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Articles
www.thelancet.com Published online December 18, 2014 http://dx.doi.org/10.1016/S0140-6736(14)61682-2
1
Global, regional, and national age–sex specifi c all-cause and
cause-specifi c mortality for 240 causes of death, 1990–2013:
a systematic analysis for the Global Burden of Disease
Study 2013
GBD 2013 Mortality and Causes of Death Collaborators*
Summary
Background Up-to-date evidence on levels and trends for age-sex-specifi c all-cause and cause-specifi c mortality is
essential for the formation of global, regional, and national health policies. In the Global Burden of Disease Study
2013 (GBD 2013) we estimated yearly deaths for 188 countries between 1990, and 2013. We used the results to assess
whether there is epidemiological convergence across countries.
Methods We estimated age-sex-specifi c all-cause mortality using the GBD 2010 methods with some refi nements to improve
accuracy applied to an updated database of vital registration, survey, and census data. We generally estimated cause of
death as in the GBD 2010. Key improvements included the addition of more recent vital registration data for 72 countries,
an updated verbal autopsy literature review, two new and detailed data systems for China, and more detail for Mexico, UK,
Turkey, and Russia. We improved statistical models for garbage code redistribution. We used six diff erent modelling
strategies across the 240 causes; cause of death ensemble modelling (CODEm) was the dominant strategy for causes with
suffi cient information. Trends for Alzheimer’s disease and other dementias were informed by meta-regression of
prevalence studies. For pathogen-specifi c causes of diarrhoea and lower respiratory infections we used a counterfactual
approach. We computed two measures of convergence (inequality) across countries: the average relative diff erence across
all pairs of countries (Gini coeffi cient) and the average absolute diff erence across countries. To summarise broad fi ndings,
we used multiple decrement life-tables to decompose probabilities of death from birth to exact age 15 years, from exact age
15 years to exact age 50 years, and from exact age 50 years to exact age 75 years, and life expectancy at birth into major
causes. For all quantities reported, we computed 95% uncertainty intervals (UIs). We constrained cause-specifi c fractions
within each age-sex-country-year group to sum to all-cause mortality based on draws from the uncertainty distributions.
Findings Global life expectancy for both sexes increased from 65·3 years (UI 65·0–65·6) in 1990, to 71·5 years
(UI 71·0–71·9) in 2013, while the number of deaths increased from 47·5 million (UI 46·8–48·2) to 54·9 million
(UI 53·6–56·3) over the same interval. Global progress masked variation by age and sex: for children, average absolute
diff erences between countries decreased but relative diff erences increased. For women aged 25–39 years and older than
75 years and for men aged 20–49 years and 65 years and older, both absolute and relative diff erences increased.
Decomposition of global and regional life expectancy showed the prominent role of reductions in age-standardised death
rates for cardiovascular diseases and cancers in high-income regions, and reductions in child deaths from diarrhoea,
lower respiratory infections, and neonatal causes in low-income regions. HIV/AIDS reduced life expectancy in southern
sub-Saharan Africa. For most communicable causes of death both numbers of deaths and age-standardised death rates fell
whereas for most non-communicable causes, demographic shifts have increased numbers of deaths but decreased age-
standardised death rates. Global deaths from injury increased by 10·7%, from 4·3 million deaths in 1990 to 4·8 million in
2013; but age-standardised rates declined over the same period by 21%. For some causes of more than 100 000 deaths per
year in 2013, age-standardised death rates increased between 1990 and 2013, including HIV/AIDS, pancreatic cancer, atrial
fi brillation and fl utter, drug use disorders, diabetes, chronic kidney disease, and sickle-cell anaemias. Diarrhoeal diseases,
lower respiratory infections, neonatal causes, and malaria are still in the top fi ve causes of death in children younger than
5 years. The most important pathogens are rotavirus for diarrhoea and pneumococcus for lower respiratory infections.
Country-specifi c probabilities of death over three phases of life were substantially varied between and within regions.
Interpretation For most countries, the general pattern of reductions in age-sex specifi c mortality has been associated
with a progressive shift towards a larger share of the remaining deaths caused by non-communicable disease and
injuries. Assessing epidemiological convergence across countries depends on whether an absolute or relative measure
of inequality is used. Nevertheless, age-standardised death rates for seven substantial causes are increasing, suggesting
the potential for reversals in some countries. Important gaps exist in the empirical data for cause of death estimates
for some countries; for example, no national data for India are available for the past decade.
Funding Bill & Melinda Gates Foundation.
Published Online
December 18, 2014
http://dx.doi.org/10.1016/
S0140-6736(14)61682-2
See Online/Comment
http://dx.doi.org/10.1016/
S0140-6736(14)62006-7
*Collaborators listed at the end
of the Article
For interactive versions of fi gure 7
and fi gure appendices 1–3, visit
http://vizhub.healthdata.org/le
Correspondence to:
Prof Christopher J L Murray,
2301 5th Avenue, Suite 600,
Seattle, WA 98121, USA
cjlm@uw.edu
Articles
2
www.thelancet.com Published online December 18, 2014 http://dx.doi.org/10.1016/S0140-6736(14)61682-2
Introduction
The Global Burden of Disease (GBD) study provides a
unique comprehensive framework to systematically assess
national trends in age-specifi c and sex-specifi c all-cause
and cause-specifi c mortality. Up-to-date and comprehensive
evidence for levels and trends for each country is critical
for informed priority setting. Trends quantify progress
against explicit health targets, whether local, national, or
global, and help to evaluate where programmes are
working or not. Quantifi cation across populations and
over time using comparable defi nitions and methods can
also enable benchmarking. Regular comprehensive
updates about causes of death will identify emerging
public health challenges. The GBD 2013 study provides the
fi rst GBD study to use a continuously updated approach to
global health surveillance.1
The GBD 2010 study, a collaboration of 488 investigators,
showed important global and regional trends for all-cause
and cause-specifi c mortality.2–8 The GBD 2010 reported
substantial decreases in child mortality driven by
reductions in diarrhoea, lower respiratory infections, and
more recently, malaria. The lowest income regions had
progressed in combating maternal mortality, HIV/AIDS,
tuberculosis, and malaria. Nevertheless, much work
remains to be done for these Millennium Development
Goal-related diseases. Outside sub-Saharan Africa,
1990–2010 saw rapid shifts towards a larger share of death
from non-communicable diseases and injuries and a
rising mean age of death. Country analyses using the
GBD 2010 database have been reported for China, Iran,
Mexico, UK, and USA, taking advantage of the comparable
methods and defi nitions of the GBD to benchmark these
countries against their peers.9–16
Much debate surrounds what should follow the
Millennium Development Goals; objective, timely, and
comprehensive evidence for the levels and trends in
causes of death can be a useful input. Ambitious goals
have been discussed,16 such as the elimination of
preventable child and maternal mortality in a generation.
Targets of zero disease have been formulated for HIV/
AIDS, tuberculosis, and malaria by various groups.17–23
The Lancet Commission on Global health 2035:
a world converging within a generation24 suggested that a
grand convergence in health can be achieved between
poor and rich countries by 2035. Advocates for
non-communicable disease programmes argue25 that
rapid epidemiological transitions in many regions of the
world require broader health goals for the development
community. Movements to focus on universal health
coverage in the post-2015 health agenda emphasise the
consequences of failure to meet basic health-care
needs.24–27
Broad interest in the GBD 2010 has led to the
expansion of the GBD collaboration to include more
than 1000 investigators in 106 countries. The GBD 2013
not only incorporates newly published or released
datasets, particularly from the past 5 years, but also
expands the analysis in other ways. We included
subnational assessments for provinces of China, states
of Mexico, and regions of the UK. These subnational
assessments will help national decision makers to
identify inequalities and local variation in leading
diseases, injuries, and risk factors. The list of causes has
been expanded and many new and more detailed data
sources incorporated. We report the new fi ndings for
the fi rst time at the country-level for 1990–2013.
Methods
Study design
The GBD approach to estimating all-cause mortality
and cause-specifi c mortality has been previously
described.2,3 Here, we describe several refi nements.28
Figure 1 shows the general analysis of all-cause
mortality and cause-specifi c mortality and their
interactions. GBD 2010 included 291 causes of death or
disability, of which 235 were causes of death; we have
expanded the list to include 306 causes of death or
disability, of which 240 are causes of death. The extra
causes were added on the basis of three considerations:
(1) causes that were for epidemiological reasons already
modelled separately but reported combined with other
causes in GBD 2010—for example, silicosis, asbestosis,
anorexia nervosa, and typhoid and paratyphoid fever;
(2) the category of other unintentional injuries was
large and heterogeneous so we broke it down further to
include pulmonary aspiration and foreign body in
trachea or lung, foreign body in other part of body, and
unintentional suff ocation; and (3) new datasets became
available to enable estimation of mesothelioma, new
maternal sub-causes, neonatal haemolytic anaemia,
and chronic kidney disease caused by glom-
erulonephritis. Appendix pp 245–251 provides the
International Classifi cation of Diseases codes for the
GBD 2013 cause list. After broad consultation, we have
removed from the cause list the pathogen-specifi c
causes of diarrhoeal diseases and lower respiratory
infections. Instead, we analysed these causes with a
counterfactual approach.
We assessed 21 regions and seven super-regions as
defi ned in the GBD 2010. The GBD 2013 also included
an assessment of subnational populations in
three countries: provinces for China, states for Mexico,
and the UK broken down into Scotland, Wales,
Northern Ireland, and nine regions of England. We
analysed these countries subnationally because of the
interest from national collaborators and because
suffi cient data were made available by the teams in each
country. In future iterations of the GBD, we hope to
include further subnational breakdowns. In addition,
we separately analysed data sources for rural and urban
regions in India. This approach improved our
estimation of mortality and causes of death and enabled
us to analyse causes of death that were specifi c to urban
or rural regions alone.
See Online for appendix
Articles
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3
Covariates
We estimated national time series (1980–2013) for a range
of covariates with data from surveys (household-level and
individual-level data), censuses, offi cial reports,
administrative data, and a systematic review. For lagged
distributed income and education, we estimated national
time series from 1950, to 2013. Details of how we imputed
series for GDP, educational attainment, tobacco prevalence,
and obesity prevalence have been published previously.29–32
Appendix (pp 4961–4974) shows the sources and imputation
methods used to generate time series for each covariate.
Generally, we estimated uncertainty in covariate values
when suffi cient infor mation was available.
All-cause mortality
We analysed all-cause mortality for 188 countries from 1950
to 2013; we present only results for 1990–2013 to coincide
with the period of the overall GBD 2013 assessment. As a
result of the split of Sudan, we re-extracted data and made
new separately generated estimates for Sudan and South
Sudan. We improved how we adjusted for data source bias
for the analysis of child mortality in two ways. First, by
using the improved functional forms between summary
mortality indicators for child (age <5 years) and adult
(age 15–59 years) age groups and other covariates, including
crude rates of death caused by HIV/AIDS. And second, by
modifi cation of the model lifetable system to use a unifi ed
standard life-table selection process and improved re-
distribution of excess mortality rate from HIV/AIDS. We
divided the analysis into eight steps. Input data and key
indicators for all countries are available online.
First, to estimate under-5 mortality (5q0), we analysed all
survey, census, sample registration, and vital registration
sources. Wherever possible, we analysed microdata from
surveys and censuses with updated methods for child
mortality.33 We synthesised all measurements of under-5
Figure 1: Components of GBD 2013 and their relations
ICD=International Classifi cation of Diseases. BTL=basic tabulation list. MI ratio=mortality:incidence ratio. CODEm=Cause of Death Ensemble model. YLLs=years of life lost. EPP=UNAIDS Estimates and
Projects Package.
CoDCorrect
algorithm
Cause
mapping
YLLs
Verbal autopsy
data
Maternal
surveillance
Household surveys
Vital registration
(ICD 9, BTL, ICD 10)
Other (ie, burial or
mortuary and census
Police data
Natural history
models Prevalence
National registries
Cause of death
database
Redistribution
Noise
reduction
Maternal census
Case fatality rate
All-cause mortality Cause-specific mortality
Intervention
coverage
Cause-specific
mortality
Vital registration,
sample registration
Complete birth
histories
Household recall from
censuses and surveys
Summary birth
histories
Vital registration,
sample registration
Household recall from
censuses and surveys
Sibling history
Under-5 mortality
estimation
Adult mortality
estimation
Age pattern of
excess mortality
from HIV
HIV-free
life table
Life table with HIV
and excess mortality
caused by war and
disaster shocks
Non-shock
life tables
Consistency adjustment
from EPP-Spectrum for
CoDCorrect
Non-shock
age-specific
mortality
Age-specific and
sex-specific
death numbers
Cancer
registries
Noise
reduction
RedistributionMI ratio Cause
mapping
Negative
binomial/fixed
proportion
DisModCODEm
Source data
Process
Results
Database
Key
For the input data and key
indicators for each country see
http://vizhub.healthdata.org/
mortality/
Articles
4
www.thelancet.com Published online December 18, 2014 http://dx.doi.org/10.1016/S0140-6736(14)61682-2
mortality with spatiotemporal regression and Gaussian
process regression.33 We corrected for bias in diff erent
sources in specifi c countries.
Second, to estimate adult mortality (45q15), we
systematically identifi ed all available vital registration
data, sibling history survey data, sample registration
data, and household recall of deaths. We assessed vital
registration data for completeness by optimised death
distribution methods.2,34 We analysed sibling history
data to account for survivor bias, zero-surviving
sibships, and recall bias.2,35 We synthesised sources with
a combination of spatiotemporal regression and
Gaussian process regression. The mean function for the
Gaussian process regression was based on the
combination of a non-linear hierarchical model with
income per person, mean years of education in age
group 15–60 years, mortality caused by HIV/AIDS,
and country random eff ects2 as covariates, and a
spatiotemporal regression in which we added to the fi rst
stage model without country random eff ects, the
smoothed residuals between the fi rst stage model and
observed data (appendix pp 66–79). We selected the
hyper-parameters for Gaussian process regression
through an out-of-sample predictive validity testing
process.2 We ranked the estimated subnational adult
mortality in China, India, Mexico, and the UK to ensure
that the sum of subnational estimates for a given age-
sex group equalled the national estimates accounting
for diff erent population sizes.
Third, we assessed HIV-free under-5 mortality and
adult mortality. HIV/AIDS causes more excess mortality
in younger people and thus changes the age pattern of
mortality that otherwise can be readily described by
Gompertz law of mortality or the Kannisto-Thatcher
model.36,37 Where HIV/AIDS is common, this pattern of
mortality should be explicitly taken into account. We
estimated the HIV-counterfactual under-5 mortality and
adult mortality rates using the estimated coeffi cients of
crude death rate from HIV from the non-linear mixed
eff ects models for under-5 mortality and adult mortality
respectively, and setting the crude death rates from
HIV/AIDS in the respective age groups to zero (appendix
pp 90–94).
Fourth, we constructed an HIV-free life-table. The GBD
2010 introduced a model life-table system that used the
under-5 death rate and adult mortality rate along with a
selected standard mortality schedule to estimate the full
age pattern of mortality for country-years of interest.33
For GBD 2013, we modifi ed how the standard mortality
schedule was selected for each country-year so that the
same approach was used for all countries. Specifi cally,
we empirically computed a set of space-time weights that
relate the observed age pattern of the probability of death
in a sex-country-year with other sex-country-year
observations. These weights were derived by comparing
every empirical life-table that is not aff ected by the HIV/
AIDS epidemic in the GBD database (10 673 life-tables)
with every other life-table for the same sex. We estimated
space-time weights as a function of the time lag between
the paired life-tables and location (ie, within the country,
region, or super-region). We estimated these weights as
the inverse of the average sum of age-specifi c diff erences
in the logit of the probability of death (appendix pp
79–91). The key observation from this spatial-temporal
analysis of age-specifi c probabilities of death is that the
mortality pattern in a country in a given year was more
strongly related to the mortality pattern in the same
country within 15 years than to mortality patterns in
other countries; however, other countries in the same
region or other regions generally are similarly related
when the lag-in time was more than 20 years.
Fifth, we assessed the age pattern of HIV/AIDS
mortality. Excess mortality from HIV/AIDS as quantifi ed
between the estimated 5q0 and 45q15 with their HIV
counterfactual counterparts leads to increased mortality
in specifi c age groups. This excess HIV/AIDS mortality
was assigned by age with the estimated relative risk of
death caused by HIV/AIDS in an age group compared
with the HIV/AIDS excess death rate in age group
40–44 years. We estimated these relative risks with data
from vital registration systems that have International
Classifi cation of Diseases 10 coded causes of death from
HIV/AIDS, which includes South Africa.38 We used
Seemingly Unrelated Regression model39 with only a
constant and generated 95% uncertainty intervals (UIs)
for the age pattern of relative risks by repeatedly sampling
from the mean and covariance matrix of the estimated βs
and the error term. Seemingly Unrelated Regression
enables the error term of a series of linear regressions to
be correlated. We used separate regressions by sex and
for the pattern of mortality in concentrated epidemics
and generalised epidemics as defi ned by UNAIDS.38–40
Sixth, we minimised the diff erence between demo-
graphic estimation of age-specifi c mortality and HIV
models. Murray and colleagues38 used a refi ned version of
the EPP-Spectrum framework to model HIV/AIDS
mortality. This analysis yielded very large UIs for HIV/
AIDS in many countries. However, in some southern
African countries, there remained a large discrepancy
between data for all-cause mortality and estimates of HIV/
AIDS mortality with demographic sources suggesting
smaller epidemics. To minimise the diff erence between
HIV/AIDS mortality and the demographic estimates,
which are also uncertain, we computed a loss function that
quantifi es the extent to which the age-sex-country-year
HIV/AIDS estimates exceed all-cause mortality:
For run (r) of a given country, excess mortality (e) is
equal to the sum of all non-zero diff erences between
HIV/AIDS mortality (mHIV) and 0·8 times a randomly
selected all-cause mortality draw (mall-cause) across all year
er = ∑∑∑max(0,mr,t,a,s – 0·8 × mall-cause)
tas r,t,a,s
HIV
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5
(t), age (a), and sex (s) combinations. 0·8 was the highest
observed cause fraction caused by HIV in any age group
in any vital registration system. We selected from the
uncertainty ranges for HIV and all-cause mortality
those that minimised the diff erence. If no draws had a
positive loss function, we sampled randomly from all
matched draws.
The CoDCorrect algorithm includes HIV/AIDS
cause-specifi c mortality and can alter the age and time
distribution of deaths from HIV/AIDS. To incorporate
the overall change in the number of HIV/AIDS deaths
over the course of the epidemic in a country implied by
the application of the CoDCorrect algorithm, but not to
distort the Spectrum estimated time and age pattern, we
adjusted the entire HIV/AIDS epidemic up or down on
the basis of the cumulative eff ects of CoDCorrect on
HIV/AIDS for all estimated years in each country.
Seventh, we used the same method as Wang and
colleagues2 to generate child mortality rate and adult
mortality rate for natural disasters and armed confl icts.
We obtained data for confl ict and war, including deaths
from one-sided violence, non-state confl ict, armed force
battles, and other national or international confl icts, from
the Uppsala Confl ict Data Program41 and the International
Institute for Strategic Studies.42 Further data for war were
obtained from countries’ vital registration systems and
classifi ed as caused by war.43 We included disaster data
from the International Disaster Database from the Center
for Research on the Epidemiology of Disasters (University
of Louvain, Brussels, Belgium).44 From this database, we
included deaths caused by complex disaster, drought,
earthquake, fl ood, and others. When these databases
were not fully up-to-date or did not contain shocks known
to exist, we supplemented with case-by-case sources.
These with-shock mortality rates were then used as entry
parameters to the GBD relational model life-table system
to generate age-specifi c mortality rates with the eff ect of
shocks added.
Eighth, we used age-specifi c and sex-specifi c death
rates from the life-table to generate numbers of death by
multiplying by population estimates from the World
Population Prospects 2012 revision45 and the Human
Mortality Database for people older than age 5 years. For
the under-5 age groups, we applied the method of Wang
and colleagues.33 In some cases, assumptions in the UN
estimation process led to implausible population
numbers for some countries and age groups—for
example, low population estimates for older age groups
in South Africa, especially for the most recent years.
For GBD 2013, we applied a Bayesian population
reconstruction model46 to re-estimate population for
South Africa for 1970–2013.
Cause of death database
Lozano and colleagues3 described the key steps in the
development of the GBD cause of death database. The
database has been expanded to capture 2233 additional
site-years of vital registration data and 52 additional verbal
autopsy site-years (table 1); a site-year is defi ned as data for
a specifi c geographical location (eg, a province of China) in
a given year. We included data up to April 15, 2014.
A major new addition was the incorporation of two
data systems in China. First, the China National Offi ce
for Maternal and Child Health Surveillance provided
detailed information for child and maternal mortality by
cause from 363 surveillance sites in China for 1996–2013.
Second, the Disease Surveillance Points system was the
main source of mortality data for 1991–2007, with
145 disease surveillance points used from 1991 to 2003,
and 161 points used from 2004 to 2007. From 2008 to
2012, all of the deaths and cause of death information
from the Disease Surveillance Points system and other
system points throughout China were collected and
reported via the Mortality Registration and Reporting
System, an online reporting system of the Chinese
Center for Disease Control and Prevention, which
included 4·0 million deaths in 2012.47,48 Because of the
discrepancy in proportions of deaths in hospital and out
of hospital in the Mortality Registration and Reporting
System, we divided each province in China into two
strata based on the degree of urbanisation from the 2010
China Census. We then applied the proportion of deaths
in hospital and out of hospital and degree of urbanisation
from the Disease Surveillance Points system to the
Mortality Registration and Reporting System to account
for biases in the latter. We disaggregated data for both
systems by province and urban and rural regions within
each province. We obtained new datasets for Russia that
provided more detailed causes (appendix pp 180–244).
Turkey expanded its vital registration system to cover
nearly all the population after 2009 and we incorporated
these new data into the analysis.
In total, we identifi ed 538 verbal autopsy site-years,
52 more than in GBD 2010. India, Bangladesh, and
Tanzania had the most verbal autopsy site-years
All geographies GBD 2013
GBD 2010 GBD 2013 Diff erence National State, province,
or region*
Local
Vital registration 2798 5039 2241† 2765 2112 162
Cancer registry 2715 3860 1145 1216 979 1665
Sibling history 1557 1798 241 1788 0 10
Police records 1129 1433 304 1429 1 3
Surveillance 128 1430 1302 73 1074 283
Verbal autopsy 486 538 52 110 0 428
Survey or census; hospital;
burial or mortuary
154 146 –8‡ 94 0 52
Total 8967 14 244 5277 7475 4166 2603
GBD=Global Burden of Disease Study. *Data were analysed at the state level for Mexico, the province level for China,
and the region level for the UK. †Signifi cant increase because of incorporation of subnational sites in China, Mexico,
and the UK. ‡Decrease caused by omission of World Health Survey data where adequate vital registration data was
available for GBD 2013.
Table 1: Number of site-years in database by source type
For the Human Mortality
Database see http://mortality.
org/
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available. We re-extracted and re-mapped data from all
verbal autopsy studies to ensure consistency with the
GBD 2013 cause list. We excluded from the database
verbal autopsy studies reporting cause assignment using
InterVA because it has very low published validity.47 We
incorporated 1145 registry-years of new cancer data,
including 128 from Cancer Incidence in Five Continents
Volume X48 and 1017 from supplemental sources. Our
analysis of cancer incidence and models of the
death:incidence ratio remains unchanged from GBD
2010. Figure 2 shows site-years of data by country for any
cause. Of note, Somalia and Equatorial Guinea had no
cause of death data for any specifi c cause.
Assessment and enhancement of quality and
comparability of cause of death data
Using the general approach of the GBD 2010, we
followed six steps to assess the quality of data and
enhance comparability. First, we adjusted cause of
death data from vital registration systems for
incompleteness. The analysis of all-cause mortality
yields a separate estimate of completeness for deaths of
children younger than 5 years and deaths of people
older than 5 years, which we used to correct the data for
cause of death. When correcting for incomplete
registration, we assumed that for each age-sex-country
group, the cause of death composition of registered
deaths and non-registered deaths were the same.
77% of datapoints were from registration or sample
registration that were more than 85% complete, 17%
from systems that were 70–84% complete, and 6% were
from systems less than 70% complete. Of the 6% of
observations less than 70% complete, most (62%) were
for children younger than 5 years. In sensitivity tests in
the GBD 2010, exclusion of data below a fi xed threshold
of completeness for child causes of death did not
substantially change the results; thus, we have used all
the data in our analysis for GBD 2013.3
Second, we developed 103 maps (excluding verbal
autopsy studies) to translate causes found in the data to
the GBD 2013 cause list. The expanded cause list of this
study required us to adjust the maps used for data
included in GBD 2010. Appendix pp 245–251 show GBD
2013 cause maps for International Classifi cation of
Diseases 9 and 10. The appendix (pp 252–253) includes
more detail about changes made to the handling of
various shorter tabulation lists used by some countries
for reporting, such as the International Classifi cation of
Diseases 9 Basic Tabulation List.
Third, a crucial aspect of enhancing the comparability
of data for cause of death is to deal with uninformative,
so-called garbage codes. Garbage codes are codes for
which deaths are assigned that cannot or should not be
considered as the underlying cause of death—for
Figure 2: Site-years for all causes of death data by country, 1980–2013
ATG=Antigua and Barbuda. VCT=Saint Vincent and the Grenadines. LCA=Saint Lucia. TTO=Trinidad and Tobago. TLS=Timor-Leste. FSM=Federated States of Micronesia.
Persian Gulf
Caribbean LCA
Dominica
ATG
TTO
Grenada
VCT
TLS
Maldives
Barbados
Seychelles
Mauritius
Comoros
West Africa Eastern
Mediterranean
Malta
Singapore Balkan Peninsula Tonga
Samoa
FSM
Fiji
Solomon Isl
Marshall Isl
Vanuatu
Kiribati
No data
1–4 site-years
5–14 site-years
15–24 site-years
25–34 site-years
≥35 site-years
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example, heart failure, ill-defi ned cancer site, senility,
ill-defi ned external causes of injuries, and septicaemia.
Figure 3 shows the number of deaths in the database for
each calendar year with the number assigned to garbage
codes. Because of lags in national reporting of cause of
death data, the number of deaths available after 2010 fell.
Important changes for the GBD 2013 in our approach to
redistributing garbage codes included the statistical
estimation of the fraction of deaths following the methods
outlined by Ahern and colleagues49 for deaths assigned to
ill-defi ned cancer site, ill-defi ned external causes of injury,
heart failure, unspecifi ed stroke, hypertension, and
atherosclerosis by region, age, and sex. Because of
variation in the coding of International Classifi cation of
Diseases 10 code X59 (exposure to unspecifi ed factor)50
and its subcauses in high-income countries, we
redistributed these garbage codes with country-specifi c
estimates for high-income countries derived from our
statistical analysis. Additionally, we did not use malaria as
a target for any garbage code redistribution in adults.51 We
also implemented geographical restrictions on garbage
code redistribution for Chagas disease based on
endemicity so that Chagas disease was not assigned
deaths in countries outside Latin America.
Fourth, for some datasets, particularly some verbal
autopsy studies, deaths were reported for broad age groups
or with both sexes combined. With the addition of new
data for GBD 2013, we identifi ed 30 new age formats,
totalling 112 unique age tabulations in the database. We
used the algorithms described in the GBD 2010 to split
these aggregated categories into estimates for specifi c
age-sex groups.
Fifth, because few overall deaths were included in verbal
autopsy studies or reported in small countries, the number
of deaths by cause can fl uctuate substantially from year to
year. For example, in Iceland, no maternal deaths were
recorded from 1991 to 2000, then one maternal death in
2001. We modifi ed our approach to smoothing these
stochastic fl uctuations used in the GBD 2010 by use of a
simple Bayesian algorithm. We assume a normally
distributed prior and a normal data likelihood, such that:
Where X is the mean of the data and μ is the mean of
the prior. We estimated the prior for vital registration
series with a negative binomial regression with fi xed
eff ects for year and age estimated separately for each
country. When the data are based on a large sample size
the variance is small and the prior has little eff ect on the
posterior. When the data have a large variance because of
a small sample size, the prior has more eff ect, eff ectively
borrowing strength on the age pattern from data within
the same country but allowing for diff erent levels in each
year. For verbal autopsy studies, we modifi ed this
approach because many published reports are for a single
site in a single year. The prior for each cause was based on
a negative binomial with fi xed eff ects for age groups and
random eff ects for study-year; the regression was
estimated independently for each region. For malaria, we
did not group studies by region but by super-region and
level of endemicity. To avoid very large negative values for
log death rates or logit cause fractions, we limited the
minimum non-zero posterior values to 1 per 10 000 000.
