional, and GBD super-regional scales for both males and females in figure 4. In 2016, 17 (of 21) GBD regions and six (of seven) GBD super-regions had a ratio of more than 1 for either men or women, representing the shift to more DALYs from NCDs than from CMNN diseases, compared with 13 regions and five super-regions in 1990. In 2016, females had more DALYs due to NCDs relative to CMNN causes than did males, with higher ratios in 14 regions. This figure also shows how these ratios shift with increasing SDI, with 18·5 as the 2016 ratio for high SDI for men and 20·5 for women and 0·52 for men in low SDI and 0·57 for women. Several regions showed large shifts toward more NCD DALYs than CMNN DALYs, with 12 regions doubling in ratio from 1990 to 2016 for females and 11 regions doubling for males over the same time period. Expected age-standardised YLL rates decreased substantially with increasing SDI, most notably because of decreases in CMNN causes, such as diarrhoea, lower respiratory infections, and other common infectious diseases; NTDs and malaria; neonatal disorders; nutritional deficiencies; and HIV/AIDS and tuberculosis (figure 5A). Several causes had their largest expected agestandardised rates at intermediate levels of SDI, such as diabetes, urogenital, blood, and endocrine diseases; cardio vascular diseases; and chronic respiratory diseases. Causes that increased in expected age-standardised YLL rates with increasing SDI included neoplasms and neurological disorders. Much less variation was estimated 1990, at age 65 years 2006, at age 65 years 2016, at age 65 years Females Males Females Males Females Males Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE Life expectancy HALE (Continued from previous page) Democratic Republic of the Congo 12·12 (11·58– 12·81) 8·73 (7·67– 9·72) 12·06 (11·75– 12·46) 8·55 (7·57– 9·47) 11·83 (11·37– 12·42) 8·65 (7·71– 9·57) 12·25 (11·94– 12·49) 8·84 (7·89– 9·74) 12·78 (12·31– 13·29) 9·45 (8·42– 10·39) 12·92 (12·42– 13·33) 9·46 (8·43– 10·38) Equatorial Guinea 9·99 (9·12– 11·11) 7·42 (6·43– 8·39) 9·97 (8·98– 11·29) 7·37 (6·34– 8·47) 13·95 (11·33– 16·90) 10·37 (8·18– 12·78) 14·22 (12·47– 16·55) 10·46 (8·83– 12·43) 15·71 (13·12– 19·29) 11·71 (9·45– 14·15) 15·35 (13·29– 18·00) 11·33 (9·53– 13·46) Gabon 12·61 (11·83– 13·42) 9·26 (8·22– 10·27) 10·76 (10·09– 11·80) 7·93 (7·01– 8·93) 12·21 (11·38– 13·06) 8·98 (7·94– 10·04) 13·15 (11·99– 13·91) 9·72 (8·51– 10·79) 14·23 (13·07– 16·01) 10·51 (9·24– 11·89) 14·30 (12·61– 16·05) 10·62 (9·01– 12·20) Data in parentheses are 95% uncertainty intervals. To download the data in this table, please visit the Global Health Data Exchange. GBD=Global Burden of Disease. HALE=healthy life expectancy. SDI=Socio-demographic index. Table 3: Global, regional, and GBD location-specific life expectancy and HALE at age 65 years, by sex, in 1990, 2006, and 2016 Global Health Metrics 1310 www.thelancet.com Vol 390 September 16, 2017 for expected age-standardised YLD rates with SDI than for YLL rates. Although the relationship between disability and SDI was generally constant for most Level 2 causes, nutritional deficiencies, NTDs, and malaria resulted in greater than expected levels of disability at lower SDI. Compared with YLLs and YLDs expressed as all-age rates, the consequences of differences in population age structure become apparent for a number of causes (figure 5B). Cardiovascular diseases, in particular, result in greater YLLs at higher SDI in terms of all-age rates, reflecting the older age of populations at higher levels of SDI. By contrast, the pattern observed when standardising for age structure—YLLs accruing at intermediate SDI—highlights health loss from diseases that typically manifest at older ages undistorted by the lower overall age structure of the populations. The expected all-age YLD rates generally increased with rising SDI, particularly for mental and substance use disorders and musculoskeletal disorders, although variation with SDI remained substantially lower than for YLLs. Figure 1: Trends of total DALYs (A) and age-standardised DALY rates (B) from 1990 to 2016 for GBD Level 1 cause groups by SDI quintile Shaded areas show 95% uncertainty intervals. DALYs=disability-adjusted life-years. GBD=Global Burden of Disease. SDI=Socio-demographic Index. Low SDI 0 200 400 600 Year All-age total DALYs (millions) A B 1990 1995 2000 2006 2010 2016 Year 1990 1995 2000 2006 2010 2016 Year 1990 1995 2000 2006 2010 2016 Year 1990 1995 2000 2006 2010 2016 Year 1990 1995 2000 2006 2010 2016 0 20 000 40 000 60 000 Age-standardised DALY rate per 100 000 High SDIHigh-middle SDIMiddle SDILow-middle SDI Communicable, maternal, neonatal, and nutritional diseases Non-communicable diseases Injuries Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1311 (Figure 2 continues on next page) Leading causes 1990 Leading causes 2006 Mean % change, number of DALYs 1990–2006 Mean % change, all-age DALY rate 1990–2006 Mean % change, agestandardised DALY rate 1990–2006 Leading causes 2016 1 Lower respiratory infection 1 Ischaemic heart disease 1 Ischaemic heart disease 2 Diarrhoeal diseases 2 Lower respiratory infection 2 Cerebrovascular disease 3 Ischaemic heart disease 3 Diarrhoeal diseases 3 Lower respiratory infection 4 Neonatal preterm birth 4 Cerebrovascular disease 4 Low back and neck pain 5 Cerebrovascular disease 5 HIV/AIDS 5 Diarrhoeal diseases 6 Measles 6 Neonatal preterm birth 6 Road injuries 7 Congenital defects 7 Malaria 7 Sense organ diseases 8 Neonatal encephalopathy 8 Road injuries 8 COPD 9 Tuberculosis 9 Low back and neck pain 9 Neonatal preterm birth 10 Road injuries 10 Neonatal encephalopathy 10 HIV/AIDS 11 Malaria 11 COPD 11 Skin diseases 12 COPD 12 Congenital defects 12 Diabetes 13 Low back and neck pain 13 Tuberculosis 13 Malaria 14 Other neonatal 14 Sense organ diseases 14 Congenital defects 15 Skin diseases 15 Skin diseases 15 Neonatal encephalopathy 16 Sense organ diseases 16 Diabetes 16 Migraine 17 Self-harm 17 Migraine 17 Depressive disorders 18 Protein-energy malnutrition 18 Depressive disorders 18 Tuberculosis 19 Drowning 19 Self-harm 19 Lung cancer 20 Meningitis 20 Other neonatal 20 Falls 21 Migraine 21 Iron-deficiency anaemia 21 Self-harm 22 Depressive disorders 22 Lung cancer 22 Chronic kidney disease 23 Diabetes 23 Falls 23 Iron-deficiency anaemia 24 Iron-deficiency anaemia 24 Chronic kidney disease 24 Other musculoskeletal 25 Falls 25 Other musculoskeletal 25 Alzheimer's disease 26 Tetanus 26 Protein-energy malnutrition 26 Anxiety disorders 27 Neonatal sepsis 27 Neonatal sepsis 27 Other neonatal 28 Lung cancer 28 Meningitis 28 Asthma 29 Asthma 29 Interpersonal violence 29 Neonatal sepsis 30 Chronic kidney disease 30 Anxiety disorders 30 Interpersonal violence 31 Drowning31 Interpersonal violence 31 Meningitis 32 Asthma33 Other musculoskeletal 33 Protein-energy malnutrition 33 Alzheimer's disease34 Anxiety disorders 38 Drowning 34 Measles36 HIV/AIDS 85 Measles 79 Tetanus43 Alzheimer's disease 116 Tetanus 1 Lower respiratory infection 1 Malaria 1 Lower respiratory infection 2 Diarrhoeal diseases 2 Diarrhoeal diseases 2 Malaria 3 Malaria 3 Lower respiratory infection 3 Diarrhoeal diseases 4 Measles 4 HIV/AIDS 4 HIV/AIDS 5 Protein-energy malnutrition 5 Neonatal preterm birth 5 Neonatal preterm birth 6 Neonatal preterm birth 6 Neonatal encephalopathy 6 Neonatal encephalopathy 7 Tuberculosis 7 Tuberculosis 7 Tuberculosis 8 Neonatal encephalopathy 8 Protein-energy malnutrition 8 Ischaemic heart disease 9 Meningitis 9 Congenital defects 9 Congenital defects 10 Congenital defects 10 Meningitis 10 Protein-energy malnutrition 11 HIV/AIDS 11 Neonatal sepsis 11 Meningitis 12 Neonatal sepsis 12 Ischaemic heart disease 12 Neonatal sepsis 13 Other neonatal 13 Other neonatal 13 Cerebrovascular disease 14 STDs 14 Cerebrovascular disease 14 Road injuries 15 Ischaemic heart disease 15 STDs 15 Other neonatal 16 Tetanus 16 Road injuries 16 Iron-deficiency anaemia 17 Cerebrovascular disease 17 Iron-deficiency anaemia 17 STDs 18 Conflict and terror 18 Measles 18 Skin diseases 19 Road injuries 19 Skin diseases 19 Low back and neck pain 20 Whooping cough 20 Whooping cough 20 Sense organ diseases 21 Iron-deficiency anaemia 21 Sense organ diseases 21 Diabetes 22 Skin diseases 22 Low back and neck pain 22 Depressive disorders 23 Low back and neck pain 23 COPD 23 Conflict and terror 24 Sense organ diseases 24 Diabetes 24 COPD 25 Drowning 25 Drowning 25 Migraine 26 COPD 26 Depressive disorders 26 Whooping cough 27 Hemoglobinopathies 27 Asthma 27 Asthma 28 Asthma 28 Haemoglobinopathies 28 Falls 29 Falls 29 Falls 29 Haemoglobinopathies 30 Depressive disorders 30 Migraine 30 Drowning 33 Tetanus32 Diabetes 34 Measles 49 Conflict and terror34 Migraine 60 Tetanus B A 13·7 4·0 –29·9 19·3 –34·7 –3·9 21·7 6·5 –23·6 –43·7 11·3 24·4 –27·3 –13·5 –22·0 14·3 13·2 –23·4 13·7 14·4 –6·3 20·0 7·5 14·1 37·5 13·1 –29·2 3·9 –8·5 –2·3 1·1 –7·5 –37·6 6·1 –41·9 –14·5 8·2 –5·3 –32·0 –49·9 –1·0 10·7 –35·3 –23·0 –30·7 1·6 0·7 –31·9 1·1 1·8 –16·7 6·8 –4·4 1·5 22·3 0·6 –37·0 –7·6 –18·6 –13·1 –11·9 –19·2 –34·4 –1·3 –39·9 –14·7 –1·9 –17·9 –24·1 –50·0 1·4 –1·7 –30·7 –16·2 –22·0 0·1 –3·6 –35·3 –11·7 –4·6 –17·7 –3·0 –1·8 –3·7 0·2 –0·7 –29·0 –12·2 –9·0 –12·0 –21·3 –32·4 –28·9 –44·5 –5·7 0·6 –10·5 24·3 8·1 –17·0 –4·2 3·3 18·6 15·2 –7·0 21·3 1·7 35·8 36·9 28·8 38·2 32·5 214·0 16·4 33·8 –26·0 15·6 17·2 6·5 –2·4 –40·5 –48·9 –46·2 –58·0 –28·7 –24·0 –32·3 –6·1 –18·3 –37·3 –27·6 –22·0 –10·3 –12·9 –29·7 –8·3 –23·2 2·7 3·4 –2·7 4·4 0·1 137·3 –12·0 1·2 –44·0 –12·7 –11·4 –19·5 –26·2 –31·3 –46·2 –38·4 –60·6 –24·2 –19·3 –32·2 –6·7 –14·0 –32·9 –22·3 –16·9 –10·3 –8·9 –25·9 –7·6 –19·1 3·1 1·3 –3·7 4·0 –2·3 119·8 –12·9 –0·5 –42·5 –15·0 –6·6 –15·0 –21·1 29·0 –35·3 –35·0 17·4 532·5 –28·0 27·9 14·7 29·8 –11·6 –0·4 –14·8 –16·4 39·0 24·6 67·4 32·3 32·4 4·0 –15·8 19·7 31·3 17·5 35·1 42·0 –26·3 5·3 –17·5 15·1 30·6 3·3 –48·2 –47·9 –6·0 406·5 –42·4 2·4 –8·2 3·9 –29·2 –20·2 –31·8 –33·1 11·3 –0·2 34·0 5·9 6·0 –16·7 –32·6 –4·2 5·2 –5·9 8·2 13·7 –41·0 –15·7 –33·9 –7·8 4·6 –13·0 –37·5 –39·3 –18·6 394·4 –28·8 23·2 –7·6 –7·4 –12·0 –30·9 –18·1 –38·1 0·1 1·8 16·8 –0·4 –1·7 –19·8 –16·0 1·5 –8·4 –9·9 –1·2 4·4 –28·9 4·1 –23·2 –8·4 –0·1 –19·2 –39·3 –42·6 186·2 –28·1 –19·6 –25·8 –43·7 –25·2 –33·2 –21·1 0·3 –28·6 –18·8 –32·3 –15·4 –10·0 –87·8 2·0 –27·5 –1·3 –2·5 –18·5 5·8 –27·0 –0·1 –24·2 –29·5 –20·1 1·3 –13·2 –30·2 –33·5 194·0 –21·3 –12·4 –22·9 –38·8 –18·7 –26·9 –13·8 1·9 –22·4 –14·4 –27·1 –9·9 –9·8 –87·1 1·7 –23·3 0·1 –2·2 –16·6 9·2 –22·0 –0·4 –22·1 –23·9 –13·2 0·5 27·7 -4·0 -9·2 352·5 13·7 27·1 -10·9 17·3 18·3 5·7 24·7 58·5 13·0 28·4 7·0 33·7 42·3 -80·7 61·3 14·7 56·1 54·1 28·8 67·3 15·4 57·9 19·9 11·4 26·3 60·2 Communicable, maternal, neonatal, and nutritional diseases Non-communicable diseases Injuries Mean % change, number of DALYs 2006–16 Mean % change, all-age DALY rate 2006–16 Mean % change, agestandardised DALY rate 2006–16 Global Health Metrics 1312 www.thelancet.com Vol 390 September 16, 2017 1 Diarrhoeal diseases 1 Ischaemic heart disease 2 Lower respiratory infection 2 Diarrhoeal diseases 3 Neonatal preterm birth 3 Lower respiratory infection 4 HIV/AIDS 4 Neonatal preterm birth 5 Ischaemic heart disease 5 Cerebrovascular disease 6 Malaria 6 Malaria 7 Malaria 7 Neonatal encephalopathy 7 COPD 8 Other neonatal 8 Tuberculosis 8 Neonatal encephalopathy 9 Ischaemic heart disease 9 Cerebrovascular disease 9 HIV/AIDS 10 Congenital defects 10 Other neonatal 10 Road injuries 11 COPD 11 Congenital defects 11 Tuberculosis 12 Cerebrovascular disease 12 COPD 12 Iron-deficiency anaemia 13 Tetanus 13 Road injuries 13 Sense organ diseases 14 Protein-energy malnutrition 14 Iron-deficiency anaemia 14 Low back and neck pain 15 Iron-deficiency anaemia 15 Sense organ diseases 15 Congenital defects 16 Road injuries 16 Low back and neck pain 16 Skin diseases 17 Meningitis 17 Measles 17 Diabetes 18 Drowning 18 Skin diseases 18 Migraine 19 Intestinal infectious 19 Self-harm 19 Other neonatal 20 Sense organ diseases 20 Migraine 20 Depressive disorders 21 Neonatal sepsis 21 Diabetes 21 Self-harm 22 Low back andneck pain 22 Meningitis 22 Chronic kidney disease 23 Skin diseases 23 Neonatal sepsis 23 Falls 24 Self-harm 24 Protein-energy malnutrition 24 Neonatal sepsis 25 Asthma 25 Depressive disorders 25 Other musculoskeletal 26 Neonatal haemolytic 26 Intestinal infectious 26 Meningitis 27 Migraine 27 Falls 27 Asthma 28 Falls 28 Drowning 28 Protein-energy malnutrition 29 STDs 29 Chronic kidney disease 29 Intestinal infectious 30 Depressive disorders 30 Asthma 30 Anxiety disorders 31 Other musculoskeletal32 Chronic kidney disease 31 Drowning 32 STDs33 Diabetes 45 STDs 33 Anxiety disorders34 Other musculoskeletal 58 Measles 37 Neonatal haemolytic37 HIV/AIDS 59 Neonatal haemolytic 42 Tetanus42 Anxiety disorders 96 Tetanus 1 Lower respiratory infection 1 Ischaemic heart disease 1 Ischaemic heart disease 2 Cerebrovascular disease 2 Cerebrovascular disease 2 Cerebrovascular disease 3 Neonatal preterm birth 3 Road injuries 3 Road injuries 4 Diarrhoeal diseases 4 HIV/AIDS 4 Low back and neck pain 5 Ischaemic heart disease 5 Lower respiratory infectio 5 Sense organ diseases 6 Congenital defects 6 COPD 6 Diabetes 7 Road injuries 7 Low back and neck pain 7 COPD 8 COPD 8 Neonatal preterm birth 8 Skin diseases 9 Neonatal encephalopathy 9 Sense organ diseases 9 Lower respiratory infection 10 Tuberculosis 10 Congenital defects 10 HIV/AIDS 11 Low back and neck pain 11 Diabetes 11 Lung cancer 12 Drowning 12 Skin diseases 12 Chronic kidney disease 13 Self-harm 13 Tuberculosis 13 Congenital defects 14 Skin diseases 14 Lung cancer 14 Neonatal preterm birth 15 Sense organ diseases 15 Depressive disorders 15 Migraine 16 Diabetes 16 Migraine 16 Depressive disorders 17 Interpersonal violence 17 Diarrhoeal diseases 17 Liver cancer 18 Depressive disorders 18 Self-harm 18 Other musculoskeletal 19 Migraine 19 Chronic kidney disease 19 Self-harm 20 Other neonatal 20 Neonatal encephalopathy 20 Interpersonal violence 21 Chronic kidney disease 21 Liver cancer 21 Falls 22 Stomach cancer 22 Interpersonal violence 22 Alzheimer's disease 23 Measles 23 Other musculoskeletal 23 Tuberculosis 24 Liver cancer 24 Stomach cancer 24 Stomach cancer 25 Lung cancer 25 Falls 25 Anxiety disorders 26 Falls 26 Drowning 26 Diarrhoeal diseases 27 Asthma 27 Anxiety disorders 27 Iron-deficiency anaemia 28 Other musculoskeletal 28 Asthma 28 Hypertensive heart disease 29 Anxiety disorders 29 Iron-deficiency anaemia 29 Asthma 30 Iron-deficiency anaemia 30 Alzheimer's disease 30 Neonatal encephalopathy 31 Other neonatal32 Hypertensive heart disease 33 Drowning 32 Hypertensive heart disease42 Alzheimer's disease 46 Other neonatal 82 Measles98 HIV/AIDS 121 Measles Leading causes 2006 Mean % change, number of DALYs 1990–2006 Mean % change, all-age DALY rate 1990–2006 Mean % change, agestandardised DALY rate 1990–2006 Leading causes 2016 Mean % change, number of DALYs 2006–16 Mean % change all-age DALY rate 2006–16 Mean % change, agestandardised DALY rate 2006–16 D 28·3 –38·7 –38·2 –27·7 12·0 –21·5 18·1 –25·4 –45·0 9·3 –25·6 6·4 22·9 26·0 –10·4 17·1 31·3 20·7 –36·3 16·1 0·2 18·9 15·9 –12·6 16·5 –15·6 3·5 –27·2 –20·2 19·2 10·0 –47·4 –47·0 –38·0 –4·0 –32·7 1·2 –36·0 –52·8 –6·3 –36·2 –8·8 5·4 8·1 –23·2 0·4 12·6 3·5 –45·4 –0·4 –14·1 1·9 –0·6 –25·0 –0·1 –27·6 –11·2 –37·6 –31·6 2·2 –2·3 –42·7 –37·5 –26·5 –15·3 –25·6 –11·1 –23·5 –55·0 –6·7 –39·9 –6·8 –3·9 –0·1 –12·3 2·4 1·8 –0·1 –34·8 –6·7 –15·4 –4·9 –3·7 –11·2 –6·5 –20·7 –18·5 –30·1 –26·8 –0·8 23·9 6·5 –8·2 19·0 22·1 25·6 –3·7 4–4 –31·2 –45·0 19·7 20·5 –26·7 –30·4 12·1 11·1 12·4 11·2 –13·4 –3·9 15·1 44·0 –30·0 –5·5 9·8 –35·9 3·2 27·1 1·1 –42·1 15·4 –0·8 –14·5 10·8 13·7 17·0 –10·3 –2·7 –35·9 –48·7 