Global Health Metrics
Global burden of 369 diseases and injuries in 204 countries
and territories, 1990–2019: a systematic analysis for the
Global Burden of Disease Study 2019
GBD 2019 Diseases and Injuries Collaborators*
Summary
Lancet 2020; 396: 1204–22
This online publication has been
corrected. The corrected version
first appeared at thelancet.com
on October 23, 2020
*For the list of Collaborators see
Viewpoint Lancet 2020;
396: 1135–59
Correspondence to:
Prof Christopher J L Murray,
Institute for Health Metrics and
Evaluation, University of
Washington, Seattle, WA 98195,
USA
cjlm@uw.edu
Background In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries
along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global
Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published,
publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and
collectively exhaustive list of diseases and injuries.
Methods GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and
disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories.
Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries,
health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific
death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian
process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD
population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate
YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence,
prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were
multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered
results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of
schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every
metric using the 25th and 975th ordered 1000 draw values of the posterior distribution.
Findings Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After
taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the
pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared
with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group.
Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower
respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough
(ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked
tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked
first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years
were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain
(fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the
top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a
marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries.
In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all
disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower
end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI.
Interpretation As disability becomes an increasingly large component of disease burden and a larger component of
health expenditure, greater research and development investment is needed to identify new, more effective
intervention strategies. With a rapidly ageing global population, the demands on health services to deal with
disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of
universal and more geographically specific influences on health reinforces the need for regular reporting on
population health in detail and by underlying cause to help decision makers to identify success stories of disease
control to emulate, as well as opportunities to improve.
Funding Bill & Melinda Gates Foundation.
Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
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Global Health Metrics
Research in context
Evidence before this study
The Global Burden of Diseases, Injuries, and Risk Factors Study
(GBD) 2017 reported on incidence, prevalence, and mortality
from 359 diseases and injuries. Information on prevalence and
mortality was also analysed in terms of summary measures:
years of life lost (YLLs), years lived with disability (YLDs),
disability-adjusted life-years (DALYs), and healthy life
expectancy. GBD is the only comprehensive assessment
providing time trends for a mutually exclusive and collectively
exhaustive list of diseases and injuries. For the first time,
GBD 2017 also produced internally consistent estimates of
population, fertility, mortality, and migration by age, sex,
and year for 1950–2017. GBD 2017 also included subnational
assessments for 16 countries at administrative level 1 and for
local authorities in England.
Added value of this study
GBD 2019 updates and expands beyond GBD 2017 in ten ways.
(1) The number of countries for which subnational assessments
have been undertaken was expanded to include Italy, Nigeria,
Pakistan, the Philippines, and Poland. (2) 12 new causes were
added to the GBD modelling framework, including pulmonary
arterial hypertension, nine new sites of cancer, and two new
sites of osteoarthritis (hand and other joints). (3) For each
disease, the preferred or reference case definition or
measurement method was clearly defined and stored in a
database. For both risks and diseases, the statistical relationship
between the alternative and reference measurement method
was analysed using network meta-regression using only data
where two different approaches were measured in the same
location–time period. Although statistical cross-walking
between alternative and reference definitions and
measurement methods has been a feature in all GBD studies,
the approach in GBD 2019 was highly standardised and used
improved methods across diseases and risks. (4) Some prior
Introduction
The Global Burden of Diseases, Injuries, and Risk Factors
Study (GBD) provides a systematic scientific assessment
of published, publicly available, and contributed data on
disease and injury incidence, prevalence, and mortality
for a mutually exclusive and collectively exhaustive list
of diseases and injuries.1–3 In an era of shifting global
agendas and expanded emphasis on non-communicable
diseases and injuries along with communicable diseases,
sound and up-to-date evidence on trends—both progress
and adverse patterns—by cause at the national level is
essential to reflect effects of public health policy and
medical care delivery.4–7
GBD 2019 provides an opportunity to incorporate
newly available datasets, enhance method performance
and standardisation, and reflect changes in scientific
understanding. Since GBD 2017,1–3 no comprehensive
update of descriptive epidemiology levels and trends has
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distributions used in DisMod-MR, the Bayesian meta-regression
tool used to simultaneously estimate incidence, prevalence,
remission, excess mortality, and cause-specific mortality,
were revised on the basis of simulation studies showing that
less informative priors helped to improve the coverage of
uncertainty intervals. (5) Redistribution algorithms for sepsis,
heart failure, pulmonary embolism, acute kidney injury, hepatic
failure, acute respiratory failure, pneumonitis, and five
intermediate causes in the central nervous system were revised
according to an analysis of 116 million deaths that were
attributed to multiple causes. (6) Processing of clinical
informatics data on hospital and clinic visits was revised to
better take into account differential access across locations to
health-care facilities. (7) To enhance the stability of models in
the presence of the addition of subnational data in different
GBD cycles, we adopted a set of standard locations for the
estimation of covariate effects in models. (8) 7333 national and
24 657 subnational vital registration systems, 16 984 published
studies, and 1654 household surveys were used in the analysis,
including many newly available data sources. (9) Results are
presented so as to integrate causes of death, incidence,
prevalence, YLDs, YLLs, and DALYs into a comprehensive
assessment of each disease and injury. (10) Closer technical
coordination with WHO has led to the addition of nine WHO
member states to the analysis and revisions of the analytical
approach for select diseases.
Implications of all the available evidence
GBD 2019 provides the most up-to-date assessment of the
descriptive epidemiology of a mutually exclusive and collectively
exhaustive list of diseases and injuries for 204 countries and
territories from 1990 to 2019. The comprehensive nature of the
assessment provides policy-relevant information on the trends
of major causes of burden globally, regionally, and by country or
territory.
been released, to our knowledge. In this study, we
summarise GBD methods and present integrated results
on fatal and non-fatal outcomes for the GBD disease
and injury hierarchical cause list. GBD 2019 includes
estimation of numerous different models for disease and
injury outcomes. This Article provides a high-level overview of our findings. Results are presented both broadly
and in detail for a selection of diseases, injuries, and
impairments in two-page summaries with a standard set
of tables and figures.
Methods
Overview
The general approach to estimating causes of death and
disease incidence and prevalence for GBD 2019 is the
same as for GBD 2017.2,3 Appendix 1 provides details on
the methods used to model each disease and injury.
Here, we provide an overview of the methods, with an
See Online for appendix 1
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For the statistical code see
http://ghdx.healthdata.org/gbd2019/code
emphasis on the main methodology changes since
GBD 2017.
For each iteration of GBD, the estimates for the whole
time series are updated on the basis of addition of new
data and change in methods where appropriate. Thus,
the GBD 2019 results supersede those from previous
rounds of GBD.
GBD 2019 complies with the Guidelines for Accurate
and Transparent Health Estimates Reporting (GATHER)
statement (appendix 1 section 1.4).8 Analyses were completed with Python version 3.6.2, Stata version 13, and
R version 3.5.0. Statistical code used for GBD estimation
is publicly available online.
Geographical units, age groups, time periods, and cause
levels
For the GHDx see http://ghdx.
healthdata.org
For the GHDx source tool see
http://ghdx.healthdata.org/gbd2019/data-input-sources
See Online for appendix 2
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GBD 2019 estimated each epidemiological quantity of
interest—incidence, prevalence, mortality, years lived
with disability (YLDs), years of life lost (YLLs), and
disability-adjusted life-years (DALYs)—for 23 age
groups; males, females, and both sexes combined; and
204 countries and territories that were grouped into
21 regions and seven super-regions. For GBD 2019,
nine countries and territories (Cook Islands, Monaco,
San Marino, Nauru, Niue, Palau, Saint Kitts and Nevis,
Tokelau, and Tuvalu) were added, such that the GBD
location hierarchy now includes all WHO member
states. GBD 2019 includes subnational analyses for
Italy, Nigeria, Pakistan, the Philippines, and Poland,
and 16 countries previously estimated at subnational
levels (Brazil, China, Ethiopia, India, Indonesia, Iran,
Japan, Kenya, Mexico, New Zealand, Norway, Russia,
South Africa, Sweden, the UK, and the USA). All
subnational 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), the UK (by local government authorities), and the Philippines (by province).
In this publication, we present subnational estimates
for Brazil, India, Indonesia, Japan, Kenya, Mexico,
Sweden, the UK, and the USA; given space constraints,
these results are presented in appendix 2. At the most
detailed spatial resolution, we generated estimates for
990 locations. The GBD diseases and injuries analytical
framework generated estimates for every year from
1990 to 2019.
Diseases and injuries were organised into a levelled
cause hierarchy from the three broadest causes of death
and disability at Level 1 to the most specific causes at
Level 4. Within the three Level 1 causes—communicable,
maternal, neonatal, and nutritional diseases; non-communicable diseases; and injuries—there are 22 Level 2 causes,
174 Level 3 causes, and 301 Level 4 causes (including
131 Level 3 causes that are not further disaggregated at
Level 4; see appendix 1 sections 3.4 and 4.12 for the full list
of causes). 364 total causes are non-fatal and 286 are fatal.
For GBD 2019, 12 new causes were added to the modelling
framework: pulmonary arterial hypertension, eye cancer,
soft tissue and other extraosseous sarcomas, malignant
neoplasm of bone and articular cartilage, and neuroblastoma and other peripheral nervous cell tumours at
Level 3, and hepatoblastoma, Burkitt lymphoma, other
non-Hodgkin lymphoma, retinoblastoma, other eye cancers, and two sites of osteoarthritis (hand and other joints)
at Level 4.
Data
The GBD estimation process is based on identifying
multiple relevant data sources for each disease or injury
including censuses, household surveys, civil registration
and vital statistics, disease registries, health service use,
air pollution monitors, satellite imaging, disease notifications, and other sources. Each of these types of data are
identified from systematic review of published studies,
searches of government and international organisation
websites, published reports, primary data sources such as
the Demographic and Health Surveys, and contributions
of datasets by GBD collaborators. 86 249 sources were
used in this analysis, including 19 354 sources reporting
deaths, 31 499 reporting incidence, 19 773 reporting prevalence, and 26 631 reporting other metrics. Each newly
identified and obtained data source is given a unique
identifier by a team of librarians and included in the
Global Health Data Exchange (GHDx). The GHDx makes
publicly available the metadata for each source included in
GBD as well as the data, where allowed by the data
provider. Readers can use the GHDx source tool to identify
which sources were used for estimating any disease or
injury outcome in any given location.
Data processing
A crucial step in the GBD analytical process is correcting
for known bias by redistributing deaths from unspecified
codes to more specific disease categories, and by adjusting
data with alternative case definitions or measurement
methods to the reference method. We highlight several
major changes in data processing that in some cases have
affected GBD results.
Cause of death redistribution
Vital registration with medical certification of cause of
death is a crucial resource for the GBD cause of death
analysis in many countries. Cause of death data obtained
using various revisions of the International Classification
of Diseases and Injuries (ICD)9 were mapped to the
GBD cause list. Many deaths, however, are assigned to
causes that cannot be the underlying cause of death
(eg, cardiopulmonary failure) or are inadequately specified (eg, injury from undetermined intent). These deaths
were reassigned to the most probable underlying causes
of death as part of the data processing for GBD.
Redistribution algorithms can be divided into three
categories: proportionate redistribution, fixed proportion
redistribution based on published studies or expert
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Global Health Metrics
judgment, or statistical algorithms. For GBD 2019, data
for 116 million deaths attributed to multiple causes were
analysed to produce more empirical redistribution algorithms for sepsis,10 heart failure, pulmonary embolism,
acute kidney injury, hepatic failure, acute respiratory
failure, pneumonitis, and five intermediate causes
(hydrocephalus, toxic encephalopathy, compression of
brain, encephalopathy, and cerebral oedema) in the
central nervous system. To redistribute unspecified
injuries, we used a method similar to that of intermediate
cause redistribution, using the pattern of the nature of
injury codes in the causal chain where the ICD codes X59
(“exposure to unspecified factor”) and Y34 (“unspecified
event, undetermined intent”) and GBD injury causes
were the underlying cause of death. These new
algorithms led to important changes in the causes to
which these intermediate outcomes were redistributed.
Additionally, data on deaths from diabetes and stroke
lack the detail on subtype in many countries; we ran
regressions on vital registration data with at least 50%
of deaths coded specifically to type 1 or 2 diabetes and
ischaemic, haemorrhagic, or subarachnoid stroke to
predict deaths by these subtypes when these were coded
to unspecified diabetes or stroke.
Correcting for non-reference case definitions or measurement
methods
In previous cycles of GBD, data reported using alternative
case definitions or measurement methods were corrected
to the reference definition or measurement method
primarily as part of the Bayesian meta-regression models.
For example, in DisMod-MR, the population data were
simultaneously modelled as a function of country covariates for variation in true rates and as a function of
indicator variables capturing alternative measurement
methods. To enhance transparency and to standardise
and improve methods in GBD 2019, we estimated
correction factors for alternative case definitions or
measurement methods using network meta-regression,
including only data where two methods were assessed in
the same location–time period or in the exact same
population. This included validation studies where two
methods had been compared in populations that were
not necessarily random samples of the general population. Details on the correction factors from alternative
to reference measurement methods are provided in
appendix 1 (section 4.4.2).
Clinical informatics
Clinical informatics data include inpatient admissions,
outpatient (including general practitioner) visits, and
health insurance claims. Several data processing steps
were undertaken. Inpatient hospital data with a single
diagnosis only were adjusted to account for nonprimary diagnoses as well as outpatient care. For each
GBD cause that used clinical data, ratios of non-primary
to primary diagnosis rates were extracted from claims
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in the USA, Taiwan (province of China), New Zealand,
and the Philippines, as well as USA Healthcare Cost and
Utilization Project inpatient data. Ratios of outpatient to
inpatient care for each cause were extracted from claims
data from the USA and Taiwan (province of China).