Sixth, we excluded outliers based on four criteria. (1)
Studies with biologically implausible values, such as
100% of mortality from a single rare cause. (2) Studies
with results that were greatly inconsistent with other
studies for the same country. (3) Studies that were greatly
inconsistent with studies from other countries with
similar sociodemographic profi les within the same
region. (4) Studies that, if included, led to abrupt changes
in model-estimated time trends that could not be
explained by contextual changes or policy initiatives.
Outliers (0·89% of database entries) are shown in the
online data visualisation of the cause of death database.
Modelling individual causes of death
As in the GBD 2010, we used six modelling strategies for
causes of death depending on the strength of the available
data. Where extensive data were available, we used cause
of death ensemble modelling (CODEm), where fewer
data were available we used simpler statistical models,
and where available cause of death data might be
substantially biased or not available we used natural
history models (appendix pp 278–282). We generated
95% UIs from all the modelling strategies. Uncertainty
in the number of deaths for an age-sex-country-year was
propagated into the computation of years of life lost
(YLLs) for the same category.
For online data visualisation of
the cause of death database see
http://vizhub.healthdata.org/cod
Figure 3: Total garbage and non-garbage coded deaths from vital
registration and verbal autopsy sources, 1990–2013
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
0
5
10
15
20
25
Deaths (millions)
Year
Garbage coded
Usable cause
Posterior mean = (X + μ)
τ2
τ2 + σ2
σ2
τ2 + σ2
Posterior variance = ()
τ2 σ2
τ2 + σ2
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We used CODEm for 155 causes of death. CODEm has
been extensively used for global health estimation
including the GBD 2010.3,52,53 An advantage is that a wide
range of diff erent models are tested; only models meeting
predetermined criteria for statistical signifi cance and
direction of regression coeffi cients are retained. We
excluded 30% of the data from the initial analysis so that
the performance of diff erent models could be assessed in
terms of how well they predict the omitted data. Through
multiple iterations of this process (cross-validation), we
obtained stable objective information about the model’s
performance. The best performing models in terms of
root-mean squared error for level and trend were
combined into a model ensemble. For some causes, we
developed separate ensemble models for GBD developed
and GBD developing regions;54 the main advantage of
this approach is that uncertainty in model estimation
from heterogeneous data in low-income regions does not
infl ate the UI for high-income countries. We used this
approach for all cancers and transport injuries.
For 13 causes, the number of deaths in the cause of
death database was too low to generate stable estimates.
For these causes, we developed negative binomial
regressions with either a constant or constant multiplied
by the mean assumption for the dispersion parameter,
using reverse step-wise model building. We selected
between the two model dispersion assumptions on the
basis of best fi t to the data. Compared with the GBD
2010, we modifi ed how we generated uncertainty from
these regressions by including in the uncertainty
sampling draws from the γ distribution with shape equal
to the expected rate (μ) divided by expected dispersion,
and scale equal to the expected dispersion if the
dispersion was assumed to be constant. For models in
which dispersion was assumed to equal a constant
multiplied by the mean, the scale parameter included μ
as a multiplicative term (instead of the shape parameter).
As in the GBD 2010, for 14 causes for which death is
rare, we fi rst modelled the parent cause in the GBD
hierarchy with CODEm and then allocated deaths to
specifi c causes using proportions of the parent cause for
each sub-cause. For these causes, we identifi ed no
signifi cant predictors in negative binomial regressions.
We estimated proportions by simple averaging based on
available vital registration data. Depending on the
availability of data, we averaged the data across age, sex,
region, and year.
We used DisMod-MR55 to estimate detailed cause
fractions for several causes of death that had suffi cient
data to estimate proportions of a parent cause resulting
from subcauses that vary across regions and countries
but insuffi cient data to run CODEm. The source code for
estimation is available online. DisMod-MR uses data for
subcause fractions gathered from systematic review and
from International Classifi cation of Diseases-coded vital
registration and sample registration systems. It uses
two types of fi xed eff ects (study characteristics and
country covariates) with hierarchical random eff ects for
super-region, region, and country to generate estimates
for each country, age group, both sexes, and six discrete
time points: 1990, 1995, 2000, 2005, 2010, and 2013. We
calculated predictions for intervening years—and back to
1980—assuming an exponential rate of change. We used
this approach for eight causes of maternal death,
four causes of meningitis, one cause of chronic kidney
disease, four causes of cirrhosis, four causes of liver
cancer, and three causes of haemoglobinopathies.
For 14 causes in the GBD 2010, we used natural history
models because data systems for cause of death did not
capture suffi cient information. The natural history
model for African trypanosomiasis was updated to
include the most recent case notifi cation data from
WHO (up to 2012). We made substantial changes to the
HIV natural history model.25 Our natural history model
for congenital syphilis was estimated as in the GBD
2010, with updated data for antenatal care coverage to
inform the number of births at risk and additional vital
registration data sources to inform age and sex
distribution of deaths. We also used simple natural
history models for typhoid and paratyphoid fever,
whooping cough, measles, visceral leishmaniasis, and
yellow fever. Additionally, because vital registration data
recording the specifi c type of hepatitis were very sparse,
we used natural history models for all the detailed
causes of hepatitis. The natural history model takes into
account the extensive serological data for the prevalence
of antibodies or antigens for hepatitis A, B, and C, and
more limited data for case-fatality rates.
Alzheimer’s disease and other dementias were
analysed with CODEm in GBD 2010. Because of the
large inconsistency between the data for prevalence and
mortality, we used a natural history model in the GBD
2013. Prevalences have not changed substantially over
time, whereas age-standardised mortality rates in
high-income countries have increased, ranging from
about 25% (Denmark, Switzerland, Norway) to 46%
(Germany). The prevalence of dementia varies between
countries by a factor of three, whereas dementia
mortality recorded in vital registration data and verbal
autopsy studies varies by more than 20-fold. On the
basis of these fi ndings, we believe that the variation in
dementia mortality rates between countries and over
time was probably aff ected by changes in coding
practices with increased propensity to assign dementia
as an underlying cause of death. To correct for this, we
assessed data from 23 high-income countries with
high-quality vital registration systems to estimate the
ratio of registered dementia deaths:prevalent cases. In
DisMod-MR, we used the mean of these ratios as an
estimate of excess mortality to estimate age-specifi c and
sex-specifi c mortality from dementia consistent with the
meta-regression of prevalence.
In GBD 2010, because single-cause models were
developed for each cause, the fi nal step was to combine
For the source code for
estimation see http://ghdx.
healthdata.org/node/156633
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9
these models into estimates that are consistent with all-
cause mortality for each age-sex-country-year group. For
each cause-specifi c model and for all-cause mortality, we
had 1000 draws from the posterior distribution for each
age-sex-country-year group. We combined causes by
taking a random draw without replacement from the
posterior distribution of each cause and all-cause
mortality. Each cause was rescaled by a scalar equal to the
draw of all-cause mortality divided by the sum of the
draws of individual causes. The GBD 2010 induced a
correlation of 1·0 between the sum of cause-specifi c and
all-cause mortality. CoDCorrect was applied in a
hierarchical fashion: fi rst to level 1 causes and then to
level 2 and level 3 causes. Level 2 causes were constrained
to sum to the level 1 parent cause. Levels of this cascade
were largely the same as those used in the GBD 2010 and
were chosen on the basis of the amount and quality of
available data for cause of death.
For GBD 2013, we made slight modifi cations to this
approach. Because tests showed no substantial eff ect of the
1·0 correlation between draws of all-cause mortality and
the sum of individual causes and because each cause is
modelled independently such that the ordering of draws
across causes were unrelated, we have removed this
assumption. Furthermore, because the modelling of HIV
through the GBD version of Spectrum uses relationships
between incidence, CD4 progression, and death that are
age-dependent and antiretroviral therapy scale-up over
time has had major eff ects, we modifi ed the way in which
HIV deaths are handled in CoDCorrect. We ran
CoDCorrect for all causes and then computed the
pre-CoDCorrect cumulative deaths over time and age and
compared with the cumulative deaths post-CoDCorrect.
This provided an overall scalar, which we used to adjust the
entire HIV epidemic. To avoid in any age-sex-country-year
the sum of individual deaths exceeding all-cause mortality,
we computed the diff erence between the cumulatively
scaled HIV deaths and the CoDCorrect HIV deaths and
added this diff erence to the estimate of all-cause mortality
at the draw level.
In GBD 2010, diarrhoea deaths and lower respiratory
infection deaths were reported for pathogen-specifi c
causes in tabulations that summed to 100% of each parent
cause. Since the GBD 2010, the GEMS study56 has been
published, which provided data for the relative risk of
diarrhoea being related to diff erent pathogens. This
relative risk approach used a diff erent conceptual
framework than did the International Classifi cation of
Diseases approach for underlying cause. Underlying
cause follows the more than 200-year history of health
statistics of assigning each death uniquely to a single
underlying cause. The relative risk approach follows the
approach used more generally for risk factors, where
cause is assigned based on comparison to a counterfactual.
Counterfactual attribution to specifi c risks or in this case
pathogens, can sum to more or less than 100%. On the
basis of the GEMS study and consultations among
experts in both diarrhoea and lower respiratory infection,
we report results for counterfactual causes in GBD 2013.
To estimate diarrhoea mortality attributable to diff erent
pathogens, we calculated the population attributable
fraction for pathogens including rotavirus, Shigella,
enteropathogenic Escherichia coli, enterotoxigenic E coli,
adenovirus (enteric adenovirus), norovirus, Aeromonas,
other Salmonella (non-typhoidal Salmonella),
Cryptosporidium, Campylobacter, and Entamoeba. We used
the Miettinen formula, which uses the distribution of
pathogens in patients and relative risks of pathogens for
diarrhoea, to provide a population attributable fraction for
each pathogen:57,58
Where PAFi is the population attributable fraction of
diarrhoea caused by pathogen i, pi is the prevalence of
pathogen i in patients with diarrhoea, and odds ratioi is
the odds ratio of diarrhoea in people with the pathogen.
We used DisMod-MR to estimate the proportion of
patients in each age-sex-country-year with each
pathogen with data from studies of inpatients and
community samples. By use of study-level covariates in
the meta-regression, we obtained diff erent estimates
for inpatients and community samples. We assumed
inpatients to be a proxy for severe diarrhoea and death.
We reanalysed GEMS59 to estimate the odds ratio for
each pathogen in a multipathogen model by conditional
logistic regression. Regression models included fi xed
eff ects for a specifi c pathogen with interaction terms
for three age groups (0–1 years, 1–2 years, and
2–6 years) to allow diff erent odds ratios by age and
interaction terms for diff erent GEMS fi eld sites
(Bangladesh, India, Kenya, Mali, Mozambique,
Pakistan, and The Gambia) to estimate site-specifi c
odds ratios. For other countries in the region, the odds
ratio we used was the average of the odds ratios (in
logarithm scale) of the countries with GEMS sites in
that region. For countries in central and southern sub-
Saharan Africa, we used the average of GEMS sites
located in eastern and western sub-Saharan Africa. For
all other countries in regions without GEMS sites, we
used the average of all odds ratio. To produce odds
ratio uncertainty while averaging odds ratio, we
generated 1000 draws of joint normal distribution
using a covariance matrix from the conditional logistic
regression for each of the GEMS countries in a region.
To produce the draws for non-GEMS countries, we
selected draws from each of the GEMS countries until
we had a full set of 1000 draws. For example, to
generate 1000 draws for countries in eastern
sub-Saharan Africa, we used draws from Kenya and
Mozambique—the two GEMS countries within that
region. We pulled 500 of the Kenya draws and 500 of
the Mozambique draws to produce our full set of
PAFi = pi(pathogen in patients)(1 – )
1
odds ratioi
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1000 draws, which were used for all the other countries
in this region. Because GEMS included only diarrhoea
in children younger than 5 years, we applied the odds
ratio of pathogens calculated for children aged
2–5 years when calculating for adults. We did not
assign diarrhoea cases or deaths to a pathogen for an
age-country-year if more than 95% of draws were
greater than 1.
For cholera, we used data from previous studies
(appendix pp 310) and compared them with WHO case
notifi cation data to estimate under-reporting for cholera
and then the number of cases (appendix pp 310). To
estimate cholera deaths, we modelled cholera case fatality
in DisMod-MR with data from previous studies.
Clostridium diffi cile as a cause of diarrhoea in children is
rarely studied; we could not estimate the epidemiological
population attributable fraction as we did for other
pathogens because C diffi cile was not included in GEMS.
Because C diffi cile is related to hospital and health-care
use, we used hospital data as the primary source for
estimation. We modelled the incidence and case fatality of
C diffi cile and assumed a 1-month risk of death60 in
DisMod-MR to estimate the number of deaths.
For GBD 2013, we split lower respiratory infection
mortality into four categories: Streptococcus pneumoniae,
Haemophilus infl uenzae type B pneumonia, respiratory
syncytial virus pneumonia, and infl uenza. The counter-
factual approach captures the complex interactions
between the these causes61 and also excludes the “other
lower respiratory infection” category included in GBD
2010. Moreover, we did not attribute lower respiratory
infection to any cause for children younger than age
1 month. We adopted a diff erent approach to estimate
bacterial and viral causes on the basis of available data.
For pneumococcal and H infl uenzae type B pneumonia,
we estimated the causal fraction from vaccine effi cacy
studies.62–64 For pneumococcal pneumonia, we included
data from controlled trials and observational studies,
such as before-after population analyses of the
introduction of pneumococcal vaccine.65–76 For
H infl uenzae type B, we excluded case-control studies
because of implausibly large estimates of vaccine effi cacy.
Furthermore, unlike for pneumococcal vaccine, little data
were available from vaccine effi cacy studies on the eff ect
outside of child ages. As a result, we did not estimate the
causal fraction of H infl uenzae type B for lower respiratory
infection in people aged 5 years or older. We adjusted data
for effi cacy, using invasive disease as a marker as well as
serotype coverage for pneumococcal vaccine.64 We
calculated pooled estimates of causal fractions by age
with DisMod-MR for pneumococcal vaccine and
random-eff ects meta-analysis for H infl uenzae type B,
adjusted post-hoc for national-level coverage of
pneumococcal vaccine and H infl uenzae type B vaccine.
For respiratory syncytial virus and infl uenza, we relied on
observational studies that measured causal fractions
among hospital admissions for lower respiratory
infection. We estimated the causal fractions among cases
by country, age, and sex with DisMod-MR and the odds
ratio of exposure from case-control studies. To account
for the higher case-fatality of bacterial versus viral lower
respiratory infections, we applied a relative case-fatality
diff erential based on in-hospital case-fatality using
hospital admissions that included cases coded to the
specifi c pneumonia causes.
Convergence measures
To test whether all-cause and cause-specifi c mortality
converged in the 188 countries since 1990, we computed
two measures: the average relative diff erence and the
average absolute diff erence between any pair of countries
included in the GBD 2013 study. The average relative
diff erence is known as the Gini coeffi cient and is the most
commonly used measure of inequality. For international
comparisons, we used the population-weighted version of
the Gini coeffi cient in age-specifi c mortality rates so that
small populations do not have an undue infl uence on the
assessment of global mortality convergence (appendix
pp 556–557).77 For the Gini coeffi cient to fall, the percent
decrease in mortality for countries with higher mortality
must in general be faster than that for countries with
lower mortality.
We also computed the mean absolute diff erence for
all-cause mortality for each age group for 1990–2013 and
for age-standardised rates for each cause (appendix
pp 556–557). Average absolute diff erence can fall while
average relative diff erence (the Gini coeffi cient) rises.
The two measures provide diff erent perspectives on
convergence.
Multiple decrement lifetables
We used age-specifi c cause of death and all-cause
mortality life-tables to compute the conditional
probability of death for three summary intervals:
childhood and adolescence (0 to exact age 15 years),
reproductive-age adults (15 years to exact age 50 years),
middle-aged adults (50 years to exact age 75 years), and
the cause-specifi c contributions to each of these
summary indicators. For each conditional probability of
death, we used the multiple decrement life-table
method78 to compute the probability of death from each
cause and the overall contribution of each cause of
death to the summary probability of death indicators
for the three broad age groups (appendix pp 556–557).
We calculated the decomposition of changes in life
expectancy by age and cause of death as detailed by
Beltran-Sanchez, Preston, and Canudas-Romo.79
Age-standardised rates and YLLs
For GBD 2010, we computed age-standardised mortality
rates and YLL rates from the world population age
standard issued by WHO in 2001.80 To account for the
substantial change in global demographics since 2001,
we updated this standard. We used the same method as
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11
WHO and computed a standard population structure
with population estimates for 2010–35 from the most
recent World Population Prospects by the United
Nations Population Division. Appendix pp 95–96
provides details of the GBD world population age-
standard. We computed YLLs by multiplying numbers
of deaths from each cause in each age group by the
reference life expectancy at the average age of death for
those who die in the age group following the standard
GBD 2010 methods.3 The appendix (pp 121–40) shows
key indicators from the new GBD standard life-table.
Ranking lists and decomposition analysis
We used the GBD 2010 approach to create ranked lists
of specifi c diseases and injuries. We modifi ed GBD
2010 ranking list to incorporate newly estimated causes
with the same overall assignment of rank causes as
GBD 2010: typhoid and paratyphoid separately,
haemolytic disease in fetus and newborn and other
neonatal jaundice, mesothelioma, unintentional
suff ocation, pulmonary aspiration and foreign body in
trachea or lung, and foreign body in other part of body.
Following the methods developed by Lozano and
colleagues,3 we decomposed changes in the number of
global deaths and global YLLs into the contributions
from population growth, population aging, and age-
specifi c death rates.
Role of the funding source
The funder of the study had no role in study design,
data collection, data analysis, data interpretation, or
writing of the report. The authors had access to the
data in the study and the fi nal responsibility to submit
the paper.
Results
Global all-cause mortality
Global life expectancy at birth for both sexes increased
from 65·3 years in 1990, to 71·5 years in 2013, an
average increase of 0·27 years per calendar year. Life
expectancy increased over this period by 6·6 years for
females and 5·8 years for males. Figure 4 shows the
yearly change in global life expectancy at birth, with a
large drop in the 1990s as a result of the Rwanda
genocide and famine in North Korea and the return to
increases of about 0·3 years or more per year since
2003. If the median rate of change of the last 23 years
continues, by 2030 global female life expectancy will be
85·3 years and male life expectancy will be 78·1 years.
Reduced fertility and the consequent demographic
shift of the world’s population to older ages has led to
the mean age of death increasing from 46·7 years in
1990, to 59·3 years in 2013.81
The number of deaths globally for both sexes all ages
increased from 47·47 (UI 46·77–48·22) million in 1990,
to 54·86 (53·57–56·33) million in 2013, partly because of
consistent increases in global population over the past
decades. Rapid falls in child death rates compared with
other age-specifi c death rates have led to a shift in the age
structure of global deaths with substantial decreases in
children and large increases in the proportion of deaths
of people older than age 80 years (fi gure 5). The number
of child deaths fell between 1990 and 2013 in southeast
Asia, east Asia, and Oceania with very substantial falls in
north Africa and the Middle East, and Latin America and
the Caribbean (fi gure 5). However, the number of child
deaths in sub-Saharan Africa only changed from 3·68
(3·63–3·73) million in 1990, to 3·20 (3·00–3·42) million
in 2013. Substantial increases in the number of deaths of
people older than age 80 years have occurred in
high-income regions as well as in southeast Asia, east
Asia, and Oceania.
Rising global life expectancy at birth has not come from
uniform progress across age-groups or countries. In all
age-groups except the 80 years and older age group, mean
mortality rate has decreased more for females than for
males (fi gure 6). Larger decreases in males older than age
80 years might be a result of the diff erences in the age
composition between males and females in this open-
ended age group. The mortality rate in the under-5 age
group has fallen much more between 1990 and 2013 than
has that for older age groups. The smallest decreases
occurred in men in age groups 30–34 years, 35–39 years,
and 80 years or older, and in women aged 80 years or
older.
For all age groups, population-weighted average relative
diff erence for age-specifi c mortality rates diff erences
across countries (ie, inequality) increased except in
age group 10–14 years and 15–19 years for females. The
divergence in age-specifi c mortality rates was greatest in
young adult age groups between ages 20 years and 44 years
for both males and females; dominant causes in these age
groups include HIV/AIDS, interpersonal violence,
maternal mortality, and road injury (data not shown). For
many age groups, in both sexes, the absolute diff erences
have fallen while relative inequalities have increased
(fi gure 6). For women aged 25–39 years and 75 years and
older, and for men aged 20–49 years and 65 years and
Figure 4: Change in global life expectancy at birth for males and females
1990 1995 2000 2005 2010 2013
–0·3
–0·2
–0·1
0
0·1
0·2
0·3
0·4
0·5
0·6
Change in life expectancy at birth (years)
Year
Male
Female
Male (data from Rwanda and North Korea for 1993−95 excluded)
Female (data from Rwanda and North Korea for 1993−95 excluded)
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older, both relative and absolute diff erences in mean age-
specifi c mortality rates have increased since 1990.
Global causes of death
We decomposed change in global and regional life
expectancy by cause (level 2 of the GBD cause hierarchy;
fi gure 7). Increased life expectancy since 1990 was mainly
caused by a fall in mortality from lower respiratory
infections and diarrhoeal diseases (contributing
2·2 years), cardiovascular and circulatory diseases
(contributing 1·1 years), neonatal conditions (contributing
0·7 years), cancers (contributing 0·4 years), and chronic
respiratory diseases (contributing 0·5 years). Decreases
in mortality from unintentional injuries added another
0·3 years to life expectancy, while female life expectancy
increased by about 0·2 years because of reductions in
maternal mortality. These gains were off set by increased
mortality from diabetes, chronic kidney diseases, and
related conditions, as well as musculoskeletal disorders,
although the net eff ect of these increases was small,
reducing life expectancy, on average, by about 0·1 years.
Five main causes reduced life expectancy: HIV/AIDS was
a major cause of death in southern sub-Saharan Africa
and to a smaller extent in western and eastern sub-
Saharan Africa; diabetes, chronic kidney disease, and
other endocrine disorders decreased life expectancy
across many regions, most notably in Oceania and central
Latin America; mental disorders made a negative
contribution in multiple regions, especially high-income
north America; intentional injuries reduced life
expectancy in south Asia, high-income Asia Pacifi c, and
southern sub-Saharan Africa; and cirrhosis made a
negative impact in eastern Europe and central Asia
(fi gure 7). Large gains in life expectancy in sub-Saharan
Africa were mainly driven by reductions of diarrhoea and
lower respiratory infections and of neonatal disorders.
Gains in high-income regions were driven by reductions
in cardiovascular disease, some cancers, transport
injuries, and chronic respiratory conditions (fi gure 7).
Between 1990 and 2013, numbers of deaths from
non-communicable diseases and injuries steadily
increased while deaths from communicable, maternal,
neonatal, and nutritional causes decreased (table 2).
However, age-standardised rates decreased in these
three broad categories. The shift to non-communicable
diseases, at least at globally, was driven by faster rates of
decline for communicable, maternal, neonatal and
nutritional causes and an ageing world population.
In 2013, 11·8 million (11·3–12·3) deaths were caused by
communicable, maternal, neonatal, and nutritional
disorders: 2·7 million (2·4–2·8) by lower respiratory
infections, 1·3 million (1·3–1·5) by HIV/AIDS, 1·3 million
(1·2–1·4) by tuberculosis, and 1·3 million (1·2–1·4) by
diarrhoeal diseases, 2·0 million (1·9–2·2) by neonatal
conditions, 854 600 (702 924–1 032 497) by malaria, and
293 336 (261 322–328 200) by maternal causes (about 20%
Figure 5: Global deaths by age and super region in 1990 and 2013
0–4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 ≥80
0
2·5
5·0
7·5
10·0
12·5
15·0
Deaths (millions)
Age group (years)
Sub-Saharan Africa
Southeast Asia, east Asia, and Oceania
South Asia
North Africa and Middle East
Latin America and Caribbean
High-income
Central Europe, eastern Europe, and central Asia
1990GBD super region 2013
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13
less than in 2000). Between 2000 and 2013, deaths from
diarrhoeal diseases fell by 31·1% (from 1·8 million
[1·7–2·0] to 1·3 million [1·2–1·4]), tuberculosis and
meningitis each by about 20% (1·6 million [1·4–1·7] to 1·3
million [1·2–1·4] for tuberculosis, 377 300 [331 400–438 000]
to 303 500 [261 400–346 300] for meningitis), while
mortality from tetanus fell by about 60% (142 400
[108 800–163 100] to 58 900 [39 800–77 300]), from measles
by about 80% (494 500 [279·4–763·8] to 95 600
[48 500–172 900]), from diphtheria by about 40% (5400
[2800–10 700] to 3300 [1700–6600]), and from whooping
cough by about 40% (111 800 [42 300–242 400] to 60 600
[22 300–136 800]). Deaths from neonatal causes fell by a
quarter since 2000 (2·8 million to 2·0 million), and by
about one-fi fth for maternal causes (364 900 to 293 300).
Comparing 2013 to 1990, malaria deaths decreased by
4·4% and HIV/AIDS increased by 368% (table 2). HIV/
AIDS mortality and malaria mortality both peaked in 2005
(1·7 million [1·6–1·9] for HIV/AIDS, 1·2 million [1·1–1·4]
for malaria); HIV/AIDS mortality fell by 21% (20·4–21·5)
from 2005 to 2013, and malaria mortality fell by 30%
(24·8–35·4).38 The risk of death from various leading
causes of communicable, maternal, neonatal, and
nutritional disorders as measured by the age-standardised
death rate (table 2), has generally declined by an even
greater amount than the risk for HIV/AIDS and malaria.
Age-standardised death rates decreased by about 40%
since 1990 for the category as a whole, as well as most
notably, for lower respiratory infections, maternal
disorders, neonatal disorders, and asthma, and by 50–60%
for tuberculosis, diarrhoeal diseases, pneumoconiosis and
several neglected tropical diseases (table 2). Despite a
small decrease in numbers of deaths, age-standardised
malaria mortality have fallen by 19% since 1990, with
much of that decline occurring in the past 5 years or so
(data not shown).33
For most of the leading non-communicable diseases,
the number of deaths has increased, by 42% between
1990 and 2013 (from 27·0 million [UI 26·3–27·6] in 1990,
to 38·3 million [37·2–39·4] in 2013), but age-standardised
mortality rates have fallen. Allowing for changes in
the age structure of the world’s population between
1990 and 2013, age-standardised death rates from
non-communicable diseases fell by 18·6%; by 22% for
cardiovascular and circulatory diseases, 13·7–14·7% for
cirrhosis of the liver and cancer, and 21·9–30·4% for
other digestive diseases and chronic respiratory diseases
(table 2). For many disorders, including stomach cancer,
Hodgkin lymphoma, rheumatic heart disease, peptic
ulcer disease, appendicitis, and schizophrenia, age-
standardised death rates have fallen by more than one-
third since 1990 (table 2). Age-standardised death rates
Figure 6: Change in age-specifi c population-weighted Gini coeffi cient versus relative change in mortality rate from 1990 to 2013
Solid points show age groups for which the mean absolute diff erence between countries has increased and hollow points show those for which mean absolute
diff erence has decreased.
0–10–20–30–40–50–60
0
0·10
0·05
0·15
Change in Gini coefficient of age-specific mortality rate
Relative change in mean age-specific mortality rate (%)
25−29
30–34
35−39
75−79
≥80
20−24
25−29
30−34
35−39
40−44
45−49
65−69
70−74
75−79
≥80
<1
1−4
5−9
10−14
15−19
20−24
40−44
45−49
50−54 55−59
60−64
65−69
70−74
<1
1−4
5−9
10−14 15−19
50−54
55−59
60−64
Female
Male
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14
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for some cancers have fallen (lung by 9%, breast by 18%,
and leukaemia by 20%), but have remained unchanged
for others (table 2). Global age-standardised death rates
have fallen by more than one-fi fth for ischaemic heart
disease and stroke (table 2).
Global age-standardised mortality rates increased
signifi cantly for very few disease between 1990 and 2013.
The largest increase was for HIV/AIDS, which peaked in
2005 and then fell by 31·0% (UI 25·7% to 35·9%) from
2005 to 2013 (from 26·9 to 18·5 per 100 000). Among the
Figure 7: Change in life expectancy at birth by GBD region and cause group from 1990 to 2013
An interactive fi gure with these data is available at http://vizhub.healthdata.org/le. Changes in life expectancy as a result of specifi c causes were decomposed from
the diff erence between all-cause lifetables and cause-deleted lifetables (mortality set to zero for a specifi c cause). Because all changes in life expectancy are based on
cross-sectional lifetables, the cause-specifi c changes add up to the total change in life-expectancy. NTDs=neglected tropical diseases.