11·6 12·2 –31·8 –35·2 4·4 3·5 4·7 3·6 –19·3 –10·4 7·2 34·2 –34·8 –12·0 2·3 –40·3 –3·8 18·4 –5·8 –46·1 –6·6 –20·4 –15·2 –1·1 –3·2 –3·2 –29·2 1·6 –33·5 –48·1 –9·9 –4·2 –21·3 –23·3 2·2 –4·9 –12·6 –4·7 –21·3 –8·1 –2·6 0·7 –41·3 –28·6 0·1 –38·4 –1·7 –4·9 –12·4 –34·5 –36·5 –33·9 –19·1 644·5 56·5 30·8 –3·0 –21·3 33·8 –16·1 5·9 9·7 38·3 21·4 43·9 42·4 –64·2 37·3 22·1 45·4 101·5 –17·3 4·5 –30·6 48·3 –19·3 20·6 –26·6 33·1 –9·6 –53·3 –51·4 –40·5 447·8 15·1 –3·8 –28·6 –42·1 –1·6 –38·3 –22·1 –19·3 1·8 –10·7 5·9 4·8 –73·7 1·0 –10·1 7·0 48·3 –39·1 –23·1 –48·9 9·1 –40·6 –11·3 –46·0 –2·1 –33·5 –44·3 –39·1 –26·1 431·9 2·6 11·8 –11·4 –46·0 –10·9 –23·4 –7·5 –28·4 3·3 –7·4 –3·1 –6·2 –68·7 2·9 –13·1 0·1 33·8 –29·9 –4·6 –39·2 1·3 –34·4 –9·3 –37·8 –7·0 –37·1 59·2 20·8 15·7 3408·5 –57·9 –10·7 34·2 –48·0 41·7 –29·4 82·0 17·8 –31·8 64·1 31·0 35·5 –64·2 –19·2 40·3 –42·9 38·7 8·1 42·0 7·4 20·2 –48·6 29·6 –1·3 15·6 74·5 32·4 0·5 –3·8 2817·9 –65·0 –25·7 11·6 –56·8 17·9 –41·3 51·3 –2·1 –43·3 36·4 9·0 12·7 –70·2 –32·8 16·7 –52·5 15·4 –10·1 18·1 –10·7 –0·1 –57·3 7·8 –17·9 –3·9 45·1 Communicable, maternal, neonatal, and nutritional diseases Non-communicable diseases Injuries 2·2 –21·2 –3·8 2736·7 –52·2 –42·7 –8·0 –38·2 –1·9 –19·5 19·6 2·3 –52·2 6·8 –3·4 2·3 –61·7 –35·6 –1·4 –30·5 –8·7 –10·8 2·7 –30·1 –6·0 –49·2 0·7 –23·9 2·4 0·0 1 Diarrhoeal diseases 2 Lower respiratory infection 3 Neonatal preterm birth 4 Measles 5 Tuberculosis 6 Neonatal encephalopathy Leading causes 1990 C (Figure 2 continues on next page) Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1313 1 Ischaemic heart disease 1 Ischaemic heart disease 1 Ischaemic heart disease 2 Cerebrovascular disease 2 Cerebrovascular disease 2 Cerebrovascular disease 3 Lower respiratory infection 3 Low back and neck pain 3 Low back and neck pain 4 Road injuries 4 Road injuries 4 Sense organ diseases 5 Low back and neck pain 5 Sense organ diseases 5 Road injuries 6 Neonatal preterm birth 6 Skin diseases 6 Skin diseases 7 Congenital defects 7 Depressive disorders 7 Diabetes 8 COPD 8 COPD 8 Depressive disorders 9 Sense organ diseases 9 Migraine 9 Migraine 10 Skin diseases 10 Lung cancer 10 Lung cancer 11 Lung cancer 11 Lower respiratory infection 11 Falls 12 Falls 12 Congenital defects 12 COPD 13 Self-harm 13 Diabetes 13 Alzheimer's disease 14 Migraine 14 Self-harm 14 Lower respiratory infection 15 Depressive disorders 15 Falls 15 Self-harm 16 Diabetes 16 Neonatal preterm birth 16 Congenital defects 17 Diarrhoeal diseases 17 Alcohol use disorders 17 Anxiety disorders 18 Stomach cancer 18 Interpersonal violence 18 Chronic kidney disease 19 Drowning 19 Drug use disorders 19 Alcohol use disorders 20 Interpersonal violence 20 Alzheimer's disease 20 Other musculoskeletal 21 Neonatal encephalopathy 21 Stomach cancer 21 Drug use disorders 22 Alcohol use disorders 22 Anxiety disorders 22 Neonatal preterm birth 23 Drug use disorders 23 Chronic kidney disease 23 Colorectal cancer 24 Anxiety disorders 24 Cardiomyopathy 24 Stomach cancer 25 Chronic kidney disease 25 Other musculoskeletal 25 Interpersonal violence 26 Tuberculosis 26 Colorectal cancer 26 Oral disorders 27 Alzheimer's disease 27 Liver cancer 27 Liver cancer 28 Asthma 28 Oral disorders 28 Cardiomyopathy 29 Other neonatal 29 Drowning 29 Osteoarthritis 30 Nature disaster 30 Tuberculosis 30 Breast cancer 31 Osteoarthritis 31 Other musculoskeletal 33 Asthma 32 Breast cancer 32 Liver cancer 40 Drowning 33 Asthma 33 Colorectal cancer 45 Tuberculosis 36 Neonatal encephalopathy 34 Cardiomyopathy 47 Neonatal encephalopathy 46 Other neonatal 35 Oral disorders 59 Diarrhoeal diseases 50 Diarrhoeal diseases 40 Breast cancer 62 Other neonatal 129 Nature disaster 42 Osteoarthritis 145 Nature disaster 1 Ischaemic heart disease 1 Ischaemic heart disease1 Ischaemic heart disease 2 Low back and neck pain 2 Low back and neck pain2 Low back and neck pain 3 Cerebrovascular disease 3 Cerebrovascular disease 3 Cerebrovascular disease 4 Road injuries 4 Lung cancer 4 Lung cancer 5 Lung cancer 5 Skin diseases 5 Alzheimer's disease 6 Skin diseases 6 Sense organ diseases 6 Sense organ diseases 7 Sense organ diseases 7 Diabetes 7 Skin diseases 8 Migraine 8 Alzheimer's disease 8 Diabetes 9 Depressive disorders 9 Depressive disorders9 COPD 10 Diabetes 10 Migraine 10 Depressive disorders 11 Self-harm 11 Road injuries 11 Migraine 12 COPD 12 COPD 12 Falls 13 Falls 13 Self-harm 13 Self-harm 14 Alzheimer's disease 14 Falls 14 Road injuries 15 Colorectal cancer 15 Drug use disorders 15 Drug use disorders 16 Congenital defects 16 Colorectal cancer 16 Colorectal cancer 17 Anxiety disorders 17 Other musculoskeletal 17 Other musculoskeletal 18 Other musculoskeletal 18 Anxiety disorders 18 Anxiety disorders 19 Lower respiratory infection 19 Lower respiratory infection 19 Chronic kidney disease 20 Drug use disorders 20 Chronic kidney disease 20 Lower respiratory infection 21 Stomach cancer 21 Breast cancer 21 Oral disorders 22 Breast cancer 22 Oral disorders 22 Other cardiovascular 23 Other cardiovascular 23 Other cardiovascular 23 Breast cancer 24 Neonatal preterm birth 24 Congenital defects 24 Osteoarthritis 25 Oral disorders 25 Stomach cancer 25 Pancreatic cancer 26 Asthma 26 Asthm a2 6 Congenital defects 27 Chronic kidney disease 27 Alcohol use disorders27 Stomach cancer 28 Alcohol use disorders 28 Pancreatic cancer 28 Alcohol use disorders 29 Interpersonal violence 29 Osteoarthritis 29 Asthma 30 HIV/AIDS 30 Liver cancer 30 Liver cancer 31 Neonatal preterm birth 32 Pancreatic cancer 37 Neonatal preterm birth 33 Interpersonal violence 34 Osteoarthritis 42 Interpersonal violence 60 HIV/AIDS 36 Liver cancer 71 HIV/AIDS Leading causes 1990 Leading causes 2006 Mean % change, number of DALYs 1990–2006 Mean % change, all-age DALY rate 1990–2006 Mean % change, agestandardised DALY rate 1990–2006 Leading causes 2016 Mean % change, number of DALYs 2006–16 Mean % change all-age DALY rate 2006–16 Mean % change, agestandardised DALY rate 2006–16 F E –5·3 –10·6 17·7 19·5 –17·2 7·9 21·1 11·4 11·5 5·6 6·9 –7·0 35·1 –20·0 –18·7 –22·2 11·9 13·2 –11·0 19·9 2·1 –27·6 12·3 –10·9 –22·1 20·2 14·9 –5·3 30·0 3·9 –14·8 –19·6 5·9 7·5 –25·5 –2·9 9·0 0·3 0·3 –5·0 –3·8 –16·3 21·6 –28·0 –26·8 –30·0 0·7 1·9 –19·9 7·9 –8·1 –34·9 1·1 –19·8 –29·9 8·2 3·4 –14·8 17·0 –6·5 –26·0 –30·0 –2·3 –3·4 –26·0 1·7 –3·8 –3·8 –1·2 –17·3 –10·9 –27·6 –0·9 –33·8 –27·8 –26·4 0·7 –9·1 –24·2 2·5 –9·8 –30·1 –11·6 –29·6 –28·9 –1·5 –8·4 –22·0 1·8 –17·5 Communicable, maternal, neonatal, and nutritional diseases Non-communicable diseases Injuries –3·6 11·1 –1·5 4·8 30·8 17·6 4·7 9·6 14·2 6·2 2·8 18·3 –2·4 –16·2 13·6 6·9 7·4 4·0 22·0 8·6 15·2 7·5 1·8 19·6 17·7 –7·9 –5·0 3·6 –0·6 12·0 –8·5 5·5 –6·5 –0·6 24·2 11·7 –0·6 4·1 8·5 0·8 –2·4 12·3 –7·3 –20·4 7·9 1·4 1·9 –1·2 15·8 3·1 9·3 2·0 –3·3 13·5 11·7 –12·6 –9·8 –1·6 –5·6 6·3 –21·4 0·9 –19·8 –13·0 –1·1 –1·0 0·5 –6·6 –6·2 0·6 –0·9 1·3 –6·3 –22·0 11·5 –10·8 –0·0 –0·3 1·1 –12·4 0·7 –10·4 –11·6 1·1 –2·2 –10·3 –21·0 –1·2 –5·8 –6·0 32·7 15·2 25·0 9·3 31·0 20·2 29·1 –13·7 26·2 17·1 –42·4 –37·7 44·8 20·0 10·3 –52·3 39·0 12·3 33·3 56·4 –11·9 30·1 27·0 53·7 42·2 34·7 22·6 31·0 –34·8 3·3 11·7 –3·0 5·2 –8·0 10·3 1·2 8·6 –27·3 6·2 –1·4 –51·5 –47·6 21·9 1·0 –7·1 –59·9 17·0 –5·5 12·2 31·7 –25·8 9·5 6·9 29·4 19·7 13·4 3·2 10·3 –45·1 –13·1 –6·3 –17·5 –7·6 –11·4 –2·1 3·2 –1·6 –37·9 –2·3 –13·0 –46·3 –33·2 6·1 –8·3 –14·6 –47·9 3·8 –10·8 3·7 5·4 –35·3 3·5 –4·4 11·8 7·6 –1·2 –10·3 –2·1 –40·6 –23·5 –27·0 16·0 –15·0 9·3 10·4 28·0 30·5 49·0 12·6 10·4 –26·0 17·3 5·7 14·1 38·5 10·8 22·1 12·6 1·5 28·1 2·4 20·5 3·1 –30·2 –21·3 –10·2 11·4 34·1 37·5 39·3 –33·5 5·8 –22·6 –0·5 0·5 16·6 18·8 35·6 2·5 0·5 –32·6 6·8 –3·8 3·9 26·1 0·9 11·2 2·5 –7·6 16·6 –6·8 9·7 –6·1 –36·5 –28·3 –18·3 1·4 22·1 25·2 26·8 –45·7 –3·4 –36·8 –16·7 3·2 –0·3 1·4 0·8 0·5 –0·8 –33·3 –11·9 –5·2 –9·4 31·8 –15·5 5·0 1·4 –25·3 –3·5 –20·8 –2·9 –21·1 –32·3 –40·0 –17·7 –1·9 2·3 5·3 6·4 Figure 2: Leading 30 Level 3 causes of total DALYs for 1990, 2006, and 2016, with percentage change in number of DALYs and all-age and age-standardised DALY rates, overall and by SDI quintile Overall (A). Low SDI (B). Low-middle SDI (C). Middle SDI (D). High-middle SDI (E). High SDI (F). Causes are connected by lines between time periods; solid lines are increases and dashed lines are decreases. For the time period of 1990–2006 and 2006–16, three measures of change are shown: percentage change in the number of DALYs, percentage change in the all-age DALY rate, and percentage change in the age-standardised DALY rate. Statistically significant changes are shown in bold. COPD=chronic obstructive pulmonary disease. DALYs=disability-adjusted life-years. STDs=sexually transmitted diseases. Global Health Metrics 1314 www.thelancet.com Vol 390 September 16, 2017 The proportion of DALYs due to YLDs versus YLLs has shifted distinctly since 1990, and these changes are shown in figure 6. More all-age DALYs as a result of YLDs rather than YLLs indicates a reduction in premature death, with individuals living longer with disability than previously. Globally, 95·9% of countries increased in the proportion of all-age DALYs due to YLDs from 1990 to 2016. In 2016, Kuwait (0·64), Qatar (0·63), and Bahrain (0·60) had the highest ratios of DALYs due to YLDs, whereas Chad (0·14), Niger (0·14), and the Central African Republic (0·13) had the lowest. This finding is a change from 1990, when the highest three proportions were for Andorra (0·49), Kuwait (0·48), and Iceland (0·47), and the lowest three were for Burundi (0·08), Malawi (0·08), and Niger (0·06). Observed versus expected total and cause-specific burden As shown in figure 7, 83 countries had greater than expected age-standardised DALY rates on the basis of SDI in 2016 (a ratio of observed to expected age-standardised DALY rate of greater than 1), generally within central Europe, eastern Europe, and central Asia (20 of 29 countries) or in the super-region of sub-Saharan Africa (24 of 46 countries). Among the 34 locations in the five regions within the high-income GBD super-region, three had greater than expected DALYs relative to their SDI. The five locations with the lowest level of age-standardised DALYs relative to the level expected on the basis of SDI were Nicaragua, Costa Rica, the Maldives, Peru, and Israel. Countries within western Europe (eg, France, Norway, and Iceland), western sub-Saharan Africa (eg, The Gambia, Senegal, and Liberia), eastern sub-Saharan Africa (eg, Ethiopia, Rwanda, and Burundi), east Asia (eg, China), and a subset of countries in south and southeast Asia (eg, Bhutan and Bangladesh) all had ratios of observed to expected DALY rates of less than 1 in 2016. The five locations with the highest age-standardised DALY rates relative to the rates expected on the basis of SDI were Lesotho, Swaziland, South Africa, Fiji, and Botswana, with Lesotho’s ratio of observed to expected measured at 2·43 in 2016. Worldwide, ischaemic heart disease and stroke remained the two leading causes of DALYs, and expected levels were close to those observed at a global scale, with ratios of 0·84 for ischaemic heart disease and 1·0 for stroke (figure 8). The ratio of observed to expected DALYs ranged from 0·24 in Qatar to 3·33 in Ukraine for ischaemic heart disease and from 0·32 in Oman to 2·98 in Bulgaria for stroke. This ratio was less than 1 for 114 of 167 global locations for ischaemic heart disease and 91 of 145 for stroke. Low back and neck pain resulted in fewer than expected DALYs globally, with a ratio of 0·95 globally and 74 locations showing a ratio of less than 1. Of the ten leading causes for each location, observed levels of DALYs were frequently greater than expected on the basis of SDI for causes such as diabetes mellitus (ratio of observed to expected DALYs of greater than 1 in 95 global locations), sense organ diseases (79 locations), and skin and subcutaneous diseases (72 locations). Causes that resulted in greater than expected DALYs for both sexes combined showed strong regional patterns. In the GBD high-income super-region, of the leading ten causes of DALYs, COPD had greater than expected DALYs in 14 of 34 locations and less than expected in none. Observed DALYs were frequently greater than expected for low back and neck pain (27 locations), although the ratio did not exceed 1·66 for any single location. Although drug use disorders were only within the leading ten causes for one location in the high-income super-region, the ratio of observed to expected DALYs in the USA was 4·37. In addition to being the most common leading causes of DALYs, ischaemic heart disease and stroke were sources of more DALYs than expected for most locations in the central Europe, eastern Europe, and central Asia super-region. Here, the highest ratio of observed to expected DALYs was 3·33 for ischaemic heart disease and 2·98 for stroke. Mental and substance use disorders were also notable in the region for the high levels of observed DALYs relative to those expected, particularly in eastern Europe, where the ratio for the Level 4 cause of alcohol use disorders was 5·38 and ranged from 3·33 in Moldova to 5·7 in Russia. For locations in the super-region of Latin America and Caribbean, the burden from interpersonal violence was greater than expected, with a ratio of 3·37 for the superregion and ranging from 1·68 in Ecuador to 7·22 in Venezuela. Diabetes was also a source of greater than expected burden in this super-region, with DALYs exceeding expected levels in 19 of 37 locations. Additionally, diabetes contributed to a higher burden of DALYs in many locations in the super-region of southeast Asia, east Asia, and Oceania, with ratios of observed to expected levels ranging from 0·68 in the Maldives to 10·48 in Fiji. In south Asia, neonatal encephalopathy due to birth asphyxia and trauma caused greater than expected DALYs for two countries, with ratios of 1·01 in Nepal and 2·72 in Pakistan. Ongoing conflicts in north Africa and the Middle East were reflected in higher than expected DALYs in seven of the 21 locations in the region. Patterns of observed DALYs compared with those expected in subSaharan Africa were dominated by the ongoing effect of HIV/AIDS, particularly in southern sub-Saharan Africa. Observed levels of DALYs exceeded those expected in 37 of 46 locations, ranging from a ratio of 8·10 in Burundi to 316·34 in Lesotho. The five locations with the largest magnitude of improvement—a decrease in the difference between observed and expected all-cause age-standardised DALY rates from 1990 to 2016—included Greenland, the Maldives, Bermuda, Ethiopia, and Liberia. The five locations that showed the smallest decrease in the difference between observed and expected DALY rates Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1315 Figure 3: Leading Level 3 causes of all-age DALY rates by location for females (A) and males (B) in 2016 ATG=Antigua and Barbuda. DALY=disability-adjusted life-year. FSM=Federated States of Micronesia. LCA=Saint Lucia. TLS=Timor-Leste. TTO=Trinidad and Tobago. VCT=Saint Vincent and the Grenadines. VUT=Vanuatu. WSM=Samoa. Persian Gulf Persian Gulf A B Caribbean LCA Dominica ATG TTO Grenada VCT TLS Maldives Barbados Seychelles Mauritius Comoros West Africa Eastern Mediterranean Malta Singapore Balkan Peninsula Tonga Samoa FSM Fiji Solomon Isl Marshall Isl Vanuatu Kiribati Caribbean LCA Dominica ATG TTO Grenada VCT TLS Maldives Barbados Seychelles Mauritius Comoros West Africa Eastern Mediterranean Malta Singapore Balkan Peninsula Tonga Samoa FSM Fiji Solomon Isl Marshall Isl Vanuatu Kiribati Tuberculosis HIV/AIDS Diarrhoeal diseases Lower respiratory infections Malaria Neonatal preterm birth complications Neonatal encephalopathy Iron-deficiency anaemia Ischaemic heart disease Cerebrovascular disease Chronic obstructive pulmonary disease Diabetes mellitus Low back and neck pain Congenital birth defects Skin and subcutaneous diseases Self-harm Conflict and terrorism Tuberculosis HIV/AIDS Diarrhoeal diseases Lower respiratory infections Malaria Neonatal preterm birth complications Ischaemic heart disease Cerebrovascular disease Chronic obstructive pulmonary disease Diabetes mellitus Chronic kidney disease Low back and neck pain Congenital birth defects Road injuries Self-harm Interpersonal violence Conflict and terrorism Global Health Metrics 1316 www.thelancet.com Vol 390 September 16, 2017 over the period 1990–2016 were Lesotho, Swaziland, Syria, South Africa, and Botswana. Discussion General findings Although the world has become healthier since 1990, this progress has been highly heterogeneous. From 1990 to 2016, global HALE at birth increased. The total number of years of functional health lost (life expectancy minus HALE) increased from 1990 to 2016, indicating an absolute expansion of morbidity. YLL rates have fallen more rapidly than YLD rates so that non-fatal health loss as a proportion of DALYs has grown from 1990 to 2016. This progress has been driven by marked reductions in DALYs due to CMNN causes and, to a lesser extent, by declines in age-standardised NCD and injury DALYs. Population growth in the past 27 years (from 5·27 billion to 7·39 billion) has resulted in a significant increase in total DALYs due to NCDs, coupled with a high total DALY burden in 2016, thus leading to an overall increasing toll on health systems.22,23 GBD 2016 continues to unequivocally show an absolute Figure 4: Ratios of all-age DALY counts from NCDs to those from CMNN diseases by SDI quintile, GBD super-region, and region, for males and females separately, for 1990, 1995, 2000, 2006, 2010, and 2016 Each point represents the ratio of DALYs from NCDs to those from CMNN diseases for each GBD region in a given year by sex. Ratios of more than 1·00 represent a greater number of DALYs from NCDs than from CMNN diseases. CMNN=communicable, maternal, neonatal, and nutritional. DALY=disability-adjusted life-year. GBD=Global Burden of Disease. NCDs=non-communicable diseases. SDI=Socio-demographic Index. 