The log of the ratios for each cause were modelled by
age and sex using MR-BRT (Meta-Regression-Bayesian
Regularised Trimmed), the Bayesian meta-regression
tool. To account for the incomplete health-care access in
populations where not every person with a disease or
injury would be accounted for in administrative clinical
records, we transformed the adjusted admission rates
using a scalar derived from the Healthcare Access and
Quality Index.11 We used this approach to produce
adjusted, standardised clinical data inputs. More details
are provided in appendix 1 (section 4.3).
Modelling
For most diseases and injuries, processed data are
modelled using standardised tools to generate estimates
of each quantity of interest by age, sex, location, and year.
There are three main standardised tools: Cause of Death
Ensemble model (CODEm), spatiotemporal Gaussian
process regression (ST-GPR), and DisMod-MR. Previous
publications2,3,12 and the appendix provide more details on
these general GBD methods. Briefly, CODEm is a highly
systematised tool to analyse cause of death data using an
ensemble of different modelling methods for rates or
cause fractions with varying choices of covariates that
perform best with out-of-sample predictive validity
testing. DisMod-MR is a Bayesian meta-regression tool
that allows evaluation of all available data on incidence,
prevalence, remission, and mortality for a disease,
enforcing consistency between epidemiological parameters. ST-GPR is a set of regression methods that
borrow strength between locations and over time for
single metrics of interest, such as risk factor exposure or
mortality rates. In addition, for select diseases, particularly
for rarer outcomes, alternative modelling strategies
have been developed, which are described in appendix 1
(section 3.2).
In GBD 2019, we designated a set of standard locations
that included all countries and territories as well as
the subnational locations for Brazil, China, India, and
the USA. Coefficients of covariates in the three main
modelling tools were estimated for these standard
locations only—ie, we ignored data from subnational
locations other than for Brazil, China, India, and the USA
(appendix 1 section 1.1). Using this set of standard
locations will prevent changes in regression coefficients
from one GBD cycle to the next that are solely due to the
addition of new subnational units in the analysis that
might have lower quality data or small populations
(appendix 1 section 1.1). Changes to CODEm for GBD 2019
included the addition of count models to the model
ensemble for rarer causes. We also modified DisMod-MR
priors to effectively increase the out-of-sample coverage of
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Global Health Metrics
uncertainty intervals (UIs) as assessed in simulation
testing (appendix 1 section 4.5).
For the cause Alzheimer’s disease and other dementias,
we changed the method of addressing large variations
between locations and over time in the assignment of
dementia as the underlying cause of death. Based on
a systematic review of published cohort studies, we
estimated the relative risk of death in individuals with
dementia. We identified the proportion of excess deaths
in patients with dementia where dementia is the underlying cause of death as opposed to a correlated risk factor
(appendix 1 section 2.6.2). We changed the strategy of
modelling deaths for acute hepatitis A, B, C, and E from
a natural history model relying on inpatient case fatality
rates to CODEm models after predicting type-specific
acute hepatitis deaths from vital registration data with
specified hepatitis type.
DisMod-MR was used to estimate deaths from three
outcomes (dementia, Parkinson’s, and atrial fibrillation),
and to determine the proportions of deaths by underlying
aetiologies of cirrhosis, liver cancer, and chronic kidney
disease deaths.
Socio-demographic Index, annual rate of change, and
data presentation
The Socio-demographic Index (SDI) is a composite
indicator of a country’s lag-distributed income per
capita, average years of schooling, and the fertility rate in
females under the age of 25 years (appendix 1 section 6).13
For changes over time, we present annualised rates of
change as the difference in the natural log of the values
at the start and end of the time interval divided by the
number of years in the interval. We examine the
relationship between SDI and the annualised rate of
change in age-standardised DALY rates for all causes,
apart from HIV/AIDS, natural disasters, and war and
conflict, by country or territory, for the time periods
1990–2010 and 2010–19. We deliberately subtracted out
DALYs due to HIV/AIDS because their magnitude in
3000
60 000
2000
40 000
30 000
20 000
1000
10 000
Age-standardised DALY rate
DALY count
0
1990
2000
Age-standardised DALY rate
DALY count (millions)
50 000
2010
0
2019
Year
Figure 1: Global DALYs and age-standardised DALY rates, 1990–2019
Shaded sections indicate 95% uncertainty intervals. DALY=disability-adjusted
life-year.
1208
some parts of the world would have obscured the trends
in all other causes; we also subtracted out DALY rates
from natural disasters and war and conflict to avoid
trends in disease burden in some countries being
dominated by these sudden and dramatic changes. As a
measure of the epidemiological transition, we present
the ratio of YLDs due to non-communicable diseases
and injuries, and due to total burden in DALYs. We
present 95% UIs for every metric based on the 25th and
975th ordered values of 1000 draws of the posterior
distribution.
Role of the funding source
The funders of this study had no role in study design,
data collection, data analysis, data interpretation, or the
writing of the report. The corresponding author had full
access to the data in the study and final responsibility for
the decision to submit for publication.
Results
Global trends
Between 1990 and 2019, the number of global DALYs
remained almost constant, but once the effects of
population growth and ageing were removed by converting counts to age-standardised rates, there were clear
improvements in overall health (figure 1). Over the past
decade, the pace of decline in global age-standardised
DALY rates accelerated in age groups younger than
50 years compared with the 1990–2010 time period
(table). The annualised rate of decline was greatest in the
0–9-year age group. In the population aged 50 years and
older, the rate of change was slower from 2010 to 2019
compared with the earlier time period.
These general trends are made up of complex trends for
specific diseases and injuries. Overall trends in the number
of DALYs across the different age groups between
1990 and 2019 are driven by some key diseases and injuries
(figure 2). The ten most important drivers of increasing
burden (ie, the causes that had the largest absolute
increases in number of DALYs between 1990 and 2019)
include six causes that largely affect older adults (ischaemic
heart disease, diabetes, stroke, chronic kidney disease,
lung cancer, and age-related hearing loss), whereas the
other four causes (HIV/AIDS, other musculoskeletal
disorders, low back pain, and depressive disorders) are
common from teenage years into old age (figure 2).
Despite these ten conditions contributing the largest
number of additional DALYs over the 30-year period, only
HIV/AIDS, other musculoskeletal disorders, and diabetes
saw large increases in age-standardised DALY rates, with
an increase of 58·5% (95% UI 37·1–89·2) for HIV/AIDS,
30·7% (27·6–34·3) for other musculoskeletal disorders,
and 24·4% (18·5–29·7) for diabetes. The burden of
HIV/AIDS, however, peaked in 2004 and has dropped
substantially after the global scale-up of antiretroviral
treatment (ART). The changes in age-standardised rates
for chronic kidney disease, age-related hearing loss, and
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Global Health Metrics
DALYs 2019
Count
(millions)
Annualised rate of change, 1990–2010
Annualised rate of change, 2010–19
Age-standardised rate
(per 100 000)
DALYs
Age-standardised rate
DALYs
Age-standardised rate
−4·0% (−4·7 to −3·2)
0–9 years
531 (458 to 621)
19 125·7 (16 495·1 to 22 382·5)
−2·3% (−2·5 to −2·2)
−2·5% (−2·6 to −2·3)
−3·7% (−4·4 to −2·9)
10–24 years
229 (194 to 270)
12 313·0 (10 399·9 to 14 478·3)
0·2% (0·1 to 0·2)
−0·7% (−0·8 to −0·6)
−1·1% (−1·4 to −0·9)
−1·3% (−1·5 to −1·1)
25–49 years
616 (533 to 709)
22 691·2 (19 613·7 to 26 116·3)
1·4% (1·4 to 1·5)
−0·4% (−0·4 to −0·3)
−0·0% (−0·2 to 0·1)
−1·2% (−1·4 to −1·0)
50–74 years
832 (752 to 919)
28 263·2 (25 527·6 to 31 213·4)
1·3% (1·2 to 1·3)
−1·0% (−1·0 to −0·9)
2·0% (1·8 to 2·1)
−0·9% (−1·1 to −0·8)
≥75 years
329 (308 to 351)
77 320·5 (72 372·5 to 82 440·3)
2·2% (2·2 to 2·2)
−0·9% (−0·9 to −0·9)
2·3% (2·3 to 2·4)
−0·8% (−0·9 to −0·8)
−0·0% (−0·1 to 0·0)
−1·4% (−1·5 to −1·3)
−0·2% (−0·4 to 0·0)
−1·3% (−1·5 to −1·1)
All ages
2540 (2290 to 2810) 32 801·7 (29 535·1 to 36 319·5)
DALY=disability-adjusted life-year.
Table: Global DALYs in 2019 and annualised rate of change in DALYs and age-standardised DALY rates over 1990–2010 and 2010–19, by age group and for all ages
depressive disorders were small (figure 2). Substantial
declines in age-standardised rates were seen in ischaemic
heart disease (28·6%, 95% UI 24·2–33·3), stroke (35·2%,
30·5–40·5), and lung cancer (16·1%, 8·2–24·0).
The ten most important contributors to declining
burden (ie, the causes that had the largest absolute
decreases in number of DALYs between 1990 and 2019)
include nine that predominantly affect children (lower
respiratory infections, diarrhoeal diseases, neonatal disorders, measles, protein-energy malnutrition, congenital
birth defects, drowning, tetanus, and malaria), as well
as tuberculosis, which largely affects adults. All of
these causes with declining burden also had substantial
decreases in age-standardised DALY rates, ranging
from 32·6% (21·2–42·1) decline for neonatal disorders
to 90·4% (87·5–92·8) decline for measles, not just
decreases in the absolute number of DALYs due to
demographic changes (figure 2A). Although most of the
ten leading Level 3 causes of DALYs were the same for
both sexes in 2019, road injuries (ranked fourth for
males), cirrhosis (ninth), and lung cancer (tenth) were in
the top ten for males only, and were replaced by low back
pain (ranked sixth for females), gynaecological diseases
(ninth), and headache disorders (tenth) for females
(appendix 2 figure S5 and tables S2–5, S7, S8, S12, S13,
S16). Congenital defects were ranked tenth for both sexes
combined in 2019 but did not make the top ten for either
sex separately.
The burden for children younger than 10 years declined
profoundly between 1990 and 2019, by 57·5% (95% UI
50·3–63·1). Key drivers of this progress included large
reductions in major infectious diseases affecting
children—namely, lower respiratory infections, diarrhoeal
diseases, and meningitis, each of which declined by
more than 60% between 1990 and 2019 (figure 2). In 2019,
neonatal disorders were the leading cause of burden in
this age group, accounting for 32·4% (30·7–34·1) of the
group’s global DALYs, increasing from 23·0% (22·0–24·1)
in 1990. Six infectious diseases were also among the
top ten causes of burden in children: lower respiratory
infections (ranked second), diarrhoeal diseases (third),
malaria (fifth), meningitis (sixth), whooping cough (ninth),
and sexually transmitted infections (which were fully
www.thelancet.com Vol 396 October 17, 2020
accounted for by congenital syphilis in this age group;
tenth). Congenital birth defects (ranked fourth) as well
as two nutritional disorders—dietary iron deficiency
(seventh) and protein-energy malnutrition (eighth)—
completed the top ten. The percentage change in agestandardised DALY rates for eight of the ten leading
causes was large, ranging from a 35·4% (23·8–44·8)
decline for neonatal disorders to 78·3% (69·9–85·5)
decline for protein-energy malnutrition over the study
period. The decreases for the remaining two top-ten
causes, sexually transmitted infections and dietary iron
deficiency, were much more modest. Sub-Saharan Africa
experienced nearly half of the total DALYs (49·9%
[47·6–52·3]) for this age group in 2019.
The change in disease burden in adolescents aged
10–24 years was much more modest (figure 2). DALYs
declined by 6·2% (95% UI 2·1–10·5) overall between
1990 and 2019. DALYs for non-communicable diseases
increased by 13·1% (9·5–16·3), whereas injuries declined
by 24·8% (19·7–29·3) and infectious diseases by 18·7%
(13·4–24·0). Three injury causes were among the top ten
causes of global DALYs in this age group in 2019: road
injuries (ranked first), self-harm (third), and interpersonal
violence (fifth; figure 2). Headache disorders, two mental
disorders (depression and anxiety), low back pain, dietary
iron deficiency, HIV/AIDS, and diarrhoeal disease were
the other causes in the top ten for adolescents. Among
the top ten causes in this age group, age-standardised
DALY rates for road injuries, self-harm, and diarrhoeal
diseases decreased by more than a third each between
1990 and 2019. As in the 0–9-year age group, the large
increase in burden due to HIV/AIDS in the 10–24-year age
group reflects a rapid increase in the first half of the study
period followed by a decline after the global scale-up of
ART; despite declining in recent years, the HIV/AIDs
burden has not yet returned to 1990 levels. The other
causes in the top ten showed small or insignificant change
(figure 2). The sex differences in the top ten rankings are
striking. The three previously mentioned injuries were
the top-ranked causes of DALYs among male adolescents
(appendix 2 figure S9), whereas headaches, depressive
disorders, and anxiety disorders were the top three causes
of DALYs among females (appendix 2 figure S10).