−10 −8 −6 −4 −2 0 0 2 4 6 8 10
Year s
Eastern sub-Saharan Africa
(9·2 years)
East Asia
(8·3 years)
South Asia
(8·3 years)
North Africa and Middle East
(7·1 years)
Andean Latin America
(6·9 years)
Global
(6·3 years)
Central sub−Saharan Africa
(5·8 years)
Tropical Latin America
(5·7 years)
Central Europe
(5·6 years)
Western sub-Saharan Africa
(5·6 years)
Southeast Asia
(5·5 years)
High-income Asia Pacific
(5·2 years)
Caribbean
(5 years)
Western Europe
(4·9 years)
Australasia
(4·9 years)
Southern Latin America
(4·1 years)
Central Latin America
(4 years)
Oceania
(3·6 years)
High-income north America
(3·6 years)
Central Asia
(2·9 years)
Eastern Europe
(1·7 years)
South sub-Saharan Africa
(−5·1 years)
Decreases Increases
Forces of nature, war, and legal intervention
Self-harm and interpersonal injuries
Unintentional injuries
Transport injuries
Other NCDs
Musculoskeletal disorders
Diabetes, urogenital, blood,
and endocrine diseases
Mental and behavioural disorders
Neurological disorders
Digestive diseases
Cirrhosis
Chronic respiratory disease
Cardiovascular diseases
Neoplasms
Other communicable, maternal, and nutritional diseases
Nutritional deficiencies
Neonatal disorders
Maternal disorders
NTDs and malaria
Diarrhoea, lower respiratory infections, and other
common infectious diseases
HIV and tuberculosis
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15
cancers, only liver cancer caused by hepatitis C increased
substantially (table 2). Although age-standardised
mortality for cardiovascular and circulatory diseases
decreased by 22%, signifi cant increases occurred for
atrial fi brillation and fl utter and peripheral vascular
disease (table 2). Mortality rates for Alzheimer’s disease
and other dementias increased by only 3·2% (UI –3·01
to 11·61) and Parkinson’s disease by 28·2% (–6·42 to
37·83; table 2). Important worldwide increases occurred
for diabetes (9·0%) and an even larger increase for
chronic kidney disease (36·9%; table 2). The age-
standardised death rate for sickle-cell disease increased
All ages deaths (thousands) Age-standardised death rate (per 100 000)
1990 2013 Median % change 1990 2013 Median % change
All causes 47 468·7
(46 771·7 to 48 223·3)
54 863·8
(53 576·1 to 56 333·6)
15·6
(12·54 to 19·11)
1160·5
(1143·2 to 1179·0)
879·7
(859·9 to 902·2)
–24·2
(–26·20 to –21·96)
Communicable, maternal, neonatal,
and nutritional diseases
16 149·4
(15 674·5 to 16 597·6)
11 809·6
(11 335·5 to 12 283·0)
–26·8
(–29·99 to –24·10)
289·5
(281·2 to 298·4)
172·2
(165·2 to 178·8)
–40·5
(–42·98 to –38·35)
HIV/AIDS and tuberculosis 2072·5
(1938·2 to 2246·1)
2631·2
(2497·3 to 2863·2)
26·8
(17·08 to 37·92)
49·3
(46·2 to 53·3)
38·0
(36·0 to 41·6)
–23·1
(–29·02 to –16·39)
Tuberculosis 1786·1
(1666·4 to 1945·4)
1290·3
(1167·3 to 1406·2)
–27·7
(–34·99 to –20·56)
43·5
(40·4 to 47·2)
19·4
(17·6 to 21·2)
–55·3
(–59·67 to –51·01)
HIV/AIDS 286·4
(227·4 to 370·3)
1341·0
(1257·8 to 1482·6)
374·2
(262·47 to 490·73)
5·8
(4·6 to 7·6)
18·5
(17·4 to 20·5)
222·0
(145·14 to 303·34)
HIV/AIDS resulting in mycobacterial
infection
27·8
(20·5 to 37·4)
84·0
(67·4 to 104·9)
205·1
(130·68 to 291·83)
0·6
(0·4 to 0·8)
1·2
(0·9 to 1·4)
105·5
(55·00 to 166·22)
HIV/AIDS resulting in other
diseases
258·6
(206·1 to 334·4)
1257·0
(1178·1 to 1391·1)
393·2
(276·86–511·66)
5·3
(4·2 to 6·8)
17·4
(16·3 to 19·2)
235·0
(154·55 to 319·43)
Diarrhoea, lower respiratory infections,
and other common infectious diseases
7880·5
(7468·3 to 8337·2)
4750·5
(4388·8 to 5029·9)
–39·4
(–45·17 to –35·89)
143·7
(137·3 to 151·9)
72·4
(66·7 to 76·5)
–49·4
(–53·54 to –46·75)
Diarrhoeal diseases 2578·7
(2412·2 to 2748·9)
1264·1
(1151·2 to 1383·2)
–51·0
(–55·55 to –46·25)
47·4
(44·4 to 50·1)
19·0
(17·4 to 20·8)
–59·8
(–63·54 to –55·96)
Intestinal infectious diseases 259·1
(145·6 to 424·6)
221·3
(122·6 to 362·6)
–14·3
(–26·08 to –2·75)
4·3
(2·4 to 7·0)
3·1
(1·7 to 5·0)
–28·7
(–38·19 to –18·98)
Typhoid fever 180·5
(96·4 to 302·3)
160·7
(85·9 to 268·0)
–10·8
(–23·70 to 4·24)
3·0
(1·6 to 5·0)
2·2
(1·2 to 3·7)
–25·9
(–36·31 to –13·57)
Paratyphoid fever 63·4
(33·6 to 106·7)
54·3
(29·3 to 92·0)
–14·9
(–30·51 to 9·48)
1·0
(0·6 to 1·7)
0·7
(0·4 to 1·3)
–28·0
(–40·99 to –7·97)
Other intestinal infectious diseases 15·3
(13·5 to 17·3)
6·3
(5·5 to 7·0)
–59·1
(–63·26 to –54·09)
0·3
(0·2 to 0·3)
0·1
(0·1 to 0·1)
–66·7
(–70·02 to –62·84)
Lower respiratory infections 3420·7
(3211·6 to 3638·4)
2652·6
(2368·0 to 2808·1)
–22·2
(–29·71 to –16·32)
66·8
(63·1 to 71·6)
41·7
(37·1 to 44·1)
–37·4
(–42·54 to –33·45)
Upper respiratory infections 4·7
(4·0 to 5·6)
3·9
(3·3 to 4·7)
–16·4
(–33·55 to 4·22)
0·1
(0·1 to 0·1)
0·1
(0·1 to 0·1)
–40·8
(–52·40 to –26·16)
Otitis media 4·9
(4·5 to 5·3)
2·4
(2·3 to 2·6)
–50·8
(–54·22 to –46·41)
0·1
(0·1 to 0·1)
0·0
(0·0 to 0·0)
–60·8
(–63·34 to –57·76)
Meningitis 464·4
(405·0 to 559·0)
303·5
(261·4 to 346·3)
–34·3
(–45·34 to –24·08)
7·7
(6·8 to 9·0)
4·3
(3·7 to 4·9)
–43·9
(–52·65 to –36·07)
Pneumococcal meningitis 112·1
(97·7 to 132·6)
79·1
(67·8 to 91·1)
–29·4
(–39·84 to –16·86)
1·9
(1·7 to 2·2)
1·1
(1·0 to 1·3)
–41·7
(–49·60 to –31·37)
Haemophilus infl uenzae type B
meningitis
118·0
(98·2 to 147·0)
64·4
(53·0 to 76·4)
–45·4
(–54·79 to –33·50)
1·8
(1·5 to 2·2)
0·9
(0·7 to 1·1)
–49·7
(–58·21 to –39·32)
Meningococcal meningitis 88·1
(76·1 to 108·0)
65·7
(55·9 to 75·8)
–24·9
(–38·82 to –12·62)
1·5
(1·3 to 1·8)
0·9
(0·8 to 1·1)
–37·3
(–47·55 to –27·80)
Other meningitis 146·1
(128·0 to 174·9)
94·2
(82·2 to 106·4)
–35·1
(–47·16 to –24·71)
2·5
(2·2 to 2·9)
1·3
(1·2 to 1·5)
–45·9
(–54·98 to –37·48)
Encephalitis 92·2
(65·2 to 116·2)
77·3
(65·4 to 97·0)
–15·2
(–40·15 to 16·01)
1·6
(1·1 to 1·9)
1·1
(0·9 to 1·4)
–28·7
(–48·10 to –4·52)
Diphtheria 8·0
(3·9 to 18·8)
3·3
(1·7 to 6·6)
–57·7
(–85·61 to 12·54)
0·1
(0·1 to 0·3)
0·0
(0·0 to 0·1)
–60·8
(–86·70 to 2·82)
Whooping cough 138·2
(52·9 to 300·2)
60·6
(22·3 to 136·8)
–56·7
(–83·76 to 14·31)
1·9
(0·7 to 4·2)
0·8
(0·3 to 1·9)
–58·2
(–84·31 to 10·44)
Tetanus 356·2
(292·9 to 578·6)
58·9
(39·8 to 77·3)
–82·1
(–92·00 to –76·10)
5·7
(4·7 to 9·1)
0·8
(0·6 to 1·1)
–83·9
(–92·72 to –77·84)
(Table 2 continues on next page)
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All ages deaths (thousands) Age-standardised death rate (per 100 000)
1990 2013 Median % change 1990 2013 Median % change
(Continued from previous page)
Measles 544·5
(304·0 to 867·8)
95·6
(48·5 to 172·9)
–83·0
(–90·23 to –67·93)
7·8
(4·3 to 12·4)
1·3
(0·7 to 2·4)
–83·7
(–90·66 to –69·19)
Varicella 8·9
(7·2 to 11·7)
7·0
(5·7 to 8·7)
–21·4
(–43·72 to 4·63)
0·2
(0·2 to 0·3)
0·1
(0·1 to 0·1)
–45·6
(–61·28 to –27·33)
Neglected tropical diseases and malaria 1 092·4
(994·1 to 1 196·7)
997·0
(840·5 to 1 185·2)
–9·2
(–25·09 to 10·78)
18·5
(17·0 to 20·2)
13·9
(11·8 to 16·5)
–24·9
(–37·19 to –9·70)
Malaria 888·1
(793·4 to 992·7)
854·6
(702·9 to 1 032·5)
–4·4
(–23·61 to 19·83)
14·6
(13·2 to 16·2)
11·9
(9·8 to 14·4)
–18·9
(–34·32 to 0·18)
Chagas disease 12·7
(5·2 to 39·4)
10·6
(4·2 to 33·0)
–19·3
(–41·12 to 25·58)
0·3
(0·1 to 1·1)
0·2
(0·1 to 0·5)
–51·7
(–66·06 to –24·52)
Leishmaniasis 52·2
(44·6 to 60·6)
62·5
(52·3 to 73·3)
19·8
(3·56 to 37·74)
0·9
(0·7 to 1·0)
0·9
(0·7 to 1·0)
–0·3
(–14·18 to 14·25)
Visceral leishmaniasis 52·2
(44·6 to 60·6)
62·5
(52·3 to 73·3)
19·8
(3·56 to 37·74)
0·9
(0·7 to 1·0)
0·9
(0·7 to 1·0)
–0·3
(–14·18 to 14·25)
African trypanosomiasis 23·0
(11·4 to 37·7)
6·9
(3·7 to 10·9)
–69·7
(–73·88 to –64·94)
0·5
(0·2 to 0·7)
0·1
(0·1 to 0·1)
–78·9
(–81·76 to –75·54)
Schistosomiasis 17·4
(14·8 to 20·6)
5·5
(4·9 to 6·2)
–68·2
(–73·56 to –61·68)
0·4
(0·4 to 0·5)
0·1
(0·1 to 0·1)
–80·7
(–84·06 to –76·71)
Cysticercosis 0·9
(0·8 to 1·1)
0·7
(0·5 to 1·0)
–28·6
(–35·23 to –15·09)
0·0
(0·0 to 0·0)
0·0
(0·0 to 0·0)
–53·0
(–57·59 to –43·63)
Cystic echinococcosis 4·0
(3·8 to 4·3)
2·2
(2·1 to 2·4)
–45·0
(–48·34 to –41·22)
0·1
(0·1 to 0·1)
0·0
(0·0 to 0·0)
–60·8
(–62·99 to –58·26)
Dengue 8·8
(5·2 to 11·3)
9·1
(5·6 to 10·8)
–1·3
(–21·69 to 74·08)
0·1
(0·1 to 0·2)
0·1
(0·1 to 0·2)
–13·6
(–31·42 to 42·41)
Yellow fever 2·2
(1·9 to 2·5)
0·5
(0·4 to 0·6)
–77·2
(–80·99 to –72·34)
0·0
(0·0 to 0·0)
0·0
(0·0 to 0·0)
–83·3
(–86·08 to –79·71)
Rabies 38·4
(26·7 to 48·7)
23·5
(17·3 to 28·6)
–38·3
(–53·14 to –24·37)
0·7
(0·5 to 0·9)
0·3
(0·2 to 0·4)
–54·0
(–64·09 to –44·23)
Intestinal nematode infections 9·1
(8·1 to 10·2)
4·5
(4·0 to 5·1)
–50·7
(–56·28 to –43·47)
0·1
(0·1 to 0·2)
0·1
(0·1 to 0·1)
–54·7
(–59·66 to –48·46)
Ascariasis 9·1
(8·1 to 10·2)
4·5
(4·0 to 5·1)
–50·7
(–56·28 to –43·47)
0·1
(0·1 to 0·2)
0·1
(0·1 to 0·1)
–54·7
(–59·66 to –48·46)
Other neglected tropical diseases 35·6
(26·4 to 44·2)
16·3
(13·9 to 19·6)
–54·4
(–64·57 to –36·37)
0·6
(0·5 to 0·8)
0·2
(0·2 to 0·3)
–62·3
(–69·58 to –49·75)
Maternal disorders 376·6
(344·0 to 408·2)
293·3
(261·3 to 328·2)
–22·3
(–31·63 to –11·38)
6·9
(6·3 to 7·5)
3·9
(3·5 to 4·4)
–43·6
(–50·45 to –35·70)
Maternal haemorrhage 71·4
(64·6 to 78·5)
44·2
(38·3 to 51·0)
–38·1
(–47·24 to –27·03)
1·3
(1·2 to 1·4)
0·6
(0·5 to 0·7)
–55·0
(–61·51 to –46·97)
Maternal sepsis and other maternal
infections
34·1
(30·5 to 38·0)
23·8
(20·1 to 28·0)
–30·4
(–42·43 to –15·10)
0·6
(0·6 to 0·7)
0·3
(0·3 to 0·4)
–50·0
(–58·40 to –39·00)
Maternal hypertensive disorders 36·6
(33·3 to 39·9)
29·3
(25·7 to 33·5)
–20·0
(–30·89 to –7·22)
0·7
(0·6 to 0·7)
0·4
(0·3 to 0·4)
–41·3
(–49·14 to –32·20)
Obstructed labour 29·3
(26·3 to 32·7)
18·8
(16·3 to 21·8)
–35·9
(–45·56 to –23·86)
0·5
(0·5 to 0·6)
0·2
(0·2 to 0·3)
–53·4
(–60·20 to –44·45)
Complications of abortion 50·0
(45·8 to 54·8)
43·7
(38·3 to 49·9)
–12·6
(–24·72 to 2·00)
0·9
(0·9 to 1·0)
0·6
(0·5 to 0·7)
–37·5
(–46·15 to –26·71)
Indirect maternal deaths 40·1
(35·5 to 44·4)
31·1
(26·8 to 35·8)
–22·7
(–34·25 to –7·62)
0·7
(0·6 to 0·8)
0·4
(0·4 to 0·5)
–43·4
(–51·81 to –32·38)
Late maternal deaths 44·9
(36·4 to 53·2)
43·5
(35·7 to 52·4)
–1·8
(–26·81 to 21·95)
0·8
(0·7 to 1·0)
0·6
(0·5 to 0·7)
–28·4
(–46·61 to –11·67)
Maternal deaths aggravated by HIV/
AIDS
0·8
(0·5 to 1·1)
2·1
(1·3 to 2·9)
161·7
(128·02 to 202·66)
0·0
(0·0 to 0·0)
0·0
(0·0 to 0·0)
94·1
(69·26 to 124·47)
Other maternal disorders 68·6
(58·4 to 80·4)
56·2
(48·7 to 64·4)
–18·2
(–31·79 to –0·04)
1·3
(1·1 to 1·5)
0·7
(0·6 to 0·9)
–40·8
(–50·36 to –27·88)
(Table 2 continues on next page)
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17
All ages deaths (thousands) Age-standardised death rate (per 100 000)
1990 2013 Median % change 1990 2013 Median % change
(Continued from previous page)
Neonatal disorders 3 433·3
(3 225·7 to 3 586·1)
2 048·0
(1 934·7 to 2 160·3)
–40·3
(–43·96 to –36·30)
46·9
(44·1 to 49·0)
27·6
(26·1 to 29·1)
–41·1
(–44·71 to –37·13)
Preterm birth complications 1 570·5
(1 285·0 to 1 803·5)
742·4
(591·3 to 910·8)
–52·9
(–58·70 to –45·50)
21·4
(17·5 to 24·6)
10·0
(8·0 to 12·3)
–53·5
(–59·23 to –46·20)
Neonatal encephalopathy (birth
asphyxia/trauma)
874·1
(688·5 to 1 055·4)
643·8
(515·0 to 760·5)
–26·1
(–38·29 to –11·35)
11·9
(9·4 to 14·4)
8·7
(6·9 to 10·3)
–27·1
(–39·06 to –12·49)
Neonatal sepsis and other neonatal
infections
346·4
(195·7 to 484·0)
366·0
(233·2 to 510·8)
6·1
(–15·94 to 38·04)
4·7
(2·7 to 6·6)
4·9
(3·1 to 6·9)
4·6
(–17·13 to 35·99)
Haemolytic disease and other neonatal
jaundice
64·8
(39·5 to 96·3)
19·6
(13·0 to 29·7)
–70·0
(–79·78 to –50·43)
0·9
(0·5 to 1·3)
0·3
(0·2 to 0·4)
–70·6
(–80·17 to –51·17)
Other neonatal disorders 577·6
(457·5 to 756·0)
276·2
(219·6 to 350·7)
–52·3
(–62·41 to –36·77)
7·9
(6·3 to 10·3)
3·7
(3·0 to 4·7)
–53·0
(–63·01 to –37·68)
Nutritional defi ciencies 757·7
(641·5 to 934·4)
681·1
(533·5 to 795·5)
–9·7
(–22·87 to 1·86)
14·7
(12·4 to 18·2)
10·4
(8·2 to 12·2)
–28·6
(–38·77 to –20·45)
Protein-energy malnutrition 507·9
(394·3 to 648·5)
468·8
(350·0 to 560·9)
–7·3
(–22·92 to 7·51)
9·2
(7·1 to 11·8)
7·1
(5·3 to 8·5)
–22·5
(–34·54 to –11·34)
Iodine defi ciency 2·1
(1·4 to 3·4)
2·7
(1·5 to 4·7)
24·5
(–29·82 to 137·21)
0·0
(0·0 to 0·1)
0·0
(0·0 to 0·1)
–7·9
(–47·13 to 80·62)
Iron-defi ciency anemia 213·4
(143·5 to 309·3)
183·4
(122·0 to 259·2)
–13·8
(–32·36 to 5·33)
4·6
(3·3 to 6·6)
2·8
(1·9 to 4·0)
–38·8
(–50·36 to –26·57)
Other nutritional defi ciencies 34·3
(23·8 to 56·1)
26·2
(17·0 to 41·2)
–22·5
(–42·66 to –4·64)
0·8
(0·5 to 1·3)
0·4
(0·3 to 0·7)
–45·4
(–56·40 to –33·85)
Other communicable, maternal,
neonatal, and nutritional diseases
536·5
(433·0 to 674·9)
408·4
(342·1 to 488·3)
–23·8
(–33·94 to –11·53)
9·4
(7·9 to 11·4)
5·9
(5·0 to 7·0)
–37·4
(–44·55 to –28·28)
Sexually transmitted diseases
excluding HIV
257·6
(154·7 to 396·4)
142·0
(87·6 to 213·9)
–44·5
(–55·96 to –32·77)
3·8
(2·4 to 5·7)
1·9
(1·2 to 2·9)
–48·4
(–58·54 to –37·36)
Syphilis 250·6
(147·4 to 389·1)
136·8
(82·4 to 208·9)
–45·1
(–56·56 to –33·02)
3·6
(2·2 to 5·5)
1·9
(1·1 to 2·9)
–48·0
(–58·61 to –36·83)
Chlamydial infection 1·5
(1·1 to 1·9)
1·1
(0·9 to 1·4)
–23·0
(–42·59 to 0·03)
0·0
(0·0 to 0·0)
0·0
(0·0 to 0·0)
–51·1
(–64·66 to –34·62)
Gonococcal infection 3·2
(2·5 to 3·8)
2·3
(2·0 to 2·9)
–26·6
(–41·91 to –6·75)
0·1
(0·1 to 0·1)
0·0
(0·0 to 0·0)
–54·0
(–64·57 to –39·74)
Other sexually transmitted diseases 2·4
(1·9 to 2·8)
1·7
(1·5 to 2·1)
–27·3
(–42·08 to –10·71)
0·1
(0·0 to 0·1)
0·0
(0·0 to 0·0)
–53·2
(–63·49 to –40·60)
Hepatitis 162·0
(152·8 to 171·4)
136·7
(123·7 to 163·2)
–16·8
(–25·24 to 2·96)
3·3
(3·1 to 3·5)
2·0
(1·8 to 2·4)
–39·6
(–45·37 to –25·80)
Hepatitis A 22·6
(7·9 to 40·2)
14·9
(5·0 to 27·7)
–36·3
(–50·41 to –2·45)
0·3
(0·1 to 0·6)
0·2
(0·1 to 0·4)
–41·9
(–54·39 to –12·04)
Hepatitis B 85·0
(65·1 to 104·0)
68·6
(52·0 to 86·6)
–19·7
(–28·94 to –4·23)
1·9
(1·5 to 2·2)
1·1
(0·8 to 1·3)
–44·6
(–50·38 to –35·37)
Hepatitis C 2·3
(0·5 to 5·3)
3·5
(0·7 to 8·2)
51·0
(24·40 to 86·56)
0·1
(0·0 to 0·1)
0·1
(0·0 to 0·1)
–4·2
(–20·86 to 17·67)
Hepatitis E 52·1
(39·0 to 67·2)
49·7
(36·1 to 67·5)
–6·1
(–17·83 to 20·41)
1·0
(0·7 to 1·3)
0·7
(0·5 to 1·0)
–30·8
(–39·35 to –11·37)
Other infectious diseases 116·9
(94·0 to 144·3)
129·8
(89·6 to 164·9)
11·3
(–21·67 to 45·00)
2·4
(1·9 to 3·0)
2·0
(1·4 to 2·5)
–17·7
(–42·92 to 7·31)
Non-communicable diseases 26 993·5
(26 298·1 to
27 639·1)
38 267·2
(37 202·2 to 39 417·6)
41·7
(36·86 to 47·00)
782·5
(765·5 to 798·2)
637·5
(620·4 to 655·7)
–18·6
(–21·08 to –15·78)
Neoplasms 5659·7
(5440·4 to 5826·6)
8235·7
(7941·4 to 8538·9)
45·6
(39·82 to 51·55)
157·0
(151·3 to 161·5)
133·8
(128·9 to 138·6)
–14·7
(–17·91 to –11·49)
Oesophageal cancer 313·1
(275·0 to 351·5)
440·2
(389·2 to 516·8)
39·9
(26·42 to 56·36)
8·8
(7·8 to 9·9)
7·2
(6·3 to 8·4)
–19·3
(–27·13 to –9·94)
Stomach cancer 763·4
(725·6 to 803·2)
841·0
(791·6 to 894·1)
10·1
(3·94 to 17·43)
21·7
(20·6 to 22·9)
13·8
(13·0 to 14·7)
–36·3
(–39·82 to –32·14)
(Table 2 continues on next page)
Articles
18
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All ages deaths (thousands) Age-standardised death rate (per 100 000)
1990 2013 Median % change 1990 2013 Median % change
(Continued from previous page)
Liver cancer 510·1
(474·9 to 543·1)
818·0
(763·7 to 879·0)
60·3
(46·21 to 75·28)
13·7
(12·7 to 14·5)
13·0
(12·1 to 13·9)
–5·0
(–13·01 to 3·61)
Liver cancer secondary to hepatitis B 198·4
(181·7 to 215·4)
300·0
(272·0 to 329·2)
51·6
(33·51 to 72·02)
5·1
(4·7 to 5·5)
4·6
(4·2 to 5·1)
–9·1
(–19·63 to 2·69)
Liver cancer secondary to hepatitis C 87·4
(79·5 to 94·7)
342·5
(317·1 to 375·3)
290·8 (251·46 to
342·52)
2·4
(2·2 to 2·6)
5·5
(5·1 to 6·0)
125·4
(103·82 to 154·85)
Liver cancer secondary to alcohol use 122·8
(113·8 to 132·5)
92·2
(84·8 to 100·3)
–25·2 (–31·20 to
–17·09)
3·5
(3·2 to 3·7)
1·5
(1·4 to 1·6)
–56·4
(–59·87 to –51·82)
Other liver cancer 101·5
(92·8 to 110·0)
83·3
(75·1 to 92·2)
–17·5
(–28·57 to –7·02)
2·6
(2·4 to 2·9)
1·3
(1·2 to 1·4)
–50·5
(–56·97 to –44·23)
Larynx cancer 76·2
(62·7 to 88·1)
87·6
(74·3 to 106·2)
14·2
(7·25 to 27·55)
2·1
(1·7 to 2·4)
1·4
(1·2 to 1·7)
–33·4
(–37·26 to –26·29)
Tracheal, bronchus, and lung cancer 1050·0
(1010·6 to 1078·2)
1639·6
(1565·6 to 1706·0)
56·5
(47·83 to 62·84)
29·6
(28·5 to 30·4)
27·0
(25·7 to 28·1)
–8·7
(–13·62 to –5·16)
Breast cancer 327·3
(289·2 to 366·8)
471·0
(412·0 to 514·2)
44·2
(35·01 to 51·82)
9·0
(7·9 to 10·1)
7·4
(6·4 to 8·1)
–17·5
(–23·42 to –13·38)
Cervical cancer 196·3
(162·9 to 212·4)
235·7
(201·9 to 257·9)
20·1
(10·74 to 30·15)
5·2
(4·3 to 5·6)
3·6
(3·1 to 4·0)
–30·1
(–35·55 to –24·13)
Uterine cancer 45·6
(36·6 to 55·6)
67·7
(53·6 to 79·0)
48·6
(30·64 to 64·39)
1·3
(1·0 to 1·6)
1·1
(0·9 to 1·3)
–15·0
(–24·61 to –6·58)
Prostate cancer 157·1
(124·0 to 193·1)
292·7
(242·2 to 373·9)
82·8
(71·77 to 109·02)
5·1
(4·0 to 6·3)
5·2
(4·3 to 6·6)
–1·0
(–7·17 to 13·05)
Colon and rectum cancer 490·2
(476·1 to 504·5)
771·1
(741·5 to 799·2)
57·4
(51·89 to 62·64)
14·5
(14·1 to 14·9)
12·8
(12·4 to 13·3)
–11·1
(–14·40 to –8·17)
Lip and oral cavity cancer 83·9
(74·0 to 96·2)
135·0
(115·3 to 154·3)
59·7
(46·91 to 78·39)
2·3
(2·0 to 2·6)
2·1
(1·8 to 2·5)
–7·9
(–14·96 to 2·32)
Nasopharynx cancer 53·7
(47·7 to 63·5)
60·5
(54·0 to 69·5)
12·7
(1·19 to 24·74)
1·4
(1·2 to 1·6)
0·9
(0·8 to 1·1)
–32·5
(–39·31 to –25·55)
Other pharynx cancer 48·9
(43·7 to 53·5)
78·6
(67·0 to 86·3)
60·9
(43·26 to 76·41)
1·3
(1·2 to 1·4)
1·2
(1·0 to 1·3)
–7·1
(–16·97 to 2·04)
Gallbladder and biliary tract cancer 115·4
(100·6 to 130·4)
139·5
(120·0 to 155·0)
22·1
(6·85 to 32·36)
3·4
(3·0 to 3·9)
2·3
(2·0 to 2·6)
–31·2
(–39·96 to –25·22)
Pancreatic cancer 186·4
(181·3 to 191·8)
352·4
(339·4 to 364·8)
89·0
(82·43 to 95·46)
5·4
(5·3 to 5·6)
5·9
(5·6 to 6·1)
7·4
(3·63 to 11·04)
Malignant skin melanoma 38·7
(30·1 to 50·9)
56·9
(43·9 to 75·7)
47·6
(31·54 to 57·81)
1·1
(0·8 to 1·4)
0·9
(0·7 to 1·2)
–14·1
(–23·86 to –8·06)
Non-melanoma skin cancer 25·0
(20·2 to 30·0)
39·2
(32·8 to 48·7)
55·0
(43·30 to 76·59)
0·8
(0·6 to 0·9)
0·7
(0·6 to 0·8)
–12·9
(–20·00 to –0·93)
Ovarian cancer 98·9
(93·1 to 106·1)
157·8
(147·5 to 169·5)
59·6
(50·81 to 68·88)
2·7
(2·6 to 2·9)
2·5
(2·3 to 2·7)
–7·8
(–12·97 to –2·56)
Testicular cancer 7·0
(5·4 to 8·2)
8·3
(6·3 to 10·4)
18·4
(8·86 to 35·42)
0·2
(0·1 to 0·2)
0·1
(0·1 to 0·1)
–23·0
(–28·60 to –11·08)
Kidney cancer 77·9
(73·8 to 83·0)
133·8
(126·0 to 141·1)
71·8
(63·06 to 81·02)
2·1
(2·0 to 2·3)
2·2
(2·0 to 2·3)
1·6
(–3·95 to 8·11)
Bladder cancer 130·8
(116·6 to 140·9)
173·9
(156·8 to 192·5)
32·2
(27·35 to 43·06)
4·0
(3·5 to 4·3)
3·0
(2·7 to 3·3)
–25·6
(–28·36 to –20·14)
Brain and nervous system cancers 136·0
(116·6 to 155·2)
203·9
(169·5 to 234·8)
50·4
(34·04 to 60·33)
3·3
(2·8 to 3·8)
3·1
(2·6 to 3·6)
–4·6
(–13·99 to 0·96)
Thyroid cancer 23·8
(20·3 to 26·1)
33·7
(29·6 to 38·2)
41·1
(29·80 to 59·27)
0·7
(0·6 to 0·8)
0·6
(0·5 to 0·6)
–19·6
(–25·50 to –9·09)
Mesothelioma 17·0
(15·2 to 20·3)
33·7
(29·4 to 38·7)
100·5
(69·94 to 115·47)
0·5
(0·4 to 0·6)
0·5
(0·5 to 0·6)
16·2
(–2·42 to 24·92)
Hodgkin lymphoma 33·6
(24·1 to 38·0)