0·12 0·25 0·50 1·00 2·00 Ratio 4·00 8·00 16·0 32·0 Central sub-Saharan Africa Eastern sub-Saharan Africa Western sub-Saharan Africa Southern sub-Saharan Africa Sub-Saharan Africa South Asia North Africa and Middle East Oceania Southeast Asia East Asia Southeast Asia, east Asia, and Oceania Tropical Latin America Caribbean Andean Latin America Central Latin America Latin America and Caribbean Central Asia Central Europe Eastern Europe Central Europe, eastern Europe, and central Asia Southern Latin America Western Europe High-income Asia Pacific Australasia High-income North America High income Low SDI Low–middle SDI Middle SDI High–middle SDI High SDI Global 1990 1995 2000 2006 2010 2016 Males Females Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1323 12345678 91 0 Trinidad and Tobago Diabetes (5·75) IHD (1·0) Stroke (0·98) Violence (4·03) Back+Neck (0·71) Road Inj (0·93) Sense (1·08) CKD (2·62) Skin (0·96) HIV (14·12) Virgin Islands IHD (1·41) Diabetes (3·6) Stroke (1·07) Back+Neck (0·66) Violence (7·04) Sense (1·35) CKD (3·17) Prostate C (5·5) Skin (1·08) HIV (29·92) Tropical Latin America IHD (0·62) Violence (4·1) Back+Neck (1·03) Road Inj (1·03) Stroke (0·68) Sense (1·13) Skin (1·03) Diabetes (1·14) LRI (1·09) Migraine (1·05) Brazil IHD (0·61) Violence (4·27) Back+Neck (1·02) Road Inj (1·04) Stroke (0·68) Sense (1·13) Skin (1·03) Diabetes (1·13) LRI (1·13) Migraine (1·04) Paraguay IHD (0·6) Road Inj (1·04) Diabetes (1·48) Back+Neck (0·91) Stroke (0·66) Skin (1·06) Congenital (1·0) NN Preterm (0·93) Sense (0·98) Violence (1·72) Southeast Asia, east Asia, and Oceania Stroke (1·61) IHD (0·73) Back+Neck (0·89) Road Inj (0·95) COPD (1·51) Sense (1·18) Diabetes (1·09) Lung C (1·42) Skin (0·87) Liver C (4·25) East Asia Stroke (1·83) IHD (0·73) Back+Neck (0·89) Road Inj (1·03) COPD (1·72) Lung C (1·68) Sense (1·2) Liver C (5·42) Skin (0·78) Depression (1·01) ChinaStroke (1·83) IHD (0·74) Back+Neck (0·9) Road Inj (1·01) COPD (1·69) Sense (1·2) Lung C (1·71) Liver C (5·45) Skin (0·78) Depression (1·02) North KoreaStroke (2·25) IHD (1·05) COPD (2·6) Lung C (7·46) Road Inj (0·96) Congenital (0·93) NN Preterm (0·56) Back+Neck (1·36) Sense (1·3) LRI (0·29) Taiwan (Province of China) Diabetes (2·5) Back+Neck (0·82) IHD (0·41) Stroke (0·81) Sense (1·19) Liver C (5·73) Lung C (0·84) Road Inj (1·05) Skin (0·84) CKD (2·23) Southeast Asia IHD (0·75) Stroke (1·12) Back+Neck (0·95) Diabetes (1·4) LRI (1·19) Road Inj (0·75) Sense (1·13) Skin (1·07) TB (5·73) NN Preterm (0·87) Cambodia LRI (1·01) Stroke (1·01) IHD (0·58) NN Preterm (0·67) Road Inj (0·73) Back+Neck (1·28) Skin (1·13) Sense (1·13) NN Enceph (0·77) Malaria (70·48) IndonesiaIHD (0·91) Stroke (1·26) Diabetes (1·69) TB (8·74) Back+Neck (0·94) NN Preterm (1·06) Sense (1·07) Road Inj (0·68) Skin (1·0) Diarrhoea (3·07) Laos LRI (1·7) NN Preterm (1·69) Congenital (1·88) IHD (0·85) NN Enceph (1·83) Stroke (1·01) Diarrhoea (0·82) Road Inj (0·8) Skin (1·16) Back+Neck (1·02) Malaysia IHD (0·79) Stroke (0·72) Road Inj (1·08) LRI (2·09) Back+Neck (0·68) Skin (1·14) Diabetes (1·29) Sense (0·72) Depression (0·89) COPD (0·9) Maldives IHD (0·56) Back+Neck (0·79) Skin (1·15) Sense (0·97) Iron (2·9) COPD (0·88) Stroke (0·34) Migraine (0·81) Diabetes (0·68) CKD (1·21) MauritiusDiabetes (5·86) IHD (0·97) CKD (5·25) Stroke (0·92) Back+Neck (0·9) Sense (1·33) Skin (1·0) COPD (1·08) Iron (3·78) Depression (0·99) MyanmarStroke (1·05) LRI (0·86) Sense (1·45) Road Inj (0·75) IHD (0·41) Back+Neck (1·04) COPD (1·25) Diabetes (1·11) TB (3·05) Skin (1·13) PhilippinesIHD (0·92) LRI (1·66) Stroke (1·03) Diabetes (1·26) Back+Neck (0·92) Skin (1·22) NN Preterm (0·93) TB (4·99) Congenital (0·99) CKD (1·96) Sri LankaIHD (0·83) Diabetes (2·14) Back+Neck (0·93) Sense (1·39) Stroke (0·68) Self Harm (1·63) Skin (0·98) COPD (1·05) Asthma (2·37) Road Inj (0·52) Seychelles IHD (0·69) LRI (2·18) HTN HD (5·93) Back+Neck (0·84) Stroke (0·74) CKD (2·92) Sense (1·19) Diabetes (1·23) Skin (0·98) Road Inj (0·63) Thailand Road Inj (1·32) IHD (0·47) Back+Neck (1·01) Stroke (0·84) Sense (1·35) LRI (1·58) Skin (1·13) Diabetes (1·31) COPD (1·39) Liver C (5·35) Timor-LesteLRI (0·77) Diarrhoea (1·46) NN Preterm (0·87) IHD (0·62) Congenital (0·94) Stroke (0·66) NN Enceph (1·07) Skin (1·13) Sense (1·09) Back+Neck (0·89) VietnamStroke (1·28) IHD (0·54) Road Inj (0·93) Back+Neck (1·07) Sense (1·19) Lung C (2·31) Diabetes (1·03) Skin (1·0) COPD (1·02) Congenital (0·69) Oceania LRI (0·94) IHD (1·39) Stroke (1·41) Diabetes (2·77) COPD (2·16) NN Preterm (0·68) Congenital (1·01) Asthma (3·88) Road Inj (0·76) Diarrhoea (0·52) American SamoaDiabetes (3·44) IHD (0·57) Skin (1·25) Stroke (0·67) Back+Neck (0·68) CKD (2·45) Sense (0·74) LRI (0·99) COPD (0·87) Asthma (1·66) Micronesia IHD (1·35) Diabetes (3·48) Stroke (1·51) CKD (2·71) LRI (0·91) COPD (1·4) Self Harm (1·98) Road Inj (0·69) Skin (1·2) Back+Neck (0·84) Fiji Diabetes (10·48) IHD (1·54) Stroke (1·17) CKD (4·59) LRI (2·54) Back+Neck (0·78) Asthma (3·59) Skin (1·09) NN Preterm (1·48) Congenital (1·28) Guam IHD (1·38) Diabetes (2·92) Stroke (1·09) Back+Neck (0·68) Self Harm (1·4) Lung C (1·07) Skin (1·04) CKD (2·84) LRI (2·06) COPD (1·44) Kiribati Diabetes (5·26) IHD (1·39) Stroke (1·77) LRI (0·41) NN Preterm (0·65) Congenital (1·12) TB (1·56) NN Enceph (0·95) Self Harm (2·91) Diarrhoea (0·41) Marshall Islands Diabetes (8·59) IHD (1·03) Stroke (1·05) LRI (0·77) CKD (2·43) NN Preterm (0·81) Road Inj (0·6) Skin (1·12) Self Harm (1·75) Back+Neck (0·88) Northern Mariana Islands Diabetes (1·77) Back+Neck (0·62) IHD (0·28) Skin (0·97) Stroke (0·47) Road Inj (0·64) Self Harm (0·8) Migraine (0·7) Sense (0·6) CKD (1·53) Papua New GuineaLRI (0·77) IHD (1·55) Stroke (1·61) COPD (2·7) NN Preterm (0·64) Diabetes (2·35) Congenital (1·08) Asthma (3·88) Road Inj (0·91) Diarrhoea (0·32) Samoa IHD (0·97) Diabetes (2·31) Stroke (0·93) LRI (0·46) Skin (1·18) CKD (1·76) Back+Neck (0·95) Sense (0·99) COPD (0·81) Self Harm (1·17) Solomon Islands IHD (1·56) Stroke (1·64) Diabetes (3·27) LRI (0·4) CKD (2·07) NN Preterm (0·46) Congenital (0·79) COPD (1·42) Road Inj (0·62) Asthma (2·52) TongaDiabetes (3·25) IHD (0·81) Stroke (0·7) LRI (0·65) NN Preterm (0·73) CKD (1·9) Skin (1·18) Back+Neck (0·93) Sense (0·94) Road Inj (0·49) VanuatuIHD (1·92) Stroke (1·73) LRI (0·66) Diabetes (2·53) NN Preterm (0·69) Congenital (1·05) COPD (1·43) CKD (1·85) Road Inj (0·63) Iron (1·41) (Figure 8 continues on next page) Global Health Metrics 1324 www.thelancet.com Vol 390 September 16, 2017 12345678 91 0 North Africa and Middle East IHD (0·92) Conflict Terror (63·83) Road Inj (1·16) Congenital (1·69) NN Preterm (1·52) Back+Neck (1·06) Stroke (0·62) Diabetes (1·23) LRI (1·06) Sense (0·98) North Africa and Middle East IHD (0·92) Conflict Terror (63·77) Road Inj (1·16) Congenital (1·69) NN Preterm (1·52) Back+Neck (1·06) Stroke (0·62) Diabetes (1·23) LRI (1·06) Sense (0·98) Afghanistan Conflict Terror (32·51) LRI (0·48) IHD (2·44) Congenital (1·99) Road Inj (2·05) NN Preterm (0·66) Stroke (1·37) Meningitis (1·0) TB (0·71) Diarrhoea (0·11) Algeria IHD (0·74) Congenital (1·66) Back+Neck (1·09) NN Preterm (1·53) Road Inj (0·96) Diabetes (1·25) Sense (1·1) Stroke (0·52) Migraine (1·18) Skin (0·88) BahrainDiabetes (2·48) IHD (0·43) Back+Neck (0·93) Migraine (1·19) Skin (0·95) Depression (1·22) Road Inj (0·63) Sense (0·83) Oth MSK (1·52) Anxiety (1·16) EgyptIHD (1·3) Road Inj (1·09) Stroke (0·81) Back+Neck (1·08) LRI (1·22) Diabetes (1·36) Congenital (1·22) Diarrhoea (3·39) Sense (1·08) Cirr HepC (5·32) Iran IHD (0·71) Road Inj (1·73) Back+Neck (0·94) NN Preterm (2·36) Depression (1·59) Congenital (1·8) Diabetes (1·39) Migraine (1·15) Stroke (0·5) Sense (0·92) Iraq Conflict Terror (166·54) NN Preterm (1·3) IHD (1·24) Congenital (1·49) Diabetes (1·59) NN Sepsis (2·3) Road Inj (0·78) Stroke (0·68) LRI (0·29) Back+Neck (1·1) Jordan Congenital (1·93) IHD (0·49) NN Preterm (1·56) Back+Neck (0·86) Conflict Terror (36·87) Diabetes (1·22) Road Inj (0·71) Skin (0·93) Migraine (1·09) Sense (0·86) Kuwait IHD (0·42) Back+Neck (0·79) Migraine (1·2) Road Inj (0·94) Congenital (1·7) Skin (0·84) Depression (1·06) Diabetes (0·95) Sense (0·72) Anxiety (1·18) LebanonIHD (0·68) Back+Neck (0·82) Conflict Terror (29·91) Sense (1·1) Migraine (1·15) Skin (0·96) Diabetes (1·21) Depression (1·09) Congenital (1·23) Anxiety (1·31) LibyaIHD (0·72) Conflict Terror (51·15) Back+Neck (0·85) Road Inj (1·36) Diabetes (1·37) Migraine (1·13) Stroke (0·52) Sense (0·95) Skin (0·91) Depression (1·07) MoroccoIHD (1·1) Back+Neck (1·45) Road Inj (0·94) Diabetes (1·5) Depression (1·88) Stroke (0·59) Sense (1·15) TB (3·75) NN Preterm (0·73) Migraine (1·32) PalestineNN Preterm (0·97) IHD (1·01) Congenital (1·23) Stroke (0·51) Back+Neck (1·09) Road Inj (0·52) Diabetes (1·06) NN Sepsis (1·26) Skin (0·92) Depression (1·32) Oman Road Inj (2·14) IHD (0·58) Back+Neck (0·9) Diabetes (1·59) Migraine (1·15) Skin (0·83) Depression (1·04) Sense (0·68) Stroke (0·32) Congenital (0·72) QatarRoad Inj (1·75) Back+Neck (0·84) Diabetes (1·43) Migraine (1·18) IHD (0·24) Depression (1·13) Skin (0·82) Congenital (1·22) Sense (0·63) Anxiety (1·14) Saudi Arabia IHD (0·47) Road Inj (1·63) Back+Neck (0·84) Migraine (1·2) Skin (0·95) Sense (0·78) Depression (0·96) Stroke (0·39) Diabetes (0·89) Congenital (1·11) SudanCongenital (2·65) NN Preterm (1·54) IHD (1·09) Road Inj (1·45) LRI (0·6) Stroke (0·67) Iron (2·05) Back+Neck (1·26) Diarrhoea (0·76) Skin (0·97) Syria Conflict Terror (851·38) IHD (1·17) Back+Neck (1·0) Stroke (0·5) Sense (0·94) Migraine (1·15) Skin (0·89) Congenital (0·69) Depression (1·01) Road Inj (0·4) TunisiaIHD (0·93) Back+Neck (1·2) Road Inj (0·97) Diabetes (1·41) Stroke (0·61) Sense (1·18) Migraine (1·22) Skin (0·91) Depression (1·23) Congenital (0·93) Turkey IHD (0·56) Back+Neck (1·09) Diabetes (1·36) Sense (1·07) Congenital (1·4) Migraine (1·12) Skin (0·84) Stroke (0·44) Depression (1·03) Lung C (0·98) United Arab Emirates Road Inj (2·17) IHD (0·65) Back+Neck (0·92) Stroke (0·62) Diabetes (1·37) Migraine (1·14) Skin (0·79) Sense (0·76) Depression (0·9) CKD (1·64) Yemen Conflict Terror (171·01) NN Preterm (1·4) IHD (1·15) Congenital (1·57) Road Inj (1·39) Diarrhoea (0·76) Iron (2·73) LRI (0·27) Stroke (0·7) STD (4·05) South Asia IHD (1·19) Diarrhoea (1·95) COPD (2·01) LRI (0·79) Stroke (0·77) NN Preterm (0·8) Iron (2·49) TB (2·88) Road Inj (0·67) Sense (1·24) South Asia IHD (1·19) Diarrhoea (1·95) COPD (2·01) LRI (0·79) Stroke (0·77) NN Preterm (0·8) Iron (2·49) TB (2·88) Road Inj (0·67) Sense (1·24) Bangladesh IHD (0·8) Stroke (1·03) LRI (0·38) Back+Neck (1·19) NN Enceph (0·95) Sense (1·23) COPD (1·1) Migraine (1·41) Oth NN (1·6) Skin (0·95) Bhutan IHD (0·73) LRI (0·73) Iron (2·89) NN Preterm (0·84) Back+Neck (0·96) COPD (1·24) Congenital (0·76) Stroke (0·48) Migraine (1·27) Diabetes (0·91) IndiaIHD (1·22) COPD (2·28) Diarrhoea (2·47) LRI (0·9) Stroke (0·74) Iron (3·0) NN Preterm (0·87) TB (3·61) Sense (1·3) Road Inj (0·69) NepalIHD (1·06) LRI (0·39) NN Enceph (1·01) COPD (1·67) Stroke (0·64) Oth NN (1·53) Back+Neck (1·23) Diarrhoea (0·34) Road Inj (0·52) Skin (1·0) Pakistan IHD (1·3) NN Enceph (2·72) Diarrhoea (1·36) NN Preterm (0·86) LRI (0·56) Stroke (0·81) Oth NN (2·71) Road Inj (0·75) TB (1·81) Congenital (0·69) Sub-Saharan Africa Malaria (138·47) HIV (54·6) Diarrhoea (1·23) LRI (0·78) NN Enceph (1·55) NN Preterm (0·77) TB (1·69) PEM (1·94) NN Sepsis (2·07) Congenital (0·91) Southern sub-Saharan Africa HIV (226·35) LRI (3·43) TB (17·53) Diarrhoea (9·23) Road Inj (1·56) Violence (4·08) Diabetes (2·03) NN Preterm (1·6) IHD (0·42) Stroke (0·64) (Figure 8 continues on next page) Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1325 12345678 91 0 Botswana HIV (166·06) Diabetes (1·95) TB (13·78) LRI (1·95) Diarrhoea (7·09) IHD (0·43) Road Inj (0·9) Skin (1·14) Stroke (0·57) Back+Neck (0·67) LesothoHIV (316·34) TB (14·68) Diarrhoea (5·4) LRI (1·71) Road Inj (1·51) Violence (3·5) NN Preterm (1·18) Diabetes (2·16) Oth NN (4·64) Stroke (1·0) NamibiaHIV (117·45) Diarrhoea (6·46) LRI (1·76) Road Inj (1·05) TB (6·58) NN Preterm (1·15) Oth NN (4·82) Violence (1·92) IHD (0·38) Skin (1·18) South Africa HIV (287·36) LRI (3·97) Road Inj (1·99) Violence (6·29) TB (23·49) Diabetes (2·47) IHD (0·42) Diarrhoea (8·27) Stroke (0·63) Back+Neck (0·7) SwazilandHIV (200·02) Diarrhoea (7·1) LRI (2·31) TB (8·93) Road Inj (1·29) Diabetes (2·1) NN Preterm (1·11) Oth NN (4·58) Violence (1·97) IHD (0·43) Zimbabwe HIV (133·99) Diarrhoea (2·09) LRI (1·09) TB (3·84) NN Enceph (2·11) NN Preterm (1·04) Congenital (0·99) PEM (3·05) NN Sepsis (2·47) Road Inj (0·73) Western sub-Saharan Africa Malaria (195·78) Diarrhoea (1·4) LRI (0·78) HIV (39·08) NN Enceph (1·95) NN Preterm (0·92) Congenital (1·44) Meningitis (2·83) NN Sepsis (2·32) PEM (1·6) BeninMalaria (23·7) Diarrhoea (0·71) LRI (0·51) NN Enceph (1·18) NN Preterm (0·74) Congenital (1·2) Meningitis (1·31) NN Sepsis (1·16) HIV (9·78) Iron (1·06) Burkina Faso Malaria (14·54) LRI (0·62) Diarrhoea (0·28) Congenital (1·7) NN Preterm (0·61) Meningitis (1·25) NN Enceph (0·7) PEM (0·77) NN Sepsis (1·18) Iron (1·32) Cameroon Malaria (274·08) HIV (76·33) LRI (1·04) Diarrhoea (0·96) Congenital (1·78) NN Preterm (0·99) NN Enceph (1·53) Meningitis (2·84) NN Sepsis (2·14) PEM (1·52) Cape VerdeIHD (0·55) HIV (14·08) LRI (0·59) Back+Neck (0·99) NN Preterm (0·6) Violence (1·45) Skin (1·06) Stroke (0·48) Congenital (0·69) Iron (1·71) Chad Diarrhoea (0·81) LRI (0·89) HIV (25·23) Malaria (5·13) NN Enceph (1·21) NN Preterm (0·8) Meningitis (1·66) PEM (1·17) STD (2·59) Congenital (1·29) Côte d’Ivoire Malaria (89·87) Diarrhoea (1·04) HIV (50·5) LRI (0·75) NN Preterm (1·01) Congenital (1·64) NN Enceph (1·42) IHD (0·75) NN Sepsis (1·78) Stroke (0·79) The Gambia LRI (0·39) NN Preterm (0·74) Diarrhoea (0·32) HIV (20·9) NN Enceph (0·9) Congenital (1·0) NN Sepsis (1·35) Iron (1·42) Meningitis (1·2) Malaria (4·5) GhanaMalaria (431·66) HIV (32·92) NN Enceph (2·24) LRI (0·72) Congenital (1·11) NN Sepsis (3·02) NN Preterm (0·59) Stroke (0·69) IHD (0·49) Meningitis (2·84) Guinea Malaria (19·15) LRI (0·7) NN Enceph (1·27) Diarrhoea (0·27) NN Preterm (0·68) Congenital (1·47) HIV (16·26) Meningitis (1·37) TB (0·78) NN Sepsis (1·2) Guinea-BissauHIV (45·01) LRI (0·49) Diarrhoea (0·4) NN Enceph (1·13) NN Preterm (0·75) TB (1·34) STD (3·3) Meningitis (1·72) IHD (1·02) Congenital (1·16) LiberiaDiarrhoea (0·57) Malaria (10·62) LRI (0·32) HIV (17·67) NN Enceph (0·86) NN Preterm (0·48) Congenital (0·97) IHD (0·61) Meningitis (0·92) TB (0·6) Mali Malaria (11·12) Diarrhoea (0·55) NN Enceph (1·38) NN Preterm (0·91) LRI (0·3) PEM (1·08) Congenital (1·32) STD (2·03) Meningitis (1·1) NN Sepsis (1·17) Mauritania LRI (0·62) Diarrhoea (1·04) NN Preterm (0·77) NN Enceph (1·22) Congenital (1·0) NN Sepsis (2·06) IHD (0·45) Back+Neck (1·13) Iron (1·43) Skin (1·11) NigerMalaria (3·36) Diarrhoea (0·52) LRI (0·51) Meningitis (1·0) NN Enceph (0·74) NN Preterm (0·48) Congenital (1·0) PEM (0·5) NN Sepsis (0·93) TB (0·32) NigeriaMalaria (594·41) Diarrhoea (2·59) HIV (50·48) NN Enceph (2·98) LRI (0·97) NN Preterm (1·15) Congenital (1·52) NN Sepsis (3·49) Meningitis (3·93) PEM (2·71) São Tomé and Príncipe LRI (0·45) Congenital (0·83) Diarrhoea (0·34) NN Enceph (0·72) NN Preterm (0·43) Stroke (0·57) NN Sepsis (1·35) Skin (1·08) Back+Neck (0·95) PEM (0·94) SenegalDiarrhoea (0·41) LRI (0·39) NN Preterm (0·58) NN Enceph (0·87) Congenital (1·11) Malaria (7·08) Meningitis (1·4) NN Sepsis (1·27) IHD (0·62) TB (0·75) Sierra LeoneMalaria (46·59) Diarrhoea (0·87) LRI (0·82) NN Enceph (1·63) Congenital (2·11) NN Preterm (0·76) Meningitis (2·1) HIV (17·7) TB (0·85) NN Sepsis (1·36) Togo Malaria (83·04) HIV (38·48) LRI (0·49) Diarrhoea (0·47) NN Enceph (1·17) NN Preterm (0·7) Congenital (1·13) IHD (0·64) TB (0·87) NN Sepsis (1·34) Eastern sub-Saharan Africa HIV (45·37) LRI (0·57) Diarrhoea (0·61) Malaria (17·5) NN Enceph (1·04) TB (1·35) NN Preterm (0·53) PEM (1·28) Meningitis (1·5) NN Sepsis (1·37) Burundi Diarrhoea (0·63) LRI (0·52) Malaria (4·13) TB (1·73) NN Enceph (1·04) PEM (1·11) NN Preterm (0·65) NN Sepsis (1·24) Meningitis (0·85) HIV (8·1) ComorosLRI (0·61) Diarrhoea (0·9) NN Enceph (1·04) NN Preterm (0·59) TB (1·53) IHD (0·47) PEM (1·5) Skin (1·24) NN Sepsis (1·57) Meningitis (1·78) Djibouti HIV (44·64) LRI (0·67) PEM (2·36) NN Preterm (0·7) TB (1·61) Diarrhoea (0·41) NN Enceph (0·87) NN Sepsis (1·73) IHD (0·56) Meningitis (1·73) EritreaDiarrhoea (0·71) LRI (0·52) TB (1·99) PEM (1·5) NN Enceph (0·7) NN Preterm (0·42) Meningitis (1·51) HIV (11·79) NN Sepsis (1·2) Skin (1·2) Ethiopia Diarrhoea (0·29) LRI (0·34) TB (0·98) NN Enceph (0·7) NN Sepsis (1·02) IHD (0·62) HIV (8·87) NN Preterm (0·31) PEM (0·61) Meningitis (0·85) Kenya HIV (64·38) Diarrhoea (3·17) LRI (1·0) NN Enceph (1·3) NN Preterm (0·63) Skin (1·25) TB (1·6) Malaria (92·97) NN Sepsis (1·89) Meningitis (2·45) Madagascar Diarrhoea (0·88) LRI (0·68) PEM (2·55) NN Preterm (0·85) Stroke (1·1) Malaria (9·15) NN Sepsis (1·48) NN Enceph (0·68) STD (2·54) Meningitis (1·27) Malawi HIV (93·83) Malaria (18·86) LRI (0·6) Diarrhoea (0·45) NN Enceph (1·32) NN Preterm (0·65) PEM (1·13) Meningitis (1·47) TB (0·89) NN Sepsis (1·35) Mozambique HIV (92·49) Malaria (8·96) LRI (0·46) NN Enceph (0·98) TB (1·18) Diarrhoea (0·19) NN Preterm (0·5) NN Sepsis (1·2) Oth NN (0·87) STD (1·41) Rwanda LRI (0·55) Diarrhoea (0·41) TB (1·25) NN Enceph (0·86) Malaria (15·27) HIV (16·46) PEM (1·21) NN Preterm (0·42) Meningitis (1·41) NN Sepsis (1·22) (Figure 8 continues on next page) Global Health Metrics 1326 www.thelancet.com Vol 390 September 16, 2017 GBD 2015 results.4 The conclusion that DALY convergence is likely to be accelerated by escalating increases in SDI remains valid. We have looked a step further in GBD 2016 and highlighted countries that have most improved and showed the largest declines in their DALY performance relative to that expected on the basis of their SDI. The pace and efficiency of a country’s transition through the SDI quintiles and the concurrent improvements in population summary health metrics is by no means inevitable, however, and analysis derived from the ratio of observed to expected DALYs on the basis of SDI has been informative. The exemplars were Nicaragua, Costa Rica, the Maldives, Peru, and Israel, which had the lowest levels of age-standardised DALYs relative to the levels expected on the basis of SDI in 2016. The appendix (pp 63–66) contains a full list of exemplars. These countries considerably outperformed health expectations relative to their development status. Conversely, Lesotho, Swaziland, South Africa, Fiji, and Botswana had the highest levels of age-standardised DALYs relative to those expected, reflecting a need for additional attention and examination of the reasons for these discrepancies. Lesotho had the highest ratio of age-standardised DALYs relative to that expected, which indicates poor performance in terms of health outcomes for mortality and morbidity relative to their SDI status. The reasons for the success in health outcomes of the five exemplars merit in-depth analyses. Conversely, with the exception of Fiji (which is heavily impacted by the high levels of diabetes seen throughout Oceania), the poor performers in 2016 were clearly those that were most devastated by the continuing HIV/AIDS epidemic and, although progress has been made in South Africa, Swaziland, Zimbabwe, and Lesotho are still disproportionately affected. Many different locations within the top ten exemplars and ten poor performers would make excellent case studies to assess the reasons for their relative rankings, particularly given the different health challenges and successes for each nation. By highlighting exemplars and poor performers across the SDI quintiles, we have seen that the underlying influences that have contributed to the relative successes and failures of nations are substantially heterogeneous. A clear corollary is that the policy implications for reform will not be the same between quintiles. Nation-by-nation complexity in how countries progress through the SDI continuum and how population health summaries track is clearly too voluminous to document in this study by 12345678 91 0 Somalia LRI (0·63) TB (1·69) Diarrhoea (0·28) PEM (1·19) Conflict Terror (12·12) Whooping (2·02) Malaria (1·64) NN Enceph (0·61) Meningitis (0·89) IHD (0·94) South SudanLRI (0·62) STD (3·37) Diarrhoea (0·3) HIV (13·67) PEM (0·99) NN Enceph (0·88) NN Preterm (0·59) Meningitis (0·8) TB (0·66) Measles (0·28) Tanzania HIV (52·38) LRI (0·78) Diarrhoea (0·68) Malaria (49·43) NN Enceph (1·37) PEM (1·83) NN Preterm (0·56) TB (1·14) NN Sepsis (1·59) Meningitis (1·8) Uganda HIV (51·43) Malaria (32·48) LRI (0·49) NN Enceph (1·33) Diarrhoea (0·43) TB (1·74) NN Preterm (0·69) Meningitis (1·84) PEM (1·03) NN Sepsis (1·44) ZambiaHIV (112·98) LRI (1·06) Diarrhoea (1·22) TB (3·62) Malaria (96·01) NN Enceph (1·66) PEM (3·27) NN Preterm (0·7) Meningitis (3·39) NN Sepsis (2·12) Central sub-Saharan Africa Malaria (81·1) LRI (0·68) Diarrhoea (0·8) TB (2·59) HIV (25·92) PEM (2·16) NN Preterm (0·73) NN Enceph (1·07) Congenital (1·05) NN Sepsis (2·04) Angola Diarrhoea (1·12) LRI (0·66) Malaria (51·12) HIV (26·41) NN Preterm (0·66) TB (1·68) PEM (1·94) NN Sepsis (2·15) NN Enceph (0·87) Congenital (0·9) Central African Republic TB (5·02) HIV (57·95) Diarrhoea (0·72) LRI (0·9) Malaria (10·77) Measles (1·76) PEM (1·65) STD (3·47) NN Preterm (0·74) Road Inj (1·64) Congo (Brazzaville) HIV (63·82) Malaria (580·54) Diarrhoea (2·16) LRI (1·0) TB (3·24) NN Preterm (0·88) NN Enceph (1·29) IHD (0·51) NN Sepsis (3·14) Road Inj (0·76) DR Congo Malaria (14·77) LRI (0·5) TB (1·7) Diarrhoea (0·31) PEM (1·3) NN Preterm (0·61) NN Enceph (0·85) Congenital (1·05) HIV (12·33) NN Sepsis (1·4) Equatorial Guinea HIV (113·42) Malaria (11942·38) LRI (1·85) NN Preterm (1·4) Road Inj (0·74) Back+Neck (0·79) Skin (1·12) NN Enceph (2·46) Congenital (1·04) NN Sepsis (5·86) GabonHIV (38·64) Malaria (2134·02) LRI (1·62) NN Preterm (1·26) Diarrhoea (3·06) NN Sepsis (6·03) Road Inj (0·88) Congenital (1·13) IHD (0·43) Skin (1·17) 0·0–0·64 0·64–0·81 0·81–0·97 0·97–1·07 1·07–1·181·18–1·371·37–1·71 1·71–2·77 ≥2·77 Figure 8: Leading ten causes of all-age DALYs with the ratio of observed to expected DALYs on the basis of Socio-demographic Index in 2016, by location The ratio of observed to expected DALYs on the basis of Socio-demographic Index is provided in brackets for each cause and cells are colour-coded by ratio ranges (calculated to place a roughly equal number of cells into each bin). Shades of blue represent much lower observed DALY levels than expected on the basis of Socio-demographic Index, whereas red shows observed DALYs that exceed expected levels. Alcohol=alcohol use disorders. Alzheimer’s=Alzheimer’s disease and other dementias. Anxiety=anxiety disorders. Back+Neck=low back and neck pain. Cirr alc=cirrhosis due to alcohol use. Cirr HepC=cirrhosis due to hepatitis C. CKD=chronic kidney disease. CMP=cardiomyopathy and myocarditis. Colorect C=colon and rectum cancer. Conflict Terror=conflict and terrorism. Congenital=congenital anomalies. COPD=chronic obstructive pulmonary disease. Depression=depressive disorders. Diabetes=diabetes mellitus. Diarrhoea=diarrhoeal diseases. DR Congo=Democratic Republic of the Congo. Drugs=drug use disorders. HIV=HIV/AIDS. HTN HD=hypertensive heart disease. IHD=ischaemic heart disease. Iron=iron-deficiency anaemia. Liver C=liver cancer. LRI=lower respiratory infections. Lung C=lung, bronchus, and trachea cancers. NN Enceph=neonatal encephalopathy due to birth asphyxia and trauma. NN Preterm=neonatal preterm birth complications. NN Sepsis=neonatal sepsis and other neonatal infections. Oth Cardio=other cardiovascular and circulatory diseases. Oth MSK=other musculoskeletal disorders. Oth NN=Other neonatal disorders. PEM=protein-energy malnutrition. Prostate C=prostate cancer. Road Inj=road injuries. Sense=sense organ diseases. Skin=skin and subcutaneous diseases. STD=sexually transmitted diseases excluding HIV. Stomach C=stomach cancer. Stroke=Cerebrovascular disease. TB=tuberculosis. Violence=interpersonal violence. Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1327 nation, but we hope that the results of this study will spawn a plethora of derivative and policy-relevant research. We have highlighted key exemplar nations and those whose progress could be improved, but there are clearly more general lessons that can be learned from the epidemiological transition across all causes and locations. The collection of London Declaration NTDs, along with malaria28 and HIV, all show how effective domestic and international collaborations in fighting of specific infectious diseases can be. Whether or not these collaborations provide evidence of health-care improvements in one area, whether or not the benefits cascade, and whether or not non-health-specific interventions, such as poverty alleviation, education,29 and family planning, are having synergistic effects are hard to differentiate,28,30 but a host of online tools exist that enable national experts to examine performance of and changes in the leading causes of disease burden, compare them with peers, and objectively assess what can be improved. Since the concept of the DALY was introduced two decades ago,31 it has become a key metric for monitoring of population health and prioritisation within health sectors.32–39 The following are examples of policy makers, funders, or foundations that use DALYs in decision making: WHO, the World Bank, the National Institutes of Health, the US Centers for Disease Control and Prevention, the Bill & Melinda Gates Foundation, Gavi, the US President’s Emergency Plan for AIDS Relief, the Global Fund, and the Wellcome Trust, as well as the Chinese Politburo, the National Institute for Health and Care Excellence, Indonesia Bappenas, and Public Health England at the national level. Comparison of GBD 2016 with other global estimates and GBD 2015 The GBD study is the only source of comprehensive quantification of population health summary measures, including YLLs, YLDs, DALYs, and HALE. Specific efforts that are relevant to policy makers are being made to estimate burden within other organisations. Since GBD 2015, most organisations that we assessed have not produced updated estimates for DALYs or HALE. The exception to this is WHO, which has released updated Global Health Estimates for DALYs and HALE for 183 countries from 2000 to 2015.40 These estimates draw heavily on the GBD 2015 results, with revisions to the all-cause mortality envelope40,41 as detailed in the GBD 2016 cause-specific mortality publication10 and to selected cause-specific disability weights and severity distributions for YLDs.42 WHO-adjusted disability weights for various causes are further detailed in the WHO Global Health Estimates Technical Paper.41 Disease-specific considerations The SDI transition was prominent for many CMNN conditions, especially diarrhoea, lower respiratory infection, HIV after 2005 (the year of the global peak), maternal disorders, and vaccine-preventable diseases like measles, tetanus, diphtheria, and pertussis. Focused programmes with community-wide targeting of health interventions—such as access to antenatal care,43,44 vaccination, malaria vector control (insecticidetreated bednets and indoor residual spraying),45 and artemisinin combination therapies,46 water and sanitation efforts,47 and ART and prevention of motherto-child transmission of HIV30,48,49—are potentially responsible for DALY reductions in these conditions.50 Further scale-up and maintenance of these interventions should continue, including a transition from nongovernmental organ isations to local ownership and financing. Corre sponding improvements in health system functions related to survival of mothers and their children have not occurred,51 shown by the comparatively slow improvement in DALYs due to maternal and neonatal disorders compared with other CMNN causes and the growing burden of congenital birth defects, haemo globinopathies and haemolytic anaemias, and sudden infant death syndrome, especially in the under-5 age group. Early detection of complications of pregnancy like micronutrient deficiencies, hypertensive disorders of pregnancy, pregnancy-transmitted and sexually trans mitted infec tions, and congenital birth defects can help identify the pregnant women and newborns at highest risk, help ensure that appropriate curative treatments are administered, and put the females and newborns in a position to receive timely perinatal care when it is needed. Newborn screening for congenital birth defects and haemoglobinopathies can, at the very least, identify children with these conditions and facilitate maternal education about the importance of presentation for care early when a mother’s child gets sick. Delays in seeking care can have deadly consequences, particularly for children with acute conditions. Education of mothers and fathers, especially first-time parents, about how to best care for their babies can help reduce avoidable injuries and deaths. At the same time, stark