1209
Global Health Metrics
A
All ages
Leading causes 1990
1 Neonatal disorders
2 Lower respiratory infections
3 Diarrhoeal diseases
4 Ischaemic heart disease
5 Stroke
6 Congenital birth defects
7 Tuberculosis
8 Road injuries
9 Measles
10 Malaria
11 COPD
12 Protein-energy malnutrition
13 Low back pain
14 Self-harm
15 Cirrhosis
16 Meningitis
17 Drowning
18 Headache disorders
19 Depressive disorders
20 Diabetes
21 Lung cancer
22 Falls
23 Dietary iron deficiency
24 Interpersonal violence
25 Whooping cough
Percentage of DALYs
1990
Leading causes 2019
Percentage of DALYs
2019
Percentage change in
number of DALYs,
1990–2019
Percentage change in
age-standardised DALY
rate, 1990–2019
10·6 (9·9 to 11·4)
8·7 (7·6 to 10·0)
7·3 (5·9 to 8·8)
4·7 (4·4 to 5·0)
4·2 (3·9 to 4·5)
3·2 (2·3 to 4·8)
3·1 (2·8 to 3·4)
2·7 (2·6 to 3·0)
2·7 (0·9 to 5·6)
2·5 (1·4 to 4·1)
2·3 (1·9 to 2·5)
2·0 (1·6 to 2·7)
1·7 (1·2 to 2·1)
1·4 (1·2 to 1·5)
1·3 (1·2 to 1·5)
1·3 (1·1 to 1·5)
1·3 (1·1 to 1·4)
1·1 (0·2 to 2·4)
1·1 (0·8 to 1·5)
1·1 (1·0 to 1·2)
1·0 (1·0 to 1·1)
1·0 (0·9 to 1·2)
1·0 (0·7 to 1·3)
0·9 (0·9 to 1·0)
0·9 (0·4 to 1·7)
1 Neonatal disorders
2 Ischaemic heart disease
3 Stroke
4 Lower respiratory infections
5 Diarrhoeal diseases
6 COPD
7 Road injuries
8 Diabetes
9 Low back pain
10 Congenital birth defects
11 HIV/AIDS
12 Tuberculosis
13 Depressive disorders
14 Malaria
15 Headache disorders
16 Cirrhosis
17 Lung cancer
18 Chronic kidney disease
19 Other musculoskeletal
20 Age-related hearing loss
21 Falls
22 Self-harm
23 Gynaecological diseases
24 Anxiety disorders
25 Dietary iron deficiency
7·3 (6·4 to 8·4)
7·2 (6·5 to 7·9)
5·7 (5·1 to 6·2)
3·8 (3·3 to 4·3)
3·2 (2·6 to 4·0)
2·9 (2·6 to 3·2)
2·9 (2·6 to 3·0)
2·8 (2·5 to 3·1)
2·5 (1·9 to 3·1)
2·1 (1·7 to 2·6)
1·9 (1·6 to 2·2)
1·9 (1·7 to 2·0)
1·8 (1·4 to 2·4)
1·8 (0·9 to 3·1)
1·8 (0·4 to 3·8)
1·8 (1·6 to 2·0)
1·8 (1·6 to 2·0)
1·6 (1·5 to 1·8)
1·6 (1·2 to 2·1)
1·6 (1·2 to 2·1)
1·5 (1·4 to 1·7)
1·3 (1·2 to 1·5)
1·2 (0·9 to 1·5)
1·1 (0·8 to 1·5)
1·1 (0·8 to 1·5)
–32·3 (–41·7 to –20·8)
50·4 (39·9 to 60·2)
32·4 (22·0 to 42·2)
–56·7 (–64·2 to –47·5)
–57·5 (–66·2 to –44·7)
25·6 (15·1 to 46·0)
2·4 (–6·9 to 10·8)
147·9 (135·9 to 158·9)
46·9 (43·3 to 50·5)
–37·3 (–50·6 to –12·8)
127·7 (97·3 to 171·7)
–41·0 (–47·2 to –33·5)
61·1 (56·9 to 65·0)
–29·4 (–56·9 to 6·6)
56·7 (52·4 to 62·1)
33·0 (22·4 to 48·2)
69·1 (53·1 to 85·4)
93·2 (81·6 to 105·0)
128·9 (122·0 to 136·3)
82·8 (75·2 to 88·9)
47·1 (31·5 to 61·0)
–5·6 (–14·2 to 3·7)
48·7 (45·8 to 51·8)
53·7 (48·8 to 59·1)
13·8 (10·5 to 17·2)
–32·6 (–42·1 to –21·2)
–28·6 (–33·3 to –24·2)
–35·2 (–40·5 to –30·5)
–62·5 (–69·0 to –54·9)
–64·6 (–71·7 to –54·2)
–39·8 (–44·9 to –30·2)
–31·0 (–37·1 to –25·4)
24·4 (18·5 to 29·7)
–16·3 (–17·1 to –15·5)
–40·0 (–52·7 to –17·1)
58·5 (37·1 to 89·2)
–62·8 (–66·6 to –58·0)
–1·8 (–2·9 to –0·8)
–37·8 (–61·9 to –6·2)
1·1 (–4·2 to 2·9)
–26·8 (–32·5 to –19·0)
–16·2 (–24·0 to –8·2)
6·3 (0·2 to 12·4)
30·7 (27·6 to 34·3)
–1·8 (–3·7 to –0·1)
–14·5 (–22·5 to –7·4)
–38·9 (–44·3 to –33·0)
–6·8 (–8·7 to –4·9)
–0·1 (–1·0 to 0·7)
–16·4 (–18·7 to –14·0)
0·8 (0·6 to 1·1)
0·8 (0·8 to 0·9)
0·8 (0·6 to 1·0)
0·8 (0·6 to 1·0)
0·7 (0·5 to 1·0)
0·7 (0·5 to 1·0)
26 Interpersonal violence
40 Meningitis
41 Protein-energy malnutrition
46 Drowning
55 Whooping cough
71 Measles
1·1 (1·0 to 1·2)
0·6 (0·5 to 0·8)
0·6 (0·5 to 0·7)
0·5 (0·5 to 0·6)
0·4 (0·2 to 0·7)
0·3 (0·1 to 0·6)
10·2 (3·2 to 19·2)
–51·3 (–59·4 to –42·0)
–71·1 (–79·6 to –59·7)
–60·6 (–65·2 to –53·6)
–54·5 (–74·6 to –16·9)
–89·8 (–92·3 to –86·8)
–23·8 (–28·6 to –17·8)
–57·2 (–64·4 to –48·6)
–74·5 (–82·0 to –64·5)
–68·2 (–71·9 to –62·8)
–56·3 (–75·6 to –20·3)
–90·4 (–92·8 to –87·5)
1 Neonatal disorders
2 Lower respiratory infections
3 Diarrhoeal diseases
4 Congenital birth defects
5 Measles
6 Malaria
7 Protein-energy malnutrition
8 Meningitis
9 Whooping cough
10 Drowning
11 Tuberculosis
12 Tetanus
13 Road injuries
14 Dietary iron deficiency
15 STIs
16 Typhoid and paratyphoid
17 Foreign body
18 HIV/AIDS
19 Encephalitis
20 Acute hepatitis
21 Haemoglobinopathies
22 Leukaemia
23 Sudden infant death
24 Asthma
25 Falls
23·0 (22·0 to 24·1)
17·0 (14·9 to 19·7)
13·1 (10·7 to 15·1)
6·6 (4·6 to 10·0)
5·7 (2·0 to 11·8)
4·6 (2·5 to 7·5)
4·1 (3·1 to 5·5)
2·3 (2·0 to 2·7)
1·9 (0·8 to 3·8)
1·8 (1·5 to 2·1)
1·8 (1·5 to 2·1)
1·7 (1·4 to 1·9)
1·3 (1·1 to 1·5)
0·9 (0·6 to 1·3)
0·7 (0·2 to 1·5)
0·7 (0·3 to 1·3)
0·6 (0·5 to 0·7)
0·6 (0·5 to 0·7)
0·5 (0·4 to 0·7)
0·5 (0·4 to 0·5)
0·4 (0·3 to 0·6)
0·4 (0·3 to 0·6)
0·4 (0·2 to 0·9)
0·4 (0·3 to 0·5)
0·4 (0·3 to 0·5)
1 Neonatal disorders
2 Lower respiratory infections
3 Diarrhoeal diseases
4 Congenital birth defects
5 Malaria
6 Meningitis
7 Dietary iron deficiency
8 Protein-energy malnutrition
9 Whooping cough
10 STIs
11 Measles
12 Road injuries
13 Tuberculosis
14 HIV/AIDS
15 iNTS
16 Drowning
17 Haemoglobinopathies
18 Typhoid and paratyphoid
19 Asthma
20 Foreign body
21 EMBID
22 Sudden infant death
23 Idiopathic epilepsy
24 Other unspecified infectious
25 Dermatitis
32·4 (30·7 to 34·1)
11·6 (10·5 to 12·6)
9·3 (7·9 to 10·8)
8·6 (7·4 to 10·7)
6·4 (3·3 to 10·8)
2·1 (1·8 to 2·5)
2·0 (1·3 to 2·9)
2·0 (1·7 to 2·3)
1·9 (0·9 to 3·3)
1·4 (0·5 to 2·8)
1·3 (0·4 to 2·7)
1·1 (1·0 to 1·4)
1·0 (0·9 to 1·2)
1·0 (0·9 to 1·2)
1·0 (0·6 to 1·5)
0·9 (0·8 to 1·1)
0·9 (0·7 to 1·0)
0·8 (0·4 to 1·5)
0·5 (0·4 to 0·8)
0·5 (0·4 to 0·5)
0·5 (0·4 to 0·6)
0·5 (0·2 to 1·0)
0·5 (0·3 to 0·6)
0·4 (0·3 to 0·6)
0·4 (0·2 to 0·7)
–36·2 (–45·4 to –24·7)
–69·1 (–75·9 to –60·9)
–67·8 (–75·3 to –57·2)
–41·6 (–54·6 to –17·4)
–36·9 (–61·4 to –2·2)
–59·7 (–68·1 to –49·3)
–0·8 (–5·3 to 3·6)
–78·1 (–85·0 to –68·9)
–54·7 (–74·7 to –17·3)
–16·3 (–30·7 to 1·7)
–90·0 (–92·6 to –86·9)
–61·5 (–68·7 to –45·0)
–74·5 (–79·8 to –67·8)
–18·6 (–35·6 to 3·6)
68·3 (27·4 to 121·2)
–77·6 (–81·3 to –70·1)
–10·3 (–30·3 to 22·5)
–46·7 (–59·1 to –31·1)
–32·2 (–46·2 to –14·5)
–62·9 (–69·6 to –56·2)
–18·9 (–33·3 to –0·9)
–50·6 (–61·6 to –29·8)
–30·7 (–45·8 to 3·6)
–28·4 (–48·3 to 7·8)
2·7 (1·7 to 3·7)
–35·4 (–44·8 to –23·8)
–69·6 (–76·3 to –61·6)
–68·5 (–75·9 to –58·4)
–40·1 (–55·1 to –17·9)
–38·5 (–63·1 to –6·5)
–61·0 (–69·2 to –51·1)
–8·2 (–12·3 to –4·1)
–78·3 (–85·5 to –69·9)
–53·2 (–75·6 to –20·4)
–14·9 (–30·1 to 2·5)
–90·5 (–92·9 to –87·6)
–63·7 (–70·8 to –48·8)
–75·5 (–80·6 to –69·2)
–25·0 (–35·3 to –13·6)
61·4 (20·6 to 109·3)
–79·0 (–82·6 to –72·2)
–13·7 (–34·3 to 14·7)
–50·7 (–62·5 to –36·9)
–37·5 (–50·0 to –21·5)
–63·6 (–70·2 to –57·1)
–22·1 (–36·1 to –6·0)
–46·9 (–61·7 to –30·0)
–34·0 (–49·1 to –3·8)
–29·3 (–50·3 to 3·3)
–6·0 (–6·9 to –5·1)
28 Idiopathic epilepsy
30 Other unspecified infectious
33 iNTS
34 EMBID
44 Dermatitis
0·3 (0·2 to 0·4)
0·3 (0·2 to 0·4)
0·3 (0·1 to 0·4)
0·3 (0·2 to 0·3)
0·2 (0·1 to 0·3)
0·4 (0·4 to 0·5)
0·4 (0·3 to 0·5)
0·4 (0·3 to 0·5)
0·3 (0·3 to 0·5)
0·3 (0·2 to 0·3)
–54·8 (–67·7 to –32·9)
–47·2 (–67·0 to –18·0)
–67·6 (–76·7 to –47·6)
–91·3 (–93·8 to –85·6)
–73·1 (–81·7 to –59·1)
–55·3 (–69·5 to –37·0)
–48·3 (–68·7 to –22·6)
–68·5 (–77·9 to –50·2)
–91·2 (–93·8 to –85·6)
–74·1 (–82·6 to –61·1)
27 Age-related hearing loss
29 Chronic kidney disease
30 HIV/AIDS
32 Gynaecological diseases
34 Anxiety disorders
35 Other musculoskeletal
B
0–9 years
26 Leukaemia
27 Falls
28 Encephalitis
32 Tetanus
39 Acute hepatitis
Communicable, maternal, neonatal, and nutritional diseases
Non-communicable diseases
Injuries
(Figure 2 continues on next page)
1210
www.thelancet.com Vol 396 October 17, 2020
Global Health Metrics
Maternal disorders, gynaecological disorders, and dietary
iron deficiency were also in the top ten causes for females
in this relatively young age group (appendix 2 figure S10).