24·2
(22·0 to 31·6)
–33·0
(–40·41 to 15·35)
0·8
(0·5 to 0·8)
0·4
(0·3 to 0·5)
–54·9
(–58·93 to –24·34)
Non-Hodgkin lymphoma 133·6
(116·3 to 158·0)
225·5
(186·3 to 245·8)
72·0
(41·51 to 84·35)
3·5
(3·1 to 4·2)
3·6
(2·9 to 3·9)
6·1
(–14·56 to 13·42)
Multiple myeloma 45·3
(37·4 to 57·4)
79·4
(65·3 to 94·2)
77·1
(53·64 to 90·08)
1·3
(1·1 to 1·7)
1·3
(1·1 to 1·6)
0·4
(–13·32 to 8·27)
(Table 2 continues on next page)
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19
All ages deaths (thousands) Age-standardised death rate (per 100 000)
1990 2013 Median % change 1990 2013 Median % change
(Continued from previous page)
Leukaemia 223·8
(215·1 to 234·2)
265·1
(253·9 to 275·8)
18·7
(11·69 to 24·19)
5·2
(5·0 to 5·4)
4·1
(3·9 to 4·3)
–20·0
(–24·12 to –16·68)
Other neoplasms 250·6
(231·8 to 305·2)
369·6
(328·0 to 400·1)
51·3
(15·57 to 62·10)
6·2
(5·8 to 7·3)
5·8
(5·2 to 6·3)
–4·2
(–23·44 to 3·32)
Cardiovascular diseases 12 279·6
(11 776·6 to 12 764·1)
17 297·5
(16 520·2 to 18 071·9)
40·8
(36·17 to 46·36)
375·5
(360·5 to 389·1)
293·2
(280·4 to 306·1)
–22·0
(–24·50 to –19·07)
Rheumatic heart disease 373·5
(302·5 to 464·6)
275·1
(222·6 to 353·9)
–26·5
(–33·64 to –17·20)
9·8
(7·9 to 12·2)
4·4
(3·5 to 5·6)
–55·4
(–59·47 to –50·11)
Ischaemic heart disease 5737·5
(5254·9 to 6148·6)
8139·9
(7322·9 to 8758·5)
41·7
(35·96 to 48·44)
177·3
(161·8 to 190·2)
137·8
(123·9 to 148·2)
–22·3
(–25·48 to –18·68)
Cerebrovascular disease 4584·8
(4162·1 to 4968·1)
6446·9
(5963·0 to 7155·2)
40·2
(34·43 to 49·56)
141·6
(128·5 to 153·9)
110·1
(101·8 to 122·2)
–22·5
(–25·56 to –17·30)
Ischaemic stroke 21 82·9
(1923·3 to 2430·9)
3272·9
(2812·7 to 3592·6)
50·2
(41·02 to 59·27)
71·3
(63·0 to 79·3)
57·3
(49·3 to 62·9)
–19·6
(–24·52 to –14·97)
Haemorrhagic stroke 2401·9
(2109·4 to 2669·1)
3174·0
(2885·7 to 3719·7)
30·7
(22·23 to 49·07)
70·3
(61·2 to 77·9)
52·8
(48·0 to 62·3)
–25·9
(–30·64 to –14·73)
Hypertensive heart disease 622·1
(525·7 to 783·9)
1068·6
(849·8 to 1 242·2)
74·1
(47·34 to 93·73)
19·3
(16·4 to 24·4)
18·2
(14·5 to 21·3)
–4·5
(–18·86 to 6·41)
Cardiomyopathy and myocarditis 293·9
(243·5 to 346·3)
443·3
(370·1 to 512·0)
51·4
(37·27 to 61·45)
8·2
(6·9 to 9·6)
7·1
(6·0 to 8·3)
–12·6
(–19·98 to –7·68)
Atrial fi brillation and fl utter 28·9
(26·0 to 32·4)
112·2
(97·7 to 126·7)
288·1
(246·32 to 335·03)
1·0
(0·9 to 1·1)
2·0
(1·8 to 2·3)
100·0
(77·55 to 124·90)
Aortic aneurysm 99·6
(82·4 to 118·5)
151·5
(124·2 to 180·0)
52·1
(43·75 to 60·91)
3·0
(2·5 to 3·6)
2·6
(2·1 to 3·1)
–15·3
(–20·06 to –10·50)
Peripheral vascular disease 15·9
(14·4 to 17·5)
40·5
(35·5 to 44·9)
155·3
(126·51 to 178·39)
0·5
(0·5 to 0·6)
0·7
(0·6 to 0·8)
34·1
(18·77 to 46·62)
Endocarditis 45·1
(35·6 to 58·6)
65·0
(48·6 to 79·4)
46·3
(23·88 to 65·52)
1·2
(1·0 to 1·6)
1·0
(0·8 to 1·3)
–12·7
(–25·81 to –2·80)
Other cardiovascular and circulatory
diseases
478·3
(403·9 to 546·4)
554·6
(499·1 to 654·2)
15·2
(9·38 to 32·52)
13·6
(11·5 to 15·5)
9·3
(8·3 to 10·8)
–32·2
(–35·44 to –22·40)
Chronic respiratory diseases 3490·2
(3280·4 to 3795·3)
4267·5
(3996·3 to 4694·2)
21·9
(14·95 to 31·48)
104·5
(98·5 to 113·3)
73·0
(68·4 to 80·2)
–30·4
(–34·19 to –25·05)
Chronic obstructive pulmonary disease 2421·3
(2151·3 to 2632·4)
2931·2
(2626·3 to 3215·8)
21·0
(12·70 to 31·26)
74·8
(66·8 to 81·2)
50·7
(45·4 to 55·6)
–32·3
(–36·75 to –26·54)
Pneumoconiosis 251·2
(184·0 to 317·8)
259·7
(201·7 to 331·2)
1·9
(–15·21 to 40·27)
7·2
(5·3 to 9·0)
4·3
(3·3 to 5·5)
–40·5
(–50·81 to –19·84)
Silicosis 55·4
(36·7 to 77·1)
46·3
(32·1 to 64·8)
–16·0
(–32·85 to 6·14)
1·6
(1·0 to 2·2)
0·8
(0·5 to 1·1)
–50·7
(–61·18 to –38·06)
Asbestosis 21·0
(13·9 to 30·4)
24·1
(17·5 to 32·3)
14·2
(–10·20 to 54·87)
0·6
(0·4 to 0·9)
0·4
(0·3 to 0·5)
–32·6
(–47·35 to –9·61)
Coal workers pneumoconiosis 28·9
(18·2 to 43·9)
25·2
(19·0 to 35·6)
–13·7
(–30·65 to 23·06)
0·8
(0·5 to 1·2)
0·4
(0·3 to 0·6)
–50·2
(–60·26 to –29·13)
Other pneumoconiosis 145·9
(100·3 to 189·2)
164·1
(123·1 to 213·6)
10·9
(–13·48 to 61·23)
4·2
(2·9 to 5·5)
2·7
(2·0 to 3·6)
–35·3
(–50·19 to –6·77)
Asthma 504·3
(399·7 to 731·8)
489·0
(397·7 to 676·8)
–2·9
(–24·58 to 19·21)
13·7
(10·8 to 20·4)
8·0
(6·5 to 11·1)
–41·5
(–55·17 to –28·01)
Interstitial lung disease and pulmonary
sarcoidosis
217·6
(128·7 to 299·4)
471·5
(372·3 to 606·8)
114·1
(53·25 to 214·86)
6·6
(3·9 to 8·9)
8·0
(6·3 to 10·3)
20·1
(–11·89 to 74·20)
Other chronic respiratory diseases 95·8
(78·3 to 113·8)
116·1
(99·2 to 136·9)
21·2
(7·78 to 40·66)
2·2
(1·8 to 2·7)
1·9
(1·6 to 2·3)
–15·1
(–24·22 to –3·39)
Cirrhosis of the liver 838·0
(807·0 to 866·7)
1 221·1
(1 170·3 to 1 284·3)
45·6
(38·47 to 54·52)
21·8
(20·9 to 22·5)
18·8
(18·0 to 19·7)
–13·7
(–17·75 to –8·49)
Cirrhosis of the liver secondary to
hepatitis B
233·9
(220·8 to 250·0)
317·4
(292·3 to 344·6)
35·6
(22·66 to 49·58)
6·1
(5·7 to 6·5)
4·9
(4·5 to 5·3)
–19·3
(–26·86 to –11·11)
Cirrhosis of the liver secondary to
hepatitis C
213·1
(200·4 to 226·7)
357·8
(334·3 to 386·1)
67·3
(54·60 to 83·86)
5·7
(5·4 to 6·0)
5·6
(5·2 to 6·0)
–2·4
(–9·58 to 7·48)
Cirrhosis of the liver secondary to
alcohol use
292·2
(276·5 to 307·1)
383·8
(356·2 to 414·7)
31·2
(20·27 to 43·97)
7·8
(7·4 to 8·2)
5·9
(5·5 to 6·4)
–24·1
(–30·24 to –16·92)
(Table 2 continues on next page)
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All ages deaths (thousands) Age-standardised death rate (per 100 000)
1990 2013 Median % change 1990 2013 Median % change
(Continued from previous page)
Other cirrhosis of the liver 98·8
(92·1 to 106·0)
162·1
(145·5 to 182·9)
63·5
(47·65 to 86·21)
2·2
(2·0 to 2·3)
2·4
(2·1 to 2·7)
9·3
(–1·52 to 24·19)
Digestive diseases 1053·2
(958·6 to 1 131·2)
1168·3
(1 064·4 to 1 275·5)
10·4
(2·11 to 22·38)
28·4
(25·8 to 30·5)
19·0
(17·4 to 20·7)
–33·5
(–38·30 to –26·51)
Peptic ulcer disease 326·9
(288·8 to 388·7)
301·4
(249·8 to 350·5)
–7·6
(–21·26 to 5·52)
9·1
(8·0 to 10·7)
4·9
(4·1 to 5·7)
–45·8
(–53·37 to –38·53)
Gastritis and duodenitis 54·2
(32·1 to 76·3)
59·5
(42·2 to 90·1)
5·8
(–13·46 to 50·38)
1·4
(0·8 to 2·0)
1·0
(0·7 to 1·5)
–35·1
(–46·54 to –6·86)
Appendicitis 87·5
(70·8 to 105·9)
71·9
(51·4 to 89·5)
–17·8
(–34·66 to 2·68)
2·0
(1·6 to 2·4)
1·1
(0·8 to 1·4)
–45·7
(–56·37 to –33·27)
Paralytic ileus and intestinal
obstruction
178·0
(116·3 to 228·5)
235·7
(175·6 to 327·0)
29·4
(11·32 to 68·61)
4·7
(3·1 to 6·1)
3·8
(2·8 to 5·3)
–20·3
(–31·07 to 1·80)
Inguinal, femoral, and abdominal
hernia
50·5
(33·2 to 61·2)
32·5
(25·6 to 48·8)
–41·8
(–49·26 to –4·88)
1·4
(0·9 to 1·7)
0·5
(0·4 to 0·8)
–64·8
(–69·29 to –44·12)
Infl ammatory bowel disease 54·9
(40·4 to 67·2)
51·2
(42·4 to 69·7)
–8·7
(–17·41 to 13·01)
1·4
(1·0 to 1·6)
0·8
(0·7 to 1·1)
–41·8
(–47·30 to –25·30)
Vascular intestinal disorders 52·6
(34·7 to 78·4)
79·5
(51·9 to 113·3)
51·8
(36·03 to 67·16)
1·6
(1·1 to 2·4)
1·4
(0·9 to 1·9)
–16·3
(–24·49 to –8·10)
Gallbladder and biliary diseases 80·5
(68·9 to 94·1)
105·9
(89·1 to 122·3)
32·2
(18·76 to 43·46)
2·4
(2·0 to 2·8)
1·8
(1·5 to 2·1)
–24·5
(–31·76 to –18·21)
Pancreatitis 83·0
(59·2 to 111·3)
122·6
(85·9 to 152·3)
49·1
(16·02 to 76·94)
2·1
(1·5 to 2·9)
1·9
(1·3 to 2·4)
–10·2
(–29·33 to 5·74)
Other digestive diseases 85·2
(73·3 to 99·5)
108·0
(90·4 to 124·2)
27·1
(14·25 to 39·04)
2·3
(2·0 to 2·6)
1·8
(1·5 to 2·0)
–21·9
(–29·04 to –15·13)
Neurological disorders 1017·5
(965·0 to 1 072·3)
1976·8
(1875·0 to 2080·7)
94·0
(83·99 to 106·61)
34·2
(32·3 to 36·1)
35·0
(33·2 to 36·9)
2·3
(–3·27 to 9·08)
Alzheimer’s disease and other
dementias
795·8
(747·9 to 844·3)
1655·1
(1563·5 to 1765·3)
107·2
(94·94 to 124·00)
28·9
(27·1 to 30·7)
29·9
(28·2 to 31·9)
3·2
(–3·01 to 11·61)
Parkinson’s disease 43·7
(38·3 to 55·1)
102·5
(79·3 to 112·6)
139·8
(77·36 to 156·99)
1·5
(1·3 to 1·9)
1·8
(1·4 to 2·0)
28·2
(–6·42 to 37·83)
Epilepsy 111·0
(95·5 to 129·9)
115·8
(93·7 to 132·0)
4·7
(–12·05 to 24·00)
2·2
(1·9 to 2·5)
1·7
(1·3 to 1·9)
–22·4
(–33·73 to –10·21)
Multiple sclerosis 12·4
(9·2 to 19·0)
19·8
(12·8 to 25·5)
64·8
(3·60 to 87·09)
0·3
(0·2 to 0·5)
0·3
(0·2 to 0·4)
–1·1
(–38·78 to 11·91)
Other neurological disorders 54·6
(47·1 to 60·8)
83·7
(71·8 to 90·8)
54·2
(37·88 to 60·72)
1·4
(1·2 to 1·5)
1·3
(1·1 to 1·4)
–3·6
(–10·27 to 0·43)
Mental and substance use disorders 188·3
(157·2 to 242·3)
282·4
(233·9 to 329·3)
51·5
(32·26 to 63·84)
4·3
(3·6 to 5·7)
4·0
(3·3 to 4·7)
–5·7
(–18·55 to 2·10)
Schizophrenia 22·8
(14·7 to 28·3)
16·0
(13·7 to 22·8)
–35·5
(–46·52 to 29·94)
0·6
(0·4 to 0·7)
0·2
(0·2 to 0·3)
–59·7
(–66·47 to –20·07)
Alcohol use disorders 111·9
(84·0 to 165·3)
139·2
(90·2 to 178·5)
28·2
(1·78 to 35·66)
2·7
(2·0 to 3·9)
2·0
(1·3 to 2·6)
–22·0
(–38·53 to –17·54)
Drug use disorders 53·2
(47·8 to 64·1)
126·6
(110·8 to 135·5)
140·4
(101·60 to 159·88)
1·1
(1·0 to 1·3)
1·8
(1·6 to 1·9)
63·0
(36·10 to 74·81)
Opioid use disorders 18·0
(16·5 to 22·2)
50·7
(43·4 to 54·4)
187·3
(134·80 to 210·24)
0·4
(0·3 to 0·5)
0·7
(0·6 to 0·8)
92·7
(56·69 to 107·16)
Cocaine use disorders 2·4
(2·2 to 2·9)
4·3
(3·9 to 4·7)
77·2
(49·11 to 102·26)
0·1
(0·0 to 0·1)
0·1
(0·1 to 0·1)
20·9
(1·97 to 36·34)
Amphetamine use disorders 2·1
(1·9 to 2·5)
3·8
(3·4 to 4·1)
80·4
(52·49 to 106·70)
0·0
(0·0 to 0·1)
0·1
(0·0 to 0·1)
23·2
(4·70 to 40·49)
Other drug use disorders 30·7
(26·1 to 37·3)
67·7
(59·5 to 72·8)
122·8
(86·51 to 146·32)
0·6
(0·6 to 0·8)
1·0
(0·8 to 1·0)
51·6
(26·34 to 65·39)
Eating disorders 0·4
(0·3 to 0·4)
0·6
(0·5 to 0·7)
60·4
(41·17 to 84·68)
0·0
(0·0 to 0·0)
0·0
(0·0 to 0·0)
13·5
(–0·42 to 30·16)
Anorexia nervosa 0·4
(0·3 to 0·4)
0·6
(0·5 to 0·7)
60·4
(41·17 to 84·68)
0·0
(0·0 to 0·0)
0·0
(0·0 to 0·0)
13·5
(–0·42 to 30·16)
(Table 2 continues on next page)
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21
All ages deaths (thousands) Age-standardised death rate (per 100 000)
1990 2013 Median % change 1990 2013 Median % change
(Continued from previous page)
Diabetes, urogenital, blood, and
endocrine diseases
1569·4
(1456·7 to 1709·2)
2955·0
(2764·0 to 3171·1)
88·9
(76·44 to 99·49)
42·2
(39·5 to 44·7)
48·3
(45·1 to 51·4)
14·4
(9·53 to 20·14)
Diabetes mellitus 684·3
(653·2 to 711·5)
1299·4
(1234·5 to 1374·8)
89·7
(81·77 to 99·94)
19·8
(18·9 to 20·6)
21·6
(20·6 to 22·9)
9·0
(4·59 to 14·69)
Acute glomerulonephritis 23·6
(13·7 to 33·4)
18·8
(14·3 to 22·8)
–21·4
(–36·96 to 14·96)
0·5
(0·3 to 0·8)
0·3
(0·2 to 0·3)
–45·8
(–55·70 to –21·57)
Chronic kidney disease 408·6
(363·8 to 433·4)
956·2
(812·9 to 1 034·5)
134·6
(115·70 to 150·20)
11·6
(10·4 to 12·3)
15·8
(13·5 to 17·1)
36·9
(25·43 to 46·11)
Chronic kidney disease due to
diabetes mellitus
46·3
(34·8 to 54·8)
173·1
(139·3 to 208·8)
274·1
(243·06 to 309·05)
1·4
(1·0 to 1·6)
2·9
(2·3 to 3·5)
106·5
(89·67 to 127·74)
Chronic kidney disease due to
hypertension
120·0
(91·6 to 144·8)
275·7
(196·9 to 336·5)
130·6
(106·28 to 151·38)
3·6
(2·8 to 4·4)
4·6
(3·3 to 5·6)
29·4
(15·20 to 40·43)
Chronic kidney disease due to
glomerulonephritis
99·0
(84·5 to 114·7)
116·3
(92·9 to 144·0)
16·9
(1·69 to 35·32)
2·5
(2·1 to 2·9)
1·8
(1·5 to 2·3)
–26·8
(–35·84 to –15·66)
Chronic kidney diseases due to other
causes
143·3
(115·3 to 168·0)
391·2
(297·0 to 452·3)
175·3
(140·05 to 202·16)
4·1
(3·3 to 4·8)
6·5
(4·9 to 7·5)
58·8
(37·59 to 74·71)
Urinary diseases and male infertility
due to other causes
151·4
(129·1 to 161·1)
245·8
(205·5 to 264·7)
62·8
(49·74 to 72·37)
4·3
(3·7 to 4·6)
4·1
(3·4 to 4·4)
–3·7
(–12·86 to 1·66)
Interstitial nephritis and urinary tract
infections
109·9
(92·9 to 118·1)
175·5
(144·6 to 192·0)
58·9
(48·57 to 74·08)
3·1
(2·7 to 3·4)
2·9
(2·4 to 3·2)
–6·3
(–14·36 to 2·19)
Urolithiasis 15·1
(11·1 to 18·7)
14·7
(12·4 to 20·1)
–5·9
(–16·02 to 44·87)
0·4
(0·3 to 0·5)
0·2
(0·2 to 0·3)
–45·3
(–50·97 to –14·32)
Other urinary diseases 26·3
(21·9 to 35·0)
55·5
(40·4 to 66·5)
122·4
(69·54 to 151·57)
0·7
(0·6 to 1·0)
0·9
(0·7 to 1·1)
32·5
(2·75 to 50·04)
Gynaecological diseases 5·3
(3·9 to 7·0)
3·4
(2·9 to 4·8)
–36·7
(–47·76 to –14·33)
0·1
(0·1 to 0·2)
0·1
(0·0 to 0·1)
–59·5
(–67·15 to –45·56)
Uterine fi broids 1·2
(0·9 to 1·7)
0·9
(0·7 to 1·2)
–29·7
(–43·96 to –6·20)
0·0
(0·0 to 0·0)
0·0
(0·0 to 0·0)
–57·8
(–66·54 to –43·72)
Polycystic ovarian syndrome 0·2
(0·1 to 0·3)
0·1
(0·1 to 0·2)
–56·2
(–63·48 to –29·90)
0·0
(0·0 to 0·0)
0·0
(0·0 to 0·0)
–68·5
(–73·81 to –49·56)
Endometriosis 0·5
(0·4 to 0·6)
0·2
(0·2 to 0·4)
–54·7
(–62·11 to –29·37)
0·0
(0·0 to 0·0)
0·0
(0·0 to 0·0)
–68·9
(–74·05 to –51·25)
Genital prolapse 0·9
(0·7 to 1·2)
0·6
(0·5 to 0·9)
–29·9
(–44·02 to –8·28)
0·0
(0·0 to 0·0)
0·0
(0·0 to 0·0)
–55·7
(–65·45 to –42·31)
Other gynaecological diseases 2·5
(1·8 to 3·2)
1·6
(1·3 to 2·2)
–37·4
(–48·19 to –15·38)
0·1
(0·0 to 0·1)
0·0
(0·0 to 0·0)
–59·7
(–67·23 to –46·18)
Haemoglobinopathies and haemolytic
anaemias
182·7
(91·5 to 302·5)
240·4
(106·0 to 456·1)
20·6
(–1·35 to 92·91)
3·1
(1·6 to 5·0)
3·4
(1·5 to 6·3)
0·4
(–19·05 to 51·95)
Thalassaemias 36·2
(21·2 to 49·4)
24·8
(16·9 to 32·1)
–36·4
(–46·90 to 14·05)
0·6
(0·3 to 0·8)
0·3
(0·2 to 0·4)
–43·7
(–52·63 to –2·83)
Sickle cell disorders 112·9
(39·4 to 222·6)
176·2
(56·3 to 385·7)
45·3
(14·30 to 131·19)
1·8
(0·6 to 3·4)
2·4
(0·8 to 5·3)
28·8
(1·53 to 94·35)
G6PD defi ciency 3·4
(2·0 to 4·6)
4·1
(2·6 to 5·6)
12·7
(–7·12 to 91·90)
0·1
(0·0 to 0·1)
0·1
(0·0 to 0·1)
–8·2
(–23·19 to 45·12)
Other haemoglobinopathies and
haemolytic anaemias
30·3
(19·8 to 38·6)
35·4
(24·3 to 44·9)
15·1
(3·42 to 46·66)
0·8
(0·5 to 1·0)
0·6
(0·4 to 0·7)
–25·8
(–31·66 to –9·80)
Endocrine, metabolic, blood, and
immune disorders
113·5
(90·6 to 134·4)
191·0
(150·5 to 218·5)
69·5
(49·11 to 85·62)
2·7
(2·2 to 3·2)
3·0
(2·4 to 3·4)
11·6
(0·55 to 20·87)
Musculoskeletal disorders 65·9
(55·0 to 73·4)
116·3
(100·4 to 137·7)
77·1
(54·42 to 105·22)
1·7
(1·5 to 1·9)
1·9
(1·6 to 2·2)
8·5
(–4·62 to 23·48)
Rheumatoid arthritis 27·8
(23·8 to 31·7)
38·1
(33·0 to 46·9)
35·6
(16·43 to 64·74)
0·8
(0·7 to 0·9)
0·6
(0·5 to 0·8)
–20·5
(–31·22 to –4·53)
Other musculoskeletal disorders 38·1
(30·3 to 44·1)
78·2
(65·2 to 90·3)
107·2
(76·94 to 145·59)
0·9
(0·8 to 1·1)
1·2
(1·0 to 1·4)
32·7
(14·17 to 51·37)
Other non-communicable diseases 831·7
(690·7 to 1 061·0)
746·6
(674·1 to 846·2)
–7·6
(–25·31 to 4·05)
12·9
(10·9 to 16·1)
10·5
(9·5 to 11·9)
–16·4
(–30·83 to –7·20)
(Table 2 continues on next page)
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22
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All ages deaths (thousands) Age-standardised death rate (per 100 000)
1990 2013 Median % change 1990 2013 Median % change
(Continued from previous page)
Congenital anomalies 750·6
(611·8 to 969·7)
632·1
(561·3 to 730·3)
–13·5
(–30·40 to –0·80)
11·0
(9·0 to 14·1)
8·7
(7·7 to 10·0)
–18·9
(–34·07 to –7·54)
Neural tube defects 151·3
(109·1 to 256·3)
68·9
(40·9 to 124·4)
–54·8
(–65·35 to –42·39)
2·1
(1·5 to 3·6)
0·9
(0·6 to 1·7)
–56·6
(–66·71 to –44·61)
Congenital heart anomalies 366·2
(293·0 to 485·2)
323·4
(288·3 to 372·6)
–9·8
(–29·83 to 7·37)
5·4
(4·4 to 7·0)
4·5
(4·0 to 5·1)
–15·8
(–33·64 to –0·61)
Orofacial clefts 7·6
(4·2 to 11·5)
3·3
(1·9 to 5·2)
–57·8
(–68·83 to –33·93)
0·1
(0·1 to 0·2)
0·0
(0·0 to 0·1)
–58·5
(–69·40 to –35·04)
Down’s syndrome 42·5
(16·4 to 73·0)
36·4
(20·4 to 52·4)
–12·9
(–39·13 to 40·14)
0·7
(0·3 to 1·1)
0·5
(0·3 to 0·7)
–22·6
(–45·11 to 19·89)
Chromosomal unbalanced
rearrangements
19·1
(8·5 to 41·2)
17·3
(10·6 to 26·8)
5·5
(–40·18 to 35·89)
0·3
(0·1 to 0·6)
0·2
(0·1 to 0·4)
–3·0
(–43·13 to 25·13)
Other congenital anomalies 163·9
(119·9 to 301·9)
182·8
(150·2 to 261·9)
18·0
(–16·45 to 40·74)
2·4
(1·8 to 4·4)
2·5
(2·1 to 3·6)
8·5
(–20·90 to 29·30)
Skin and subcutaneous diseases 59·1
(54·1 to 63·1)
99·4
(89·9 to 107·3)
68·3
(51·36 to 86·44)
1·6
(1·5 to 1·7)
1·6
(1·5 to 1·8)
2·5
(–7·66 to 10·83)
Cellulitis 27·0
(19·0 to 34·1)
29·5
(22·5 to 39·3)
8·0
(–5·23 to 30·03)
0·7
(0·5 to 0·8)
0·5
(0·4 to 0·6)
–29·8
(–38·47 to –16·25)
Pyoderma 16·7
(11·6 to 23·1)
37·7
(30·2 to 42·9)
129·9
(71·83 to 186·99)
0·4
(0·3 to 0·6)
0·6
(0·5 to 0·7)
44·0
(14·02 to 75·43)
Decubitus ulcer 13·7
(11·5 to 16·9)
28·5
(24·4 to 33·0)
109·6
(82·53 to 132·42)
0·5
(0·4 to 0·6)
0·5
(0·4 to 0·6)
8·3
(–4·81 to 19·09)
Other skin and subcutaneous
diseases
1·7
(1·2 to 2·2)
3·8
(3·0 to 4·9)
138·4
(74·18 to 201·77)
0·0
(0·0 to 0·1)
0·1
(0·0 to 0·1)
35·9
(4·37 to 69·36)
Sudden infant death syndrome 22·0
(12·7 to 40·2)
15·1
(9·6 to 21·4)
–29·5
(–54·51 to –4·22)
0·3
(0·2 to 0·6)
0·2
(0·1 to 0·3)
–31·0
(–55·52 to –6·34)
Injuries 4325·8
(4095·7 to 4524·0)
4786·9
(4507·9 to 5072·8)
10·4
(4·23 to 18·04)
88·5
(84·5 to 93·0)
70·0
(65·9 to 74·2)
–21·0
(–25·27 to –15·85)
Transport injuries 1150·0
(1092·9 to 1253·9)
1482·7
(1364·8 to 1588·8)
29·3
(17·11 to 38·66)
23·2
(22·1 to 25·1)
21·2
(19·5 to 22·7)
–8·4
(–16·54 to –2·02)
Road injuries 1058·4
(1005·0 to 1167·2)
1395·8
(1286·1 to 1492·7)
32·4
(19·21 to 41·84)
21·3
(20·3 to 23·4)
20·0
(18·4 to 21·3)
–6·1
(–15·12 to 0·28)
Pedestrian road injuries 389·7
(339·6 to 472·0)
543·8
(452·0 to 629·1)
40·2
(17·55 to 58·63)
8·0
(7·0 to 9·6)
8·0
(6·7 to 9·2)
0·1
(–15·84 to 12·51)
Cyclist road injuries 68·6
(58·5 to 83·3)
90·6
(74·2 to 106·6)
32·0
(13·51 to 51·06)
1·4
(1·2 to 1·7)
1·3
(1·1 to 1·6)
–7·5
(–20·14 to 5·51)
Motorcyclist road injuries 202·0
(169·3 to 236·4)
248·5
(200·9 to 294·4)
22·8
(7·72 to 40·45)
3·9
(3·2 to 4·5)
3·4
(2·8 to 4·0)
–12·2
(–22·77 to 0·56)
Motor vehicle road injuries 364·4
(324·4 to 409·1)
492·7
(431·0 to 555·1)
35·5
(24·24 to 46·49)
7·3
(6·5 to 8·2)
6·9
(6·1 to 7·8)
–4·9
(–12·90 to 2·69)
Other road injuries 33·8
(21·9 to 44·1)
20·2
(14·4 to 25·5)
–40·7
(–54·01 to –7·44)
0·7
(0·4 to 0·9)
0·3
(0·2 to 0·4)
–57·3
(–67·23 to –34·33)
Other transport injuries 91·6
(77·4 to 102·0)
86·9
(72·0 to 97·2)
–5·3
(–18·27 to 10·72)
1·9
(1·6 to 2·1)
1·2
(1·0 to 1·4)
–33·6
(–42·61 to –22·72)
Unintentional injuries other than
transport injuries
2017·2
(1848·6 to 2 165·7)
2006·7
(1857·1 to 2 183·1)
–1·0
(–8·96 to 10·95)
40·8
(38·2 to 43·8)
30·4
(28·0 to 32·9)
–25·7
(–31·89 to –18·23)
Falls 340·5
(311·7 to 411·5)
556·4
(448·5 to 610·7)
66·7
(21·25 to 82·98)
9·0
(8·2 to 10·8)
9·1
(7·3 to 9·9)
3·4
(–23·97 to 11·61)
Drowning 544·9
(409·2 to 635·8)
368·1
(311·0 to 515·4)
–35·0
(–43·35 to 17·98)
9·4
(7·2 to 10·8)
5·2
(4·4 to 7·3)
–46·3
(–52·33 to –6·00)
Fire, heat, and hot substances 299·6
(262·7 to 352·9)
237·5
(199·3 to 282·9)
–21·2
(–33·96 to –1·98)
5·9
(5·2 to 7·0)
3·5
(2·9 to 4·1)
–41·9
(–50·85 to –29·74)
Poisonings 120·2
(104·1 to 168·5)
98·0
(70·2 to 110·8)
–12·7
(–44·83 to –0·08)
2·4
(2·1 to 3·3)
1·4
(1·0 to 1·6)
–36·0
(–58·97 to –27·57)
Exposure to mechanical forces 232·7
(186·3 to 273·9)
196·8
(177·9 to 244·6)
–15·6
(–31·65 to 18·68)
4·1
(3·4 to 4·8)
2·8
(2·5 to 3·5)
–32·6
(–43·48 to –9·05)
(Table 2 continues on next page)
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23
while that of other haemoglobinopathies fell (table 2).