increases in neonatal sepsis DALYs, in which nosocomial infections of the newborn are included, highlight the crucial importance of being vigilant in infection control within hospitals and clinics. This iteration of GBD estimated diseases and injuries for 5 year age groups older than 80 years for the first time: 80–84 years, 85–89 years, 90–94 years, and 95 years and older. This addition has provided a clearer picture than from previous GBD publications of the burden of disease in ageing populations, who are disproportionately affected by dementias and other NCDs compared with younger age groups. Ageing populations have increased substantially in size since 1990. As a result of this population shift and the epidemiological transition (which increases the proportion of burden of disease due to NCDs), age groups older than 80 years had increases in all-age DALYs between Global Health Metrics 1328 www.thelancet.com Vol 390 September 16, 2017 1990 and 2016, with increases across all SDI quintiles. The proportion due to NCDs also increased. Therefore, research into these age groups and the diseases that continue to affect them over time should be prioritised. Tuberculosis We made important changes to the modelling strategy for tuberculosis in GBD 2016. For fatal tuberculosis, we first modelled the prevalence of active disease and latent infection, which we then used as covariates for the Cause of Death Ensemble model. For non-fatal tuberculosis, we strengthened our statistical triangulation approach, which enforces consistency between data for different parameters by modelling tuberculosis incidence, prevalence, and mortality among those with latent infection. Application of MIRs estimated on the basis of SDI to better reflect incidence in low-income and middle-income countries has also enhanced consistency between fatal and non-fatal estimates of tuberculosis. All of these changes have resulted in global tuberculosis DALYs that are 15% higher than in GBD 2015 for the year 2010, with prominent increases occurring in several African countries, including Uganda, the Central African Republic, and Zambia. HIV/AIDS A major HIV methods change for GBD 2016 was the distribution of ART coverage by age, sex, and CD4positive cell count. We used two AIDS Indicator Surveys52,53 to predict the age-sex-CD4 cell distribution of ART coverage and applied the distributions to the input counts of people receiving ART in our HIV estimation model. This method shifted the coverage distribution to groups with higher CD4 cell counts, as was seen in the data. All of these changes have resulted in higher global HIV/AIDS DALYs than in GBD 2015 for the year 2010, with the most prominent increases occurring in several African countries, including South Africa, Botswana, Lesotho, and Swaziland. We have also systematically updated other key input parameters to the HIV/AIDS estimation process, such as the on-ART mortality rate and other demographic inputs, including HIV-free mortality. Lower respiratory infection and diarrhoea Total lower respiratory infection (increased by 0·701%) and diarrhoeal disease (increased by 15·5%) DALYs increased compared with GBD 2015 estimates for the year 2010. Like most CMNN causes, the lower respiratory infection and diarrhoeal disease DALY burden is due primarily to YLLs; more than 90% of global DALYs are from these causes. Several changes have been made to the under-5 mortality models that have large impacts on the DALY totals for these causes. We added several new model covariates on the basis of risk factors associated with lower respiratory infections and diarrhoeal diseases, including childhood stunting and suboptimal breastfeeding. Additionally, because of India’s large population size and disease burden, its Sample Registration System data changed the magnitude of YLLs. Inclusion of diarrhoeal diseases as a fatal discontinuity in YLL estimation also affected the overall estimation for GBD 2016. Overall, the YLLs due to lower respiratory infections decreased slightly (by 0·626%) for the year 2010 from GBD 2015 to GBD 2016 and those due to diarrhoeal diseases increased (by 14·3%). We believe that these modelling changes improved under-5 mortality estimates for lower respiratory infections and diarrhoea. Although responsible for a smaller contribution to DALYs than cause of death models, the non-fatal models of lower respiratory infections and diarrhoea included new data sources and processing. An example is that we now account for seasonality from the populationrepresentative surveys that provide most prevalence data from sub-Saharan Africa and south Asia in the lower respiratory infection and diarrhoeal disease models. Another major update is a change in the estimated mean duration of illness on the basis of a set of updated systematic reviews. The duration of diarrhoea remained nearly the same as in GBD 2015, but that of lower respiratory infection decreased by about 20% compared with GBD 2015. Malaria Refinements to the methodological approach and addition of substantially more data than in GBD 2015 have led to changes in estimates of both malaria mortality and morbidity estimates in the GBD 2016 iteration, with resulting changes in DALYs. Globally, predicted trends in malaria were similar to GBD 2015, rising to a peak in 2005 before steadily declining. Overall malaria DALYs are lower in GBD 2016 than in GBD 2015, reflecting mainly lower estimates outside of Africa and particularly for India. Outside of Africa, and for lowerburden countries within Africa, estimates were informed for the first time by extensive subnational case-reporting data from routine surveillance systems. These were subsequently adjusted to account for under-reporting, misdiagnosis, and incompleteness, and then entered into a spatiotemporal geostatistical model to infer continuous surfaces of incidence rate before reaggregating them to national and subnational totals. This approach led to notable reductions in estimated cases, YLDs, and DALYs in India, Myanmar, Indonesia, and Pakistan. In high-burden countries in sub-Saharan Africa, where the methods remained similar to GBD 2015, changes were relatively modest and reflected the inclusion of newly available cross-sectional parasite rate surveys or updates to data for malaria intervention coverage in recent years. London Declaration diseases The London Declaration was established in 2012 as a partnership of pharmaceutical companies, private Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1329 foundations, and global health organisations, com mitted to providing resources and expertise for controlling, eliminating, or eradicating ten NTDs: human African trypanosomiasis, Chagas disease, Guinea worm disease, leprosy, lymphatic filariasis, oncho cerciasis, schistosomiasis, soil-transmitted hel minths, blinding trachoma, and visceral leishmaniasis.54 With this year’s addition of Guinea worm disease to the GBD cause list, we now estimate DALYs for all ten of the London Declaration diseases and GBD estimates can, therefore, offer insight into progress. We estimate that total DALYs from the London Declaration NTDs have declined by 21·1% from 11·4 million (95% UI 6·8 million to 18·5 million) DALYs in 2010 before the London Declaration in 2012 to 9·0 million (5·3 million to 14·5 million) in 2016. Moreover, we find that DALY rates have declined for all ten of the London Declaration NTDs between 1990 and 2016, with the largest declines occurring for Guinea worm disease, reflecting the particular effectiveness of the Guinea worm eradication initiative,55 and for human African trypanosomiasis.56 Zika virus disease The appearance of Zika virus cases in the Americas in 2015, along with the reported associations between Zika virus infection and microcephaly and Guillain-Barré syndrome, led WHO to declare Zika virus a Public Health Emergency of International Concern in February 2016.57–59 We estimated Zika virus disease for GBD 2016 in response to this heightened global concern and interest. We estimated four non-fatal outcomes of Zika virus infection: asympto matic infection, symptomatic infection, Guillain-Barré syndrome caused by Zika virus infection, and congenital Zika syndrome. Despite the high incidence observed in the Americas in 2016 and understandable public concern, especially surrounding congenital Zika syndrome, we estimate that less than 0·01% of Zika virus infections result in Guillain-Barré syndrome, congenital Zika syndrome, or death. We estimate that 7·60 million (95% UI 5·70 million to 10·7 million) infections occurred in 2016, resulting in 2·70 million symptomatic Zika virus cases, 1880 cases of Zika-attributable Guillain-Barré syndrome, and 2400 congenital Zika syndrome births.8 Our estimate for congenital Zika syndrome is in line with official reports from the Pan American Health Organization of circa 2500 congenital Zika syndrome births.60 However, those born with congenital Zika syndrome will have future disability. Injuries Global DALYs due to injuries generally contributed to a lower proportion of total burden than did those due to CMNN diseases and NCDs, and remained relatively stable as a proportion of total DALYs over time. Examination of these trends by SDI quintile, region, and cause of injury reveals much more variable patterns than the global pattern, which are potentially related to a confluence of demographic changes, policy adoption and enforcement, access to high-quality trauma care, and political stability. Among high-SDI and highmiddle-SDI countries, all-age DALYs due to road injuries have substantially declined since 1990, while DALYs due to road injuries in low-middle-SDI and lowSDI countries have risen, a trend that is particularly evident in recent years. By contrast, DALYs from drowning markedly declined among middle-SDI and low-middle-SDI countries. Disease burden from falls rose across the development spectrum, a trend primarily driven by population growth and ageing. Finally, the consequences of ongoing conflict, particularly in north Africa and the Middle East, and interpersonal violence, particularly in Latin America, on population health cannot be overlooked.61 Fatalities due to such conflict and violence have resulted in stagnated or decreasing life expectancy in many of these countries,7,62 and for those who survive, the long-term effects of such injuries could easily result in impaired movement and functioning, heightened risk of other disorders (eg, musculoskeletal conditions), and mental health challenges for an extended period after the end of the conflict. Cerebrovascular disease For GBD 2016, we modelled each stroke subtype independently to produce more reliable ratios of ischaemic to haemorrhagic stroke than for GBD 2015 and better match our independent estimation of subtype-specific stroke mortality. We avoided undercounting of stroke by reclassifying hospital admissions and deaths ascribed to unspecified stroke. Despite this method, haemorrhagic stroke remains a heterogeneous category that includes neonatal intraventricular haemorrhage and all other nontraumatic intracranial bleeding. These deaths in children younger than 5 years result in many more YLLs, and therefore many more DALYs, than from ischaemic stroke. Consistent with population-based and multinational studies of stroke subtype, GBD estimates higher incidence but much lower case fatality and YLLs due to ischaemic stroke among adults than due to haemorrhagic stroke.63 This pattern appears true even for locations where this pattern was not previously thought to be the case, such as in China.64 Future estimates can be improved by production of separate estimates of non-traumatic subarachnoid haemorrhage and paediatric stroke. Mental and substance use disorders Throughout multiple iterations of GBD, mental and substance use disorders have consistently been shown as the leading causes of YLDs worldwide, with burden present in both sexes across the lifespan. They are also strongly associated with premature mortality, although this association is not reflected in GBD YLL estimates for mental disorders as they are rarely coded as the direct Global Health Metrics 1330 www.thelancet.com Vol 390 September 16, 2017 cause of death. Nevertheless, they contribute a substantial number of DALYs and this large contribution to burden has remained constant across time in all countries, including those with high or substantially improving SDI. Treatment rates remain very low65–67 and, even in highincome countries where treatment coverage has increased, the prevalence of the most common disorders has not changed.68 To reduce the burden of these disorders, improved treatment coverage needs to include a focus on the quality of the intervention delivered. Additionally, identification and quantification of modifiable risk factors for mental and substance use disorders are vital for development of effective prevention strategies and are an area noted for expansion in future iterations of GBD. Diabetes We made several important improvements to the process of estimation of diabetes prevalence, including use of more data sources than GBD 2015 and development of a novel approach to standardise the definition of diabetes across different sources. In our assessment of diagnostic criteria for diabetes across different surveys, we identified more than 50 different definitions for diabetes based on various biomarkers (eg, fasting plasma glucose concentration, oral glucose tolerance test result, and glycated haemoglobin A1c concentration) and different levels of each biomarker. To standardise the definition of diabetes, we mostly focused on the surveys that had included fasting plasma glucose concentration as a diagnostic criterion and developed an ensemble model to characterise the distribution of fasting plasma glucose concentration at the population level in each age and sex group. Then, we used the fasting plasma glucose concentration distribution to convert various definitions of diabetes into the standard case definition. Using the ensemble model, we also estimated the prevalence of diabetes on the basis of the mean fasting plasma glucose concentration in places for which we only had data for mean fasting plasma glucose concentration. These changes allowed us to be more consistent than in GBD 2015 in our estimation of the prevalence of diabetes across countries and over time. As a result, our estimates of the prevalence of diabetes globally and in most regions are slightly lower than those reported in GBD 2015. The strong relationship between SDI and diabetes remained, mirroring global rises in overweight and obesity.69 Cancer DALYs for cancer have changed compared with GBD 2015; this change is predominantly due to lower YLD estimates for GBD 2016 than for GBD 2015. These improvements stem from adjustments made in the modelling of MIRs to better reflect differences in MIR based on SDI in data-sparse locations than in GBD 2015. In addition to stricter inclusion criteria for data used in the MIR modelling than in GBD 2015, we changed the modelling approach and used the most parsimonious model with just SDI as a predictor of MIR.8 MIRs are used to estimate cancer incidence and prevalence from GBD cancer mortality estimates and therefore directly determine YLDs. Until cancer registry incidence data and accurate mortality statistics are widely available, validation of MIR is difficult in countries that lack these data sources. The Global Initiative for Cancer Registry Development and expansion of civil registration systems are therefore crucial to further improve estimation of cancer burden. Future directions Challenging data gaps exist in the