Five causes that were in the top ten for ages 10–24
in 2019 were also in the top ten in the 25–49 age group:
road injuries (ranked first), HIV/AIDS (second), low back
C
pain (fourth), headache disorders (fifth), and depressive
disorders (sixth; figure 2). Tuberculosis and four noncommunicable causes—ischaemic heart disease, gynaecological disorders, other musculoskeletal disorders, and
stroke—completed the top ten rankings. There were
substantial improvements since 1990 in DALY rates of
10–24 years
Leading causes 1990
Percentage of DALYs
1990
Leading causes 2019
Percentage of DALYs
2019
Percentage change in
number of DALYs,
1990–2019
Percentage change in
age-standardised DALY
rate, 1990–2019
1 Road injuries
2 Self-harm
3 Headache disorders
4 Tuberculosis
5 Diarrhoeal diseases
6 Interpersonal violence
7 Maternal disorders
8 Depressive disorders
9 Low back pain
10 Drowning
11 Typhoid and paratyphoid
12 Anxiety disorders
13 Dietary iron deficiency
14 Malaria
15 Lower respiratory infections
16 Conflict and terrorism
17 Gynaecological diseases
18 Falls
19 Congenital birth defects
20 Idiopathic epilepsy
21 Conduct disorder
22 Drug use disorders
23 Asthma
24 Stroke
25 Meningitis
7·8 (6·9 to 8·8)
4·9 (4·1 to 5·6)
3·8 (0·4 to 8·2)
3·6 (3·1 to 4·1)
3·2 (2·1 to 4·9)
3·2 (2·8 to 3·6)
3·0 (2·6 to 3·4)
2·8 (2·0 to 3·9)
2·8 (1·9 to 3·8)
2·7 (2·3 to 3·2)
2·6 (1·2 to 4·9)
2·6 (1·8 to 3·5)
2·1 (1·6 to 2·8)
2·1 (1·3 to 3·3)
1·7 (1·4 to 2·0)
1·5 (1·3 to 1·9)
1·5 (1·1 to 2·1)
1·5 (1·3 to 1·6)
1·5 (1·3 to 1·7)
1·4 (1·1 to 1·8)
1·3 (0·8 to 2·0)
1·3 (1·0 to 1·6)
1·2 (1·0 to 1·6)
1·2 (1·0 to 1·3)
1·1 (1·0 to 1·3)
1 Road injuries
2 Headache disorders
3 Self-harm
4 Depressive disorders
5 Interpersonal violence
6 Anxiety disorders
7 Low back pain
8 Dietary iron deficiency
9 HIV/AIDS
10 Diarrhoeal diseases
11 Neonatal disorders
12 Tuberculosis
13 Gynaecological diseases
14 Typhoid and paratyphoid
15 Maternal disorders
16 Malaria
17 Conduct disorder
18 Drug use disorders
19 Acne vulgaris
20 Idiopathic epilepsy
21 Congenital birth defects
22 Falls
23 Drowning
24 Lower respiratory infections
25 Age-related hearing loss
6·6 (5·6 to 7·7)
5·0 (0·6 to 10·9)
3·7 (3·1 to 4·5)
3·7 (2·6 to 5·0)
3·5 (2·9 to 4·1)
3·3 (2·3 to 4·4)
3·2 (2·2 to 4·3)
2·6 (1·9 to 3·4)
2·6 (1·9 to 3·5)
2·6 (1·9 to 3·6)
2·3 (1·8 to 2·8)
2·1 (1·8 to 2·5)
1·9 (1·4 to 2·6)
1·8 (0·8 to 3·3)
1·8 (1·5 to 2·2)
1·8 (1·0 to 3·0)
1·8 (1·1 to 2·6)
1·6 (1·3 to 2·1)
1·6 (1·0 to 2·4)
1·6 (1·2 to 2·1)
1·5 (1·3 to 1·7)
1·4 (1·3 to 1·6)
1·4 (1·2 to 1·7)
1·4 (1·2 to 1·7)
1·3 (0·9 to 1·8)
–20·1 (–28·3 to –12·9)
24·6 (20·6 to 27·1)
–28·4 (–36·3 to –18·9)
20·7 (17·4 to 23·5)
2·1 (–5·0 to 11·1)
17·9 (15·7 to 20·3)
6·0 (4·4 to 7·6)
15·9 (8·6 to 22·4)
159·0 (115·4 to 211·1)
–25·7 (–40·1 to –0·3)
143·6 (114·3 to 174·6)
–44·3 (–50·7 to –36·9)
19·1 (15·8 to 22·0)
–35·5 (–46·0 to –26·4)
–42·7 (–51·9 to –33·8)
–19·4 (–50·8 to 15·8)
24·7 (22·2 to 27·0)
21·8 (15·2 to 28·7)
41·5 (39·8 to 43·2)
6·5 (–7·1 to 25·7)
–5·6 (–15·6 to 7·4)
–8·4 (–16·9 to 0·4)
–50·7 (–55·9 to –44·7)
–20·9 (–29·9 to –10·5)
18·6 (13·4 to 24·2)
–33·6 (–40·4 to –27·7)
3·3 (0·2 to 5·6)
–40·5 (–47·2 to –32·8)
0·0 (–2·8 to 2·4)
–15·4 (–21·3 to –7·9)
–2·0 (–3·8 to –0·1)
–12·0 (–13·3 to –10·6)
–3·5 (–9·5 to 2·0)
112·8 (84·3 to 141·9)
–37·0 (–50·2 to –17·0)
103·6 (78·4 to 128·5)
–53·8 (–59·1 to –47·7)
–1·4 (–4·2 to 1·0)
–46·2 (–54·9 to –38·5)
–52·5 (–60·2 to –45·3)
–31·9 (–59·0 to –3·6)
4·4 (2·3 to 6·3)
0·6 (–4·8 to 6·2)
18·1 (16·7 to 19·5)
–11·4 (–22·8 to 4·6)
–21·2 (–29·7 to –10·5)
–23·9 (–30·9 to –16·7)
–58·8 (–63·2 to –53·9)
–34·1 (–41·6 to –25·5)
–1·2 (–5·7 to 3·2)
27 Acne vulgaris
28 Age-related hearing loss
33 HIV/AIDS
35 Neonatal disorders
1·1 (0·7 to 1·6)
1·1 (0·7 to 1·5)
0·9 (0·6 to 1·5)
0·9 (0·7 to 1·1)
27 Asthma
30 Stroke
34 Meningitis
46 Conflict and terrorism
1·3 (1·0 to 1·8)
1·1 (0·9 to 1·3)
0·9 (0·7 to 1·1)
0·6 (0·5 to 0·8)
–1·1 (–8·3 to 5·1)
–12·8 (–21·5 to –2·9)
–26·0 (–34·0 to –16·4)
–62·1 (–65·7 to –57·9)
–18·0 (–23·8 to –12·4)
–27·6 (–34·8 to –19·4)
–38·3 (–45·0 to –30·4)
–68·5 (–71·6 to –65·1)
1 Road injuries
2 Tuberculosis
3 Ischaemic heart disease
4 Low back pain
5 Self-harm
6 Stroke
7 Headache disorders
8 Depressive disorders
9 Cirrhosis
10 Gynaecological diseases
11 Maternal disorders
12 Interpersonal violence
13 HIV/AIDS
14 Other musculoskeletal
15 Diarrhoeal diseases
16 Falls
17 Anxiety disorders
18 Alcohol use disorders
19 Neck pain
20 Diabetes
21 Chronic kidney disease
22 Drug use disorders
23 Schizophrenia
24 Age-related hearing loss
25 Lower respiratory infections
5·6 (5·1 to 6·1)
5·5 (4·8 to 6·2)
4·4 (3·8 to 4·9)
3·9 (2·9 to 5·1)
3·8 (3·3 to 4·4)
3·5 (3·1 to 3·9)
3·1 (0·7 to 6·4)
3·0 (2·2 to 3·9)
2·8 (2·5 to 3·2)
2·8 (2·2 to 3·7)
2·6 (2·3 to 2·9)
2·5 (2·3 to 2·8)
2·3 (1·6 to 3·2)
2·0 (1·5 to 2·8)
2·0 (1·3 to 3·1)
1·8 (1·6 to 2·0)
1·7 (1·2 to 2·2)
1·7 (1·4 to 2·0)
1·3 (0·9 to 2·0)
1·3 (1·2 to 1·5)
1·3 (1·2 to 1·4)
1·3 (1·0 to 1·6)
1·3 (0·9 to 1·6)
1·3 (0·9 to 1·7)
1·2 (1·1 to 1·4)
1 Road injuries
2 HIV/AIDS
3 Ischaemic heart disease
4 Low back pain
5 Headache disorders
6 Depressive disorders
7 Gynaecological diseases
8 Other musculoskeletal
9 Stroke
10 Tuberculosis
11 Self-harm
12 Cirrhosis
13 Interpersonal violence
14 Diabetes
15 Anxiety disorders
16 Drug use disorders
17 Falls
18 Chronic kidney disease
19 Neck pain
20 Alcohol use disorders
21 Age-related hearing loss
22 Schizophrenia
23 Maternal disorders
24 Diarrhoeal diseases
25 Oral disorders
5·1 (4·6 to 5·7)
4·8 (4·0 to 5·9)
4·7 (4·0 to 5·4)
3·9 (2·9 to 5·0)
3·7 (0·8 to 7·7)
3·5 (2·5 to 4·5)
3·3 (2·5 to 4·2)
3·2 (2·3 to 4·2)
3·2 (2·8 to 3·6)
3·0 (2·6 to 3·4)
2·9 (2·4 to 3·4)
2·8 (2·4 to 3·2)
2·3 (2·0 to 2·6)
2·2 (1·9 to 2·5)
2·0 (1·4 to 2·7)
1·9 (1·5 to 2·2)
1·8 (1·6 to 2·0)
1·6 (1·4 to 1·8)
1·6 (1·1 to 2·4)
1·6 (1·3 to 1·9)
1·5 (1·1 to 2·1)
1·5 (1·1 to 1·9)
1·4 (1·2 to 1·6)
1·3 (1·0 to 1·9)
1·2 (0·7 to 2·1)
23·2 (11·1 to 33·2)
176·2 (131·1 to 244·3)
42·7 (28·4 to 57·3)
33·0 (29·2 to 36·9)
61·2 (56·5 to 64·5)
53·2 (49·3 to 56·8)
52·7 (49·7 to 56·0)
107·1 (101·0 to 114·3)
19·9 (8·0 to 31·1)
–27·0 (–34·7 to –18·7)
–0·9 (–10·3 to 9·1)
29·6 (19·0 to 44·5)
18·1 (10·7 to 26·5)
123·9 (110·1 to 135·3)
61·6 (57·5 to 65·4)
92·0 (82·7 to 102·5)
34·4 (25·8 to 41·7)
67·3 (53·9 to 80·3)
60·2 (52·4 to 67·9)
28·2 (22·9 to 33·2)
64·3 (58·7 to 69·1)
59·6 (57·5 to 61·9)
–28·9 (–39·6 to –19·2)
–13·5 (–32·6 to 15·5)
70·7 (66·4 to 74·1)
–22·5 (–30·1 to –16·2)
72·2 (52·4 to 91·9)
–18·5 (–26·7 to –10·1)
–19·2 (–20·5 to –18·0)
0·2 (–3·7 to 2·3)
–4·9 (–6·4 to –3·4)
–4·5 (–6·3 to –2·5)
26·7 (23·4 to 30·5)
–31·0 (–37·9 to –24·6)
–55·5 (–60·2 to –50·5)
–37·2 (–43·2 to –30·9)
–23·8 (–30·1 to –15·1)
–24·4 (–29·0 to –19·0)
29·2 (21·1 to 36·0)
1·1 (0·0 to 2·1)
25·4 (19·3 to 31·6)
–18·0 (–23·4 to –13·5)
0·7 (–7·3 to 8·4)
–3·6 (–6·0 to –1·5)
–20·9 (–24·2 to –17·9)
–0·5 (–3·1 to 1·9)
–0·9 (–2·0 to 0·2)
–53·4 (–60·5 to –47·2)
–46·2 (–59·0 to –29·6)
2·8 (0·5 to 5·1)
32 Oral disorders
1·0 (0·5 to 1·6)
27 Lower respiratory infections
1·2 (1·0 to 1·4)
26·8 (15·2 to 38·5)
–23·1 (–30·2 to –16·0)
D
25–49 years
Communicable, maternal, neonatal, and nutritional diseases
Non-communicable diseases
Injuries
(Figure 2 continues on next page)
www.thelancet.com Vol 396 October 17, 2020
1211
Global Health Metrics
E
50–74 years
Leading causes 1990
1 Ischaemic heart disease
2 Stroke
3 COPD
4 Tuberculosis
5 Lung cancer
6 Diabetes
7 Cirrhosis
8 Low back pain
9 Diarrhoeal diseases
10 Stomach cancer
11 Road injuries
12 Lower respiratory infections
13 Age-related hearing loss
14 Chronic kidney disease
15 Asthma
16 Hypertensive heart disease
17 Falls
18 Colorectal cancer
19 Depressive disorders
20 Blindness and vision loss
21 Liver cancer
22 Breast cancer
23 Oesophageal cancer
24 Osteoarthritis
25 Self-harm
26 Other musculoskeletal
28 Oral disorders
29 Headache disorders
32 Neck pain
F
Percentage of DALYs
1990
12·5 (11·6 to 13·4)
10·9 (10·0 to 11·8)
6·5 (5·5 to 7·1)
4·0 (3·6 to 4·4)
3·6 (3·3 to 3·9)
3·1 (2·8 to 3·4)
2·8 (2·6 to 3·1)
2·8 (2·1 to 3·7)
2·6 (1·6 to 4·0)
· 2·6)
2·4 (2·2 to
1·9 (1·8 to 2·0)
1·8 (1·6 to 2·0)
1·7 (1·2 to 2·3)
1·6 (1·4 to 1·7)
1·5 (1·2 to 1·9)
1·5 (1·2 to 1·7)
1·4 (1·3 to 1·6)
1·4 (1·3 to 1·5)
1·3 (0·9 to 1·7)
1·2 (0·9 to 1·6)
1·2 (1·0 to 1·3)
1·2 (1·1 to 1·2)
1·1 (0·9 to 1·2)
1·1 (0·6 to 2·2)
1·1 (1·0 to 1·2)
1·1 (0·7 to 1·5)
1·0 (0·6 to 1·5)
0·9 (0·3 to 1·9)
0·8 (0·5 to 1·2)
Leading causes 2019
1 Ischaemic heart disease
2 Stroke
3 Diabetes
4 COPD
5 Lung cancer
6 Low back pain
7 Cirrhosis
8 Chronic kidney disease
9 Age-related hearing loss
10 Road injuries
11 Other musculoskeletal
12 Tuberculosis
13 Lower respiratory infections
14 Depressive disorders
15 Colorectal cancer
16 Falls
17 Stomach cancer
18 Osteoarthritis
19 Blindness and vision loss
20 Breast cancer
21 Diarrhoeal diseases
22 Hypertensive heart disease
23 Headache disorders
24 Oral disorders
25 Neck pain
27 Oesophageal cancer
28 Asthma
29 Liver cancer
31 Self-harm
Percentage of DALYs
2019
Percentage change in
number of DALYs,
1990–2019
Percentage change in
age-standardised DALY
rate, 1990–2019
11·8 (10·7 to 12·9)
9·3 (8·5 to 10·1)
5·1 (4·6 to 5·7)
4·7 (4·2 to 5·2)
3·9 (3·4 to 4·3)
3·1 (2·3 to 4·0)
2·7 (2·4 ·to 3·0)
2·3 (2·1 to 2·5)
2·2 (1·5 to 3·0)
2·1 (1·9 to 2·3)
1·9 (1·4 to 2·6)
1·9 (1·7 to 2·1)
1·8 (1·6 to 1·9)
1·7 (1·3 to 2·3)
1·7 (1·6 to 1·9)
1·7 (1·5 to 2·0)