The residual category of endocrine, metabolic, blood, and
immune disorders increased by 11·6% (table 3).
Age-standardised death rates for injuries and most
specifi c causes of injury also fell between 1990 and 2013,
but typically much less than for diseases (table 2).
Age-standardised death rates for transport injury
decreased since 1990, with most deaths from road injuries.
The number of deaths from unintentional injuries as a
whole remained essentially unchanged since 1990,
although age-standardised death rates fell by more than a
quarter (table 2). Especially large falls occurred for
drowning, fi res, unintentional suff ocation, and venomous
animal contact (table 2). Age-standardised death rates also
fell for self-harm and interpersonal violence (table 2).
Among injuries, only falls, foreign body in other body
part, adverse eff ects of medical treatment, and pedestrian
road injuries had increased age-standardised death rates,
but these were not statistically signifi cant.
For only three level 2 causes did the age-standardised
death rates increase: neurological disorders; diabetes,
urogenital, blood, and endocrine diseases; and
musculoskeletal disorders (fi gure 8). Increases in the
musculoskeletal disorders were driven by the category
other musculoskeletal disorders; causes with high
number of death in this category were systemic lupus
All ages deaths (thousands) Age-standardised death rate (per 100 000)
1990 2013 Median % change 1990 2013 Median % change
(Continued from previous page)
Unintentional fi rearm injuries 51·4
(44·5 to 60·0)
47·3
(41·0 to 55·7)
–7·6
(–26·30 to 13·15)
1·0
(0·9 to 1·2)
0·7
(0·6 to 0·8)
–32·8
(–46·94 to –18·02)
Unintentional suff ocation 84·0
(26·9 to 124·6)
37·6
(29·1 to 80·7)
–58·3
(–74·65 to 90·29)
1·2
(0·4 to 1·8)
0·5
(0·4 to 1·1)
–59·7
(–75·13 to 71·19)
Other exposure to mechanical forces 97·2
(88·8 to 119·9)
111·9
(95·5 to 122·4)
16·6
(–13·64 to 30·93)
1·9
(1·7 to 2·3)
1·6
(1·4 to 1·7)
–14·7
(–36·50 to –5·26)
Adverse eff ects of medical treatment 93·5
(77·2 to 110·2)
141·7
(107·5 to 165·9)
53·2
(27·88 to 73·38)
2·1
(1·8 to 2·6)
2·2
(1·7 to 2·6)
4·6
(–11·30 to 16·34)
Animal contact 95·5
(59·8 to 126·6)
79·6
(62·3 to 138·7)
–22·0
(–35·47 to 34·57)
1·9
(1·2 to 2·5)
1·2
(0·9 to 2·0)
–43·0
(–52·23 to –2·49)
Venomous animal contact 76·3
(47·5 to 104·7)
57·2
(44·1 to 102·5)
–30·1
(–43·14 to 28·05)
1·5
(0·9 to 2·0)
0·8
(0·6 to 1·5)
–49·3
(–58·49 to –6·80)
Non-venomous animal contact 19·2
(11·5 to 26·3)
22·4
(16·4 to 36·8)
11·2
(–9·33 to 73·85)
0·4
(0·2 to 0·5)
0·3
(0·2 to 0·6)
–16·9
(–31·27 to 25·18)
Foreign body 142·2
(99·5 to 211·6)
165·7
(114·8 to 219·1)
16·2
(–10·22 to 45·91)
2·9
(2·1 to 4·3)
2·6
(1·8 to 3·4)
–10·2
(–29·81 to 5·96)
Pulmonary aspiration and foreign
body in airway
139·8
(97·2 to 209·7)
162·1
(109·8 to 214·7)
15·5
(–11·10 to 45·78)
2·9
(2·0 to 4·2)
2·5
(1·7 to 3·4)
–10·6
(–30·35 to 5·87)
Foreign body in other body part 2·5
(1·6 to 3·5)
3·6
(2·7 to 5·4)
51·5
(–3·11 to 84·33)
0·1
(0·0 to 0·1)
0·1
(0·0 to 0·1)
6·8
(–25·59 to 31·38)
Other unintentional injuries 148·1
(119·9 to 162·8)
162·8
(143·9 to 180·3)
9·3
(–4·23 to 33·11)
3·1
(2·5 to 3·4)
2·4
(2·1 to 2·7)
–21·1
(–30·53 to –4·99)
Self-harm and interpersonal violence 1052·8
(929·3 to 1152·0)
1247·1
(1067·2 to 1390·9)
18·2
(8·38 to 29·00)
22·4
(19·8 to 24·6)
17·8
(15·3 to 19·8)
–20·8
(–27·32 to –13·83)
Self-harm 712·0
(630·6 to 784·7)
842·4
(718·1 to 939·0)
17·8
(6·04 to 32·20)
15·8
(13·9 to 17·3)
12·2
(10·4 to 13·6)
–23·1
(–30·45 to –13·97)
Interpersonal violence 340·7
(253·9 to 415·1)
404·7
(298·7 to 496·6)
18·4
(10·24 to 29·34)
6·6
(4·9 to 8·1)
5·6
(4·1 to 6·9)
–16·0
(–21·65 to –8·41)
Assault by fi rearm 127·6
(89·9 to 165·1)
180·4
(120·5 to 231·3)
41·3
(26·05 to 55·53)
2·5
(1·7 to 3·2)
2·5
(1·6 to 3·2)
0·0
(–10·58 to 10·11)
Assault by sharp object 94·0
(65·1 to 127·8)
114·3
(77·1 to 163·2)
21·0
(6·27 to 40·68)
1·8
(1·3 to 2·5)
1·6
(1·1 to 2·2)
–15·2
(–25·63 to –1·29)
Assault by other means 119·1
(84·4 to 142·9)
110·0
(78·7 to 142·1)
–8·5
(–16·48 to 7·97)
2·3
(1·6 to 2·8)
1·5
(1·1 to 2·0)
–33·9
(–39·44 to –22·31)
Forces of nature, war, and legal
intervention
105·8
(77·2 to 170·7)
50·4
(34·4 to 88·8)
–53·1
(–58·41 to –46·15)
2·2
(1·5 to 3·6)
0·7
(0·5 to 1·3)
–66·2
(–70·18 to –61·18)
Exposure to forces of nature 33·4
(19·4 to 63·5)
19·2
(13·5 to 32·0)
–43·9
(–58·70 to –6·92)
0·7
(0·4 to 1·4)
0·3
(0·2 to 0·5)
–60·4
(–71·50 to –33·98)
Collective violence and legal
intervention
72·4
(54·7 to 106·5)
31·2
(20·3 to 57·0)
–57·9
(–66·76 to –45·85)
1·5
(1·1 to 2·2)
0·5
(0·3 to 0·8)
–69·3
(–75·76 to –61·23)
Data in parentheses are 95% uncertainty intervals.
Table 2: Global deaths for 235 causes in 1990 and 2013 for all ages and both sexes combined and age-standardised death rates
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24
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Neonates age <1 month Children age 1–59 months
1990 (thousands) 2013 (thousands) Median % change 1990 (thousands) 2013 (thousands) Median % change
All causes 4506·8
(4394·5 to
4612·8)
2614·3
(2506·4 to
2723·2)
–42·0
(–44·5 to –39·5)
7608·5
(7447·7 to 7757·3)
3665·7
(3449·4 to
3905·8)
–52·0
(–54·7 to –48·6)
Communicable, maternal,
neonatal, and nutritional
diseases
4063·8
(3935·0 to 4181·4)
2275·5
(2163·6 to
2374·6)
–44·0
(–46·6 to –41·4)
6012·6
(5724·0 to 6256·5)
2766·2
(2528·6 to 3010·5)
–54·0
(–57·5 to –50·4)
HIV/AIDS ·· ·· ·· 35·2
(32·3 to 38·3)
63·8
(58·5 to 71·6)
81·1
(64·5 to 99·9)
Diarrhoeal diseases 124·8
(110·5 to 140·5)
44·8
(36·8 to 53·3)
–64·2
(–70·6 to –56·7)
1482·4
(1339·7 to 1633·3)
474·9
(398·1 to 545·0)
–68·0
(–72·8 to –62·5)
Intestinal infectious diseases ·· ·· ·· 82·0
(46·7 to 135·2)
62·4
(33·0 to 103·6)
–24·2
(–37·2 to –8·1)
Lower respiratory infections 399·3
(362·2 to 436·9)
196·5
(169·3 to 224·5)
–50·9
(–57·3 to –43·7)
1768·8
(1597·7 to 1926·6)
708·6
(628·6 to 791·4)
–59·9
(–64·8 to –54·5)
Meningitis 35·6
(27·7 to 47·0)
20·6
(14·9 to 26·8)
–42·6
(–52·7 to –28·4)
262·5
(212·1 to 346·3)
121·4
(90·2 to 157·0)
–54·0
(–63·4 to –41·5)
Whooping cough ·· ·· ·· 129·8
(49·9 to 280·7)
56·4
(20·7 to 127·0)
–57·1
(–83·9 to 12·7)
Tetanus 216·9
(174·8 to 370·5)
26·0
(12·3 to 38·9)
–87·0
(–95·8 to –80·1)
65·4
(48·2 to 121·6)
5·5
(3·9 to 7·7)
–91·2
(–95·5 to –86·9)
Measles ·· ·· ·· 472·4
(265·3 to 749·9)
82·1
(41·7 to 145·0)
–83·1
(–90·3 to –68·5)
Malaria 18·1
(13·4 to 23·5)
16·8
(11·0 to 26·4)
–9·6
(–42·6 to 52·8)
566·3
(470·8 to 662·0)
570·0
(437·5 to 733·2)
–1·3
(–27·7 to 40·9)
Preterm birth complications 1452·1
(1190·6 to 1677·3)
693·0
(553·6 to 853·9)
–52·5
(–58·6 to –45·0)
118·4
(80·1 to 157·3)
49·4
(34·5 to 69·8)
–58·3
(–69·4 to –41·3)
Neonatal encephalopathy
(birth asphyxia/trauma)
820·8
(651·3 to 993·1)
611·5
(491·9 to 724·0)
–25·3
(–38·1 to –9·8)
53·3
(35·5 to 72·6)
32·3
(21·4 to 48·7)
–40·6
(–58·2 to –9·0)
Neonatal sepsis and other
neonatal infections
328·3
(186·3 to 462·1)
342·2
(214·9 to 479·3)
4·6
(–17·3 to 38·5)
18·1
(9·2 to 28·3)
23·8
(13·3 to 38·0)
30·3
(–15·9 to 114·6)
Other neonatal disorders 489·9
(381·6 to 654·9)
238·2
(187·7 to 297·0)
–51·3
(–62·5 to –35·0)
87·7
(58·5 to 123·5)
38·1
(25·0 to 59·8)
–57·4
(–73·9 to –27·8)
Nutritional defi ciencies ·· ·· ·· 451·6
(376·5 to 560·5)
260·7
(197·9 to 316·6)
–42·3
(–52·4 to –30·1)
Syphilis 122·6
(68·9 to 194·3)
63·7
(37·0 to 98·4)
–47·9
(–58·6 to –35·5)
100·9
(56·9 to 157·6)
56·9
(32·5 to 90·9)
–43·3
(–56·8 to –28·5)
Other communicable diseases 55·3
(37·9 to 79·1)
22·4
(16·4 to 30·7)
–59·0
(–70·6 to –44·7)
317·7
(283·8 to 355·0)
159·9
(134·5 to 192·6)
–49·7
(–58·7 to –39·8)
Non-communicable diseases 366·4
(316·1 to 443·9)
292·3
(258·4 to 349·7)
–19·8
(–32·4 to 4·3)
906·4
(766·4 to 1138·2)
578·8
(462·6 to 739·5)
–36·6
(–43·4 to –23·6)
Congenital anomalies 303·6
(256·8 to 375·4)
246·6
(219·0 to 280·0)
–17·6
(–33·1 to –0·1)
343·6
(255·5 to 493·5)
248·7
(198·8 to 322·9)
–25·7
(–40·6 to –13·7)
Sudden infant death
syndrome
3·0
(1·2 to 6·1)
2·4
(1·1 to 4·5)
–13·8
(–60·1 to 54·0)
19·1
(11·2 to 34·3)
12·7
(8·4 to 18·4)
–32·2
(–55·5 to –4·9)
Other non-communicable
diseases
59·8
(52·3 to 70·5)
43·3
(32·4 to 76·9)
–31·3
(–43·3 to 18·7)
543·7
(469·1 to 642·8)
317·4
(244·2 to 422·8)
–42·8
(–52·6 to –23·3)
Injuries 76·6
(58·5 to 95·1)
46·4
(37·5 to 63·5)
–41·7
(–53·4 to –2·9)
689·5
(567·1 to 776·3)
320·7
(277·8 to 371·5)
–54·6
(–61·1 to –41·9)
Road injuries 4·1
(3·3 to 5·1)
3·8
(2·6 to 5·3)
–6·6
(–30·8 to 30·5)
105·2
(88·7 to 128·2)
64·5
(51·3 to 79·5)
–38·3
(–52·6 to –23·7)
Drowning 2·7
(1·9 to 3·5)
1·8
(1·1 to 2·8)
–38·2
(–56·7 to 16·9)
212·3
(132·4 to 275·1)
80·1
(61·7 to 111·4)
–63·3
(–74·1 to –20·4)
Other injuries 69·9
(52·2 to 88·2)
40·8
(32·1 to 58·0)
–44·1
(–55·9 to –1·0)
372·0
(280·9 to 424·3)
176·2
(153·1 to 203·7)
–53·7
(–60·8 to –37·6)
Data in parentheses are 95% uncertainty intervals. Shows major causes of death within each level 1 group that accounted for deaths in children.
Table 3: Selected causes of global child deaths in 1990 and 2013
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25
erythematosus, systemic sclerosis (scleroderma),
pyogenic arthritis, and chronic osteomyelitis (data not
shown). The average relative diff erence between
countries (the inter-country Gini coeffi cient) ranged from
0·31 for non-communicable diseases to 0·90 for forces
of nature, war, and legal intervention. Mean diff erences
in the age-standardised rates between countries ranged
from 0·32 for musculoskeletal disorders to 104·87 for
communicable, maternal, neonatal, and nutritional
diseases. Generally, inequality was much greater for
communicable, maternal, neonatal, and nutritional
causes than for non-communicable causes or injuries.
An important exception to that general pattern was war
and disaster, which were extraordinarily unequal across
countries. For neoplasms, chronic respiratory diseases,
and forces of nature, war, and legal intervention the
age-standardised death rate had fallen and the
two convergence metrics improved signifi cantly from
1990 to 2013. For many communicable, maternal,
neonatal, and nutritional causes, age-standardised death
rates and mean absolute diff erences decreased but
relative diff erences increased. For digestive diseases,
unintentional injuries, and other communicable,
maternal, neonatal, and nutritional diseases, death rates
and mean absolute diff erences were falling and relative
diff erence was not signifi cantly diff erent than zero.
Global causes of child death
We divided child causes of death into those occurring in
children younger than age 1 month and those aged
1–59 months (table 3). The number of neonatal deaths
decreased from 4·5 [UI 4·4–4·6] million in 1990, to
2·6 [2·5–2·7] million in 2013, a 42% (40–45) decrease. The
most important cause of neonatal death in 2013 was
neonatal encephalopathy, followed by neonatal sepsis,
congenital anomalies, and lower respiratory infections
(table 3). Causes with more than a 50% reduction in the
number of neonatal deaths include tetanus, diarrhoeal
diseases, lower respiratory infections, other neonatal
disorders, and other communicable diseases.
For children aged 1–59 months, the global number of
deaths fell by 52·0% from 1990 (7·6 [UI 7·4–7·8]
million) to 2013 (3·7 [3·4–3·9] million). Communicable,
neonatal, and nutritional causes accounted for
three-quarters of deaths in 2013, the remainder from
non-communicable diseases and injuries (table 3). For
this age group, two causes each accounted for more
than half a million deaths and collectively accounted for
Figure 8: Measures of convergence for causes of death in 188 countries
All causes
Communicable, maternal, neonatal, and nutritional diseases
HIV/AIDS and tuberculosis
Diarrhoea, lower respiratory, and other common infectious diseases
Neglected tropical diseases and malaria
Maternal disorders
Neonatal disorders
Nutritional deficiencies
Other communicable, maternal, neonatal, and nutritional diseases
Non-communicable diseases
Neoplasms
Cardiovascular diseases
Chronic respiratory diseases
Cirrhosis
Digestive diseases
Neurological disorders
Mental and substance use disorders
Diabetes, urogenital, blood, and endocrine diseases
Musculoskeletal disorders
Other non-communicable diseases
Injuries
Transport injuries
Unintentional injuries
Self-harm and interpersonal violence
Forces of nature, war, and legal intervention
–24·2%
–40·5%
–23·0%
–49·6%
–24·5%
–43·4%
–41·1%
–28·9%
–37·2%
–18·5%
–14·8%
–21·9%
–30·1%
–13·6%
–33·2%
2·5%
–6·4%
14·5%
8·3%
–17·6%
–20·8%
–8·5%
–25·5%
–20·7%
–65·9%
Age-standardised
death rates (% change)
Mean absolute difference in
age-standardised death rate
182·04
104·87
35·91
40·44
9·28
2·88
9·75
8·00
2·96
86·74
18·65
59·97
32·35
6·45
7·31
5·51
1·77
16·67
0·32
2·49
17·31
5·80
9·21
6·06
0·85
2013
–19·40
–42·23
–1·37
–29·80
–4·37
–1·64
–5·07
–1·40
–1·54
2·02
–6·44
7·51
–18·44
–0·08
–3·31
0·24
–0·04
3·99
0·01
0·27
–0·33
0·84
–2·67
0·23
–1·65
Change
(1990–2013)
Change
(1990–2013)
Gini coefficient
0·19
0·54
0·69
0·49
0·78
0·69
0·44
0·59
0·49
0·13
0·15
0·18
0·36
0·30
0·32
0·18
0·44
0·30
0·18
0·24
0·23
0·25
0·28
0·34
0·90
2013
0·024
0·068
0·114
0·027
0·023
0·085
0·068
0·044
0·011
0·021
–0·021
0·044
–0·030
0·028
–0·001
–0·003
0·002
0·026
–0·012
0·071
0·039
0·048
0·001
0·072
–0·038
Significant increase Significant decreaseNo significant increase
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26
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more than a third of deaths: lower respiratory infections,
and malaria (table 3). Four causes accounted for
100 000–500 000 deaths: diarrhoeal disease, meningitis,
congenital anomalies, and nutritional defi ciencies
(table 3). Another seven causes each caused
50
000–100 000 deaths: drowning, syphilis, measles,
whooping cough, intestinal infectious diseases, HIV/
AIDS, and road injuries (table 3). Deaths fell by more
than 50% between 1990, and 2013, for diarrhoeal
diseases, lower respiratory infections, meningitis,
whooping cough, tetanus, measles, preterm birth
complications, drowning, other neonatal disorders, and
other injuries. Of the causes detailed in table 3, only
HIV/AIDS increased signifi cantly from 1990, to 2013,
although from 2005, to 2013, deaths fell from 25·95
(UI 24·54–27·50) per 100 000 to 9·83 (9·01–11·04) per
100 000. In high-income countries, cancers accounted
for 5·86% (5·06–6·66) of deaths for children younger
than 5 years compared with only 1·02% (0·89–1·16%)
in low-income countries.
Figure 9 shows death rates of children for 19 major
cause groups for the 21 GBD regions. Because infants
younger than age 1 month are exposed to, at most,
1 person-month, rates in that group were high compared
with children aged 1–59 months. Across regions, death
rates varied widely for preterm birth complications,
neonatal encephalopathy (birth asphyxia and birth
trauma), other neonatal disorders and jaundice, sepsis,
and lower respiratory infections (fi gure 9). In addition,
lower respiratory infections, HIV/AIDS, congenital
syphilis, malaria, diarrhoeal diseases and congenital
abnormalities had an important contribution. Congenital
anomalies varied by more than fi ve-times from a high in
central sub-Saharan Africa to a low in high-income Asia
Pacifi c. Eastern, central, and western sub-Saharan Africa
had substantially high death rates for children aged
1–59 months compared with other regions including
south Asia (fi gure 9B). These higher rates were largely
related to malaria, diarrhoeal diseases, measles, and
nutritional disorders.
Global YLLs
Between 1990 and 2013, large falls occurred for measles,
meningitis, tetanus, syphilis, and whooping cough
(fi gure 10). Increases of 50% or more are evident for
diabetes, HIV/AIDS, hypertensive heart disease, chronic
kidney disease, Alzheimer’s disease and other dementias,
interstitial lung disease, and pancreatic cancer. Among
the top ten causes in 1990, nine remain in the top ten in
2013, with HIV/AIDS moving in and tuberculosis
moving to 11th. The largest percentage increases in YLLs
were for HIV/AIDS (343·97%, 95% UI 245·48–444·17),
atrial fi brillation and fl utter (211·89%, 182·55–242·63),
peripheral vascular disease (119·79%, 101·04–136·78),
and drug use disorders (119·22%, 83·77–140·02).
Identifi cation of key transition points in the
comparative importance of diff erent causes of premature
mortality will help to better inform programme
evaluation. From 1990 to 2013, worldwide crude YLLs fell
by about 16% (from 2005·5 million to 1685·4 million),
more so for communicable, maternal, neonatal, and
nutritional diseases (39% decrease, 1098·3 million to
667·8 million) compared with non-communicable
diseases (20% increase, 674·6 million to 806·5 million),
and injuries (9% decrease, 232·6 million to
210·8 million). Recent progress with disease control
programmes for HIV/AIDS and malaria is clear, as is the
substantial and steady progress to prevent child deaths
from neonatal disorders, diarrhoeal diseases, and lower
respiratory infections; YLLs from these diseases fell by
40–65% since 1990. The success of vaccination
programmes since 1990 is also evident (fi gure 11), with
YLLs from measles and tetanus, in particular, at very low
levels in 2013. Specifi c trends for major non-
communicable diseases are much less evident, with
incremental decreases for several leading causes of
cancer, as well as from major vascular and chronic
respiratory diseases, contributing to the 30% reduction
in YLLs since 1990 (37 021·0 to 24 493·4 per 100 000).
With the exception of drowning, only modest progress
was made in reducing premature mortality from other
leading causes of injury, with the eff ect of the 1994
genocide in Rwanda clearly visible (fi gure 11).
Causes of diarrhoea and lower respiratory infection
Deaths caused by diarrhoea fell by 51% (46–56) between
1990 and 2013 (table 2). Rotavirus was the main cause of
diarrhoea in children younger than 5 years. It was also
the most common cause of diarrhoea deaths in this age
group in 2013, with a slight decrease in the population
attributable fraction since 1990, followed by cholera,
Cryptosporidium, and shigellosis (table 4). At least 55·6%
of diarrhoea in 2013 was unexplained by these pathogens
in all ages, an increase from 48·1% in 1990 (table 4).
The distribution of Shigella and Aeromonas in patients
had a signifi cant ecological association with sanitation
(data not shown) and along with non-typhoid Salmonella,
deaths from these pathogens fell by 5·4% (28 062 deaths)
since 1990. Rotavirus was the most important pathogen
for children younger than age 5 years in east and
southeast Asia and eastern Europe, with a population
attributable fraction of 35–41% (104–6390); although it
had the lowest population attributable fraction in
high-income north America, central sub-Saharan Africa,
and Caribbean. Shigella was an important pathogen in
north Africa and Middle East and Oceania (causing
19·4% [11·8–29·4] of deaths, 3790 [1976–6381] deaths and
13·9% [9·5–19·3] of deaths, 118·4 [45·4–249·6] deaths,
respectively). Cryptosporidium in sub-Saharan Africa,
cholera in central sub-Saharan Africa, Andean Latin
America, and Oceania, and enterotoxigenic E coli were
important causes of diarrhoea death. Campylobacter did
not have a signifi cant epidemiological relationship with
diarrhoea in most countries and was an important cause
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27
only in some age groups in the GEMS data in India,
Bangladesh, and Mozambique (data not shown).