severity distributions across sequelae for most diseases in the YLD literature. Most data sources for severity are from high-income countries, which probably leads to an underestimation of YLDs in low-income and middle-income countries where the severity of presentation of non-fatal illnesses might be worse than in high-income countries, frequently as a result of late diagnosis and underdiagnosis. Improvements can be made if disease-specific research focuses on routine use of a single established measure of severity in surveys and patient populations and if countries are able to link survey data to a general health assessment instrument, along with improvements in early diagnosis and treatment procedures before illnesses progress to high-severity presentations. Although uncertainty and sample variance will persist at various levels, gaining of greater geographical information on severity of diseases than at present will increase the accuracy of GBD models. Potential is clearly huge for exploration of this work in relation to development assistance for health,70,71 as the connection between health financing and outcomes needs better understanding than at present. Improved understanding will probably help identify the reasons why certain countries have such an impressive record, whereas others are so ineffective with the resources that they have available, at all levels of SDI. More precise measurement of health burden than at present, across countries and at the subnational level, could be tied to health services financing and delivery to identify systematic associations and causal relationships. Additionally, risk factor data, health outcome data, health financing data, and other socioeconomic indicators can be combined to measure and assess health system performance. Furthermore, a core focus moving forward with GBD is to progressively increase the spatial resolution at which we implement estimations to help realise aspirations in precision public health.72 A key goal of a geographically refined DALY, at 5 × 5 km spatial resolution—starting with some key CMNN causes and under-5 mortality—is part of a long-term aspiration.73 Finally, upcoming GBD work will focus on exploration of future health scenarios, examining the likely burden of disease under different possible trajectories of independent drivers of health. This framework will capture the complex past trends and interdependent Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1331 relationships in socioeconomic development, risk factors, interventions, morbidity, mortality, and population to both estimate the likely future burden of disease and enable comparison between scenarios based on different sets of assumptions. For instance, it can be used to analyse the likely effect of the introduction and scale-up of a new type of vaccination or different trends in funding for ART for HIV/AIDS. Extension of this work through to DALY and HALE analyses is also part of these future plans. To look at cause-deleted HALE would also be valuable so that we could understand the remaining DALY burden in the absence of diseases and injuries that might be highly amenable to preventive or curative measures. Compelling examples would include HIV, vaccine-preventable and malnutrition-related diseases, a subset of NCDs that are particularly amenable to health-care or preventive measures (eg, tobacco-related or alcohol-related or some congenital birth defects), and injuries (eg, firearms or transport injuries). This approach would not be fully counterfactual, but would be a first and more rigorous way of showing what conditions contributed to the changes in HALE from 1990 (or 2006) to 2016 than was possible in this study. Limitations Despite our continued methodological advancements and data enrichments, this study has limitations. First, all limitations documented in the elements of the GBD estimation process that allow for DALY and HALE estimation2–4,12 will contribute to uncertainty in these summary measures. Second, these summary measures will also be influenced by data availability. Time lags in the reporting of health information by national authorities and thus their subsequent incorporation into the GBD estimation mean that some of the most recent changes in health states will not be captured. Relatedly, data deficiencies from populations in conflict zones (eg, Syria, Iraq, Yemen, South Sudan, and Afghanistan), autonomous subnational regions, and certain nongeographically based subpopulations (ie, migrants, refugees, and some indigenous people) limit the precision of some of our estimated levels and trends of disease burden.74 Third, the relationship between DALYs, HALE, and SDI, although explanatory, cannot be viewed as causal. Fourth, a non-trivial assumption of the analyses is the independence of the uncertainty calculated for YLLs and YLDs. Because of the link between death and prevalence, a positive correlation probably exists between these uncertainties that we do not capture. As such, we probably underestimate the aggregated uncertainty for DALYs, although the primary source of uncertainty for DALYs comes from uncertainty in disability weights, which are unaffected by this limitation. In future iterations of GBD, this potential correlation will be explored using copula, a statistical method that models the dependence structure among multiple independent marginal probability distributions to estimate correlation between them.75 Conclusion Many improvements have been made to GBD 2016 to allow for a clearer and more nuanced picture of the changing picture of global health than in GBD 2015. Among these improvements are inclusion of new studies and subnational data (notably in India) and many substantial improvements to modelling and analyses. Prominent results of these changes include higher global DALY estimates for tuberculosis, HIV/AIDS, lower respiratory infection, and diarrhoeal disease than in GBD 2015. We have for the first time discussed our DALY exemplars, countries with the lowest ratio of observed to expected DALYs, as well as our poor performers, those with the largest ratio of observed to expected DALYs. These exemplars and poor performers suggest a need to learn lessons from those with clear health gains and implement system-based strategies for those struggling. This analysis and increasingly more detailed results will allow for more informed public policy and health financing decisions as the world faces an absolute expansion of morbidity. Globally, individuals could expect to live substantially longer lives in 2016 than they could in 1990. This improvement was due to a rapid decline in YLLs and more modest age-standardised declines in YLDs, leading to lower age-standardised DALY rates across the entire socioeconomic development spectrum in 2016 than in 1990, with a decrease of approximately a third for all causes. At the same time, populations can expect to spend more time with functional health loss due to absolute morbidity expansion than previously. Such improvements have been accompanied by rapid population growth and ageing and, against the backdrop of the epidemiological transition, have resulted in the paradox of a total expansion in DALY burden and thus an ever-increasing demand on health systems, domestic health financing, development assistance for health, and associated global health organisations. This increasing demand on health systems is ubiquitous across time, location, GBD region, and SDI, posing a huge opportunity for health-care innovators in prevention and treatment for morbidity reduction. Our analysis of exemplars and poor performers is also indicative of various practices that can hasten or slow the epidemiological transition and warrant deep investigation. GBD 2016 DALYs and HALE Collaborators Simon I Hay, Amanuel Alemu Abajobir, Kalkidan Hassen Abate, Cristiana Abbafati, Kaja M Abbas, Foad Abd-Allah, Abdishakur M Abdulle, Teshome Abuka Abebo, Semaw Ferede Abera, Victor Aboyans, Laith J Abu-Raddad, Ilana N Ackerman, Isaac A Adedeji, Olatunji Adetokunboh, Ashkan Afshin, Rakesh Aggarwal, Sutapa Agrawal, Anurag Agrawal, Aliasghar Ahmad Kiadaliri, Muktar Beshir Ahmed, Amani Nidhal Aichour, Ibtihel Aichour, Miloud Taki Eddine Aichour, Sneha Aiyar, Tomi F Akinyemiju, Nadia Akseer, Faris Hasan Al Lami, Global Health Metrics 1332 www.thelancet.com Vol 390 September 16, 2017 Fares Alahdab, Ziyad Al-Aly, Khurshid Alam, Noore Alam, Tahiya Alam, Deena Alasfoor, Kefyalew Addis Alene, Raghib Ali, Reza Alizadeh-Navaei, Juma M Alkaabi, Ala’a Alkerwi, François Alla, Peter Allebeck, Christine Allen, Fatma Al-Maskari, Mohammad AbdulAziz AlMazroa, Rajaa Al-Raddadi, Ubai Alsharif, Shirina Alsowaidi, Benjamin M Althouse, Khalid A Altirkawi, Nelson Alvis-Guzman, Azmeraw T Amare, Erfan Amini, Walid Ammar, Yaw Ampem Amoako, Mustafa Geleto Ansha, Carl Abelardo T Antonio, Palwasha Anwari, Johan Ärnlöv, Megha Arora, Al Artaman, Krishna Kumar Aryal, Solomon W Asgedom, Tesfay Mehari Atey, Niguse Tadele Atnafu, Leticia Avila-Burgos, Euripide Frinel G Arthur Avokpaho, Ashish Awasthi, Shally Awasthi, Beatriz Paulina Ayala Quintanilla, Mahmoud Reza Azarpazhooh, Peter Azzopardi, Tesleem Kayode Babalola, Umar Bacha, Alaa Badawi, Kalpana Balakrishnan, Marlena S Bannick, Aleksandra Barac, Suzanne L Barker-Collo, Till Bärnighausen, Simon Barquera, Lope H Barrero, Sanjay Basu, Robert Battista, Katherine E Battle, Bernhard T Baune, Shahrzad Bazargan-Hejazi, Justin Beardsley, Neeraj Bedi, Yannick Béjot, Bayu Begashaw Bekele, Michelle L Bell, Derrick A Bennett, James R Bennett, Isabela M Bensenor, Jennifer Benson, Adugnaw Berhane, Derbew Fikadu Berhe, Eduardo Bernabé, Balem Demtsu Betsu, Mircea Beuran, Addisu Shunu Beyene, Anil Bhansali, Samir Bhatt, Zulfiqar A Bhutta, Sibhatu Biadgilign, Kelly Bienhoff, Boris Bikbov, Charles Birungi, Stan Biryukov, Donal Bisanzio, Habtamu Mellie Bizuayehu, Fiona M Blyth, Dube Jara Boneya, Dipan Bose, Ibrahim R Bou-Orm, Rupert R A Bourne, Michael Brainin, Carol E G Brayne, Alexandra Brazinova, Nicholas J K Breitborde, Paul S Briant, Gabrielle Britton, Traolach S Brugha, Rachelle Buchbinder, Lemma Negesa Bulto Bulto, Blair Bumgarner, Zahid A Butt, Lucero Cahuana-Hurtado, Ewan Cameron, Ismael Ricardo Campos-Nonato, Hélène Carabin, Rosario Cárdenas, David O Carpenter, Juan Jesus Carrero, Austin Carter, Felix Carvalho, Daniel Casey, Carlos A Castañeda-Orjuela, Jacqueline Castillo Rivas, Chris D Castle, Ferrán Catalá-López, Jung-Chen Chang, Fiona J Charlson, Pankaj Chaturvedi, Honglei Chen, Mirriam Chibalabala, Chioma Ezinne Chibueze, Vesper Hichilombwe Chisumpa, Abdulaal A Chitheer, Rajiv Chowdhury, Devasahayam Jesudas Christopher, Liliana G Ciobanu, Massimo Cirillo, Danny Colombara, Leslie Trumbull Cooper, Cyrus Cooper, Paolo Angelo Cortesi, Monica Cortinovis, Michael H Criqui, Elizabeth A Cromwell, Marita Cross, John A Crump, Abel Fekadu Dadi, Koustuv Dalal, Albertino Damasceno, Lalit Dandona, Rakhi Dandona, José das Neves, Dragos V Davitoiu, Kairat Davletov, Barbora de Courten, Diego De Leo, Hans De Steur, Louisa Degenhardt, Selina Deiparine, Robert P Dellavalle, Kebede Deribe, Amare Deribew, Don C Des Jarlais, Subhojit Dey, Samath D Dharmaratne, Preet K Dhillon, Daniel Dicker, Shirin Djalalinia, Huyen Phuc Do, Klara Dokova, David Teye Doku, E Ray Dorsey, Kadine Priscila Bender dos Santos, Tim R Driscoll, Manisha Dubey, Bruce Bartholow Duncan, Beth E Ebel, Michelle Echko, Ziad Ziad El-Khatib, Ahmadali Enayati, Aman Yesuf Endries, Sergey Petrovich Ermakov, Holly E Erskine, Setegn Eshetie, Babak Eshrati, Alireza Esteghamati, Kara Estep, Fanuel Belayneh Bekele Fanuel, Tamer Farag, Carla Sofia e Sa Farinha, André Faro, Farshad Farzadfar, Mir Sohail Fazeli, Valery L Feigin, Andrea B Feigl, Seyed-Mohammad Fereshtehnejad, João C Fernandes, Alize J Ferrari, Tesfaye Regassa Feyissa, Irina Filip, Florian Fischer, Christina Fitzmaurice, Abraham D Flaxman, Nataliya Foigt, Kyle J Foreman, Richard C Franklin, Joseph J Frostad, Nancy Fullman, Thomas Fürst, Joao M Furtado, Neal D Futran, Emmanuela Gakidou, Alberto L Garcia-Basteiro, Teshome Gebre, Gebremedhin Berhe Gebregergs, Tsegaye Tewelde Gebrehiwot, Johanna M Geleijnse, Ayele Geleto, Bikila Lencha Gemechu, Hailay Abrha Gesesew, Peter W Gething, Alireza Ghajar, Katherine B Gibney, Richard F Gillum, Ibrahim Abdelmageem Mohamed Ginawi, Melkamu Dedefo Gishu, Giorgia Giussani, William W Godwin, Kashish Goel, Shifalika Goenka, Ellen M Goldberg, Philimon N Gona, Amador Goodridge, Sameer Vali Gopalani, Richard A Gosselin, Carolyn C Gotay, Atsushi Goto, Alessandra Carvalho Goulart, Nicholas Graetz, Harish Chander Gugnani, Rajeev Gupta, Prakash C Gupta, Tanush Gupta, Vipin Gupta, Rahul Gupta, Reyna A Gutiérrez, Vladimir Hachinski, Nima Hafezi-Nejad, Alemayehu Desalegne Hailu, Gessessew Bugssa Hailu, Randah Ribhi Hamadeh, Samer Hamidi, Mouhanad Hammami, Alexis J Handal, Graeme J Hankey, Yuantao Hao, Hilda L Harb, Habtamu Abera Hareri, Josep Maria Haro, Kimani M Harun, James Harvey, Mohammad Sadegh Hassanvand, Rasmus Havmoeller, Roderick J Hay, Mohammad T Hedayati, Delia Hendrie, Nathaniel J Henry, Ileana Beatriz Heredia-Pi, Pouria Heydarpour, Hans W Hoek, Howard J Hoffman, Masako Horino, Nobuyuki Horita, H Dean Hosgood, Sorin Hostiuc, Peter J Hotez, Damian G Hoy, Aung Soe Htet, Guoqing Hu, John J Huang, Chantal Huynh, Kim Moesgaard Iburg, Ehimario Uche Igumbor, Chad Ikeda, Caleb Mackay Salpeter Irvine, Kathryn H Jacobsen, Nader Jahanmehr, Mihajlo B Jakovljevic, Peter James, Simerjot K Jassal, Mehdi Javanbakht, Sudha P Jayaraman, Panniyammakal Jeemon, Paul N Jensen, Vivekanand Jha, Guohong Jiang, Denny John, Catherine O Johnson, Sarah Charlotte Johnson, Jost B Jonas, Mikk Jürisson, Zubair Kabir, Rajendra Kadel, Amaha Kahsay, Ritul Kamal, Chittaranjan Kar, Nadim E Karam, André Karch, Corine Kakizi Karema, Seyed M Karimi, Chante Karimkhani, Amir Kasaeian, Getachew Mullu Kassa, Nicholas J Kassebaum, Nigussie Assefa Kassaw, Anshul Kastor, Srinivasa Vittal Katikireddi, Anil Kaul, Norito Kawakami, Peter Njenga Keiyoro, Laura Kemmer, Andre Pascal Kengne, Andre Keren, Chandrasekharan Nair Kesavachandran, Yousef Saleh Khader, Ibrahim A Khalil, Ejaz Ahmad Khan, Young-Ho Khang, Abdullah T Khoja, Ardeshir Khosravi, Jagdish Khubchandani, Christian Kieling, Yun Jin Kim, Daniel Kim, Ruth W Kimokoti, Yohannes Kinfu, Adnan Kisa, Katarzyna A Kissimova-Skarbek, Niranjan Kissoon, Mika Kivimaki, Ann Kristin Knudsen, Yoshihiro Kokubo, Dhaval Kolte, Jacek A Kopec, Soewarta Kosen, Georgios A Kotsakis, Parvaiz A Koul, Ai Koyanagi, Michael Kravchenko, Kristopher J Krohn, Barthelemy Kuate Defo, Burcu Kucuk Bicer, G Anil Kumar, Pushpendra Kumar, Hmwe H Kyu, Anton Carl Jonas Lager, Dharmesh Kumar Lal, Ratilal Lalloo, Tea Lallukka, Nkurunziza Lambert, Qing Lan, Van C Lansingh, Anders Larsson, Janet L Leasher, Paul H Lee, James Leigh, Cheru Tesema Leshargie, Janni Leung, Ricky Leung, Miriam Levi, Yichong Li, Yongmei Li, Xiaofeng Liang, Misgan Legesse Liben, Stephen S Lim, Shai Linn, Angela Liu, Patrick Y Liu, Shiwei Liu, Yang Liu, Rakesh Lodha, Giancarlo Logroscino, Katharine J Looker, Alan D Lopez, Stefan Lorkowski, Paulo A Lotufo, Rafael Lozano, Timothy C D Lucas, Raimundas Lunevicius, Ronan A Lyons, Erlyn Rachelle King Macarayan, Emilie R Maddison, Hassan Magdy Abd El Razek, Mohammed Magdy Abd El Razek, Carlos Magis-Rodriguez, Mahdi Mahdavi, Marek Majdan, Reza Majdzadeh, Azeem Majeed, Reza Malekzadeh, Rajesh Malhotra, Deborah Carvalho Malta, Abdullah A Mamun, Helena Manguerra, Treh Manhertz, Lorenzo G Mantovani, Chabila C Mapoma, Lyn M March, Laurie B Marczak, Jose Martinez-Raga, Paulo Henrique Viegas Martins, Francisco Rogerlândio Martins-Melo, Ira