1·7 (1·5 to 1·9)
1·5 (0·8 to 2·9)
1·4 (1·1 to 2·0)
1·4 (1·3 to 1·5)
1·4 (0·9 to 2·1)
1·3 (1·0 to 1·5)
1·2 (0·4 to 2·5)
1·2 (0·8 to 1·8)
1·1 (0·7 to 1·7)
46·1 (35·6 to 56·4)
31·5 (19·5 to 42·9)
156·1 (143·4 to 167·9)
12·0 (0·9 to 32·3)
64·3 (48·8 to 80·2)
72·1 (70·0 to 74·3)
44·6 (33·2 to 57·1)
130·2 (113·0 to 145·6)
100·8 (96·0 to 104·9)
72·9 (56·5 to 83·9)
172·0 (160·6 to 187·4)
–27·8 (–36·2 to –16·9)
49·8 (37·9 to 62·4)
107·3 (104·7 to 110·1)
95·1 (80v8 to 108·2)
88·3 (76·5 to 100·0)
6·3 (–5·0 to 18·9)
113·6 (110·9 to 116·4)
88·8 (81·9 to 95·8)
85·0 (69·9 to 99·4)
–21·0 (–42·4 to 11·9)
36·7 (20·8 to 58·8)
102·5 (88·7 to 108·2)
90·5 (86·0 to 94·7)
115·9 (110·5 to 122·2)
–29·1 (–34·2 to –24·1)
–36·3 (–42·1 to –30·8)
24·5 (18·5 to 30·4)
–45·9 (–51·4 to –36·2)
–19·8 (–27·3 to –12·1)
–15·9 (–16·9 to –14·9)
–29·1 (–34·7 to –23·0)
12·1 (3·7 to 19·5)
–2·6 (–4·9 to –0·5)
–15·2 (–23·2 to –9·9)
33·6 (280 to 40·2)
–64·7 (–68·9 to –59·4)
–27·5 (–33·3 to –21·5)
1·5 (0·2 to 2·9)
–5·1 (–12·1 to 1·2)
–8·4 (–14·1 to –2·6)
–48·1 (–536 to –42·0)
4·1 (28 to 5·4)
–8·6 (–12·0 to –5·0)
–9·5 (–16·9 to –2·5)
–61·0 (–72·1 to –45·8)
–33·8 (–41·7 to –23·4)
–1·2 (–7·4 to 2·3)
–7·4 (–9·6 to –5·1)
5·7 (3·0 to 8·5)
1·0 (0·9 to 1·1)
1·0 (0·8 to 1·1)
0·9 (0·8 to 1·0)
0·9 (0·8 to 1·0)
38·2 (18·9 to 71·9)
–1·3 (–14·3 to 11·2)
22·2 (5·2 to 44·0)
20·4 (11·3 to 33·7)
–32·1 (–41·9 to –16·1)
–51·8 (–58·3 to –46·0)
–39·9 (–48·5 to –29·5)
–41·0 (–45·5 to –34·5)
16·2 (14·6 to 17·6)
13·0 (11·7 to 14·0)
8·5 (7·5 to 9·2)
5·6 (2·6 to 12·2)
4·0 (3·6 to 4·3)
3·3 (2·9 to 3·6)
2·6 (2·3 to 2·8)
2·6 (2·2 to 2·9)
2·5 (2·3 to 2·7)
2·5 (1·9 to 3·3)
2·4 (1·8 to 2·7)
1·9 (1·2 to 3·0)
1·8 (1·3 to 2·4)
1·7 (1·5 to 1·8)
1·7 (1·3 to 2·2)
1·3 (1·1 to 1·5)
1·3 (1·1 to 1·4)
1·1 (1·0 to 1·4)
1·1 (1·0 to 1·2)
1·1 (1·0 to 1·2)
1·1 (06 to 21)
0·9 (0·6 to 1·3)
0·9 (0·8 to 1·0)
0·8 (0·7 to 1·0)
0·8 (0·7 to 0·9)
66·6 (57·7 to 74·2)
60·5 (48·7 to 72·5)
63·6 (49·1 to 86·1)
180·0 (168·0 to 194·7)
190·7 (179·4 to 201·0)
87·4 (76·2 to 99·6)
164·3 (143·6 to 183·8)
166·4 (151·1 to 183·4)
196·0 (173·9 to 211·1)
137·8 (132·0 to 143·9)
106·0 (68·5 to 131·7)
15·1 (–16·8 to 65·3)
105·7 (100·2 to 111·4)
126·9 (113·4 to 138·3)
124·7 (119·3 to 130·7)
148·6 (134·8 to 161·9)
55·0 (43·8 to 66·6)
117·0 (102·1 to 142·3)
82·3 (62·1 to 100·9)
153·7 (138·7 to 166·6)
139·5 (136·5 to 142·6)
112·0 (106·4 to 117·6)
–6·3 (–16·9 to 14·6)
25·2 (3·2 to 41·2)
110·0 (99·8 to 118·1)
–32·4 (–35·8 to –29·4)
–33·4 (–38·3 to –28·5)
–31·0 (–37·1 to –21·9)
2·6 (–2·1 to 6·6)
23·1 (18·6 to 27·5)
–25·3 (–29·3 to –20·4)
16·4 (7·4 to 24·9)
6·4 (0·4 to 13·3)
21·6 (12·6 to 27·4)
–2·2 (–4·3 to –0·2)
–15·1 (–31·5 to –5·0)
–51·0 (–64·9 to –30·4)
–12·5 (–13·8 to –11·3)
–4·5 (–9·7 to 0·1)
–7·4 (–9·9 to –4·8)
–1·8 (–6·9 to 2·5)
–32·9 (–37·5 to –28·0)
–8·5 (–14·6 to 2·1)
–21·3 (–30·2 to –13·5)
6·0 (0·0 to 11·1)
0·8 (–0·4 to 2·1)
–10·9 (–12·9 to –8·8)
–59·2 (–64·0 to –50·3)
–46·2 (–55·9 to –39·8)
–9·3 (–13·5 to –5·9)
0·6 (0·5 to 0·6)
34·0 (22·8 to 46·2)
–43·8 (–48·4 to –38·7)
75 years and older
1 Ischaemic heart disease
2 Stroke
3 COPD
4 Alzheimer's disease
5 Lower respiratory infections
6 Diarrhoeal diseases
7 Diabetes
8 Hypertensive heart disease
9 Age-related hearing loss
10 Lung cancer
11 Falls
12 Tuberculosis
13 Low back pain
14 Chronic kidney disease
15 Stomach cancer
16 Blindness and vision loss
17 Colorectal cancer
18 Asthma
19 Cirrhosis
20 Prostate cancer
21 Atrial fibrillation
22 Osteoarthritis
23 Oral disorders
24 Parkinson's disease
25 Upper digestive diseases
26 Road injuries
18·6 (17·1 to 19·7)
15·5 (14·3 to 16·7)
9·9 (8·6 to 10·7)
3·8 (1·7 to 8·6)
3·3 (3·0 to 3·6)
3·1 (2·0 to 4·5)
2·6 (2·4 to 2·9)
2·3 (1·9 to 2·5)
2·0 (1·5 to 2·7)
1·9 (1·8 to 2·0)
1·8 (1·6 to 2·1)
1·8 (1·6 to 2·1)
1·7 (1·2 to 2·3)
1·6 (1·5 to 1·8)
1·6 (1·4 to 1·7)
1·4 (1·1 to 1·8)
1·4 (1·3 to 1·5)
1·2 (1·0 to 1·7)
1·2 (1·0 to 1·3)
1·0 (0·8 to 1·2)
1·0 (0·8 to 1·2)
0·9 (0·5 to 1·7)
0·8 (0·6 to 1·2)
0·8 (0·8 to 0·9)
0·8 (0·7 to 0·9)
0·7 (0·6 to 0·8)
1 Ischaemic heart disease
2 Stroke
3 COPD
4 Alzheimer's disease
5 Diabetes
6 Lower respiratory infections
7 Lung cancer
8 Falls
9 Chronic kidney disease
10 Age-related hearing loss
11 Hypertensive heart disease
12 Diarrhoeal diseases
13 Low back pain
14 Colorectal cancer
15 Blindness and vision loss
16 Atrial fibrillation
17 Stomach cancer
18 Prostate cancer
19 Cirrhosis
20 Parkinson's disease
21 Osteoarthritis
22 Oral disorders
23 Tuberculosis
24 Asthma
25 Road injuries
32 Upper digestive diseases
Communicable, maternal, neonatal, and nutritional diseases
Non-communicable diseases
Injuries
Figure 2: Leading 25 Level 3 causes of global DALYs and percentage of total DALYs (1990 and 2019), and percentage change in number of DALYs and
age-standardised DALY rates from 1990 to 2019 for both sexes combined for all ages (A), children younger than 10 years (B), and ages 10–24 years (C),
25–49 years (D), 50–74 years (E), and 75 years and older (F)
Causes are connected by lines between time periods; solid lines are increases in rank and dashed lines are decreases. Age-related hearing loss=age-related and other
hearing loss. Alzheimer’s disease=Alzheimer’s disease and other dementias. Atrial fibrillation=atrial fibrillation and flutter. Cirrhosis=cirrhosis and other chronic liver
diseases. COPD=chronic obstructive pulmonary disease. EMBID=endocrine, metabolic, blood, and immune disorders. DALY=disability-adjusted life-year. iNTS=invasive
non-typhoidal salmonella. Haemoglobinopathies=haemoglobinopathies and haemolytic anaemias. Lung cancer=tracheal, bronchus, and lung cancer. Other
musculoskeletal=other musculoskeletal disorders. Other unspecified infectious=other unspecified infectious diseases. Sudden infant death=sudden infant death
syndrome. STI=sexually transmitted infections excluding HIV.
1212
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Global Health Metrics
tuberculosis, road injuries, stroke, and, to a lesser extent,
low back pain and ischaemic heart disease. For similar
reasons as in the previous age group, HIV/AIDS
DALY rates increased substantially. The increase in the
residual “other musculoskeletal disorder” category is
more difficult to interpret, as it is a collection of several
individual diseases. HIV/AIDS, ischaemic heart disease,
stroke, and headache disorders appeared in the top-ten
rankings for DALYs for both males and females in 2019.
Three injury causes (road injuries, self-harm, and interpersonal violence) and cirrhosis ranked prominently
among males but not females. Among females, gynaecological disorders, depressive disorders, other musculoskeletal disorders, maternal disorders, and anxiety
disorders were top ten causes (appendix 2 figures S9, S10).
In 2019, the ten leading causes of DALYs in age groups
50–74 years and 75 years and older largely overlapped.
Ischaemic heart disease and stroke were ranked first
and second, respectively, in both age groups. Chronic
obstructive pulmonary disease (COPD), diabetes, lung
cancer, chronic kidney disease, and age-related hearing
loss appeared in the top ten in both age groups. For ages
50–74 years, low back pain, cirrhosis, and road injuries
were the remaining top-ten-ranking causes of DALYs,
whereas Alzheimer’s disease and other dementias, lower
respiratory infections, and falls appeared in the top ten for
those aged 75 years and older. The most notable changes
in top ten causes in these two age groups between
1990 and 2019 were large declines in age-standardised
DALY rates for ischaemic heart disease, stroke, COPD,
cirrhosis, and road injuries, but increases in DALY rates
for diabetes and chronic kidney disease. There was a
decline in age-standardised lung cancer rates for ages
50–74 years, but an increase in the oldest age category. The
ten leading causes for DALYs by sex in both of these older
age groups largely overlapped in 2019. Among 50–74-yearolds, breast cancer, other musculoskeletal disorders, and
depressive disorders appeared in the top ten for females
only, while road injuries, cirrhosis, and tuberculosis made
it into the top ten for males. For the oldest age group, falls
and hypertensive heart disease ranked in the top ten
among females, but not males; lung cancer and prostate
cancer ranked among the top ten in males (appendix 2
figures S9, S10).
National trends
Countries and territories vary widely in their stages of the
epidemiological transition. With increasing SDI, we
expect to see a shift in the burden of disease from communicable, maternal, neonatal, and nutritional diseases
towards non-communicable causes. We also expect to
see a shift towards a larger fraction of the burden due to
YLDs compared with YLLs. These two major trends can
be summarised by the percentage of all-cause DALYs
made up of non-communicable disease and injury YLDs.
Figure 3 shows this proportion across 204 countries and
territories in 1990 and 2019. In 2019, this measure of the
www.thelancet.com Vol 396 October 17, 2020
epidemiological transition ranged from 8·4% (95% UI
6·2–10·9) in Chad to 56·9% (48·7–64·3) in Qatar.
The values in 1990 ranged from 3·5% (2·6–4·7) in Niger
to 47·5% (37·6–56·0) in Andorra. In 2019, non-communicable and injury YLDs contributed to more than half of
all disease burden in 11 countries. All but two countries,
Ukraine and Lesotho, had higher ratios in 2019 compared
with 1990.