Clostridium diffi cile caused more than 45% of diarrhoea
deaths in western Europe, high-income north America,
high-income Asia Pacifi c, and Australasia in 2013,
ranging from eight deaths in Australasia to 92 in high-
income North America, and to a lesser extent in central
Europe (20·1% [14·0–27·1] or 16 [14–19] deaths, and
eastern Europe (19·0% [12·6–26·6] or 47 [38–57] deaths).
Cholera caused 45 000 (23 000–68 000) deaths of
children younger than age 5 years, with most deaths
(34 000 [76%]) in central and eastern sub-Saharan Africa
and south and southeast Asia. Cholera was the third
leading cause of diarrhoea deaths for all ages in 2013,
behind rotavirus and shigellosis (table 4).
Clostridium diffi cile was particularly important in adults
in high-income countries, where it caused as many as
95% of diarrhoea deaths in elderly people.
Globally in 2013, pneumococcus was responsible for
the largest number of lower respiratory infection deaths
in children younger than age 5 years—followed by
H infl uenzae type B, respiratory syncytial virus, and
infl uenza—and in people of all ages (table 4). The
fraction of deaths caused by lower respiratory infection
in children younger than age 5 years attributable to
H infl uenzae type B has decreased substantially since
1990, as a result of the global scale-up of H infl uenzae
type B vaccine, with the largest decreases in high-income
regions and Latin America (data not shown), where
vaccine coverage is highest. The fraction of lower
respiratory infection deaths in children younger than
5 years attributable to pneumococcus also fell in high-
income regions such as western Europe, north America
and Australasia, caused by the scale-up of pneumococcal
conjugate vaccine, but continued to account for a large
proportion of such deaths in eastern Europe and
elsewhere. Many lower respiratory infection deaths
attributable to the four pathogens occurred in older
populations.
Country-specifi c probabilities of death during childhood
and young adolescence
We computed conditional probabilities of death for
three phases of life (children and young adolescents,
reproductive age, and middle age) by country and
cause; conditional probabilities are a useful summary
because the values are readily interpretable. The
probability of death in children and adolescents (age
0–14 years) varied greatly between and within regions,
from a low of three per 1000 girls in Iceland to a high of
179 per 1000 boys in Guinea-Bissau (fi gure appendix 1).
In the more demographically advanced regions
(measured by mean age of death, fertility, and mortality
change) the probability of death was below ten per 1000
people for both sexes in all countries except Albania,
Bulgaria, Belarus, Brunei, Moldova, Macedonia,
Romania, Russia, and Ukraine. Causes of death were
Figure 9: Child death rates by region and cause groups in 2013
(A) Of children younger than age 1 month per person-year of exposure. (B) Of children aged 1–59 months per
person-year of exposure. The set of causes is mutually exclusive and collectively exhaustive. SIDS=sudden infant
death syndrome.
Death rate (per 100
000 people)
High-income Asia Pacific
Western Europe
Australasia
High-income North America
Central Europe
Eastern Europe
Southern Latin America
East Asia
Central Latin America
Tropical Latin America
North Africa and Middle East
Southeast Asia
Andean Latin America
Central Asia
Caribbean
Southern sub-Saharan Africa
Oceania
South Asia
Eastern sub-Saharan Africa
Central sub-Saharan Africa
Western sub-Saharan Africa
A
B
Death rate (per 100
000 people)
Other injuries
Drowning
Road injuries
Other non-communicable diseases
SIDS
Congenital anomalies
Other communicable diseases
Nutritional deficiencies
Other neonatal disorders and jaundice
Neonatal sepsis
Neonatal encephalopathy
Preterm birth
Syphilis
Malaria
Measles
Diphtheria/tetanus/pertussis
Meningitis and encephalitis
Lower respiratory infections
Intestinal infectious
Diarrhoeal diseases
HIV/AIDS
0
10
000
20
000
30
000
40
000
0
500
1000
1500
2000
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28
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dominated by congenital anomalies and neonatal
causes. In southeast Asia, Malaysia had the lowest
probability of death and Laos the highest, with
substantial mortality from lower respiratory infections,
diarrhoea, and neonatal disorders. Drowning was an
important cause of death for children in this region. In
1990 mean rank (95% UI) 2013 mean rank (95% UI) Median % change
44·9 (42 to 48) 44 Alzheimer’s disease
28·6 (22 to 33) 28 Asthma
41·3 (38 to 44) 41 Breast cancer
11·5 (11 to 13) 11 COPD
41·3 (37 to 46) 42 Cardiomyopathy
5·1 (5 to 6) 5 Cerebrovascular disease
35·8 (33 to 40) 36 Chronic kidney disease
18·0 (17 to 19) 18 Cirrhosis
37·2 (35 to 39) 38 Colorectal cancer
8·9 (6 to 11) 9 Congenital anomalies
26·8 (25 to 29) 26 Diabetes
2·0 (2 to 2) 2 Diarrhoeal diseases
13·5 (12 to 18) 13 Drowning
49·2 (44 to 59) 49 Encephalitis
59 Encephalitis
49·3 (44 to 54) 50 Epilepsy
52 Epilepsy
45·8 (42 to 49) 45 Oesophageal cancer
32·6 (28 to 36) 31 Falls
25·2 (22 to 29) 25 Fire and heat
28·4 (23 to 34) 27 HIV/AIDS
34·3 (29 to 39) 34 Hypertensive heart disease
23·5 (21 to 30) 23 Interpersonal violence
35·6 (25 to 46) 35 Iron–deficiency anaemia
4·0 (4 to 4) 4 Ischaemic heart disease
38·6 (36 to 41) 39 Leukaemia
28·7 (27 to 32) 29 Liver cancer
1·0 (1 to 1) 1 Lower respiratory infections
19·6 (18 to 21) 20 Lung cancer
8·0 (6 to 10) 8 Malaria
21·4 (20 to 23) 21 Maternal disorders
12·2 (7 to 18) 12 Measles
15·0 (12 to 17) 15 Meningitis
6·4 (5 to 9) 6 Neonatal encephalopathy
3·0 (3 to 4) 3 Preterm birth
17·1 (12 to 25) 17 Neonatal sepsis
31·3 (28 to 35) 30 Other cardiovascular
41·0 (36 to 45) 40 Peptic ulcer disease
48·4 (40 to 54) 47 Poisonings
60 Poisonings
14·5 (12 to 18) 14 Protein–energy malnutrition
43·3 (33 to 53) 43 Pulmonary aspiration
33·2 (29 to 39) 32 Rheumatic heart disease
9·6 (8 to 11) 10 Road injuries
15·9 (14 to 18) 16 Self–harm
46·1 (24 to 71) 46 Sickle cell
24·1 (23 to 26) 24 Stomach cancer
22·7 (16 to 33) 22 Syphilis
18·5 (12 to 21) 19 Tetanus
69 Tetanus
7·5 (6 to 9) 7 Tuberculosis
33·4 (22 to 48) 33 Typhoid fever
49·2 (38 to 79) 48 Unintentional suffocation
77 Unintentional suffocation
37·2 (19 to 61) 37 Whooping cough
58 Whooping cough
29·3 (27 to 31)29 Alzheimer disease 89% (81 to 103)
33·7 (27 to 37)32 Asthma –22% (–35 to –4)
47·1 (42 to 54)47 Brain cancer 27% (10 to 40)
57 Brain cancer
31·9 (30 to 35)30 Breast cancer 37% (28 to 46)
11·3 (10 to 12)12 COPD –1% (–9 to 9)
33·3 (30 to 38)31 Cardiomyopathy 32% (14 to 47)
2·7 (2 to 3)3 Cerebrovascular disease 24% (18 to 32)
46·2 (42 to 54)46 Cervical cancer 14% (4 to 23)
53 Cervical cancer
20·6 (19 to 25)19 Chronic kidney disease 90% (74 to 103)
13·4 (13 to 15)13 Cirrhosis 36% (28 to 45)
27·9 (26 to 30)27 Colorectal cancer 44% (38 to 49)
10·3 (8 to 12)10 Congenital anomalies –18% (–33 to –4)
17·2 (16 to 19)17 Diabetes 67% (59 to 77)
5·5 (4 to 8)4 Diarrhoeal diseases –62% (–66 to –57)
20·7 (16 to 24)20 Drowning –46% (–54 to 3)
48·4 (43 to 54)49 Endocrine, metabolic, blood,
and immune disorders
29% (7 to 49)
58 Endocrine, metabolic, blood,
and immune disorders
37·2 (34 to 40)38 Oesophageal cancer 31% (18 to 48)
28·8 (26 to 33)28 Falls 18% (–14 to 40)
34·5 (30 to 38)34 Fire and heat –35% (–46 to –15)
6·0 (4 to 8)6 HIV/AIDS 344% (245 to 444)
24·5 (20 to 29)24 Hypertensive heart disease 56% (33 to 75)
21·2 (18 to 27)22 Interpersonal violence 10% (2 to 21)
40·8 (36 to 48)40 Interstitial lung disease 86% (26 to 194)
64 Interstitial lung disease
45·2 (36 to 59)45 Iron–deficiency anaemia –37% (–52 to –21)
1·0 (1 to 1)1 Ischaemic heart disease 31% (24 to 41)
38·7 (37 to 41)39 Leukaemia –9% (–16 to –3)
21·1 (19 to 24)21 Liver cancer 42% (26 to 58)
2·3 (2 to 3)2 Lower respiratory infections –48% (–54 to –43)
15·0 (14 to 16)15 Lung cancer 39% (31 to 48)
49·6 (45 to 55)50 Lymphoma 43% (23 to 57)
62 Lymphoma
6·9 (4 to 10)8 Malaria –5% (–26 to 24)
26·1 (24 to 29)26 Maternal disorders –23% (–32 to –12)
43·8 (30 to 62)43 Measles –83% (–90 to –68)
22·9 (19 to 26)23 Meningitis –43% (–53 to –33)
8·7 (6 to 11)9 Neonatal encephalopathy –26% (–38 to –11)
6·3 (4 to 9)7 Preterm birth –53% (–59 to –45)
15·7 (12 to 22)16 Neonatal sepsis 6% (–16 to 38)
33·7 (30 to 37)33 Other cardiovascular –12% (–17 to 4)
44·2 (42 to 48)44 Pancreatic cancer 74% (67 to 80)
66 Pancreatic cancer
43·8 (40 to 51)42 Peptic ulcer disease –20% (–36 to –6)
17·9 (16 to 22)18 Protein–energy malnutrition –28% (–40 to –15)
47·4 (39 to 59)48 Pulmonary aspiration –22% (–40 to 18)
41·9 (37 to 48)41 Rheumatic heart disease –37% (–44 to –26)
5·9 (4 to 8)5 Road injuries 15% (2 to 23)
14·4 (13 to 16)14 Self–harm 9% (–3 to 24)
35·0 (17 to 63)36 Sickle cell 42% (8 to 138)
25·0 (23 to 27)25 Stomach cancer –2% (–9 to 5)
34·8 (25 to 46)35 Syphilis –46% (–57 to –33)
11·1 (10 to 12)11 Tuberculosis –31% (–40 to –24)
35·7 (24 to 52)37 Typhoid fever –13% (–27 to 1)
Group 1
Non-communicable
Injuries
Figure 10: Top 50 causes of global years of life lost in 1990 and 2013
An interactive version of this fi gure is available at http://vizhub.healthdata.org/gbd-compare/. COPD=chronic obstructive pulmonary disease.
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29
central Asia, probabilities of death ranged from 17 per
1000 people in Armenia to 65 per 1000 people in
Turkmenistan, with unusually high contributions from
lower respiratory infections; drowning was also an
important cause. Bahrain, Oman, and United Arab
Emirates had death rates below ten per 1000 for girls,
whereas in Sudan and Yemen, they were more than 40
per 1000. Mortality in these countries was dominated by
congenital and neonatal causes. Pulmonary aspiration
and foreign body in trachea and lung was an unusually
high cause of child death in Bolivia based on the
available vital registration data. In south Asia, the
contribution of drowning in Bangladesh was notable
but mortality was still dominated by neonatal causes,
diarrhoea, and pneumonia in all the countries of the
region. Throughout sub-Saharan Africa, patterns of
causes of death varied but malaria, HIV/AIDS,
tuberculosis, nutritional defi ciencies, and haemo-
globinopathies played an important part in many
countries. Mauritius, Seychelles, and Cape Verde all
had much lower probabilities of death during childhood
than did other countries in this region. Countries with
the highest probabilities of death in central and western
sub-Saharan Africa had important contributions from
malaria. Interpersonal violence was an important
contributor to the probability of death in children and
adolescents in many countries of Latin America and
several in southern Africa.
Country-specifi c probabilities of death during
reproductive age
The probability of death in reproductive-age adults
(exact age 15 years to exact age 50 [35q15]) ranged from
1·2% for women in Andorra to 52% for men in Lesotho
(fi gure appendix 2). In high-income regions, the
probability of death was generally twice as high for men
as for women. Across all countries, transport injuries
made an important contribution, especially in men.
Among men in low-income countries, suicide and
transport accidents made important contributions. In
some countries, such as Norway and USA, drug and
alcohol use disorders account for more than 8% of the
total probability of dying in this age interval for either
sex. In central Europe, interpersonal violence was high
in Albania, drug and alcohol disorders were notable in
Poland, Croatia, Slovakia, and Montenegro, and
cirrhosis was a common cause in Bulgaria, Croatia,
Hungary, Poland, Romania, Slovakia, and Slovenia. In
east Asia, liver cancer was an important cause of death.
In southeast Asia, maternal mortality in Laos,
Cambodia, Myanmar, and Timor-Leste were important
contributors for women. For men in the same region,
interpersonal violence in Philippines, Sri Lanka, and
Thailand were notable; suicide in Sri Lanka also
accounted for a large probability of death (2·2%). Other
studies have suggested that interpersonal violence in
Philippines is concentrated in Mindanao.82
Figure 11: Global years of life lost by large cause groupings for 1990 to 2013
COPD=chronic obstructive pulmonary disease.
Other injuries
Interpersonal violence
Self-harm
Drowning
Road injuries
Other non-communicable
diseases
Congenital anomalies
Diabetes
Cirrhosis
COPD
Other cardiovascular
diseases
Cerebrovascular disease
Ischaemic heart disease
Other cancers
Lung cancer
Other communicable,
maternal, neonatal, and
nutritional disorders
Syphilis
Protein-energy
malnutrition
Neonatal disorders
Maternal disorders
Malaria
Measles
Tetanus
Meningitis
Lower respiratory
infections
Diarrhoeal diseases
HIV/AIDS
Tuberculosis
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
0
5000
10
000
15
000
20
000
25
000
30
000
35
000
40
000
Years of life lost (per 100
000 people)
Year
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Risk of death from transport injuries was greater than
2% for men in Afghanistan, Côte d’Ivoire, Cameroon,
Ecuador, Gabon, Guinea-Bissau, Equatorial Guinea,
Kazakhstan, Mauritania, Nigeria, Oman, Sierra Leone,
El Salvador, Thailand, and Uganda. Probability of death
from cirrhosis in Myanmar was 1·9% for men.
Tuberculosis stood out in Cambodia, Indonesia, Laos,
Myanmar, and Philippines for men. The gap between
the probability of death for men and women was
particularly large in eastern Europe and central Asia.
Mongolia had the highest probability of death in
reproductive age for men in these two regions, as a
result of unusually high probabilities of interpersonal
violence, self-harm, alcohol and drug use, cirrhosis, liver
cancer, and tuberculosis. More generally for men, in
eastern Europe and central Asia, there were major
Children younger than age 5 years All ages
1990 2013 1990 2013 Rate of change
for deaths
1990–2013 (%)
Deaths
(thousands)
Population
attributable
fractions (%)
Deaths
(thousands)
Population
attributable
fractions (%)
Deaths
(thousands)
Population
attributable
fractions (%)
Deaths
(thousands)
Population
attributable
fractions (%)
Diarrhoea
Adenovirus 63·0
(44·4 to 89·9)
3·9
(2·7 to 5·5)
18·3
(11·8 to 25·5)
3·5
(2·4 to 4·9)
81·3
(55·9 to 115·9)
3·2
(2·1 to 4·4)
31·8
(21·5 to 44·7)
2·5
(1·7 to 3·6)
–61·2%
(–67·8 to –52·5)
Aeromonas 12·3
(6·4 to 21·4)
0·8
(0·4 to 1·3)
5·5
(2·6 to 10·1)
1·1
(0·5 to 1·9)
28·0
(16·7 to 44·9)
1·1
(0·7 to 1·7)
13·0
(7·3 to 22·0)
1·0
(0·6 to 1·7)
–53·6%
(–66·1 to –37·8)
Amoebiasis 5·8
(2·6 to 10·0)
0·4
(0·2 to 0·6)
1·3
(0·6 to 2·3)
0·2
(0·1 to 0·5)
18·3
(8·9 to 30·6)
0·7
(0·3 to 1·2)
11·3
(5·0 to 19·7)
0·9
(0·4 to 1·6)
–39·1%
(–57 to –17·5)
Campylobacter enteritis 20·8
(11·0 to 31·9)
1·3
(0·7 to 2·0)
9·5
(3·7 to 15·7)
1·8
(0·7 to 3·0)
28·4
(16·4 to 42·8)
1·1
(0·6 to 1·6)
14·1
(6·9 to 22·4)
1·1
(0·5 to 1·8)
–50·7%
(–63·7 to –37·5)
Cholera 81·7
(39·1 to 117·0)
5·1
(2·5 to 7·2)
45·2
(23·4 to 67·6)
8·7
(4·5 to 12·7)
125·3
(61·1 to 173·4)
4·9
(2·4 to 6·7)
69·9
(37·7 to 97·0)
5·5
(3·0 to 7·7)
–44·3%
(–56·8 to –26·6)
Clostridium diffi cile 2·1
(2·0 to 2·3)
0·1
(0·1 to 0·1)
2·5
(2·4 to 2·7)
0·5
(0·4 to 0·6)
20·8
(19·9 to 21·9)
0·8
(0·7 to 0·9)
41·5
(39·1 to 43·9)
3·3
(3·0 to 3·6)
99·4%
(84·3 to 114·3)
Cryptosporidiosis 92·4
(68·7 to 125·1)
5·8
(4·3 to 7·8)
35·2
(25·9 to 48·2)
6·8
(5·1 to 9·3)
98·8
(72·8 to 132·7)
3·8
(2·8 to 5·2)
41·9
(30·0 to 58·4)
3·3
(2·4 to 4·7)
–57·8%
(–64·8 to –49·6)
Enteropathogenic
Escherichia coli infection
4·3
(1·3 to 7·8)
0·3
(0·1 to 0·5)
1·8
(0·7 to 3·4)
0·3
(0·1 to 0·7)
4·3
(1·3 to 7·8)
0·2
(0·1 to 0·3)
1·8
(0·7 to 3·4)
0·1
(0·1 to 0·3)
–57·5%
(–76·5 to –32)
Enterotoxigenic
Escherichia coli infection
86·0
(61·3 to 114·1)
5·4
(3·8 to 7·2)
23·1
(17·0 to 30·4)
4·4
(3·4 to 5·9)
134·7
(97·5 to 178·2)
5·2
(3·8 to 6·8)
59·2
(44·2 to 77·7)
4·7
(3·5 to 6·1)
–56·0%
(–63·0 to –46·9)
Norovirus 7·3
(2·7 to 11·8)
0·5
(0·2 to 0·7)
1·8
(0·7 to 3·1)
0·3
(0·1 to 0·6)
7·3
(2·7 to 11·8)
0·3
(0·1 to 0·5)
1·8
(0·7 to 3·2)
0·1
(0·1 to 0·2)
–75·8%
(–85·1 to –61·6)
Other Salmonella
infections
19·2
(11·1 to 29·1)
1·2
(0·7 to 1·8)
3·8
(1·6 to 6·7)
0·7
(0·3 to 1·2)
58·9
(42·4 to 77·8)
2·3
(1·6 to 3·0)
24·3
(16·0 to 33·3)
1·9
(1·3 to 2·6)
–58·9%
(–65·4 to –51·1)
Rotaviral enteritis 398·9
(334·5 to 464·2)
24·8
(21·4 to 28·2)
122·4
(96·6 to 152·1)
23·5
(20·1 to 27·2)
477·5
(397·9 to 555·1)
18·5
(15·8 to 21·2)
176·6
(140·4 to 218·4)
14·0
(11·4 to 16·6)
–63·2%
(–68·5 to –57·1)
Shigellosis 161·0
(130·0 to 200·3)
10·0
(8·2 to 12·2)
33·4
(24·9 to 43·5)
6·4
(5·1 to 7·9)
254·2
(207·9 to 311·7)
9·9
(8·2 to 11·9)
73·9
(58·9 to 93·8)
5·8
(4·7 to 7·3)
–70·9%
(–74·4 to –67·3)
No identifi ed aetiology* 652·4
(542·4 to 783·1)
40·6
(35·3 to 46·8)
215·9
(169·6 to 265·2)
41·5
(35·7 to 48·1)
1240·9
(1096·8 to 1421·5)
48·1
(43·5 to 53·5)
702·9
(619·2 to 796)
55·6
(51·4 to 60·3)
–43·2%
(–49·3 to –36·1)
Lower respiratory infections
Haemophilus infl uenzae
type B pneumonia
427·1
(–39·8 to 853·4)
19·7
(–1·8 to 39·1)
108·7
(–9·9 to 226·9)
12·0
(–1·1 to 25·5)
427·1
(–39·8 to 853·4)
12·5
(–1·2 to 24·8)
108·7
(–9·9 to 226·9)
4·1
(–0·4 to 8·8)
–75·1%
(–79·1 to –71·2)
Infl uenza 36·3
(14·2 to 73·5)
1·7
(0·7 to 3·3)
15·1
(5·7 to 30·4)
1·7
(0·6 to 3·3)
85·1
(36·1 to 156·2)
2·5
(1·1 to 4·5)
105·4
(45·3 to 188·1)
4·0
(1·7 to 7·3)
24·0%
(3·4 to 47·7)
Pneumococcal
pneumonia
652·4
(402·6 to 879·4)
30·1
(19·0 to 40·2)
264·0
(155·7 to 365·8)
29·2
(18·0 to 39·2)
919·5
(553·1 to 1320·5)
26·9
(16·3 to 38·4)
594·4
(295·6 to 970·2)
22·4
(11·4 to 35·9)
–36·1%
(–52·7 to –19·8)
Respiratory syncytial
virus pneumonia
145·1
(82·7 to 228·0)
6·7
(3·8 to 10·5)
41·1
(23·0 to 65·5)
4·5
(2·6 to 7·1)
185·5
(114·4 to 268·6)
5·4
(3·5 to 7·9)
81·5
(53·6 to 109·9)
3·1
(2·0 to 4·2)
–55·7%
(–63 to –47·2)
No identifi ed aetiology* 907·2
(407·3 to 1451·3)
41·9
(19·0 to 66·7)
476·2
(313·3 to 651·6)
52·6
(35·2 to 70·2)
1803·4
(1211·8 to 2452·5)
52·7
(35·4 to 71·1)
1762·6
(1385·3 to 2134·1)
66·5
(52·8 to 79·9)
–0·8%
(–19·8 to 22·9)
Data in parentheses are 95% uncertainty intervals. *Number or proportion of diarrhea or lower respiratory infection deaths attributable to any diarrhea or lower respiratory infection pathogens. Because of
interaction between pathogens (especially for lower respiratory infection), the value is the minimum amount of unexplained deaths.
Table 4: Counterfactual deaths and population attributable fractions for diarrhoea and lower respiratory infection pathogens for 1990 and 2013
Articles
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31
contributions from ischaemic heart disease, self-harm,
alcohol and drug use, cirrhosis, and tuberculosis; HIV
played an important part in Ukraine and Russia. In
north Africa and Middle East, transport injuries and
ischaemic heart disease were predominant. For women
in the region, breast cancer in all countries and maternal
mortality in Sudan and Yemen were also major factors.
In Latin America and Caribbean, there was a large
contribution of interpersonal violence in men, with the
probability of death exceeding 2% in Brazil, Colombia,
El Salvador, Guatemala, Honduras, and Venezuela.
Despite generally high violence in the region, Cuba, Costa
Rica, Bolivia, and Peru had low probabilities of death
from violence. In men, the probability of death because of
HIV/AIDS exceeded 1% in Belize, Haiti, Saint Vincent
and the Grenadines, The Bahamas, Grenada, Guyana,
Suriname, and Trinidad and Tobago. Cirrhosis
contributed more than 1% to the probability of death of
men in Guatemala, Mexico, and Guyana. Cervical cancer
was a larger contributor to the probability of death than
was breast cancer in eight countries of Latin America and
Caribbean (Bolivia, Ecuador, Guatemala, Nicaragua, Peru,
Paraguay, El Salvador, and Venezuela). Probabilities of
death for men and women in Afghanistan were more
than twice that of other countries in the region; for
women, maternal mortality was the largest cause. In
Oceania, Samoa and Tonga had much lower probabilities
of death than did other countries in the region.
Throughout sub-Saharan Africa, there were major
contributions for women from maternal mortality,
HIV/AIDS, and tuberculosis. For men in the region, HIV/
AIDS, tuberculosis, and transport injuries dominated in
most countries. Liver cancer was also a major factor,
particularly for men in western sub-Saharan Africa.
Country-specifi c probabilities of death during middle age
The probability of death in middle age (exact age 50 years
to exact age 75 years [25q50]) ranged from 10·3% for women
in Andorra to 76·3% for men in Lesotho (fi gure appendix 3).
In all countries, ischaemic heart disease and stroke were
important contributors to the risk of death in middle age,
and were greater for men than for women. Probabilities of
death from ischaemic heart disease ranged from 0·8% in
Japan for women to more than 24% in Belarus for men. In
high-income regions, lung cancer was as a major
contributor to the risk of death for men. Breast cancer for
women and prostate cancer for men also made substantial
contributions. Probability of death from liver cancer was
greater than 2% in China, Mongolia, Myanmar, North
Korea, South Korea, Taiwan (province of China), Thailand,
and Vietnam.
In central Europe, chronic respiratory diseases and
cirrhosis made clear contributions in Bulgaria, Croatia,
Hungary, Poland, Romania, and Slovenia. In some high-
income countries, including Singapore, Argentina, and
Uruguay, lower respiratory infections were important
causes of death for this age group, more so for men than
for women. In southeast Asia, liver cancer, diabetes, and
tuberculosis made larger contributions than in many other
regions, particularly in Myanmar, Philippines, Laos,
Indonesia, and Cambodia. Elsewhere in the region, stroke,
ischaemic heart disease, other cardiovascular and
circulatory diseases, and chronic respiratory diseases were
predominant. In all countries of eastern Europe and
central Asia, stroke and ischaemic heart disease were
particularly prominent for both sexes. Liver cancer in
Mongolia had the highest probability of causing death in
the world for this age group.
Egypt had extraordinarily high cirrhosis mortality,
particularly from hepatitis C, in middle aged men and
women. Deaths caused by diabetes were particularly
high in Morocco, Bahrain, Oman, and Qatar. In central
Latin America and Caribbean, diabetes made large
contributions to causes of death in men and women; the
highest probability of death in these regions from
diabetes for males was 9·2% in Trinidad and Tobago and
8·4% for women in Guyana. Chronic kidney disease was
particularly high in El Salvador, Mexico, and Nicaragua;
more so for men than for women.75,76 In the Caribbean,
diabetes, stroke, and ischaemic heart disease accounted
for 33·4% of the probability of death in this age group in
Haitian men, and 54·8% in Guyanese women.
In all the countries of Oceania, diabetes accounted for
an extremely large fraction of mortality in middle-aged
women. For nearly all countries in sub-Saharan Africa,
stroke and other cardiovascular diseases (including
cardiomyopathies) were important. HIV/AIDS and
tuberculosis, diarrhoea, and lower respiratory infections
were also estimated to be important causes in almost
every country in the region. The probability of death
from liver cancer was high in most countries of western
sub-Saharan Africa.
Country-specifi c leading causes of YLLs
Worldwide, the top ten causes of YLLs were ischaemic
heart disease, lower respiratory infections, stroke,
diarrhoea, road injury, HIV/AIDS, preterm birth,
malaria, neonatal encephalopathy, and congenital
causes (fi gure 12). The diff erences between high-
income and low-income countries was substantial. Self-
harm was the fourth highest cause of YLLs in
high-income countries and the 14th in low-income
countries. Lung cancer, self harm, Alzheimer’s disease
and other dementias, cirrhosis, chronic obstructive
pulmonary disease, and colorectal cancer were in the
top ten causes in high-income countries but not in low-
income countries. Conversely, diarrhoea, malaria, HIV/
AIDS, preterm birth complications, neonatal
encephalopathy, and congenital disorders were in the
top ten in low-income, but not high-income, regions.
