Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017.

Yasir Waheed; et al.

Abstract:

Background How long one lives, how many years of life are spent in good and poor health, and how the population's state of health and leading causes of disability change over time all have implications for policy, planning, and provision of services. We comparatively assessed the patterns and trends of healthy life expectancy (HALE), which quantifies the number of years of life expected to be lived in good health, and the complementary measure of disability-adjusted life-years (DALYs), a composite measure of disease burden capturing both premature mortality and prevalence and severity of ill health, for 359 diseases and injuries for 195 countries and territories over the past 28 years. Methods We used data for age-specific mortality rates, years of life lost (YLLs) due to premature mortality, and years lived with disability (YLDs) from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 to calculate HALE and DALYs from 1990 to 2017. We calculated HALE using age-specific mortality rates and YLDs per capita for each location, age, sex, and year. We calculated DALYs for 359 causes as the sum of YLLs and YLDs. We assessed how observed HALE and DALYs differed by country and sex from expected trends based on Socio-demographic Index (SDI). We also analysed HALE by decomposing years of life gained into years spent in good health and in poor health, between 1990 and 2017, and extra years lived by females compared with males. Findings Globally, from 1990 to 2017, life expectancy at birth increased by 7·4 years (95% uncertainty interval 7·1–7·8), from 65·6 years (65·3–65·8) in 1990 to 73·0 years (72·7–73·3) in 2017. The increase in years of life varied from 5·1 years (5·0–5·3) in high SDI countries to 12·0 years (11·3–12·8) in low SDI countries. Of the additional years of life expected at birth, 26·3% (20·1–33·1) were expected to be spent in poor health in high SDI countries compared with 11·7% (8·8–15·1) in low-middle SDI countries. HALE at birth increased by 6·3 years (5·9–6·7), from 57·0 years (54·6–59·1) in 1990 to 63·3 years (60·5–65·7) in 2017. The increase varied from 3·8 years (3·4–4·1) in high SDI countries to 10·5 years (9·8–11·2) in low SDI countries. Even larger variations in HALE than these were observed between countries, ranging from 1·0 year (0·4–1·7) in Saint Vincent and the Grenadines (62·4 years [59·9–64·7] in 1990 to 63·5 years [60·9–65·8] in 2017) to 23·7 years (21·9–25·6) in Eritrea (30·7 years [28·9–32·2] in 1990 to 54·4 years [51·5–57·1] in 2017). In most countries, the increase in HALE was smaller than the increase in overall life expectancy, indicating more years lived in poor health. In 180 of 195 countries and territories, females were expected to live longer than males in 2017, with extra years lived varying from 1·4 years (0·6–2·3) in Algeria to 11·9 years (10·9–12·9) in Ukraine. Of the extra years gained, the proportion spent in poor health varied largely across countries, with less than 20% of additional years spent in poor health in Bosnia and Herzegovina, Burundi, and Slovakia, whereas in Bahrain all the extra years were spent in poor health. In 2017, the highest estimate of HALE at birth was in Singapore for both females (75·8 years [72·4–78·7]) and males (72·6 years [69·8–75·0]) and the lowest estimates were in Central African Republic (47·0 years [43·7–50·2] for females and 42·8 years [40·1–45·6] for males). Globally, in 2017, the five leading causes of DALYs were neonatal disorders, ischaemic heart disease, stroke, lower respiratory infections, and chronic obstructive pulmonary disease. Between 1990 and 2017, age-standardised DALY rates decreased by 41·3% (38·8–43·5) for communicable diseases and by 49·8% (47·9–51·6) for neonatal disorders. For non-communicable diseases, global DALYs increased by 40·1% (36·8–43·0), although age-standardised DALY rates decreased by 18·1% (16·0–20·2). Interpretation With increasing life expectancy in most countries, the question of whether the additional years of life gained are spent in good health or poor health has been increasingly relevant because of the potential policy implications, such as health-care provisions and extending retirement ages. In some locations, a large proportion of those additional years are spent in poor health. Large inequalities in HALE and disease burden exist across countries in different SDI quintiles and between sexes. The burden of disabling conditions has serious implications for health system planning and health-related expenditures. Despite the progress made in reducing the burden of communicable diseases and neonatal disorders in low SDI countries, the speed of this progress could be increased by scaling up proven interventions. The global trends among non-communicable diseases indicate that more effort is needed to maximise HALE, such as risk prevention and attention to upstream determinants of health. Funding Bill & Melinda Gates Foundation. Previous article in issueNext article in issue Introduction Understanding global trends in the health status of populations and changes in the leading causes of disease burden over time is crucial to tracking progress towards the Sustainable Development Goal to ensure healthy lives and promote wellbeing for all at all ages.1 Robust assessment of these trends requires objective and comparable measures of population health that can help countries identify priorities and address challenges to achieving this goal. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, the third annual update in the series, uses all available up-to-date epidemiological data and improved standardised methods to provide a comparative assessment of health loss across 359 diseases and injuries and 73 age and sex groups for 195 countries and territories. The availability of GBD 2017 data for years of life lost (YLLs) because of premature mortality and years lived with disability (YLDs) provides an opportunity to assess trends in population health over the past 28 years by analysing two complementary summary measures: healthy life expectancy (HALE), which quantifies the number of years expected to be lived in good health, and disability-adjusted life-years (DALYs), which quantifies the health loss due to specific diseases and injuries. HALE provides a snapshot of overall population health and DALYs are useful for quantifying and ranking disease burden due to specific causes. DALYs can be utilised to help decision makers and the public understand the leading causes of health burden and whether improvement occurs over time. Research in context Evidence before this study The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) estimated 333 causes of disability-adjusted life-years (DALYs) for 195 countries and territories from 1990 to 2016. GBD 2016 also provided estimates for life expectancy and healthy life expectancy (HALE) at birth and at age 65 years, by sex, for each location over time. GBD 2016 included analysis of the epidemiological transition as a function of the Socio-demographic Index. The WHO Global Health Estimates has also published estimates of HALE and DALYs, although these estimates largely relied on GBD 2016 results. Added value of this study In GBD 2017, we expanded the scope of the study compared with previous iterations to include subnational estimates for five more countries (Ethiopia, Iran, New Zealand, Norway, and Russia) and 19 additional causes. The new causes estimated are invasive non-typhoidal salmonella disease; liver cancer due to non-alcoholic steatohepatitis; cirrhosis due to non-alcoholic steatohepatitis; myelodysplastic, myeloproliferative, and other haemopoietic neoplasms; benign and in-situ intestinal neoplasms; benign and in-situ cervical and uterine neoplasms; other benign and in-situ neoplasms; subarachnoid haemorrhage; non-rheumatic valvular heart disease; non-rheumatic calcific aortic valve disease; non-rheumatic degenerative mitral valve disease; other non-rheumatic valve diseases; gastro-oesophageal reflux disease; type 1 diabetes; type 2 diabetes; chronic kidney disease due to type 1 diabetes; chronic kidney disease due to type 2 diabetes; poisoning by carbon monoxide; and poisoning by other means. In addition to broadening our estimation by cause, location, and time, a substantial amount of new data were added for GBD 2017. For cause-specific non-fatal estimations, we added new data from epidemiological surveillance, disease registries, scientific literature sources, and survey sources. Similarly, for cause-specific fatal estimation, we added new data from verbal autopsy studies, vital registration, and cancer registries. For age-specific all-cause mortality estimations, we added vital registration data, complete birth history sources, summary birth history sources, and sibling history surveys. These improvements are reflected in the summary measures of population health, DALYs and HALE, reported in this paper. We also provided a more detailed assessment for HALE than in previous GBD papers by examining the following: distinguishing the years of life gained over the past 28 years into years spent in good health and in poor health, by sex, for each location; determining which extra years lived were spent in good health and in poor health for females compared with males for each location; and assessing the male–female difference in HALE and years lived in poor health for the period 1990–2017 across Socio-demographic Index (SDI) quintiles. With increasing longevity, such information has relevance for policy development, health systems planning, and resource allocation. Implications of all the available evidence Over the past 28 years, the world has had tremendous gains in life expectancy; however, in many locations simply gaining years of life has not meant living those years in good health. In some locations, a large proportion of those years are spent in poor health. By distinguishing where, among whom, and how many of these additional years of life gained are spent in good health versus poor health, we have more insight to inform policy, planning, and resource prioritisation for improving health and reducing disparities. Our results showed large disparities in health and disease burden by SDI and sex, suggesting that much could be done to narrow these gaps, such as targeted approaches to reduce risk factors and scale up proven cost-effective interventions to decrease the burden of disease and make additional improvements to HALE more equitable. Our results not only provide the most up-to-date evidence, but also serve as a baseline for evaluating the effectiveness of interventions and programmes over time. The continuing trend of increasing life expectancy and decreasing mortality because of improvements in living conditions, income per capita, education, and medical practices is well known and understood.2, 3, 4, 5 Previous GBD papers have reported that increases in HALE have been slower than increases in life expectancy, resulting in more years of poor health, and suggesting an absolute expansion of morbidity.6, 7, 8, 9 However, details of how many of the additional years of life gained are spent in good health versus poor health across countries and sociodemographic groups have not been well characterised. As people live longer, such information becomes increasingly relevant for policy development, health systems planning, and resource allocation, the effects of which cannot be understated for population health. The estimates herein provide insight into the importance of access to services and appropriate health care, and the potential societal burden of caregiving and excess health-care expenditure for years lived in poor health.10 In this