When comparing the annualised rate of change in agestandardised DALY rates for all causes except HIV/AIDS,
natural disasters, and war and conflict between the time
periods 1990–2010 and 2010–19 for each country and
territory, the rate, as shown by a simple linear regression
line, is steeper in the latter time period, suggesting that
change has accelerated over the last decade in countries
and territories at the lower end of the SDI range (figure 4).
Improvements have started to stagnate, or even reverse,
in countries with higher SDI, as is the case in Dominica,
the Dominican Republic, Guam, Jamaica, Saint Lucia,
Saint Vincent and the Grenadines, Ukraine, the USA, and
Venezuela. Countries with greater than 2% annual reductions in age-standardised DALY rates over both time
periods were Ethiopia, Angola, Burundi, Malawi, Sudan,
Myanmar, Laos, and Bangladesh. Four countries from the
former Soviet Union—Russia, Belarus, Kazakhstan, and
Uzbekistan—experienced increases in age-standardised
DALY rates between 1990 and 2010, but recovered in
the following decade; Russia, Kazakhstan, and Belarus
experienced an estimated annual decline of 2% or greater
between 2010 and 2019, and Uzbekistan experienced an
estimated 1·5% annual decline. Another former Soviet
Union republic, Ukraine, saw modest decline in the
1990 to 2010 period, but a worsening trend in the decade
after.
Cause-specific trends
Two-page cause-specific summaries provide detailed
results on mortality, prevalence, incidence, YLLs, YLDs,
and DALYs for a selection of diseases, injuries, and
impairments in the GBD cause hierarchy. These summaries include 2019 counts, age-standardised rates, and
rankings; the fraction of DALYs attributed to risk factors;
patterns over time and age; and the relationship between
SDI and DALY rates by country or territory. They were
written to increase the accessibility to and transparency
of GBD estimates for each cause. Summaries for select
causes are highlighted in print (pp S2–213); summaries
for all diseases, injuries, and impairments can be found
online.
For all two-page summaries see
https://www.thelancet.com/
gbd/summaries
Discussion
Main findings
Global health has steadily improved over the past
30 years, as measured by changes in age-standardised
DALY rates. While health has improved, after accounting
for population growth and ageing, the absolute number
of DALYs has remained stable. The shift to a much
1213
Global Health Metrics
A
1990
YLD proportion of DALYs
<10%
10% to 14%
15% to 19%
20% to 24%
25% to 29%
30% to 34%
35% to 39%
40% to 44%
45% to 49%
≥50%
Caribbean and central America
Persian Gulf
Balkan Peninsula
Southeast Asia
West Africa
Eastern
Mediterranean
Northern Europe
B
2019
Caribbean and central America
Persian Gulf
Balkan Peninsula
Southeast Asia
West Africa
Eastern
Mediterranean
Northern Europe
Figure 3: Proportion of total DALYs contributed by injury and non-communicable disease YLDs, by country or territory, 2019
Proportions were rounded to the nearest whole number. DALY=disability-adjusted life-year. YLD=year lived with disability.
1214
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Global Health Metrics
GBD super-region
High income
Central Europe, eastern Europe, and central Asia
North Africa and Middle East
Sub-Saharan Africa
South Asia
A
Latin America and Caribbean
B
1990–2010
0·01
ZWE
2010–19
UZB
UKR
LSO
SWZ
BLR
Annualised rate of change in DALY rate
0
GUM
DOM
NRU
LCA
VCT
DMA
USA
JAM
RUS
KAZ
VEN
–0·01
YEM
–0·02
MOZ
BDI
NER
MRT
ERI
SDN
AGO
KHM MMR BOL
MWI
NPL
–0·03
LBR
ETH
GTM CHN
BTN
RWA
SGP
AFG
TUR
SOM
KOR
TGO
NGA
GNB
MWI CIV
PER
SLE
LAO TLS
BGD
CMR
LAO
ETH
COD
SDN
IND
GHA
ZMB
KGZ
ZAF
SWZ
BGD MMR
OMN
ARE
BLR
RUS
MDA
KAZ
AGO
MDV
GNQ
–0·04
0
50
SDI
100
0
50
100
SDI
Figure 4: Annualised rate of change in age-standardised DALY rates for all causes excluding HIV/AIDS, natural disasters, and war and conflict, and SDI by
country or territory, for 1990–2010 (A) and 2010–19 (B)
A simple linear regression line is shown in each figure for the relationship between annualised rate of change and the average SDI value of each country and territory
for each time period. AFG=Afghanistan. AGO=Angola. ARE=United Arab Emirates. BDI=Burundi. BGD=Bangladesh. BLR=Belarus. BOL=Bolivia. BTN=Bhutan.
CHN=China. CIV=Côte d’Ivoire. CMR=Cameroon. COD=Democratic Republic of the Congo. DALY=disability-adjusted life-year. DMA=Dominica. DOM=Dominican
Republic. ERI=Eritrea. ETH=Ethiopia. GHA=Ghana. GNB=Guinea-Bissau. GNQ=Equatorial Guinea. GTM=Guatemala. GUM=Guam. IND=India. JAM=Jamaica.
KAZ=Kazakhstan. KHM=Cambodia. KOR=South Korea. KNA=Saint Kitts and Nevis. LAO=Laos. LBR=Liberia. LCA=Saint Lucia. LSO=Lesotho. MDA=Moldova.
MDV=Maldives. MMR=Myanmar. MOZ=Mozambique. MRT=Mauritania. MWI=Malawi. NER=Niger. NGA=Nigeria. NPL=Nepal. NRU=Nauru. OMN=Oman. PER=Peru.
RUS=Russia. RWA=Rwanda. SDN=Sudan. SGP=Singapore. SLE=Sierra Leone. SOM=Somalia. SWZ=eSwatini. TGO=Togo. TLS=Timor-Leste. TUR=Turkey. UKR=Ukraine.
UZB=Uzbekistan. VCT=Saint Vincent and the Grenadines. VEN=Venezuela. YEM=Yemen. ZAF=South Africa. ZWE=Zimbabwe. SDI=Socio-demographic Index.
greater number of DALYs occurring at older ages, despite
reductions in age-standardised DALY rates, illustrates
the importance of understanding how ageing shapes
future health needs. Policy makers should remain aware
that the number of DALYs represents the burden of
disease that the world’s health systems must manage.
Although most diseases showed a pattern of stable or
slowly changing rates of death and disability over the
study period, there are some notable exceptions. Deaths
due to drug use disorders have risen sharply over the
past decade. In 2019, more than half of all global overdose
deaths occurred in the USA. Liberal prescribing of highdose opioids, inadequate provision of opioid substitution
therapy, and the lacing of street drugs with highly
potent opioids such as fentanyl are considered major
contributors to this public health crisis.14–17 By contrast, a
positive, rapid change in disease rates has taken place in
Egypt, where close to 80% of the population aged 12 years
and older has been screened for hepatitis C, and those
with detectable virus are treated with a low-cost treatment
regimen.18,19 We estimated that the number of cases of
chronic hepatitis C has dropped by 65·9% (95% UI
51·1–79·7) since screening and treatment were initiated
through regular health services in 2014 and an enhanced
www.thelancet.com Vol 396 October 17, 2020
national screening programme for the whole population
aged 12 years and older was established in 2019.19 Egypt
had the highest prevalence of chronic hepatitis C in the
world, ascribed to iatrogenic infection during treatment
campaigns for schistosomiasis in the 1960s and 1970s.20–22
The sharp decline in chronic infections in Egypt is
expected to be reflected in a large decline in deaths
from cirrhosis and liver cancer in coming years. Unlike
hepatitis B vaccination in children, where the effect of
intervention cannot be expected until several decades
later, removal of hepatitis C virus in the adult population
leads to more immediate health impact.
In children younger than 10 years, the decline in
neonatal disorders was slower than for the major
infectious diseases, thus increasing neonatal disorders’
share of total DALYs. Among injuries in this age group,
drowning saw the largest decline in DALYs. The position
of congenital syphilis among the top ten causes of
DALYs in children is indicative of health system failure.
With testing and treatment in the second trimester of
pregnancy, this cause could be eliminated.23 The main
reasons for failure are limited access to health services,
the low use of rapid diagnostic tests, the failure of
antenatal clinics to screen or treat when a woman is
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tested positive, and the recent global shortage of
benzathine penicillin, the treatment of choice.24 Despite
the large health gains among children younger than
10 years, considerable burdens still remain in subSaharan Africa. Sustaining the global pace of progress
will become more challenging as an ever-increasing
proportion of the global birth cohort is born in subSaharan Africa,25 with the highest rates of burden in
these age groups. It is encouraging, however, that the
largest decreases in DALY rates globally have occurred in
sub-Saharan African countries, such as Ethiopia, Angola,
Rwanda, and Malawi, although there are others that
have seen much less progress.
Among the top ten causes of DALYs in adolescents
aged 10–24 years, self-harm had the largest decline
(28·4% [95% UI 18·9–36·3]) over the study period. The
prevalence of depressive disorders and other mental
disorders, which are major underlying causes of selfharm,26 did not change, suggesting that the decline in
self-harm deaths was largely due to other factors such as
better access to mental health services, urbanisation, and
a reduction in access to more lethal means of suicide.27–30
The increase in DALY rates of neonatal disorders in this
age group is a downside to the large improvements in
neonatal survival, causing a greater proportion of the
surviving babies to have long-term neurological and
sensory deficits.
In the 25–49-year age group, HIV/AIDS was the second
leading cause of DALYs in 2019 despite a drop since 2005,
when ART became more widely available.31 To be on
course to end HIV/AIDS as a public health threat
by 2030, UNAIDS estimates that a substantial increase in
global funding would be required, whereas high-income
countries have reduced their funding.32 The prominent
position of headache disorders in the DALY rankings in
the 10–24-year and 25–49-year age groups has received
little attention in global health policy debates. While
there is no cure for these disorders, there are effective
symptomatic and preventive treatments available.33
Ischaemic heart disease, stroke, and diabetes were not
among the 25 leading causes in the two younger age
groups, but emerged as major contributors to burden in
the 25–49-year age group and, more prominently, in the
older age groups that follow. These diseases share many
common risk factors and treatment approaches. The
burden in high-income countries has been rapidly
declining since the 1980s, but a more recent downturn in
this decline over the past 5 years has been noted as an
important explanation for the slowdown in life expectancy
gains.34 Low-income and middle-income countries still
have ample opportunity to make greater use of known
effective intervention strategies (tobacco control, blood
pressure-lowering and cholesterol-lowering treatments,
and emergency response and treatment for acute events)
that have been so effective in high-income countries.35
However, the rising prevalence of diabetes, linked to the
almost ubiquitous increase in body-mass index globally,36
1216
is mitigating the pathway to reducing the burden of
cardiovascular diseases.37,38 In the 25–49-year age group,
tuberculosis that is not associated with HIV infection
ranked among the top ten causes in 2019. There are
similar worries about sustained global funding of
tuberculosis control as mentioned for HIV/AIDS, let
alone having the additional resources and research
development effort that would be required to reach
WHO’s goals to reduce the 2015 levels of tuberculosis
deaths and incidence by 90% and 80%, respectively, by
the year 2030.39–41
The prominent rankings of COPD and lung cancer
in the 50–74-year and 75-years-and-older age groups
emphasise the continuing need for tobacco-control
measures and attention to reducing exposure to indoor
and outdoor air pollution. Already, low-income and
middle-income countries account for 62·6% of the
global burden of COPD and lung cancer, and this share
is likely to increase sharply over coming decades due to
ageing populations and less successful tobacco and air
pollution control. The finding that lung cancer DALY
rates are declining in the 50–74-year age group but not in
those aged 75 years and older is probably due to a cohort
effect; this could be encouraging if it reflects a greater
response to tobacco control in younger generations that
will drive further declines in coming years. Chronic
kidney disease is strongly linked to cardiovascular
diseases and diabetes, and shares common risks and
intervention approaches.42 Given its prominent position
in the top ten rankings of DALYs in older age groups and
the costs associated with end-stage kidney disease
treatments, screening and low-cost treatments at earlier
stages of chronic kidney disease should be more widely
implemented.43 Cirrhosis ranked seventh among those
aged 50–74 years in 2019. With low-cost treatments
available to low-income and middle-income countries,
there is an opportunity to eradicate hepatitis C as an
underlying cause—a strategy that Egypt is well on the
way to achieving in coming years.19 Childhood vaccinations for hepatitis B will eventually also reduce
cirrhosis (and liver cancer) outcomes, but the full effect
will probably not be apparent for years. Alcohol is the
third modifiable cause of cirrhosis; there is strong
evidence that taxation and regulations can reduce alcohol
use to less harmful levels.44 Age-related hearing loss is a
top ten cause of DALYs in the two older age groups.
While some reduction in burden can be achieved by
control of loud noises during leisure or occupational
activities, most of the burden cannot be prevented
through currently known strategies. For a large proportion of the elderly, hearing aids can relieve some of the
symptoms and associated social isolation. The quality of
hearing aids has improved over the past decade, but lowcost appliances are not readily available in low-income
and middle-income countries.45
Alzheimer’s disease and other dementias, and falls are
two causes that appear in the top ten ranking of DALYs
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Global Health Metrics
only for those aged 75 years and older. The ability to
intervene by prevention or treatment for dementia is still
limited despite a large research and development effort
to identify drugs, but efforts continue.46 There is good
evidence that a range of modifiable risks (tobacco,
physical inactivity, metabolic risks, and hearing loss)
contribute to the development of dementia,47,48 but little
evidence of the effectiveness of interventions addressing
these risk factors.47,49 Falls in the elderly are common and
linked to psychotropic and cardiovascular medications,50
cognitive impairment, depression, and general frailty.51,52
There is evidence for the effectiveness of multifactorial
interventions combining education, exercise, and home
safety modification interventions.53
The trend towards disability as an increasing share of
overall burden has continued. In 11 countries, more than
half the burden was from YLDs of NCDs and injuries in
2019. To some extent, the absence of a discernible trend
in disability might be an artifact of the poor availability of
data on severity, and, therefore, an inability to quantify
the effect of health service interventions that modulate
severity. The larger issue, however, is that most of the
focus of global public health has been on life-saving
interventions directed at the main causes of death.7,54,55
The large contributors to disability, such as musculoskeletal conditions and mental disorders, are associated
with few deaths. As disability becomes an increasingly
large component of disease burden and, as importantly,
a larger component of health expenditure, a greater
research development investment is needed to identify
new, and more effective, intervention strategies.56–58 With
a rapidly ageing global population, the demands on
health services to deal with disabling outcomes, which
increase with age, will require policy makers to anticipate
these changes. GBD provides key information on the
changes in types of health services in terms of facilities
and adequately trained personnel that will be needed.