Ischaemic heart disease, stroke, and lung cancer were
the top three causes in 32 GBD developed countries.
More notable diff erences in the rankings across high-
income countries were self-harm as the second highest
Articles
32
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Stroke
Lung C
Stroke
Stroke
Stroke
Stroke
Stroke
Self harm
Diabetes
LRI
Stroke
Lung C
Alzheimer's
Alzheimer's
COPD
LRI
LRI
Cirrhosis
Lung C
Stroke
Stroke
Stroke
Stroke
Lung C
Stroke
Alzheimer's
Stroke
Stroke
Lung C
Stroke
Stroke
Alzheimer's
Lung C
Stroke
Lung C
Stroke
Stroke
Lung C
Stroke
Lung C
Stroke
Stroke
Stroke
Stroke
Stroke
Stroke
LRI
Stroke
Lung C
Stroke
COPD
Self harm
LRI
LRI
NN encephalitis
Stroke
Stroke
Lung C
LRI
Lung C
COPD
Lung C
Lung C
Global
Developed
Developing
High-income
Australasia
Australia
New Zealand
High-income Asia Pacific
Brunei
Japan
Singapore
South Korea
High-income North America
Canada
USA
Southern Latin America
Argentina
Chile
Uruguay
Western Europe
Andorra
Austria
Belgium
Cyprus
Denmark
Finland
France
Germany
Greece
Iceland
Ireland
Israel
Italy
Luxembourg
Malta
Netherlands
Norway
Portugal
Spain
Sweden
Switzerland
UK
England
Northern Ireland
Scotland
Wales
Central and eastern Europe and central Asia
Central Asia
Armenia
Azerbaijan
Georgia
Kazakhstan
Kyrgyzstan
Mongolia
Tajikistan
Turkmenistan
Uzbekistan
Central Europe
Albania
Bosnia and Herzegovina
Bulgaria
Croatia
Czech Republic
IHD
IHD
LRI
IHD
IHD
IHD
IHD
Stroke
IHD
Stroke
IHD
Stroke
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
Stroke
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
LRI
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
LRI
Stroke
IHD
Lung C
Lung C
Lung C
Lung C
IHD
Stroke
IHD
LRI
Self harm
Lung C
Lung C
Lung C
Stroke
Stroke
Stroke
Stroke
Lung C
Lung C
Lung C
Lung C
Stroke
Lung C
Stroke
Lung C
Lung C
Stroke
Lung C
Lung C
Lung C
Stroke
Lung C
Stroke
Lung C
Lung C
IHD
Lung C
Stroke
Lung C
Lung C
Lung C
Lung C
Lung C
Lung C
Stroke
LRI
Stroke
LRI
Stroke
Stroke
Stroke
Stroke
IHD
LRI
LRI
Stroke
Stroke
Stroke
Stroke
Stroke
Stroke
Diarrhoea
Self harm
Diarrhoea
Alzheimer's
Self harm
Self harm
Colorectal C
Lung C
Road injuries
Lung C
Lung C
Liver C
COPD
Stroke
Alzheimer's
COPD
COPD
Road injuries
Alzheimer's
Alzheimer's
Alzheimer's
Alzheimer's
Self harm
Road injuries
COPD
Lung C
Self harm
Alzheimer's
Alzheimer's
Alzheimer's
Self harm
Diabetes
Alzheimer's
Self harm
Colorectal C
Colorectal C
Alzheimer's
LRI
Alzheimer's
Colorectal C
Alzheimer's
COPD
COPD
COPD
COPD
Alzheimer's
Self harm
NN encephalitis
Diabetes
NN encephalitis
Cirrhosis
Road injuries
Cirrhosis
Liver C
NN preterm
NN encephalitis
NN encephalitis
Cirrhosis
Lung C
CMP
HTN HD
Colorectal C
Colorectal C
Road injuries
Alzheimer's
HIV/AIDS
COPD
Colorectal C
Alzheimer's
COPD
LRI
Congenital
Self harm
Colorectal C
IHD
Stroke
Self harm
Stroke
Road injuries
Road injuries
Self harm
COPD
Colorectal C
Colorectal C
Self harm
COPD
Diabetes
Colorectal C
Self harm
Colorectal C
Colorectal C
COPD
Self harm
COPD
Stroke
Colorectal C
COPD
Breast C
COPD
Colorectal C
Colorectal C
Colorectal C
Self harm
Self harm
Alzheimer's
Alzheimer's
LRI
Alzheimer's
COPD
Cirrhosis
Cirrhosis
Road injuries
Congenital
Lung C
Cirrhosis
NN encephalitis
NN encephalitis
Diarrhoea
Diarrhoea
Cirrhosis
COPD
Other cardio
Diabetes
Lung C
Cirrhosis
Cirrhosis
HIV/AIDS
Cirrhosis
NN preterm
Self harm
Alzheimer's
Colorectal C
Self harm
Stomach C
Lung C
Stomach C
Self harm
Stomach C
Road injuries
Colorectal C
Road injuries
Lung C
Congenital
Stomach C
LRI
COPD
COPD
Cirrhosis
Alzheimer's
Alzheimer's
Alzheimer's
Cirrhosis
Alzheimer's
COPD
Road injuries
Colorectal C
Colorectal C
Colorectal C
COPD
Colorectal C
COPD
Alzheimer's
COPD
Alzheimer's
COPD
Alzheimer's
Colorectal C
LRI
LRI
Colorectal C
Colorectal C
LRI
Lung C
Congenital
Cirrhosis
Cirrhosis
Road injuries
LRI
NN preterm
Cirrhosis
Congenital
Cirrhosis
Road injuries
Self harm
Road injuries
COPD
Other cardio
COPD
Self harm
NN preterm
COPD
Malaria
Colorectal C
COPD
COPD
Alzheimer's
Liver C
LRI
Liver C
CKD
Cirrhosis
Self harm
COPD
Self harm
Congenital
Lung C
LRI
Road injuries
Self harm
LRI
Colorectal C
LRI
Breast C
Cirrhosis
Colorectal C
Cirrhosis
Cirrhosis
Colorectal C
COPD
LRI
Road injuries
Diabetes
Cirrhosis
Congenital
Breast C
Self harm
Cirrhosis
Cirrhosis
COPD
Breast C
Colorectal C
Colorectal C
Alzheimer's
LRI
Colorectal C
CMP
Road injuries
Congenital
Road injuries
LRI
Congenital
Congenital
Self harm
Stroke
Congenital
Congenital
Colorectal C
COPD
Colorectal C
Colorectal C
Alzheimer's
Alzheimer
'
s
Malaria
Colorectal C
Road injuries
LRI
Road injuries
Road injuries
Road injuries
Colorectal C
HIV/AIDS
Colorectal C
Breast C
Road injuries
Cirrhosis
Road injuries
Cirrhosis
Cirrhosis
NN preterm
Alzheimer's
Self harm
Cirrhosis
Self harm
COPD
Colorectal C
Colorectal C
Self harm
Pancreatic C
Breast C
Self harm
Breast C
Breast C
Alzheimer's
CKD
Breast C
Alzheimer's
LRI
LRI
LRI
COPD
LRI
Prostate C
Other cardio
Breast C
Breast C
Self harm
Cirrhosis
Breast C
Road injuries
Self harm
LRI
NN preterm
NN encephalitis
NN encephalitis
Road injuries
Congenital
Drowning
NN preterm
HTN HD
Alzheimer's
Violence
Alzheimer's
Alzheimer's
Self harm
COPD
NN encephalitis
LRI
NN encephalitis
Road injuries
Breast C
Breast C
Breast C
Cirrhosis
COPD
COPD
COPD
Diabetes
Diabetes
Breast C
Diabetes
Self harm
Colorectal C
Congenital
Colorectal C
Breast C
HIV/AIDS
Breast C
Breast C
COPD
LRI
Falls
Road injuries
Breast C
LRI
Prostate C
Breast C
Congenital
Cirrhosis
Breast C
Pancreatic C
Self harm
Drugs
Stomach C
Breast C
LRI
COPD
Cirrhosis
Cirrhosis
Breast C
Self harm
Cirrhosis
COPD
NN preterm
COPD
Diabetes
Other cardio
Violence
COPD
Road injuries
Cirrhosis
Road injuries
Drowning
HTN HD
Stomach C
Self harm
Cirrhosis
Road injuries
LRI
Congenital
Road injuries
Congenital
Cirrhosis
Diabetes
Diabetes
Congenital
COPD
Colorectal C
Pancreatic C
Liver C
Colorectal C
Colorectal C
Diabetes
Colorectal C
Colorectal C
Self harm
Lung C
Congenital
LRI
Pancreatic C
Diabetes
Road injuries
LRI
Diabetes
Alcohol
Other cardio
Pancreatic C
CKD
Road injuries
Congenital
Breast C
Road injuries
Road injuries
Diabetes
Pancreatic C
Breast C
Road injuries
Road injuries
Breast C
Pancreatic C
Self harm
Self harm
Cirrhosis
Breast C
Self harm
Colorectal C
Drowning
Breast C
TB
Alzheimer's
COPD
Self harm
NN preterm
Meningitis
Drowning
Self harm
Road injuries
Congenital
Cirrhosis
LRI
HTN HD
Other cardio
12345678910
Figure 12 continues on next page
Articles
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33
Lung
C
Lung C
Lung C
Lung C
Lung C
Lung C
Cirrhosis
CMP
Lung C
Lung C
Self harm
Self harm
Lung C
CMP
Self harm
Cirrhosis
CMP
Cirrhosis
Road injuries
IHD
Road injuries
IHD
Road injuries
LRI
Diabetes
Stroke
Violence
Lung C
Diabetes
Stroke
Diabetes
HIV/AIDS
Diarrhoea
Violence
Diabetes
Diabetes
NN preterm
Diabetes
Stroke
Road injuries
Road injuries
Congenital
Road injuries
Diarrhoea
Congenital
Diabetes
CKD
Congenital
Road injuries
Stroke
Stroke
Stroke
Road injuries
Road injuries
Road injuries
Lung C
Liver C
Diabetes
Stroke
Stroke
IHD
LRI
Diarrhoea
Stroke
Stroke
C
roa
ti
a
Czech Republic
Hungary
Macedonia
Montenegro
Poland
Romania
Serbia
Slovakia
Slovenia
Eastern Europe
Belarus
Estonia
Latvia
Lithuania
Moldova
Russia
Ukraine
Latin America and Caribbean
Andean Latin America
Bolivia
Ecuador
Peru
Caribbean
Antigua and Barbuda
Barbados
Belize
Cuba
Dominica
Dominican Republic
Grenada
Guyana
Haiti
Jamaica
Saint Lucia
VCT
Suriname
The Bahamas
TTO
Central Latin America
Colombia
Costa Rica
El Salvador
Guatemala
Honduras
Mexico
Nicaragua
Panama
Venezuela
Tropical Latin America
Brazil
Paraguay
Southeast and east Asia and Oceania
East Asia
China
North Korea
Taiwan (province of China)
Oceania
FSM
Fiji
Kiribati
Marshall Islands
PNG
Samoa
Solomon Islands
IHD
IHD
IHD
Stroke
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
LRI
LRI
LRI
LRI
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
IHD
HIV/AIDS
Stroke
IHD
IHD
IHD
IHD
IHD
Violence
Violence
IHD
Violence
LRI
IHD
IHD
Congenital
IHD
Violence
IHD
IHD
IHD
Stroke
Stroke
Stroke
Stroke
IHD
LRI
IHD
IHD
Stroke
IHD
LRI
Diabetes
IHD
Stroke
Stroke
Stroke
IHD
Stroke
Stroke
Stroke
Stroke
Stroke
Stroke
Stroke
Stroke
Stroke
Stroke
Stroke
Stroke
Stroke
Stroke
Violence
Road injuries
F Body Asp
Road injuries
IHD
Stroke
Stroke
Diabetes
Diabetes
Stroke
Stroke
Road injuries
Stroke
Stroke
LRI
Diabetes
Stroke
Stroke
Stroke
Stroke
Diabetes
IHD
IHD
Road injuries
IHD
Violence
Violence
CKD
LRI
Violence
IHD
Violence
Violence
Road injuries
IHD
IHD
IHD
IHD
Stroke
IHD
Diabetes
Diabetes
Diabetes
Diabetes
IHD
IHD
Diabetes
Colorectal
C
Colorectal C
Cirrhosis
Diabetes
Self harm
Self harm
Lung C
Lung C
Cirrhosis
Self harm
CMP
Lung C
Self harm
Lung C
Lung C
LRI
Self harm
Self harm
Stroke
Congenital
NN preterm
Congenital
Congenital
HIV/AIDS
LRI
LRI
Stroke
LRI
LRI
NN preterm
LRI
Diabetes
Stroke
IHD
LRI
NN preterm
Congenital
HIV/AIDS
Violence
CKD
Stroke
CKD
CKD
NN preterm
NN preterm
Cirrhosis
IHD
Road injuries
Stroke
Road injuries
Road injuries
Congenital
COPD
COPD
COPD
COPD
Lung C
Diarrhoea
LRI
LRI
LRI
Stroke
Diabetes
LRI
LRI
Cirrhosis
Cirrhosis
Colorectal C
HTN HD
Road injuries
COPD
LRI
Self harm
Colorectal C
Cirrhosis
Cirrhosis
Road injuries
HTN HD
Self harm
Cirrhosis
Self harm
Cirrhosis
HIV/AIDS
LRI
NN preterm
IHD
Violence
Stroke
Road injuries
Road injuries
CKD
Road injuries
Self harm
Road injuries
Congenital
Road injuries
Road injuries
PEM
NN preterm
Violence
Violence
Diabetes
Violence
Road injuries
Congenital
Congenital
Stroke
LRI
IHD
Stroke
Violence
NN preterm
Stroke
Congenital
LRI
LRI
NN preterm
Lung C
Lung C
Lung C
Road injuries
Diabetes
Congenital
Road injuries
Congenital
Congenital
Congenital
Malaria
Congenital
Diarrhoea
COPD
Self harm
Self harm
Colorectal C
CMP
Cirrhosis
HTN HD
Colorectal C
LRI
Colorectal C
Lung C
Cirrhosis
Alzheimer's
Alzheimer's
Alzheimer's
Lung C
Lung C
Lung C
Congenital
Stroke
Congenital
Stroke
NN preterm
Diarrhoea
HIV/AIDS
Road injuries
NN preterm
COPD
Violence
LRI
HIV/AIDS
LRI
NN sepsis
CKD
Road injuries
HIV/AIDS
LRI
Road injuries
HIV/AIDS
LRI
LRI
Cirrhosis
Congenital
PEM
COPD
Road injuries
Road injuries
LRI
Diabetes
Congenital
Congenital
LRI
LRI
Liver C
Liver C
Liver C
Cirrhosis
Malaria
Congenital
NN preterm
Road injuries
NN preterm
NN preterm
Road injuries
Congenital
Alzheimer s
Alzheimer's
COPD
COPD
Breast C
Road injuries
Alzheimer's
COPD
Self harm
Alzheimer's
Road injuries
Stomach C
Cirrhosis
Colorectal C
Road injuries
Road injuries
Road injuries
Road injuries
Diabetes
F Body Asp
NN encephalitis
CKD
Cirrhosis
Diabetes
NN preterm
Violence
Congenital
Road injuries
NN preterm
Violence
Violence
Congenital
IHD
LRI
HIV/AIDS
LRI
Road injuries
CKD
LRI
Diabetes
COPD
Violence
Alcohol
Congenital
Diarrhoea
Congenital
Stroke
CKD
CKD
Diabetes
Diabetes
Violence
Liver C
Stomach C
Stomach C
Stomach C
Self harm
NN preterm
Asthma
CKD
Diarrhoea
Road injuries
Congenital
CKD
NN preterm
Self
harm
COPD
Alzheimer's
Stomach C
Diabetes
Colorectal C
CMP
Alzheimer's
Alzheimer's
COPD
LRI
COPD
Alcohol
Cirrhosis
Colorectal C
COPD
LRI
Colorectal C
Cirrhosis
Cirrhosis
Stroke
NN preterm
F Body Asp
NN preterm
Violence
HTN HD
LRI
Colorectal C
CKD
NN sepsis
CKD
Self harm
NN preterm
Congenital
NN preterm
Road injuries
HIV/AIDS
LRI
CKD
Cirrhosis
NN preterm
Self harm
Cirrhosis
Cirrhosis
LRI
LRI
Cirrhosis
Diabetes
LRI
NN preterm
Cirrhosis
Diabetes
Congenital
Congenital
Congenital
Self harm
Road injuries
Stroke
Self harm
Road injuries
Asthma
CKD
Road injuries
Violence
Asthma
Road
injuries
LRI
HTN HD
Alzheimer's
Alzheimer's
Alzheimer's
COPD
Diabetes
Road injuries
CMP
Violence
CMP
Colorectal C
Stomach C
CMP
Colorectal C
Violence
Alzheimer's
NN preterm
NN encephalitis
Cirrhosis
Self harm
NN encephalitis
Congenital
CKD
Breast C
HIV/AIDS
Alzheimer's
Congenital
Diabetes
Self harm
Violence
Congenital
HIV/AIDS
CKD
Congenital
Self harm
HTN HD
Congenital
Stroke
Diabetes
Stomach C
Diabetes
Diabetes
Road injuries
Stroke
Violence
NN preterm
Self harm
Cirrhosis
NN preterm
CKD
Cirrhosis
LRI
LRI
LRI
LRI
Road injuries
CKD
Breast C
Self harm
Diarrhoea
HIV/AIDS
Asthma
TB
HTN
HD
Other cardio
Breast C
Road injuries
Colorectal C
LRI
Colorectal C
Breast C
Other cardio
Road injuries
Alcohol
Alcohol
CMP
Road injuries
Stomach C
Congenital
Alcohol
CMP
CKD
CKD
NN sepsis
Cirrhosis
NN sepsis
NN sepsis
Congenital
HIV/AIDS
Self harm
CKD
HTN HD
CKD
Congenital
NN preterm
NN encephalitis
Alzheimer's
Congenital
CKD
CKD
NN preterm
Self harm
NN preterm
Self harm
COPD
Stroke
Road injuries
Cirrhosis
NN preterm
Diabetes
HIV/AIDS
NN preterm
COPD
COPD
NN encephalitis
Stomach C
Cirrhosis
Cirrhosis
Congenital
Colorectal C
Asthma
COPD
COPD
NN preterm
Self harm
Asthma
Self harm
Road injuries
Figure 12 continues on next page
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34
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Stroke
Stroke
LRI
Diabetes
LRI
NN preterm
LRI
IHD
Stroke
Congenital
Cirrhosis
Stroke
Stroke
Stroke
Congenital
LRI
NN encephalitis
IHD
NN encephalitis
NN encephalitis
TB
NN encephalitis
Diarrhoea
Congenital
Stroke
Diabetes
Cirrhosis
Congenital
Congenital
NN preterm
Road injuries
Stroke
Congenital
Diabetes
Stroke
Stroke
IHD
Congenital
Congenital
Stroke
Stroke
Lung C
Congenital
Diarrhoea
LRI
Malaria
HIV/AIDS
Diarrhoea
Malaria
PEM
Malaria
Malaria
Malaria
Diarrhoea
Diarrhoea
Malaria
TB
HIV/AIDS
Diarrhoea
Stroke
Diarrhoea
Stroke
LRI
S
amoa
Solomon Islands
Tonga
Vanuatu
Southeast Asia
Cambodia
Indonesia
Laos
Malaysia
Maldives
Myanmar
Philippines
Sri Lanka
Thailand
Timor-Leste
Vietnam
South Asia
Afghanistan
Bangladesh
Bhutan
India
Nepal
Pakistan
North Africa and Middle East
Algeria
Bahrain
Egypt
Iran
Iraq
Jordan
Kuwait
Lebanon
Libya
Morocco
Oman
Palestine
Qatar
Saudi Arabia
Sudan
Syria
Tunisia
Turkey
UAE
Yemen
Sub-Saharan Africa
Central sub-Saharan Africa
Angola
CAR
Republic of Congo
DR Congo
Equator Guinea
Gabon
Eastern sub-Saharan Africa
Burundi
Comoros
Djibouti
Eritrea
Ethiopia
Kenya
Madagascar
Malawi
Mauritius
Mozambique
Diabetes
IHD
IHD
IHD
Stroke
IHD
Stroke
LRI
IHD
IHD
Stroke
IHD
IHD
IHD
LRI
Stroke
IHD
LRI
Stroke
IHD
IHD
LRI
LRI
IHD
NN preterm
IHD
IHD
IHD
NN preterm
Congenital
IHD
IHD
IHD
NN preterm
Road injuries
Congenital
Road injuries
Road injuries
NN preterm
War
IHD
IHD
Road injuries
NN preterm
HIV/AIDS
LRI
LRI
HIV/AIDS
HIV/AIDS
Diarrhoea
HIV/AIDS
HIV/AIDS
HIV/AIDS
Malaria
LRI
HIV/AIDS
Diarrhoea
LRI
HIV/AIDS
LRI
HIV/AIDS
Diabetes
HIV/AIDS
IHD
Diabetes
Diabetes
LRI
IHD
LRI
IHD
NN preterm
Road injuries
Stroke
LRI
LRI
Self harm
Road injuries
NN preterm
Road injuries
LRI
NN preterm
IHD
LRI
LRI
IHD
NN encephalitis
NN preterm
IHD
Road injuries
Stroke
NN preterm
IHD
IHD
Congenital
Congenital
Stroke
IHD
IHD
IHD
Congenital
IHD
IHD
IHD
Road injuries
Stroke
IHD
IHD
Malaria
Diarrhoea
Diarrhoea
LRI
LRI
LRI
LRI
LRI
LRI
LRI
TB
LRI
LRI
Diarrhoea
LRI
Diarrhoea
LRI
IHD
Malaria
LRI
LRI
Stroke
Stroke
Road injuries
Stroke
TB
Stroke
LRI
NN encephalitis
TB
TB
Diabetes
LRI
Diarrhoea
Liver C
NN preterm
Diarrhoea
LRI
Stroke
NN encephalitis
Stroke
IHD
Stroke
Congenital
Self harm
Congenital
Road injuries
Stroke
LRI
NN preterm
Lung C
Road injuries
Stroke
Diabetes
LRI
NN preterm
NN preterm
Diarrhoea
Congenital
Congenital
Congenital
Stroke
Congenital
Diarrhoea
PEM
Malaria
Malaria
Congenital
Malaria
Congenital
Stroke
Diarrhoea
TB
NN preterm
Diarrhoea
HIV/AIDS
TB
TB
NN preterm
PEM
CKD
Diarrhoea
Congenital
Diarrhoea
NN preterm
NN preterm
TB
Congenital
Road injuries
Diarrhoea
Lung C
NN preterm
IHD
NN preterm
COPD
Liver C
IHD
NN preterm
Diarrhoea
Congenital
NN preterm
NN preterm
NN preterm
Diarrhoea
NN preterm
Road injuries
Road injuries
Congenital
LRI
Stroke
LRI
Stroke
Stroke
Road injuries
NN preterm
LRI
Congenital
NN preterm
Self harm
Stroke
LRI
Road injuries
NN preterm
COPD
Self harm
LRI
NN preterm
HIV/AIDS
Congenital
TB
Stroke
NN preterm
Road injuries
Road injuries
TB
HIV/AIDS
Malaria
TB
Malaria
NN preterm
NN preterm
PEM
TB
Cirrhosis
TB
Road
injuries
Congenital
Congenital
Congenital
NN preterm
Road injuries
NN encephalitis
Road injuries
Congenital
Drowning
Malaria
Congenital
Road injuries
Cirrhosis
Stroke
IHD
TB
Road injuries
Drowning
Road injuries
Diarrhoea
Self harm
NN sepsis
LRI
Diabetes
Stroke
COPD
LRI
CKD
Road injuries
LRI
Diabetes
Diabetes
Road injuries
LRI
Road injuries
Stroke
CKD
Stroke
COPD
Lung C
Road injuries
LRI
Stroke
NN encephalitis
NN preterm
PEM
NN preterm
NN preterm
HIV/AIDS
Diarrhoea
Congenital
NN preterm
NN preterm
NN encephalitis
Stroke
NN preterm
NN encephalitis
NN encephalitis
Syphilis
NN preterm
LRI
NN sepsis
CKD
NN preterm
Road injuries
Road injuries
Diabetes
NN encephalitis
Diabetes
Congenital
COPD
COPD
NN preterm
Diabetes
Cirrhosis
CKD
NN encephalitis
Drowning
Stroke
Stroke
NN sepsis
Congenital
Stroke
TB
Stroke
Cirrhosis
LRI
NN preterm
Other cardio
Other cardio
Road injuries
Drowning
CKD
Breast C
LRI
Congenital
Other cardio
CKD
Diabetes
LRI
Road injuries
NN preterm
LRI
NN preterm
Drugs
Malaria
PEM
Congenital
NN preterm
PEM
Diarrhoea
Congenital
Stroke
NN encephalitis
NN encephalitis
PEM
NN sepsis
NN encephalitis
PEM
Malaria
Malaria
Malaria
Malaria
Road injuries
NN encephalitis
Violence
Asthma
Lung C
Diarrhoea
Congenital
TB
Diarrhoea
NN encephalitis
Diabetes
Road injuries
Road injuries
Violence
LRI
HIV/AIDS
Road injuries
Cirrhosis
COPD
Meningitis
Cirrhosis
Cirrhosis
COPD
NN preterm
Meningitis
COPD
NN encephalitis
Drugs
NN preterm
HTN HD
Violence
Diabetes
HTN HD
NN preterm
CKD
NN encephalitis
NN preterm
Diabetes
Falls
Falls
Malaria
LRI
COPD
Diabetes
NN preterm
Road injuries
Congenital
TB
NN encephalitis
NN encephalitis
Measles
TB
PEM
TB
PEM
NN encephalitis
Stroke
PEM
Maternal
NN sepsis
Congenital
NN sepsis
Congenital
Self harm
Syphilis
Asthma
TB
Breast C
Asthma
Cirrhosis
Diarrhoea
NN preterm
TB
HIV/AIDS
CKD
Lung C
Road injuries
Congenital
Self harm
Drowning
Congenital
Road injuries
Maternal
Self harm
COPD
Self harm
NN sepsis
Congenital
Diabetes
CKD
CKD
CKD
COPD
Diabetes
CKD
Diabetes
COPD
COPD
Drugs
Drowning
Violence
Oth mech
NN sepsis
HIV/AIDS
Endocrine
Diabetes
LRI
Falls
Maternal
NN sepsis
NN encephalitis
TB
Syphilis
NN encephalitis
NN encephalitis
NN preterm
IHD
NN sepsis
NN sepsis
Road injuries
NN preterm
NN encephalitis
Congenital
NN sepsis
Congenital
NN encephalitis
HTN HD
NN preterm
Self
harm
Road injuries
Meningitis
TB
NN encephalitis
Self harm
Cirrhosis
Drowning
CKD
LRI
NN encephalitis
NN encephalitis
Violence
Diabetes
Maternal
Lung C
Self harm
TB
Congenital
NN sepsis
Road injuries
COPD
TB
Diarrhoea
NN sepsis
Breast C
Road injuries
Self harm
Diarrhoea
NN encephalitis
Cirrhosis
CKD
Lung C
NN sepsis
CKD
Drugs
Drowning
Drugs
Vis Leish
Typhoid
CKD
Stomach C
Diabetes
COPD
TB
Meningitis
Road injuries
Meningitis
TB
Meningitis
NN encephalitis
NN preterm
Congenital
Congenital
Congenital
Congenital
NN sepsis
Stroke
PEM
Meningitis
Meningitis
Congenital
Road injuries
Figure 12 continues on next page
Articles
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35
cause of YLLs in South Korea, Alzheimer’s disease and
other dementias as the third highest cause in Canada,
Finland, and Israel, and lower respiratory infections as
the second cause in Singapore and the third highest
cause in Argentina and Japan. Cirrhosis was the third
highest cause in Chile. Colorectal cancer was a top fi ve
cause in 13 high-income countries and diabetes was in
three high-income countries.