study, we present GBD 2017 results for HALE and DALYs by age and sex from 1990 to 2017 for 195 countries and territories. GBD 2017 includes new morbidity and mortality data (epidemiological surveillance data, disease registry data, scientific literature sources, survey sources, verbal autopsy studies, vital registration systems, cancer registries, complete birth history sources, summary birth history sources, and sibling history surveys); refined methods; and new estimations at the subnational level for Ethiopia, Iran, Norway, and Russia, and stratified by ethnicity for New Zealand. Also, the disaggregation of larger cause categories (eg, diabetes) has allowed separate estimation for several additional diseases (eg, type 1 and type 2 diabetes). GBD 2017 provides a complete reanalysis of all available data by country from 1990 to 2017, and thus supersedes all previously published GBD estimations of HALE and DALYs. Methods Overview The GBD study comprehensively and systematically quantifies the comparative magnitude of health loss due to diseases and injuries by age, sex, and location over time. We estimated all-cause and cause-specific mortality using the following key principles: identification of all data sources that are available, assessment of the quality of the data and correction for known bias, application of highly standardised analytical procedures, and assessment of model performance using cross-validation analysis. We used similar principles to identify, enhance comparability, and analyse data to estimate the incidence, prevalence, and YLDs of diseases and injuries.7 Using the GBD 2017 results for YLLs and YLDs, we calculated DALYs for 359 diseases and injuries.11, 12 We used age-specific mortality and YLDs per person to calculate HALE, defined as the average number of years that a person at a given age can expect to live in good health, taking into account mortality and loss of functional health.13 Additional details for computing HALE can be found in appendix 1. We calculated years lived in poor health (ie, years lived with functional health loss) as life expectancy minus HALE. Estimations for GBD 2017 cover the period 1990 to 2017 for 195 countries and territories. We did analyses using Python versions 2.7.12 and 2.7.3, Stata version 13.1, and R version 3.2.2. For this study, we followed the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER),14 which include recommendations on documentation of data sources, estimation methods, and statistical analysis (appendix 1). Interactive online tools are available to explore GBD 2017 data sources in detail using our online sourcing tool, the Global Health Data Exchange. Data before and after adjustments and the fit of the model to the data for causes of death and non-fatal outcomes can be explored with the available data visualisation tool. Cause and location hierarchies In GBD 2017, as in previous GBDs, causes of mortality and morbidity are structured using a four-level classification hierarchy to produce results that are mutually exclusive and collectively exhaustive. GBD 2017 estimates 359 causes of DALYs, 77 of which are a source of disability but not a cause of death (eg, attention-deficit hyperactivity disorder, headache disorders, low back pain, 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). In the GBD hierarchy, the number of mutually exclusive and collectively exhaustive fatal and non-fatal causes in each level for which GBD estimates is three at Level 1, 22 at Level 2, 169 at Level 3, and 293 at Level 4. The full GBD cause hierarchy, including corresponding International Classification of Diseases (ICD)-9 and ICD-10 codes and detailed cause-specific methods, is in GBD 2017 publications on cause-specific mortality11 and non-fatal health outcomes12 in the corresponding appendices. GBD 2017 includes 195 countries and territories that are grouped into 21 regions on the basis of epidemiological similarities and geographical proximity.15 For the purposes of statistical analyses, we further grouped regions into seven super-regions (central Europe, eastern Europe, and central Asia; high income; Latin America and Caribbean; north Africa and Middle East; south Asia; southeast Asia, east Asia and Oceania; and sub-Saharan Africa). Each year, GBD includes subnational analyses for a few new countries and continues to provide subnational estimates for countries that were added in previous cycles. Subnational estimation in GBD 2017 includes five new countries (Ethiopia, Iran, New Zealand, Norway, and Russia) and countries previously estimated at subnational levels (GBD 2013: China, Mexico, and the UK [regional level]; GBD 2015: Brazil, India, Japan, Kenya, South Africa, Sweden, and the USA; and GBD 2016: Indonesia and the UK [local government authority level]). All analyses are at the first level of administrative organisation within each country except for New Zealand (by Māori ethnicity), Sweden (by Stockholm and non-Stockholm), and the UK (by local government authorites). All subnational estimates for these countries were incorporated into model development and evaluation as part of GBD 2017. To meet data use requirements, we present all subnational estimates excluding those pending publication (Brazil, India, Japan, Kenya, Mexico, Sweden, the UK, and the USA); these results are presented in appendix tables and figures (appendix 2). Subnational estimates for countries with populations larger than 200 million people (as measured according to our most recent year of published estimates) that have not yet been published elsewhere are presented wherever estimates are illustrated with maps but are not included in data tables.

research from:

• Foundation University Medical College

Year:

2018

Type of Publication:

Article

Journal:

The Lancet

Volume:

392

Number:

10159

Pages:

1859-1922

Month:

11

DOI:

10.1016/S0140-6736(18)32335-3