The finding that health gains in countries at the lower
end of the SDI scale have, on average, accelerated over the
past decade compared with the two decades before
indicates the potential for low-income countries to make
a real difference by investing in health. Progress, however,
has been uneven. The more recent downturn in reductions in DALY rates in countries and territories with
higher SDI is striking and near universal, although an
actual reversal into increases of age-standardised DALY
rates has only happened in a small number of countries
in the Caribbean and the USA. Plausible drivers of this
change include obesity, diminishing potential for further
reductions in smoking, and improvements in coverage of
treatments for high blood pressure and cholesterol to
maintain the past declines in cardiovascular mortality.34
Inequalities in access to preventive and curative services
by lower socioeconomic groups might be a further
obstacle to continued improvements in cardiovascular
mortality.59 The large increase in drug overdose deaths in
the USA and the increasing number of deaths from
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violence in Latin American countries, in addition to
the decelerated decline of cardiovascular mortality, are
driving the patterns in these locations. The mix of
universal and more geographically specific influences on
health reinforces the need for regular, detailed reporting
on population health by underlying cause to help decision
makers to identify success stories of disease control, as
well as opportunities to improve and emulate countries
that are performing well.
Limitations
The major limitation of the GBD analysis of the burden
of diseases and injuries is the availability of primary data.
Where data are not available, the results depend on the
out-of-sample predictive validity of the modelling efforts.
While improvements to data processing and modelling
can lead to incremental improvements in the accuracy of
our estimates, fundamental improvements require more
and better primary data collection. Even when data are
available, they might not have been obtained using
the preferred case definition or measurement method.
The more explicit identification of the preferred and
alternative measurement method for each outcome, and
the bias mapping from alternative to reference method
undertaken as part of GBD 2019, have led to greater
stability in data adjustments. These improvements will
also aid in identifying priorities for data collection and
in determining preferred case definitions and study
methods. Moving to use of standard locations for
estimating fixed effects in the models will aid in cycle-tocycle stability of models. Through the use of standard
locations, the addition of more subnational units in a
given GBD cycle should not shift the regression model
predictions as much as they previously would have.
Nevertheless, collinearity between covariates in some of
these models might contribute to some instability in
fixed effects between cycles. Future work on ensemble
models might help to solve the collinearity problem. Of
note, because the cause of death models developed using
CODEm are an ensemble of all high-performing possible
models, they avoid the instability due to collinearity.
Although our statistical modelling is designed to capture
uncertainty from stochastic variation in input data, age
and sex splitting of data, corrections for alternative case
definitions or uninformative cause of death codes, other
data manipulations, and model choice, it remains a
challenge to fully represent the UIs around estimates,
particularly in locations with sparse or absent data. This
will remain a major focus of GBD by tapping into existing
knowledge in other estimation fields as well as our own
development of methods.
The shift to adjusting dementia deaths to reflect
only those with end-stage disease is conceptually more
appealing than the past crude adjustment for the large
variation in coding practices. We will, however, need to
replicate the methods of determining the share of excess
mortality in people with dementia who are in the last
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Global Health Metrics
stages of the disease and for whom an assignment of
dementia as the underlying cause of death is therefore
justified. A greater focus in future rounds of GBD will
need to be directed to identifying data of treatment effects
on severity distributions of the large contributors to
YLDs, such as mental, neurological, and musculoskeletal
disorders, for which we currently do not distinguish
geographical variation in severity. This is of particular
importance as these conditions represent an increasing
share of total burden. Our effort to improve the consistency between mortality rates, prevalence, and incidence
for selected conditions by providing more explicit
guidance on excess mortality rates in DisMod-MR has
revealed that more attention will be required in future
rounds of GBD. After imposing a pattern of excess
mortality that follows an expected pattern of lower rates
in countries with better health systems, the models might
predict prevalence or incidence estimates that are far
removed from observed data. The challenge is then to
identify whether the inconsistency is due to error in the
cause of death estimates, the non-fatal data sources, or a
combination of the two. In addition to these general
limitations, there are many limitations for each specific
modelling exercise reported in this study. Appendix 1
(sections 3.4 and 4.12) provides more insight into some of
these issues.
Future directions
Several method improvements signalled in previous
GBD publications have not yet been implemented but
remain a priority. For instance, DisMod-AT, a new version
of our main non-fatal modelling tool that simultaneously
solves for patterns over age and time, is still undergoing
testing before it can be implemented in GBD. Methods to
make dependent comorbidity corrections computationally
feasible, and imposing greater variation in severity
distributions based on access to and quality of health
care, are also still under development. More generally,
imposing GBD principles and methods to the estimation
of access to health interventions and the effectiveness
thereof, and being able to link those estimates with our
future health scenario platform25 is a direction we are
keen to take. Developing this comprehensively is a large
endeavour that will take many years to complete. As this
would greatly add value to the policy relevance of GBD,
we will also aim to develop less comprehensive methods
that will nevertheless allow us to respond to policy makers
seeking information on major policy decisions in a more
timely fashion.
Conclusion
Taking into account population growth and shifts in age
structure, health continues to improve at the global level.
The absolute burden of disease and its associated impact
on health systems, however, remain resolutely constant.
Some diseases, such as diabetes, are increasing in
burden, and more general all-cause DALY stagnation in
1218
some high SDI countries points out that further gains
are not inevitable. Close monitoring of health trends and
careful policy evaluation of the options to counteract
adverse trends is required. Leading causes of DALYs, as
well as solutions, differ substantially across age groups,
highlighting the need to formulate policy for different
phases of the life course.
Contributors
Please see appendix 1 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
I N Ackerman reports grants from Victorian Government, outside of the
submitted work. C A T Antonio reports personal fees from Johnson &
Johnson (Philippines), outside of the submitted work. E Beghi reports
grants from Italian Ministry of Health and SOBI and personal fees from
Arvelle Therapeutics, outside of the submitted work. Y Béjot reports
personal fees from AstraZeneca, Bristol Myers Squibb, Pfizer, and
Medtronic, Merck Sharpe & Dohme, and Amgen; grants and personal
fees from Boehringer-Ingelheim; personal fees and non-financial
support from Servier; and non-financial support from Biogen, outside of
the submitted work. P S Briant reports personal fees from WHO,
outside of the submitted work. H Christensen reports personal fees
from Bristol Myers Squibb, Bayer, and Boehringer Ingelheim, outside of
the submitted work. L Degenhardt reports grants from Indivior and
Seqirus, outside of the submitted work. S J Dunachie reports grants
from the Fleming Fund at the UK Department of Health and Social
Care, during the conduct of the study. L M Haile reports personal fees
from WHO, outside of the submitted work. S M S Islam reports grants
from National Heart Foundation of Australia and Deakin University,
during the conduct of the study. S L James reports grants from Sanofi
Pasteur and employment from Genentech, outside of the submitted
work. P Jeemon reports and Clinical and Public Health intermediate
fellowship (grant number IA/CPHI/14/1/501497) from the Wellcome
Trust—Department of Biotechnology, India Alliance (2015–2020). V Jha
reports grants from Baxter Healthcare, GlaxoSmithKline, Zydus Cadilla,
NephroPlus, and Biocon, outside of the submitted work. J J Jozwiak
reports personal fees from Amgen, ALAB Laboratoria, Teva, Synexus,
and Boehringer Ingelheim, outside of the submitted work.
S V Katikireddi reports grants from NRS Senior Clinical Fellowship,
Scottish Government Chief Scientist Office, and the UK Medical
Research Council, during the conduct of the study. S Lewington reports
grants from the UK Medical Research Council and the CDC Foundation
(with support from Amgen), outside of the submitted work. K J Looker
reports grants from WHO and GlaxoSmithKline, outside of the
submitted work. S Lorkowski reports personal fees from Akcea
Therapeutics, Amedes, Amgen, Berlin-Chemie, Boehringer Ingelheim
Pharma, Daiichi Sankyo, Merck Sharp & Dohme, Novo Nordisk, SanofiAventis, Synlab, Unilever, and Upfield and non-financial support from
Preventicus, outside of the submitted work. R A Lyons reports grants
from Health Data Research UK, outside of the submitted work.
J Massano reports personal fees from Abbvie, Bial, Merck Sharp &
Dohme, and Zambon and other support from Boston Scientific,
GE Healthcare, Medtronic, and Roche, outside of the submitted work.
W Mendoza is a Program Analyst in Population and Development at the
UN Population Fund Country Office in Peru, an institution that does not
necessarily endorse this study. M Moradi-Lakeh reports personal fees
from Novartis, outside of the submitted work. J F Mosser reports grants
from the Bill & Melinda Gates Foundation, during the conduct of the
study. S Nomura reports grants from the Japanese Ministry of
Education, Culture, Sports, Science, and Technology. S B Patten reports
funding from the Cuthbertson & Fischer Chair in Pediatric Mental
www.thelancet.com Vol 396 October 17, 2020
Global Health Metrics
Health, during the conduct of the study. T Pilgrim reports grants and
personal fees from Biotronik and Boston Scientific, grants from Edwards
Lifesciences, and personal fees from HighLife SAS for his work as a
member of clinical event committee for a study sponsored by HighLife
SAS, outside of the submitted work. M J Postma reports grants and
personal fees from Merck Sharp & Dohme, GlaxoSmithKline, Pfizer,
Boehringer Ingelheim, Novavax, Bristol Myers Squibb, AstraZeneca,
Sanofi, IQVIA, and Seqirus; personal fees from Quintiles, Novartis,
and Pharmerit; grants from Bayer, BioMerieux, WHO, EU, FIND,
Antilope, DIKTI, LPDP, and Budi; and other support from Ingress
Health, PAG, and Asc Academics, outside of the submitted work.
E Pupillo reports grants from AIFA, outside of the submitted work.
A E Schutte reports personal fees from Omron, Servier, Novartis,
Takeda, and Abbott, outside of the submitted work. M G Shrime reports
grants from Damon Runyon Cancer Research Foundation and Mercy
Ships, outside of the submitted work. J A Singh reports personal fees
from Crealta/Horizon, Medisys, Fidia, UBM, Trio Health, Medscape,
WebMD, Clinical Care Options, Clearview Healthcare Partners, Putnam
Associates, Spherix, Practice Point Communications, National Institutes
of Health, and the American College of Rheumatology; personal fees
from Simply Speaking; other support from Amarin Pharmaceuticals and
Viking Pharmaceuticals; and non-financial support from the steering
committee of OMERACT (an international organisation that develops
measures for clinical trials and receives arm’s length funding from
12 pharmaceutical companies), US Food and Drug Administration,
Arthritis Advisory Committee, Veterans Affairs Rheumatology Field
Advisory Committee, and the editor and director of the UAB Cochrane
Musculoskeletal Group Satellite Center on Network Meta-analysis,
outside of the submitted work. S T S Skou reports personal fees from
Journal of Orthopaedic & Sports Physical Therapy and Munksgaard and
grants from The Lundbeck Foundation, outside of the submitted work;
and being co-founder of GLA:D. GLA:D is a non-profit initiative hosted
at University of Southern Denmark aimed at implementing clinical
guidelines for osteoarthritis in clinical practice. J D Stanaway reports
grants from Bill & Melinda Gates Foundation, during the conduct of the
study. R Uddin reports travel and accommodation reimbursement from
Deakin University Institute for Physical Activity and Nutrition, outside
of the submitted work. All other authors declare no competing interests.
Data sharing
To download the data used in these analyses, please visit the Global
Health Data Exchange GBD 2019 website.
Acknowledgments
Research reported in this publication was supported by the Bill &
Melinda Gates Foundation; the University of Melbourne; Queensland
Department of Health, Australia; the National Health and Medical
Research Council, Australia; Public Health England; the Norwegian
Institute of Public Health; St Jude Children’s Research Hospital;
the Cardiovascular Medical Research and Education Fund; the National
Institute on Ageing 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 funders. The authors alone are
responsible for the views expressed in this Article and they do not
necessarily represent the views, decisions, or policies of the institutions
with which they are affiliated, the National Health Service (NHS),
the National Institute for Health Research (NIHR), the UK Department
of Health and Social Care, or Public Health England; the United States
Agency for International Development (USAID), the US Government,
or MEASURE Evaluation; or the European Centre for Disease
Prevention and Control (ECDC). This research used data from the Chile
National Health Survey 2003, 2009–10, and 2016–17. The authors are
grateful to the Ministry of Health, the survey copyright owner, for
allowing them to have the database. All results of the study are those of
the authors and in no way committed to the Ministry. The Costa Rican
Longevity and Healthy Aging Study project is a longitudinal study by the
University of Costa Rica’s Centro Centroamericano de Población and
Instituto de Investigaciones en Salud, in collaboration with the
University of California at Berkeley. The original pre-1945 cohort was
funded by the Wellcome Trust (grant 072406), and the 1945–55
www.thelancet.com Vol 396 October 17, 2020
Retirement Cohort was funded by the US National Institute on Aging
(grant R01AG031716). The principal investigators are Luis Rosero-Bixby
and William H Dow and co-principal investigators are Xinia Fernández
and Gilbert Brenes. The accuracy of the authors’ statistical analysis and
the findings they report are not the responsibility of ECDC. ECDC is not
responsible for conclusions or opinions drawn from the data provided.