Martopullo, Winfried März, Manu Raj Mathur, Mohsen Mazidi, Colm McAlinden, Madeline McGaughey, John J McGrath, Martin McKee, Suresh Mehata, Toni Meier, Kidanu Gebremariam Meles, Peter Memiah, Ziad A Memish, Walter Mendoza, Melkamu Merid Mengesha, Mubarek Abera Mengistie, Desalegn Tadese Mengistu, George A Mensah, Atte Meretoja, Tuomo J Meretoja, Haftay Berhane Mezgebe, Renata Micha, Anoushka Millear, Ted R Miller, Shawn Minnig, Mojde Mirarefin, Erkin M Mirrakhimov, Awoke Misganaw, Shiva Raj Mishra, Philip B Mitchell, Karzan Abdulmuhsin Mohammad, Alireza Mohammadi, Shafiu Mohammed, Kedir Endris Mohammed, Muktar Sano Kedir Mohammed, Murali B V Mohan, Ali H Mokdad, Sarah K Mollenkopf, Lorenzo Monasta, Julio Cesar Montañez Hernandez, Marcella Montico, Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1339 Tobacco & Other Drug Research Unit (Prof C D Parry PhD), South African Medical Research Council, Cape Town, South Africa (A P Kengne PhD,); Krishan Institute of Medical Sciences, Deemed University, School of Dental Sciences, Karad, India (S T Patil MDS); Department of Community Health Sciences (Prof S B Patten PhD), University of Calgary, Calgary, AB, Canada (Prof M Tonelli MD); UK Department for International Development, Lalitpur, Nepal (D Paudel PhD); REQUIMTE/LAQV, Laboratório de Farmacognosia, Departamento de Química, Faculdade de Farmácia, Universidade do Porto, Porto, Portugal (Prof D M Pereira PhD); Aalborg University, Aalborg Esst, Denmark (C B Peterson PhD); Department of Anesthesiology (A S Terkawi MD), University of Virginia, Charlottesville, VA, USA (W A Petri MD); Health Metrics Unit, University of Gothenburg, Gothenburg, Sweden (Prof M Petzold PhD); University of the Witwatersrand, Johannesburg, South Africa (Prof M Petzold PhD); Shanghai Jiao Tong University School of Medicine, Shanghai, China (Prof M R Phillips MD); Durban University of Technology, Durban, South Africa (J D Pillay PhD); Exposure Assessment and Environmental Health Indicators, German Environment Agency, Berlin, Germany (D Plass DrPH, M Tobollik MPH); Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India (Prof N Prasad DM); Intergrowth 21st Study Research Centre, Nagpur, India (Prof M Purwar MD); Non-Communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran (M Qorbani PhD); A T Still University, Kirksville, MO, USA (A Radfar MD); Contech International Health Consultants, Lahore, Pakistan (A Rafay MS); Contech School of Public Health, Lahore, Pakistan (A Rafay MS); Research and Evaluation Division, BRAC, Dhaka, Bangladesh (M Rahman PhD); Society for Health and Demographic Surveillance, Suri, India (R K Rai MPH); ERAWEB Program, University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria (S Rajsic MD); Department of Preventive Medicine, Wonju College of Medicine, Yonsei University, Wonju, South Korea (C L Ranabhat PhD); Health Science Foundation and Study Center, Kathmandu, Nepal (C L Ranabhat PhD); Schizophrenia Research Foundation, Chennai, India (T Rangaswamy PhD); Diabetes Research Society, Hyderabad, India (Prof P V Rao MD); Diabetes Research Center, Hyderabad, India (Prof P V Rao MD); Azienda Socio-Sanitaria Territoriale, Papa Giovanni XXIII, Bergamo, Italy (Prof G Remuzzi MD); Department of Biomedical and Clinical Sciences “L Sacco”, University of Milan, Milan, Italy (Prof G Remuzzi MD); Research Center for Environmental Determinants of Health, School of Public Health (S Rezaei PhD), Kermanshah University of Medical Sciences, Kermanshah, Iran (S Siabani PhD); Hospital das Clínicas da Universidade Federal de Minas Gerais, Belo Horizonte, Brazil (Prof A L Ribeiro MD); RMIT University, Bundoora, VIC, Australia (Prof S R Robinson PhD); Campus MAR, Barcelona Biomedical Research Park (PRBB), ISGlobal Instituto de Salud Global de Barcelona, Barcelona, Spain (D Rojas-Rueda PhD); Golestan Research Center of Gastroenterology and Hepatology, Golestan University of Medical Sciences, Gorgan, Iran (G Roshandel PhD); Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany (Prof G Nagel PhD, Prof D Rothenbacher MD); Universidad Tecnica del Norte, Ibarra, Ecuador (E Rubagotti PhD); Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania (G M Ruhago PhD, B F Sunguya PhD); Managerial Epidemiology Research Center, Department of Public Health, School of Nursing and Midwifery, Maragheh University of Medical Sciences, Maragheh, Iran (S Safiri PhD); Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia (R Sahathevan PhD); Ballarat Health Service, Ballarat, VIC, Australia (R Sahathevan PhD); Development Research and Projects Center, Abuja, Nigeria (M M Saleh MPH); Chest Research Foundation, Pune, India (S S Salvi MD); Faculty of Science, Ain Shams University, Cairo, Egypt (A M Samy PhD); J Edwards School of Medicine (J R Sanabria MD), Department of Public Health (M Sawhney PhD), Marshall University, Huntington, WV, USA; Case Western Reserve University, Cleveland, OH, USA (J R Sanabria MD); IIS-Fundacion Jimenez Diaz, Madrid, Spain (M D Sanchez-Niño PhD); Department of Community Medicine, Information and Health Decision Sciences, Center for Health Technology and Services Research - CINTESIS, Porto, Portugal (J V Santos MD); Centre of Advanced Study in Psychology, Utkal University, Bhubaneswar, India (M Satpathy PhD); Federal University of Santa Catarina, Florianópolis, Brazil (I J C Schneider PhD); Division of Clinical Epidemiology and Ageing Research, German Cancer Research Center, Heidelberg, Germany (B Schöttker MPH); Institute of Health Care and Social Sciences, FOM University, Essen, Germany (B Schöttker MPH); Hypertension in Africa Research Team (HART), North-West University, Potchefstroom, South Africa (Prof A E Schutte PhD); UKZN Gastrointestinal Cancer Research Centre (Prof B Sartorius PhD), South African Medical Research Council, Potchefstroom, South Africa (Prof A E Schutte PhD); Charité Berlin, Berlin, Germany (F Schwendicke PhD); Department of Public Health, An-Najah University, Nablus, Palestine (A Shaheen PhD); Independent Consultant, Karachi, Pakistan (M A Shaikh MD); The George Institute for Global Health, Sydney, NSW, Australia (S M Shariful Islam PhD); Indian Institute of Technology Ropar, Rupnagar, India (R Sharma MA); Ministry of Health, Thimphu, Bhutan (J Sharma MPH); Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai, China (J She MD); National Institute of Infectious Diseases, Tokyo, Japan (M Shigematsu PhD); Sandia National Laboratories, Albuquerque, NM, USA (M Shigematsu PhD); Washington State University, Spokane, WA, USA (K Shishani PhD); University of Technology Sydney, Sydney, NSW, Australia (S Siabani PhD); Reykjavik University, Reykjavik, Iceland (I D Sigfusdottir PhD); Federal University of Santa Catarina, Florianopolis, Brazil (D A S Silva PhD); Brasília University, Brasília, Brazil (D G A Silveira MD); University of Pennsylvania, Philadelphia, PA, USA (D H Silberberg MD); Asthma Bhawan, Jaipur, India (V Singh MD); Department of Medicine, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India (O P Singh PhD); Max Hospital, Gaziabad, India (Prof N P Singh MD); School of Preventive Oncology, Patna, India (D N Sinha PhD); WHO FCTC Global Knowledge Hub on Smokeless Tobacco, National Institute of Cancer Prevention, Noida, India (D N Sinha PhD); Hywel Dda University Health Board, Carmarthen, UK (E Skiadaresi MD); Bristol Eye Hospital, Bristol, UK (E Skiadaresi MD); King Khalid University Hospital, Riyadh, Saudi Arabia (B H A Sobaih MD); University of Yaoundé, Yaoundé, Cameroon (Prof E Sobngwi PhD); Yaoundé Central Hospital, Yaoundé, Cameroon (Prof E Sobngwi PhD); National School of Public Health/Oswaldo Cruz Foundation, Rio de Janeiro, Brazil (Prof T C M Sousa MPH); Department of Community Medicine, International Medical University, Kuala Lumpur, Malaysia (C T Sreeramareddy MD); Attikon University Hospital, Athens, Greece (V Stathopoulou PhD); University of East Anglia, Norwich, UK (Prof N Steel PhD); Public Health England, London, UK (Prof N Steel PhD); South African Medical Research Council Unit on Anxiety & Stress Disorders, Cape Town, South Africa (Prof D J Stein PhD); Department of Dermatology, University Hospital Muenster, Muenster, Germany (S Steinke DrMed); Deakin University, Burwood, VIC, Australia (Prof M A Stokes PhD); Department of Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway (Prof L J Stovner PhD); Norwegian Advisory Unit on Headache, St Olavs Hospital, Trondheim, Norway (Prof L J Stovner PhD); Ministry of Health, KSA, Riyadh, Saudi Arabia (R Suliankatchi Abdulkader MD); Indian Council of Medical Research, New Delhi, India (S Swaminathan MD); Departments of Criminology, Law & Society, Sociology, and Public Health, University of California, Irvine, Irvine, CA, USA (Prof B L Sykes PhD); Griffith University, Gold Coast, QLD, Australia (S K Tadakamadla PhD); WSH Institute, Ministry of Manpower, Singapore, Singapore (J S Takala DSc); Tampere University of Technology, Tampere, Finland (J S Takala DSc); Chaim Sheba Medical Center, Tel Hashomer, Israel (Prof D Tanne MD); Tel Aviv University, Tel Aviv, Israel (Prof D Tanne MD); Ethiopian Public Health Association, Addis Ababa, Ethiopia (Y L Tarekegn MS); New York Medical Center, Valhalla, NY, USA (M Tavakkoli MD); Instituto Superior de Ciências da Saúde Egas Moniz, Almada, Portugal (Prof N Taveira PhD); Faculty of Pharmacy, Universidade de Lisboa, Lisboa, Portugal (Prof N Taveira PhD); Department of Anesthesiology, King Fahad Medical City, Riyadh, Saudi Arabia (A S Terkawi MD); Outcomes Research Consortium Global Health Metrics 1340 www.thelancet.com Vol 390 September 16, 2017 (A S Terkawi MD), Cleveland Clinic, Cleveland, OH, USA (Prof E M Tuzcu MD); School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh, India (Prof J Thakur MD); Christian Medical College Vellore, Vellore, India (Prof N Thomas PhD); Faculty of Health Sciences, Wroclaw Medical University, Wroclaw, Poland (R Topor-Madry PhD); School of Medicine, University of Valencia, Valencia, Spain (M Tortajada PhD); INSERM (French National Institute for Health and Medical Research), Paris, France (M Touvier PhD); University of Southern Santa Catarina, Palhoça, Brazil (Prof J Traebert PhD); Johns Hopkins University, Baltimore, MD, USA (B X Tran PhD); Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark (T Truelsen DMSc); Hanoi Medical University, Hanoi, Vietnam (B X Tran PhD); Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, Universitat de Barcelona, CIBERSAM, Barcelona, Spain (S Tyrovolas PhD); Department of Internal Medicine, Federal Teaching Hospital, Abakaliki, Nigeria (K N Ukwaja MD); School of Government, Pontificia Universidad Catolica de Chile, Santiago, Chile (E A Undurraga PhD); Ebonyi State University, Abakaliki, Nigeria (C J Uneke PhD); Warwick Medical School, University of Warwick, Coventry, UK (O A Uthman PhD); University of Nigeria, Nsukka, Enugu Campus, Enugu, Nigeria (Prof B S C Uzochukwu MD); UKK Institute for Health Promotion Research, Tampere, Finland (Prof T Vasankari PhD); National Centre for Disease Control, Delhi, India (S Venkatesh MD); Raffles Neuroscience Centre, Raffles Hospital, Singapore, Singapore (N Venketasubramanian MBBS); Weill Cornell Medical College, New York, NY, USA (R Vidavalur MD); VHS SNEHA, Chennai, India (L Vijayakumar PhD); University of Bologna, Bologna, Italy (Prof F S Violante MD); Federal Research Institute for Health Organization and Informatics, Moscow, Russia (S K Vladimirov PhD); National Research University Higher School of Economics, Moscow, Russia (Prof V V Vlassov MD); VA Medical Center, Washington, DC, USA (M T Wallin MD); Neurology Department, Georgetown University, Washington, DC, USA (M T Wallin MD); University of São Paulo Medical School, São Paulo, Brazil (Y Wang PhD); McGill University, Ottawa, ON, Canada (S Weichenthal PhD); Department of Research, Cancer Registry of Norway, Institute of Population-Based Cancer Research, Oslo, Norway (E Weiderpass PhD); Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, The Arctic University of Norway, Tromsø, Norway (E Weiderpass PhD); Genetic Epidemiology Group, Folkhälsan Research Center, Helsinki, Finland (E Weiderpass PhD); Royal Children’s Hospital, Melbourne, VIC, Australia (R G Weintraub MBBS); University of Melbourne, Melbourne, VIC, Australia (R G Weintraub MBBS); German National Cohort Consortium, Heidelberg, Germany (R Westerman PhD); West Herts Hospitals NHS Trust, Watford, UK (M E Murdoch MD); Western Health, Footscray, VIC, Australia (Prof T Wijeratne MD); Centre of Evidence Based Dermatology, University of Nottingham, Nottingham, UK (Prof H C Williams DSc); South African Medical Research Council, Cochrane South Africa, Cape Town, South Africa (Prof C S Wiysonge PhD); National Institute for Health Research Comprehensive Biomedical Research Centre, Guy’s & St Thomas’ NHS Foundation Trust and King’s College London, London, UK (Prof C D Wolfe MD); Royal Cornwall Hospital, Truro, UK (Prof A D Woolf MBBS); St John’s Medical College and Research Institute, Bangalore, India (Prof D Xavier MD); Department of Neurology, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China (Prof G Xu PhD); School of Public Health, University of Saskatchewan, Saskatoon, SK, Canada (M Yaghoubi MSc); Global Health Research Center, Duke Kunshan University, Kunshan, China (Prof L L Yan PhD); Department of Preventive Medicine, Northwestern University, Chicago, IL, USA (Y Yano MD); Social Work and Social Administration Department (Prof P Yip PhD), The Hong Kong Jockey Club Centre for Suicide Research and Prevention (Prof P Yip PhD), University of Hong Kong, Hong Kong, China; Department of Biostatistics, School of Public Health, Kyoto University, Kyoto, Japan (N Yonemoto MPH); Department of Preventive Medicine, College of Medicine, Korea University, Seoul, South Korea (S Yoon PhD); School of Public Health, University of Kinshasa, Kinshasa, Democratic Republic of the Congo (M Yotebieng PhD); Jackson State University, Jackson, MS, USA (Prof M Z Younis DrPH); Department of Epidemiology and Biostatistics, School of Public Health and Global Health Institute, Wuhan University, Wuhan, China (Prof C Yu PhD); University Hospital of Setif, Setif, Algeria (Prof Z Zaidi DSc); Faculty of Medicine, Mansoura University, Mansoura, Egypt (Prof M E Zaki PhD); Ethiopian Public Health Institute, Addis Ababa, Ethiopia (E A Zegeye MS); University of Texas School of Public Health, Houston, TX, USA (X Zhang MS); MD Anderson Cancer Center, Houston, TX, USA (X Zhang MS); and Red Cross War Memorial Children’s Hospital, Cape Town, South Africa (L J Zuhlke PhD). Contributors Please see the appendix for more detailed information about individual authors’ contributions to the research, divided into the following categories: managing the estimation process; writing the first draft of the manuscript; providing data or critical feedback on data sources; developing methods or computational machinery; applying analytical methods to produce estimates; providing critical feedback on methods or results; drafting the work or revising it critically for important intellectual content; extracting, cleaning, or cataloguing data; designing or coding figures and tables; and managing the overall research enterprise. Declaration of interests Laith J Abu-Raddad acknowledges the support of the Qatar National Research Fund (NPRP 9-040-3-008) who provided the main funding for generating the data provided to the GBD-IHME effort. Anurag Arawal acknowledges support from the Wellcome Trust DBT India Alliance Senior Fellowship; and reports personal fees from AstraZeneca. Ashish Awasthi received funding from DST, Government of India through INSPIRE Faculty scheme. The scientific work of Aleksandra Barac is part of the Project No III45005 granted by Ministry of Education, Science and Technological Development of the Republic of Serbia. Till Bärnighausen was supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the Federal Ministry of Education and Research; the Wellcome Trust; the European Commission; the Clinton Health Access Initiative; and from NICHD of NIH (R01-HD084233), NIA of NIH (P01-AG041710), NIAID of NIH (R01-AI124389 and R01-AI112339) as well as FIC of NIH (D43-TW009775); this research was supported by NIH National Center for Advancing Translational Science (NCATS) UCLA CTSI Grant Number UL1TR001881. Yannick Béjot reports grants and personal fees from AstraZeneca, personal fees from Daiichi-Sankyo, personal fees from Pfizer-BMS, personal fees from MSD, personal fees from Bayer, personal fees from Covidiem, and grants and personal fees from Boehringer-Ingelheim. Boris Bikbov has received funding from the European Union’s Horizon 2020 research and innovation programme under Marie Sklodowska-Curie grant agreement No 703226; and acknowledges that work related to this paper has been done on the behalf of the GBD Genitourinary Disease Expert Group supported by the International Society of Nephrology (ISN). Rupert Bourne acknowledges support from the Brien Holden Vision Institute. Rachelle Buchbinder is funded by an Australian National Health and Medical Research Council (NHMRC) Senior Principal Research Fellowship. Lucero Cahuana-Hurtado acknowledges support from the Instituto Nacional de Salud Pública (INSP) in Mexico. Juan-Jesus Carrero acknowledges support from Stockholm County Council Swedish Heart and Lung Foundation. Cyrus Cooper reports personal fees from Alliance for Better Bone Health, Amgen, Eli Lilly, GSK, Medtronic, Merck, Novartis, Pfizer, Servier, Takeda, and UCB. José das Neves was supported in his contribution to this work by a Fellowship from Fundação para a Ciência e a Tecnologia, Portugal (SFRH/BPD/92934/2013.; Barbora de Courten is supported by National Heart Foundation Future Leader Fellowship (100864). Louisa Degenhardt is supported by an Australian National Health and Medical Research Council (NHMRC) Principal Research Fellowship; the National Drug and Alcohol Research Centre at the University of NSW is supported by funding from the Australian Government under the Substance Misuse Prevention and Service Improvements Grants Fund. Kebede Deribe is funded by a Wellcome Trust Intermediate Fellowship in Public Health and Tropical Medicine (grant number 201900); Mir Sohail Fazeli reports personal fees from Doctor Evidence LLC. Global Health Metrics www.thelancet.com Vol 390 September 16, 2017 1341 João Fernandes gratefully acknowledges funding from FCT—Fundação para a Ciência e a Tecnologia (grant number UID/Multi/50016/2013). Katherine B Gibney is supported by an NHMRC Early Career Fellowship. Shifalika Goenka is supported by the Bernard Lown Scholars in Cardiovascular Health Program, Harvard School of Public Health (2015−17) and a Wellcome Trust (Grant No 096735/B/11/Z). Amador Goodridge acknowledges support from Sistema Nacional de Investigación (SNI) de Panamá and Secretaría Nacional de Ciencia, Tecnología e Innovación (SENACYT). Simon I Hay is funded by grants from the Bill & Melinda Gates Foundation (OPP1106023, OPP1119467, OPP1093011, and OPP1132415). Shariful Islam received postdoctoral research fellowship from The George Institute for Global Health and career transition grants from High Blood Pressure Research Foundation of Australia; the Ministry of Education Science and Technological Development of the Republic of Serbia has co-financed Serbian parts of this GBD related contribution throughout the Grant OI 175 014; publication of results was not contingent upon Minsitry’s prior censorship or approval. Peter James is supported by R00 CA201542 from NCI. Panniyammakal Jeemon acknowledges support from the Clinical and public health intermediate fellowship from the Wellcome Trust and Department of Biotechnology, India Alliance (2015-2020). Srinivasa Vittal Katikireddi reports grants from Chief Scientist Office and grants from Medical Research Council, during the conduct of the study. Anil Kaul has received funding (HR14-065) from the Oklahoma Council for Advancement of Science & Technology (OCAST). Christian Kieling has received support from Brazilian governmental research funding agencies Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (Fapergs), and Hospital de Clínicas de Porto Alegre (FIPE/HCPA). Ai Koyanagi’s work was supported by the Miguel Servet contract financed by the CP13/00150 and PI15/00862 projects, integrated into the National R + D + I and funded by the ISCIII—General Branch Evaluation and Promotion of Health Research—and the European Regional Development Fund (ERDF-FEDER). Tea Lallukka is supported by the Academy of Finland (Grants #287488 and #294096). Cheru T Leshargie would like to thank Debremarkos University for the arrangement of internate service to communicate for tasks such as registering and communicating with GBD organiser and to download the manuscript and other supportive document, send on comments, and sending the co-author form for the GBD. Miriam Levi acknowledges institutional support received from CeRIMP, Regional Centre for Occupational Diseases and Injuries, Local Health Unit Tuscany Center, Florence, Italy. Katharine J Looker thanks the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Evaluation of Interventions at the University of Bristol, in partnership with Public Health England (PHE), for research support; and received separate funding from the World Health Organization and Sexual Health 24 during the course of this study; the views expressed are those of the authors and not necessarily those of the National Health Service, the NIHR, the Department of Health or Public Health England. Azeem Majeed and Imperial College London are grateful for support from the NW London NIHR Collaboration for Leadership in Applied Health Research & Care. Francisco Martins-Melo acknowledges support from the postdoctoral Fellowship, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES; Brazilian public agency). Winifred März reports grants and personal fees from Siemens Diagnostics, grants and personal fees from Aegerion Pharmaceuticals, grants and personal fees from AMGEN, grants and personal fees from AstraZeneca, grants and personal fees from Danone Research, personal fees from Hoffmann LaRoche, personal fees from MSD, grants and personal fees from Pfizer, personal fees from Sanofi, personal fees from Synageva, grants and personal fees from BASF, grants from Abbott Diagnostics, grants and personal fees from Numares AG, grants and personal fees from Berlin-Chemie, and other support from Synlab Holding Deutschland GmbH. Mohsen Mazidi was supported by The World Academy of Sciences studentship of the Chinese Academy of Sciences. John McGrath received John Cade Fellowship APP1056929 from the National Health and Medical Research Council, and Niels Bohr Professorship from the Danish National Research Foundation. Toni Meier would like to acknowledge institutional support from the “Competence Cluster of Nutrition and Cardiovascular Health (nutriCARD)—Jena-Halle-Leipzig. Philip Mitchell’s research is supported by an Australian NHMRC Program Grant number 1037196. Ulrich Mueller has received financial support from the German National Cohort Study (grant 01ER1511D). Charles Newton is supported by the Wellcome Trust, UK. Olanrewaju Oladimeji is a Senior Research Specialist at the Human Sciences Research Council (HSRC), South Africa and Doctoral Candidate at the University of KwaZulu-Natal (UKZN), South Africa; we acknowledge the institutional support by leveraging on the existing organizational research facilities at HSRC and UKZN. AO was supported by intensificacion ISCIII FEDER funds and RETIC REDINREN. Richard Osborne was funded in part through an Australian National Health and Medical Research Council (NHMRC) Senior Research Fellowship #APP1059122. MO is supported by U54HG007479 from the NIH, USA. Charles Parry would like to acknowledge support from the South African Medical Research Council. Norberto Perico acknowledges that work related to this paper has been done on behalf of the GBD Genitourinary Disease Expert Group supported by the International Society of Nephrology (ISN). Konrad Pesudovs is support by Flinders University. William Petri is support by NIH grant AI043596; Kazem Rahimi is funded by an NIHR Career Development Fellowship and is supported by the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) and the Oxford Martin School. Giuseppe Remuzzi acknowledges that the work related to this paper has been done on behalf of GBD Genitourinary Disease Expert Group supported by the International Society of Nephrology (ISN). Maria Dolores Sanchez-Niño is supported by FIS PI15/00298 (ISCIII, Spanish Government). Aletta E Schutte received support from the South African Medical Research Council and National Research Foundation (DST/NRF SARChI Programme). Jeffrey Stanaway reports grants from Merck. Cassandra E I Szoeke reports grants from National Medical Health Research Council, during the conduct of the study; grants from Lundbeck, grants from Alzheimer’s Association, outside the submitted work; and has a patent PCT/AU2008/001556 issued; Rafael Tabarés-Seisdedos was supported in part by grant PROMETEOII/2015/021 from Generalitat Valenciana and the national grands PI14/00894 and PIE14/00031 from ISCIIIFEDER. Amanda G Thrift was provided fellowship support from the National Health & Medical Resarch Council (NHMRC; 1042600). Stefano Tyrovola’s work was supported by the Foundation for Education and European Culture (IPEP), the Sara Borrell postdoctoral programme (reference no CD15/00019 from the Instituto de Salud Carlos III (ISCIII - Spain) and the Fondos Europeo de Desarrollo Regional (FEDER). Job F M van Boven’s work was supported by the University Medical Center Groningen, University of Groningen, The Netherlands. Ronny Westerman would like to acknowledge that this work is on behalf of the German National Cohort funded by the German Ministry of Education and Research. Lijing L Yan is partly supported by the National Natural Sciences Foundation of China grants (71233001 and 71490732). Marcel Yotebieng is partially supported by the NIAID U01AI096299-01 and the NICHD R01HD087993. All other authors declare no competing interests. Acknowledgments Research reported in this publication was supported by the Bill & Melinda Gates Foundation, the National Institute on Aging of the National Institutes of Health (award P30AG047845), and the National Institute of Mental Health of the National Institutes of Health (award R01MH110163). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Bill & Melinda Gates Foundation or the National Institutes of Health. The Palestinian Central Bureau of Statistics granted the researchers access to relevant data in accordance with licence number SLN2014-3-170, after subjecting data to processing aiming to preserve the confidentiality of individual data in accordance with the General Statistics Law - 2000. The researchers are solely responsible for the conclusions and inferences drawn upon available data. We thank the Russia Longitudinal Monitoring Survey, RLMS-HSE, conducted by the National Research University Higher School of Economics and ZAO “Demoscope” together with the Carolina Population Center, University of North Global Health Metrics 1342 www.thelancet.com Vol 390 September 16, 2017 Carolina at Chapel Hill, and the Institute of Sociology RAS for making these data available. This study has been realised using the data collected by the Swiss Household Panel (SHP), which is based at the Swiss Centre of Expertise in the Social Sciences FORS. The project is financed by the Swiss National Science Foundation from the Framingham Heart Study of the National Heart Lung and Blood Institute of the National Institutes of Health and Boston University School of Medicine. This work was supported by the National Heart, Lung and Blood Institute’s Framingham Heart Study (contract number N01-HC-25195). The Health and Retirement Study (HRS) is sponsored by the National Institute on Aging (grant number NIA U01AG009740) and is conducted by the University of Michigan. This research used data from the National Health Survey 2003. We are grateful to the Ministry of Health, Survey copyright owner, allowing him to have the database. All results of the study are those of the authors and in no way committed to the Ministry. This research used data from the National Health Survey 2009–10. All results of the study are those of the authors and in no way committed to the Ministry. This research uses data from Add Health, a programme project designed by J Richard Udry, Peter S Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due to Ronald R Rindfuss and Barbara Entwisle for assistance in the original design. People interested in obtaining data files from Add Health should contact Add Health, Carolina Population Center, 123 West Franklin Street, Chapel Hill, NC 27516-2524, USA ([email protected]). No direct support was received from grant P01HD31921 for this analysis. The data reported here have been supplied by the US Renal Data System (USRDS). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy or interpretation of the US Government. HBSC is an international study carried out in collaboration with WHO/ EURO. The International Coordinator of the 1997–98, 2001–02, 2005–06 and 2009–10 surveys was Candace Currie and the Data Bank Manager for the 1997–98 survey was Bente Wold, whereas for the following survey, Oddrun Samdal was the Databank Manager. A list of principal investigators in each country can be found at http://www.hbsc.org. Data used in the preparation of this article were obtained from the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) Database. In 2011, Prize4Life, in collaboration with the Northeast ALS Consortium, and with funding from the ALS Therapy Alliance, formed the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) Consortium. The data available in the PRO-ACT Database has been volunteered by PROACT Consortium members. This paper uses data from SHARE Waves 1, 2, 3 (SHARELIFE), 4, 5, and 6 (DOIs: 10.6103/SHARE.w1.600, 10.6103/ SHARE.w2.600, 10.6103/SHARE.w3.600, 10.6103/SHARE.w4.600, 10.6103/SHARE.w5.600, 10.6103/SHARE.w6.600); see Börsch-Supan and colleagues (2013) for methodological details. The SHARE data collection has been primarily funded by the European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RIICT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812), and FP7 (SHARE-PREP: 211909, SHARE-LEAP: 227822, SHARE M4: 261982). Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the US National Institute on Aging (U01_ AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_ AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064, HHSN271201300071C), and various national funding sources is gratefully acknowledged (see www.share-project.org). This manuscript is based on data collected and shared by the International Vaccine Institute (IVI). This manuscript was not prepared in collaboration with investigators of IVI and does not necessarily reflect the opinions or views of IVI. Collection of these data was made possible by the US Agency for International Development (USAID) under the terms of cooperative agreement GPO-A-00-08-000_D3-00. The opinions expressed are those of the authors and do not necessarily reflect the views of USAID or the US Government. 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