ECDC is not responsible for the correctness of the data and for data
management, data merging and data collation after provision of the data.
ECDC shall not be held liable for improper or incorrect use of the data.
The Health Behaviour in School-Aged Children (HBSC) study 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 databank manager for
the 1997–98 survey was Bente Wold, whereas for the following surveys
Oddrun Samdal was the databank manager. A list of principal
investigators in each country can be found on the HBSC website.
Data used in this paper come from the 2009–10 Ghana Socioeconomic
Panel Study Survey, which is a nationally representative survey of more
than 5000 households in Ghana. The survey is a joint effort undertaken
by the Institute of Statistical, Social and Economic Research (ISSER) at
the University of Ghana and the Economic Growth Centre (EGC) at Yale
University. It was funded by EGC. ISSER and the EGC are not
responsible for the estimations reported by the analysts. The Palestinian
Central Bureau of Statistics granted the researchers access to relevant
data in accordance with license 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. Data for this research was provided by MEASURE
Evaluation, funded by USAID. The authors thank the Russia
Longitudinal Monitoring Survey, conducted by the National Research
University Higher School of Economics and ZAO Demoscope together
with Carolina Population Center, University of North Carolina at
Chapel Hill and the Institute of Sociology, Russia Academy of Sciences
for making data available. This paper uses data from the Bhutan 2014
STEPS survey, implemented by the Ministry of Health with the support
of WHO; the Kuwait 2006 and 2014 STEPS surveys, implemented by the
Ministry of Health with the support of WHO; the Libya 2009 STEPS
survey, implemented by the Secretariat of Health and Environment with
the support of WHO; the Malawi 2009 STEPS survey, implemented by
Ministry of Health with the support of WHO; and the Moldova 2013
STEPS survey, implemented by the Ministry of Health, the National
Bureau of Statistics, and the National Center of Public Health with the
support of WHO. This paper uses data from Survey of Health, Ageing
and Retirement in Europe (SHARE) Waves 1 (DOI:10.6103/SHARE.
w1.700), 2 (10.6103/SHARE.w2.700), 3 (10.6103/SHARE.w3.700),
4 (10.6103/SHARE.w4.700), 5 (10.6103/SHARE.w5.700),
6 (10.6103/SHARE.w6.700), and 7 (10.6103/SHARE.w7.700);
see Börsch-Supan and colleagues (2013) for methodological details.
The SHARE data collection has been funded by the European
Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3:
RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE:
CIT4-CT-2006-028812), FP7 (SHARE-PREP: GA N°211909,
SHARE-LEAP: GA N°227822, SHARE M4: GA N°261982) and Horizon
2020 (SHARE-DEV3: GA N°676536, SERISS: GA N°654221) and by
DG Employment, Social Affairs & Inclusion. 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 from various national funding sources is
gratefully acknowledged. This study has been realised using the data
collected by the Swiss Household Panel, which is based at the Swiss
Centre of Expertise in the Social Sciences. The project is financed by the
Swiss National Science Foundation. The United States Aging,
Demographics, and Memory Study is a supplement to the Health and
Retirement Study (HRS), which is sponsored by the National Institute of
Aging (grant number NIA U01AG009740). It was conducted jointly by
Duke University and the University of Michigan. The HRS is sponsored
by the National Institute on Aging (grant number NIA U01AG009740)
and is conducted by the University of Michigan. This paper uses data
For the HBSC website see
http://www.hbsc.org
For the Global Health Data
Exchange GBD 2019 website
see http://ghdx.healthdata.org/
gbd−2019
For more on SHARE see
http://www.share-project.org
1219
Global Health Metrics
For the Add Health website see
http://www.cpc.unc.edu/
addhealth
1220
from Add Health, a program 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. Information
on how to obtain the Add Health data files is available on the Add Health
website. No direct support was received from grant P01-HD31921 for this
analysis. The data reported here have been supplied by the United States
Renal Data System. The interpretation and reporting of these data are
the responsibility of the authors and in no way should be seen as an
official policy or interpretation of the US Government. Collection of data
for the Mozambique National Survey on the Causes of Death 2007–08 was
made possible by USAID under the terms of cooperative agreement
GPO-A-00-08-000_D3-00. This manuscript is based on data collected and
shared by the International Vaccine Institute (IVI) from an original study
IVI conducted. L G Abreu acknowledges support from Coordenação de
Aperfeiçoamento de Pessoal de Nível Superior (Brazil; finance code 001)
and Conselho Nacional de Desenvolvimento Científico e Tecnológico
(CNPq, a Brazilian funding agency). I N Ackerman was supported by a
Victorian Health and Medical Research Fellowship awarded by the
Victorian Government. O O Adetokunboh acknowledges the South
African Department of Science and Innovation and the National Research
Foundation. A Agrawal acknowledges the Wellcome Trust DBT India
Alliance Senior Fellowship. S M Aljunid acknowledges the Department of
Health Policy and Management, Faculty of Public Health, Kuwait
University and International Centre for Casemix and Clinical Coding,
Faculty of Medicine, National University of Malaysia for the approval and
support to participate in this research project. M Ausloos, C Herteliu,
and A Pana acknowledge partial support by a grant of the Romanian
National Authority for Scientific Research and Innovation,
CNDS-UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084.
A Badawi is supported by the Public Health Agency of Canada.
D A Bennett was supported by the NIHR Oxford Biomedical Research
Centre. R Bourne acknowledges the Brien Holden Vision Institute,
University of Heidelberg, Sightsavers, Fred Hollows Foundation,
and Thea Foundation. G B Britton and I Moreno Velásquez were
supported by the Sistema Nacional de Investigación, SNI-SENACYT,
Panama. R Buchbinder was supported by an Australian National Health
and Medical Research Council (NHMRC) Senior Principal Research
Fellowship. J J Carrero was supported by the Swedish Research Council
(2019-01059). F Carvalho acknowledges UID/MULTI/04378/2019 and
UID/QUI/50006/2019 support with funding from FCT/MCTES through
national funds. A R Chang was supported by National Institutes of
Health/National Institute of Diabetes and Digestive and Kidney Diseases
grant K23 DK106515. V M Costa acknowledges the grant
SFRH/BHD/110001/2015, received by Portuguese national funds through
Fundação para a Ciência e Tecnologia, IP, under the Norma Transitária
DL57/2016/CP1334/CT0006. A Douiri acknowledges support and funding
from the National Institute for Health Research Collaboration for
Leadership in Applied Health Research and Care South London at King’s
College Hospital NHS Foundation Trust and the Royal College of
Physicians, and support from the NIHR Biomedical Research Centre
based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College
London. B B Duncan acknowledges grants from the Foundation for the
Support of Research of the State of Rio Grande do Sul (IATS and PrInt)
and the Brazilian Ministry of Health. H E Erskine is the recipient of an
Australian NHMRC Early Career Fellowship grant (APP1137969).
A J Ferrari was supported by a NHMRC Early Career Fellowship grant
(APP1121516). H E Erskine and A J Ferrari are employed by and
A M Mantilla-Herrera and D F Santomauro affiliated with the Queensland
Centre for Mental Health Research, which receives core funding from the
Queensland Department of Health. M L Ferreira holds an NHMRC
Research Fellowship. C Flohr was supported by the NIHR Biomedical
Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust.
M Freitas acknowledges financial support from the EU (European
Regional Development Fund [FEDER] funds through COMPETE
POCI-01-0145-FEDER-029248) and National Funds (Fundação para a
Ciência e Tecnologia) through project PTDC/NAN-MAT/29248/2017.
A L S Guimaraes acknowledges support from CNPq. C Herteliu was
partially supported by a grant co-funded by FEDER through Operational
Competitiveness Program (project ID P_40_382). P Hoogar acknowledges
Centre for Bio Cultural Studies, Directorate of Research, Manipal
Academy of Higher Education and Centre for Holistic Development and
Research, Kalaghatagi. F N Hugo acknowledges the Visiting
Professorship, PRINT Program, CAPES Foundation, Brazil. B-F Hwang
was supported by China Medical University (CMU107-Z-04), Taichung,
Taiwan. S M S Islam was funded by a National Heart Foundation Senior
Research Fellowship and supported by Deakin University. R Q Ivers was
supported by a research fellowship from the National Health and Medical
Research Council of Australia. M Jakovljevic acknowledges the Serbian
part of this GBD-related contribution was co-funded through Grant
OI175014 of the Ministry of Education Science and Technological
Development of the Republic of Serbia. P Jeemon was supported by a
Clinical and Public Health intermediate fellowship (grant number
IA/CPHI/14/1/501497) from the Wellcome Trust—Department of
Biotechnology, India Alliance (2015–20). O John is a recipient of UIPA
scholarship from University of New South Wales, Sydney. S V Katikireddi
acknowledges funding from a NRS Senior Clinical Fellowship
(SCAF/15/02), the Medical Research Council (MC_UU_12017/13,
MC_UU_12017/15), and the Scottish Government Chief Scientist Office
(SPHSU13, SPHSU15). C Kieling is a CNPq researcher and a UK
Academy of Medical Sciences Newton Advanced Fellow. Y J Kim was
supported by Research Management Office, Xiamen University Malaysia
(XMUMRF/2018-C2/ITCM/00010). K Krishan is supported by UGC
Centre of Advanced Study awarded to the Department of Anthropology,
Panjab University, Chandigarh, India. M Kumar was supported by
K43 TW 010716 FIC/NIMH. B Lacey acknowledges support from the
NIHR Oxford Biomedical Research Centre and the BHF Centre of
Research Excellence, Oxford. J V Lazarus was supported by a Spanish
Ministry of Science, Innovation and Universities Miguel Servet grant
(Instituto de Salud Carlos III [ISCIII]/ESF, the EU [CP18/00074]).
K J Looker thanks the NIHR Health Protection Research Unit in
Evaluation of Interventions at the University of Bristol, in partnership
with Public Health England, for research support. S Lorkowski was
funded by the German Federal Ministry of Education and Research
(nutriCARD, grant agreement number 01EA1808A). R A Lyons is
supported by Health Data Research UK (HDR-9006), which is funded by
the UK Medical Research Council, Engineering and Physical Sciences
Research Council, Economic and Social Research Council, NIHR
(England), Chief Scientist Office of the Scottish Government Health and
Social Care Directorates, Health and Social Care Research and
Development Division (Welsh Government), Public Health Agency
(Northern Ireland), British Heart Foundation, and Wellcome Trust.
J J McGrath is supported by the Danish National Research Foundation
(Niels Bohr Professorship), and the Queensland Health Department
(via West Moreton HHS). P T N Memiah acknowledges support from
CODESRIA. U O Mueller gratefully acknowledges funding by the
German National Cohort Study BMBF grant number 01ER1801D.
S Nomura acknowledges the Ministry of Education, Culture, Sports,
Science, and Technology of Japan (18K10082). A Ortiz was supported by
ISCIII PI19/00815, DTS18/00032, ISCIII-RETIC REDinREN RD016/0009
Fondos FEDER, FRIAT, Comunidad de Madrid B2017/BMD-3686
CIFRA2-CM. These funding sources had no role in the writing of the
manuscript or the decision to submit it for publication. S B Patten was
supported by the Cuthbertson & Fischer Chair in Pediatric Mental Health
at the University of Calgary. G C Patton was supported by an åNHMRC
Senior Principal Research Fellowship. M R Phillips was supported in part
by the National Natural Science Foundation of China (NSFC, number
81371502 and 81761128031). A Raggi, D Sattin, and S Schiavolin were
supported by grants from the Italian Ministry of Health (Ricerca Corrente,
Fondazione Istituto Neurologico C Besta, Linea 4—Outcome Research:
dagli Indicatori alle Raccomandazioni Cliniche). P Rathi and
B Unnikrishnan acknowledge Kasturba Medical College, Mangalore,
Manipal Academy of Higher Education, Manipal. A L P Ribeiro was
supported by Brazilian National Research Council, CNPq, and the
Minas Gerais State Research Agency, FAPEMIG. D C Ribeiro was
supported by The Sir Charles Hercus Health Research Fellowship
(#18/111) Health Research Council of New Zealand. D Ribeiro
acknowledges financial support from the EU (FEDER funds through the
Operational Competitiveness Program; POCI-01-0145-FEDER-029253).
P S Sachdev acknowledges funding from the NHMRC of Australia
www.thelancet.com Vol 396 October 17, 2020
Global Health Metrics
Program Grant. A M Samy was supported by a fellowship from the
Egyptian Fulbright Mission Program. M M Santric-Milicevic acknowledges
the Ministry of Education, Science and Technological Development of the
Republic of Serbia (contract number 175087). R Sarmiento-Suárez received
institutional support from Applied and Environmental Sciences University
(Bogotá, Colombia) and ISCIII (Madrid, Spain). A E Schutte received
support from the South African National Research Foundation SARChI
Initiative (GUN 86895) and Medical Research Council. S T S Skou is
currently funded by a grant from Region Zealand (Exercise First) and a
grant from the European Research Council under the EU’s Horizon 2020
research and innovation program (grant agreement number 801790).
J B Soriano is funded by Centro de Investigación en Red de Enfermedades
Respiratorias, ISCIII. R Tabarés-Seisdedos was supported in part by the
national grant PI17/00719 from ISCIII–FEDER. N Taveira was partially
supported by the European & Developing Countries Clinical Trials
Partnership, the EU (LIFE project, reference RIA2016MC-1615).
S Tyrovolas was supported by the Foundation for Education and European
Culture, the Sara Borrell postdoctoral programme (reference number
CD15/00019 from ISCIII–FEDER). S B Zaman received a scholarship
from the Australian Government research training programme in support
of his academic career.
Editorial note: the Lancet Group takes a neutral position with respect to
territorial claims in published maps and institutional affiliations.
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