In central Europe, eastern Europe, and central Asia,
ischaemic heart disease and stroke dominated but in
Bosnia and Herzegovina, Serbia, Latvia, and Russia
cardiomyopathies were also in the top fi ve. As a result of
higher child mortality in these regions, Azerbaijan,
Kyrgyzstan, Mongolia, Tajikistan, Turkmenistan, and
Uzbekistan had preterm, or neonatal encephalopathy, in
the top fi ve causes. In eastern Europe, fi ve causes
(ischaemic heart disease, stroke, self-harm, cirrhosis,
and road injury) made up 49·7% of YLLs (95% UI
48·5–51·3; or 29·3 million [28·5–30·2]). In Latin
America and Caribbean, more variation exists in the
leading cause of YLLs. Lower respiratory infections were
the leading cause in Bolivia, Peru, Guatemala, and
Ecuador; HIV/AIDS was in Haiti; interpersonal violence
was in Colombia, El Salvador, and Venezuela; stroke was
in Jamaica; congenital anomalies were in Nicaragua, and
ischaemic heart disease was in the rest. Road injury was
in the top fi ve for 17 of 29 countries. Diabetes was also in
the top fi ve for 13 countries. Chronic kidney disease was
in the top fi ve for Barbados, Costa Rica, El Salvador,
Mexico, and Nicaragua. Perhaps most unusually,
interpersonal violence was in the top fi ve causes in 15
countries in the region, but only one country outside
Latin America and Caribbean, namely South Africa.
In east Asia, the top fi ve causes of YLLs, in order, were
stroke, ischaemic heart disease, road injury, chronic
obstructive pulmonary disorder, and lung cancer. These are
Figure 12: Top ten causes in 2013 of years of life lost by location
The top 15 global causes of years of life lost are coloured. VCT=Saint Vincent and the Grenadines. TTO=Trinidad and Tobago. FSM=Federated States of Micronesia. PNG=Papua New Guinea. UAE=United
Arab Emirates. CAR=Central African Republic. STP=São Tomé and Princípe. IHD=ischaemic heart disease. LRI=lower respiratory infections. Road inj=road injuries. NN Preterm=preterm birth
complications. NN enceph=neonatal encephalitis. Congenital=congenital disorders. C=cancer. COPD=chronic obstructive pulmonary disease. CKD=chronic kidney disease. CMP=cardiomyopathies.
Other cardio=other cardiovascular disease. Drugs=drug use disorders. Alcohol=alcohol use disorders. Violence=interpersonal violence. HTN HD=hypertensive heart disease. F body asp=pulmonary
aspiration and foreign body in airway. NN sepsis=neonatal sepsis. PEM=protein–energy malnutrition. TB=tuberculosis. Vis leish=visceral leishmaniasis. Other mech=other mechanical forces.
Endocrine=endocrine, metabolic, blood, and immune disorders. Maternal=maternal disorders. Sickle=sickle cell disorders.
LRI
Malaria
LRI
Malaria
HIV/AIDS
Malaria
LRI
LRI
Diarrhoea
LRI
Diarrhoea
LRI
TB
Diarrhoea
Diarrhoea
HIV/AIDS
HIV/AIDS
Diarrhoea
Malaria
Congenital
Malaria
Malaria
HIV/AIDS
Diarrhoea
LRI
Diarrhoea
LRI
Diarrhoea
LRI
HIV/AIDS
Stroke
Diarrhoea
HIV/AIDS
Diarrhoea
HIV/AIDS
Mozambique
Rwanda
Seychelles
Somalia
South Sudan
Tanzania
Uganda
Zambia
Southern sub-Saharan Africa
Botswana
Lesotho
Namibia
South Africa
Swaziland
Zimbabwe
Western sub-Saharan Africa
Benin
Burkina Faso
Cameroon
Cape Verde
Chad
Côte d’Ivoire
Ghana
Guinea
Guinea-Bissau
Liberia
Mali
Mauritania
Niger
Nigeria
São Tomé and Príncipe
Senegal
Sierra Leone
The Gambia
Togo
HIV/AIDS
LRI
IHD
Diarrhoea
LRI
HIV/AIDS
HIV/AIDS
HIV/AIDS
HIV/AIDS
HIV/AIDS
HIV/AIDS
HIV/AIDS
HIV/AIDS
HIV/AIDS
HIV/AIDS
Malaria
Malaria
Malaria
HIV/AIDS
Stroke
Diarrhoea
LRI
Malaria
Malaria
Malaria
Malaria
Malaria
LRI
Malaria
Malaria
LRI
Malaria
Malaria
Malaria
Malaria
Malaria
HIV/AIDS
Stroke
LRI
Diarrhoea
LRI
Malaria
Malaria
LRI
TB
TB
TB
LRI
LRI
LRI
LRI
LRI
LRI
LRI
IHD
LRI
HIV/AIDS
LRI
LRI
HIV/AIDS
LRI
Diarrhoea
Malaria
Diarrhoea
LRI
Malaria
LRI
LRI
LRI
LRI
Diarrhoea
Diarrhoea
HTN HD
TB
TB
Diarrhoea
Diarrhoea
Diarrhoea
TB
Diarrhoea
LRI
Diarrhoea
Diarrhoea
TB
TB
Diarrhoea
Diarrhoea
NN preterm
Diarrhoea
LRI
HIV/AIDS
Diarrhoea
NN sepsis
HIV/AIDS
Diarrhoea
HIV/AIDS
PEM
NN encephalitis
PEM
Sickle
NN preterm
NN preterm
PEM
Congenital
Diarrhoea
TB
NN preterm
Cirrhosis
PEM
PEM
TB
NN preterm
PEM
Violence
Road injuries
NN preterm
Stroke
Violence
Road injuries
NN preterm
NN preterm
NN preterm
Congenital
Road injuries
Stomach C
PEM
NN preterm
NN preterm
NN preterm
NN preterm
NN preterm
NN preterm
NN preterm
NN preterm
Road injuries
NN encephalitis
NN encephalitis
NN preterm
NN preterm
NN preterm
NN
sepsis
NN encephalitis
Drowning
NN preterm
Syphilis
Congenital
NN encephalitis
TB
Stroke
Self harm
Violence
Self harm
Stroke
NN preterm
NN encephalitis
NN encephalitis
NN encephalitis
Meningitis
NN preterm
NN encephalitis
NN preterm
NN encephalitis
PEM
NN encephalitis
PEM
NN encephalitis
NN encephalitis
Road injuries
Meningitis
NN preterm
NN sepsis
NN sepsis
Diarrhoea
HIV/AIDS
NN encephalitis
NN
encephalitis
NN sepsis
Road injuries
Meningitis
Meningitis
PEM
NN sepsis
NN encephalitis
NN preterm
NN preterm
NN encephalitis
Road injuries
Road injuries
Self harm
Stroke
Sickle
Congenital
NN encephalitis
NN encephalitis
Liver C
NN encephalitis
NN sepsis
NN encephalitis
PEM
NN encephalitis
NN sepsis
NN sepsis
NN sepsis
NN encephalitis
NN encephalitis
Congenital
HIV/AIDS
NN encephalitis
NN sepsis
Congenital
Syphilis
TB
Self harm
NN encephalitis
Maternal
NN encephalitis
TB
NN sepsis
Road injuries
NN encephalitis
Self harm
NN preterm
IHD
Violence
PEM
Road injuries
NN sepsis
NN sepsis
Congenital
Violence
Meningitis
Road injuries
Stroke
NN sepsis
Meningitis
PEM
Meningitis
Congenital
Congenital
Diarrhoea
PEM
Congenital
Congenital
NN encephalitis
PEM
NN
preterm
Road injuries
Congenital
Tetanus
Malaria
Syphilis
PEM
Congenital
IHD
Maternal
Stroke
IHD
Diabetes
Stroke
Malaria
PEM
Road injuries
Road injuries
PEM
COPD
Tetanus
Congenital
Road injuries
Meningitis
Road injuries
Congenital
Congenital
Stroke
NN sepsis
PEM
Diarrhoea
Road injuries
NN sepsis
Road injuries
NN sepsis
Road
injuries
PEM
CKD
NN sepsis
NN preterm
NN sepsis
Road injuries
Meningitis
NN encephalitis
Violence
Road injuries
Violence
NN preterm
NN encephalitis
Meningitis
NN sepsis
PEM
HIV/AIDS
NN sepsis
NN preterm
Congenital
PEM
Congenital
Congenital
NN sepsis
Stroke
HIV/AIDS
Maternal
TB
NN sepsis
IHD
PEM
Meningitis
PEM
Road injuries
Articles
36
www.thelancet.com Published online December 18, 2014 http://dx.doi.org/10.1016/S0140-6736(14)61682-2
almost the same top fi ve causes as in USA: the only
diff erence is Alzheimer’s disease and other dementias,
which was fourth and road injury was sixth, providing
evidence of epidemiological convergence between east
Asian countries and some high-income countries. In
Oceania, ischaemic heart disease, lower respiratory
infections, diabetes, and diarrhoea were important. In
Papua New Guinea, malaria was also a top fi ve cause.
Southeast Asia as a whole, Indonesia, Myanmar, and
Philippines have tuberculosis as a top fi ve cause of YLLs.
Road injury was a top fi ve cause in Indonesia, Malaysia,
Thailand, and Vietnam. Cirrhosis was in the top fi ve in
Myanmar and liver cancer in Thailand and Vietnam.
Among the countries of south Asia, the leading causes are
a mix of neonatal causes and ischaemic heart disease, lower
respiratory infections, and stroke in most countries.
Tuberculosis was the third highest cause in India.
In north Africa and Middle East, ischaemic heart
disease and stroke, preterm birth complications,
congenital anomalies, and road injury were prominent
leading causes of YLLs. In four countries—Oman, Qatar,
Saudi Arabia, and United Arab Emirates—road injury
was the leading cause of YLLs. Cirrhosis was the third
highest cause of YLLs in Egypt. Self-harm was in the top
fi ve in Bahrain, Qatar, and United Arab Emirates. The
profi le of leading causes of YLLs in sub-Saharan Africa
was greatly diff erent from the rest of the world with the
exception of Cape Verde, Mauritius, and Seychelles. HIV/
AIDS was the leading cause in 18 countries. Malaria was
the leading cause in 14 countries. Lower respiratory
infection was the leading cause in Angola, Comoros,
Ethiopia, Madagascar, Rwanda, South Sudan, Côte
d’Ivoire, Mauritania, and São Tomé and Princípe.
Diarrhoea was the leading cause in D R Congo, Eritrea,
Somalia, and Chad. Tuberculosis was in the top
fi ve causes in 18 countries. Violence was the fi fth highest
cause in South Africa. Road injury was the fi fth highest
cause in Equatorial Guinea, Gabon, Botswana, Swaziland,
Cameroon, and Nigeria.
Discussion
Main fi ndings
The GBD 2013 incorporates many new datasets for cause
of death, particularly from China, and new data for
155 other countries. Compared with the GBD 2010, it
provides the most comprehensive and up-to-date
assessment of causes of death. The results for the GBD
2013 are based on re-estimation of all causes from 1990 to
2013, and thus supersede all previously published GBD
time series (panel). Publication of country-level results
provides many opportunities for comparing a country’s
performance with that of its peers.
On the broadest level, our analysis of 240 causes of
death for 188 countries confi rms that global life
expectancy at birth has continued to improve over the
past 23 years and these improvements are driven largely
by falls in diarrhoea, lower respiratory infections, and
neonatal causes in low-income countries, and decreases
in cardiovascular diseases and some cancers in middle-
income and high-income countries. HIV/AIDS has had a
large enough eff ect to negate progress made in other
causes contributing to decreases in life expectancy,
particularly in southern sub-Saharan Africa.
This general progress masks enormous heterogeneity
across countries and age groups. Even within regions,
substantially diff erent mortality, leading causes of death,
and trends exist. Outside sub-Saharan Africa, premature
mortality is dominated by relatively few causes including
ischaemic heart disease, stroke, lower respiratory
infections, road injury, diarrhoea, preterm birth
complications, neonatal encephalopathy, congenital
anomalies, tuberculosis, chronic obstructive pulmonary
disease, cirrhosis, self-harm, and lung cancer. In
addition to these common causes, great regional and
country variation exists, such as the dominant role of
interpersonal violence in most countries of central Latin
America and Brazil.
Our study points to extraordinary epidemiological
progress: global age-standardised death rates fell
signifi cantly for 157 of 240 causes from 1990 to 2013.
The largest decreases were for some of the major
communicable diseases including diarrhoeal diseases,
lower respiratory infections, tuberculosis, and measles.
Age-standardised rates for many non-communicable
causes are also falling. At the same time, numbers of
deaths from 115 of these 240 causes, have increased,
driven by both growth in population and shifts in the
population age-structure towards older ages. For a
further 58 causes, changes in the age-standardised
death rate over the 23 year period were not statistically
diff erent from no change. For some of these causes,
sparse data might have contributed to wide UIs and in
other cases uncertainty might have arisen from
inconsistent coding across countries. However, eight
specifi c causes account for more than 100 000 deaths
and their age-standardised death rates have increased
signifi cantly since 1990: HIV/AIDS, liver cancer caused
by hepatitis C, pancreatic cancer, atrial fi brillation and
fl utter, drug use disorders, diabetes, chronic kidney
disease, and sickle cell disorders. Of these causes,
three (HIV/AIDS, diabetes, and chronic kidney disease)
account for more than a half a million deaths each.
HIV/AIDS, however, has been decreasing as a cause of
death since 2005. These causes, which run counter to
an extraordinary global trend towards lower age-
standardised death rates, deserve special attention.
The rise and subsequent fall of HIV/AIDS is well
known as is the rise in diabetes. Increases for atrial
fi brillation and fl utter, pancreatic cancer, drug use
disorders, and chronic kidney disease have received
far less global attention. Drug use disorders and
chronic kidney diseases cause many more deaths in
some regions and countries than in others.
Nevertheless, they are important emerging global
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37
challenges that show the potential adverse eff ects of
some behaviours and socioeconomic developments. In
view of the important behavioural component for
some of these causes, there is potentially an important
role for public health policy and resources to modify
these causes of death. These diseases, particularly
HIV/AIDS and drug use disorders are also subject to
social stigmatisation, which adds an important
challenge for eff ective policy interventions. Although
global age-standardised death rates have increased for
very few causes, there is remarkable and important
variation in trends across countries such that causes
with falling global age-standardised rates are
increasing in some countries—for example, ischaemic
heart disease in China.
Convergence or divergence?
Ambitious goals have been set for maternal and child
mortality,84–86 such as the end of preventable maternal and
child death in a generation. The Lancet Commission
Global health 2035: a world converging within a generation
has argued that a grand convergence in health is possible
between high-income, middle-income, and low-income
countries.24 Trends in the past 23 years provide an
important starting point for framing how great a challenge
achieving these aspirations will be and the political will
and fi nancial resources required. Part of the answer
depends on how the goals are framed—for example, what
does convergence mean? In the development literature on
economic convergence,87–89 convergence has been framed
in terms of poverty rates or in terms of income inequality
measured by the Gini coeffi cient or other measures of
inequality. Work on convergence in life expectancy has
tended to focus on measures of absolute diff erence90–92
rather than relative diff erence.93 We found unequivocal
divergence in mortality rates for women aged 25–39 years
and older than 80 years and for men aged 20–44 years and
65 years and older, similar to previous estimates of
divergence of life expectancy at birth since the 1980s.94 In
these age groups, both the Gini coeffi cient and the mean
absolute diff erence in death rates are rising. In all other
age groups, except girls aged 10–14 years, relative
inequality is increasing but the absolute gap is narrowing.
Framing a grand convergence as simply achieving a
reduction in the diff erences in mortality rates across
countries might not be suffi ciently ambitious to meet the
goals of many national policy makers. If mortality
decreases in all countries by the same percent per year,
absolute diff erence will decrease and relative diff erences
will stay constant. For age groups in which global relative
and absolute diff erences in death rates are diverging,
extraordinary eff orts will be needed to achieve laudable
goals such as a grand convergence. If convergence
includes reducing the ratio of the highest to lowest death
rates, even for under-5 mortality, major new eff orts will be
needed to have faster percent decreases in countries with
higher mortality.
Arguments that convergence is technically and
fi nancially feasible are grounded on the rapid
improvements of some countries.24 For example, from
1990 to 2013, 13 countries (all low-income), achieved
increases in life expectancy greater than 10 years
(appendix pp 141–151). The real challenge is whether the
strategies to decrease mortality used by these countries
are generalisable or transferable to those countries who
are making the least progress. The Lancet Commission
on global health 2035 drew attention to the four Cs
(Cuba, Costa Rica, Chile, and China). Life expectancy has
improved faster than the global aggregate trend in China
and Chile in the past 23 years.
The good news is that some countries that were
low-income in 1990 have achieved remarkable progress in
the past 23 years—for example, in Nepal, life expectancy
has increased by 12·16 years since 1990, reaching
70·64 years in 2013 for both sexes combined (appendix
pp 141–151). Other examples of improvements greater
than 12 years for both sexes combined include Rwanda,
Ethiopia, Niger, Maldives, Timor-Leste, and Iran. Because
Panel: Research in context
Systematic review
The GBD 2013 assessment of causes of death is a major
improvement in the evidence base compared with GBD 2010
through the inclusion of new data from vital registration
systems, verbal autopsy studies, maternal mortality
surveillance, injury surveillance and other sources. Through
the inclusion of sub-national data on China, Mexico, and UK
the evidence base for causes of death has been greatly
expanded. Redistribution algorithms for ill-defi ned causes of
death used to enhance the comparability of data were based
on new statistical models. GBD 2013 also benefi ts from
several improvements in the methods used to estimate all-
cause mortality and specifi c causes of death such as HIV/AIDS.
GBD 2013 provides a more up-to-date and comprehensive
assessment of causes of death than do other studies of cause
of death in particular age groups (CHERG), for particular
causes (GLOBOCAN),83 and previous GBD analyses
(GBD 2010).2–8
Interpretation
This study provides a comprehensive description of mortality
levels and patterns worldwide, and provides the evidence to
assess progress of global development goals, including
control of non-communicable diseases, and priorities for
further global health and development debates. Because the
study provides a complete re-analysis of trends for each cause
from 1990 to 2013, it supersedes the results of the GBD 2010
study. This is the fi rst time that country-specifi c results for all
188 countries with populations of more than 50 000 people
have been comprehensively published. Country-specifi c data
provide the opportunity to examine the extent to which
epidemiological convergence is occurring across countries.
For CHERG estimates see
http://cherg.org/main.html
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38
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the Rwandan genocide occurred after 1990, the progress
from the peak of mortality during the genocide until 2013
is even larger, 49·63 years. Studies have already assessed
progress in Bangladesh, Ethiopia, and Niger, particularly
in reducing child mortality.95 Further study of these
countries might provide insights about how to achieve
low mortality, including the role of development
assistance for health, rapid economic growth, and
addressing chronic challenges such as famines. Simple
assessments built up from individual technology
analyses, such as the Disease Control Priorities-2,96
assume a high-level of health system effi ciency and
contextual factors that enable technology to be delivered
such as levels of maternal education. Plans to achieve a
grand convergence in the face of diverging mortality will
need to take into account low levels of health system
effi ciency and low levels of health system resourcing in
some countries and the greater eff orts needed to achieve
high intervention coverage in low-income countries with
inadequate primary health-care systems and low levels of
educational attainment. The challenge of improving
health system management, particularly locally, is a
crucial component of the future plans.
The analysis of average relative diff erence between
countries and average absolute diff erence between
countries by cause (data not shown) shows the general
pattern that many communicable, maternal, and neonatal
causes, along with war and natural disasters, are highly
unequal across countries; almost all have average relative
diff erences of more than 50%. Among the
non-communicable disease categories, mental and
substance use disorders is the only cause with a mean
relative diff erence greater than 40%. Following the more
stringent criteria for convergence—in which global rates
and the Gini coeffi cient are both falling—only neoplasms
and chronic respiratory diseases are converging. As more
countries go through the epidemiological transition, it
seems likely that cross-country inequalities or relative
diff erences for communicable causes will rise and
inequalities for non-communicable causes will narrow.
Narrowing inequalities across countries will not necessarily
narrow inequalities for non-communicable disease within
countries. Because mortality exponentially rises with age,
at least after age 50 years, relative diff erences at older ages,
when mortality becomes concentrated, tend to be small.
Causes such as diabetes, chronic kidney disease, and
alcohol and drug use disorders—for which global death
rates are rising and inequality is increasing—are
exceptions to this general pattern.
Non-communicable diseases
Age-standardised death rates for cardiovascular and
circulatory diseases have fallen in high-income and many
middle-income countries since 1990. Rapid falls have
occurred in some countries. For example, fi ve countries
(Israel, Denmark, Norway, South Korea, and UK), had at
least a 65% decrease in age-standardised death rates for
ischaemic heart disease. Many other countries have had
decreases of 40–65%. Age-adjusted death rates caused by
haemorrhagic stroke fell by three-quarters in South Korea.
The ageing and growth of populations has led to an
increase in the total number of cardiovascular deaths,
accounting for almost a third of all deaths globally in
2013. Ischaemic heart disease, ischaemic stroke, and
haemorrhagic stroke continue to cause most
cardiovascular and circulatory deaths in almost all
countries. Some Balkan countries are an exception;
cardiomyopathy was a leading cause of death, possibly as
a result of alcohol exposure or local patterns of garbage
codes.97 Additional studies are needed to establish
whether this fi nding is driven by medical certifi cation
practices or is related to alcohol or some other factor.97
Age-standardised death rates for atrial fi brillation and
fl utter and peripheral vascular disease have increased,
possibly because of increased awareness of these
conditions or better survival from cardiovascular diseases
that share the same risk factors. Much uncertainty
remains for trends in mortality caused by rheumatic
heart disease, partly because endemic populations are
concentrated within poorer subnational regions where
data collection is limited and rheumatic heart disease
might not always be coded as the underlying cause of
death.98 Eff orts to benchmark changes in cardiovascular
and circulatory diseases will benefi t from increasing
access to verbal autopsy in India and sub-Saharan Africa,
household surveys focused on chronic diseases, and
improvements in electronic health records.
Generally, cancer deaths are increasing but
age-standardised cancer death rates are falling. Some
cancer-related risk factors, such as tobacco consumption,
have decreased, but others, such as obesity, have
increased. The substantial general fall in cancers require
further explanation. Death rates for fi ve cancers increased
(non-Hodgkin lymphoma, mesothelioma, kidney cancer,
pancreatic cancer, and multiple myeloma); some
explanations, such as the potential link between the rise
of diabetes and pancreatic cancer might account for some
of these reversals. Because of diff erent rates of decrease
for other sites, the mix of cancers is steadily changing,
particularly in low-income regions, such as the relative
importance of breast cancer compared with cervical
cancer. These local changes have important implications
for the development of cancer care programmes and
training. Because of the strong relation between cancer
mortality and age, ageing of the world’s population is the
most important driver of the rising number of cancer
deaths in most countries. Most countries can expect to
have to deal with more patients who need diagnosis,
treatment, and palliation in coming years.
Alzheimer’s disease and other dementias
We used a substantially diff erent approach to estimate
Alzheimer’s disease and other dementia mortality in the
GBD 2013 by focusing on studies of prevalence and
For the age-standardised death
rates by cause for each country
see http://vizhub.healthdata.
org/cod
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39
using data from countries with the highest death to
prevalence ratios in 2013 to estimate mortality in other
regions and back in time. This change greatly lowers the
increase compared with GBD 2010 in the age-
standardised death rate for dementia although the
numbers of dementia deaths nevertheless increased.
Lower increases in the age-standardised rate were
because the meta-regression of prevalence studies did
not show a rapidly rising trend; one study, reported
decreases in age-specifi c rates, although our overall
assessment suggests a slight increase in age-specifi c
rates.99 Even in high-income countries with complete
medical certifi cation of causes of death, we argue that
dementia was systematically underestimated as a cause
of death in earlier periods. Other studies, such as the
National Mortality Followback Study in USA, support
this idea.100 Dementia deaths might have been
misclassifi ed into categories such as senility. Our
garbage code redistribution algorithms for this broad
category might have under-allocated dementia deaths in
earlier periods. For future research, we may want to
more carefully trace to which garbage codes dementia
deaths might have been assigned using hospital linkage
or other approaches.
The other eff ect of using this approach is that we
estimated considerably more dementia deaths in
middle-income countries than in the GBD 2010.
Prevalence studies suggest dementia occurs in these
countries although it is rarely recorded on a death
certifi cate as a cause of death. Our overall conclusion is
that dementia is more common worldwide and that
numbers are increasing because of population ageing
with only a small component of the increase caused by
rising age-specifi c rates. The analysis of dementia will
benefi t from further population-based prevalence
surveys, especially with repeated measurement over time
using standardised defi nitions and methods. As further
studies of this type become available and incorporated
into the GBD, our estimates of dementia burden might
be substantially revised. Trends in the category
Alzheimer’s disease and other dementias might mask
upward trends in Alzheimer’s and downward trends in
vascular dementia; however, these disorders are diffi cult
to tell apart in population-based prevalence studies and
cause of death data. Nevertheless, our fi nding that the
number of dementia deaths is increasing implies that
governments should remain concerned about the rising
demands for care that will come with population ageing
even if future rates do not increase substantially.
Diarrhoea and lower respiratory infections
We report that the distribution of the causes of diarrhoea
is diff erent around the world. The distribution of
pathogens has also changed signifi cantly since 1990—for
example, almost 50% (20 343 [9054–41 216] deaths) of all
cholera deaths in children occur in sub-Saharan Africa.
Because we used GEMS data to estimate relative risks, it
is perhaps not surprising that our results are comparable
to their fi ndings.57 The population attributable fraction for
pathogens such as Campylobacter, Shigella, and Salmonella
were not signifi cant in some countries and some ages.
Because of the nature of case notifi cation data, we had to
estimate all types of cholera and were unable to
breakdown cholera into O1, non-O1, and O-139.
In high-income countries, C diffi cile is an important
threat that has increased during the past two decades.
65% (744 413 deaths) of unexplained diarrhoea in people
older than 5 years is an important knowledge gap.
Although the new counterfactual approach is successful
for estimating attributable death empirically and
adjusts for the overall pathogen load in the country, it
still suff ers from limitations such as the potential low
sensitivity of diagnostic tests. Some pathogens are
more prevalent in controls than in cases, which might
present a distorted causal picture because of continuous
shedding of pathogen long after the acute phase.101–105
These fi ndings could also suggest a protective eff ect of
infection from one or more pathogens against other
pathogens or could be simply caused by a diff erential
decrease in the sensitivity of diagnostic tests (for other
pathogens) where diarrhoea presents assuming a single
pathogen caused the diarrhoea. More sensitive
diagnostic tests help to improve sensitivity but at the
price of decreased specifi city because of contamination
and post-diarrhoea pathogen excretion. Follow-up
studies with multiple measurements of pathogens in
children during healthy and diarrhoea periods could
help to elucidate the true causal associations. Better
case defi nition and more strict criteria for pathogens
such as excluding recent cases of diarrhoea could
decrease exposure misclassifi cations.
Our estimates of the fraction of under-5 lower
respiratory infection deaths attributable to the four
causes of pneumonia (pneumococcus, H infl uenzae B,
respiratory syncytial virus, and infl uenza) are much the
same as previous estimates, with pneumococcus and
H infl uenzae B the predominant causes.63,64,106–108 The
large fraction of lower respiratory infection attributable
to pneumococcus and H infl uenzae B, particularly in
low-income regions where the absolute burden is
highest, shows the potential benefi t of continuing to
scale up pneumococcal conjugate and H infl uenzae B
vaccination. We calculated the contribution of each
cause with a counterfactual approach. This approach
means that they do not add up to 100% but also that
there might be overlap; for example, death from lower
respiratory infection might involve viral and bacterial
co-infection. These results should also be interpreted
with caution because of the data used to generate these
estimates. Data for cause are sparse and prone to several
biases, which is shown in the large UIs.
Estimates of the mortality burden of pneumococcal
pneumonia in children rely on data from vaccine probe
studies, which showed that disease in infants fell after
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pneumococcal conjugate vaccination; there are no
sensitive diagnostic tests to detect non-bacteraemic
pneumococcal pneumonia in children. To calculate
burden in the absence of a diagnostic assay, the
pneumococcal conjugate vaccination probe studies
assumed a vaccine effi cacy against non-bacteraemic
pneumonia caused by vaccine types equal to that of
protection from vaccine type bacteraemia (75%). Data
from a large randomised trial of pneumococcal conjugate
vaccination in adults confi rmed effi cacy against
bacteraemic pneumonia of 75%, but effi cacy against
non-bacteraemic pneumonia was only 45%.109 If similar
effi cacy estimates are applied to infants, then the
contribution of the pneumococcus to pneumonia
mortality in infants could be as high as 63%
(166 324 deaths). Because data were sparse, we did not
estimate the fraction of deaths caused by H infl uenzae B
among people aged 5 years and older. The before-and-after
vaccine effi cacy studies used to estimate the burden of
pneumococcus were limited to high-income settings.
These types of studies might also be biased because of
underlying temporal trends in hospital admissions for
lower respiratory infection. Furthermore, the only
variation included for pneumococcus and H infl uenzae B
is a result of diff erences in vaccination coverage.
The observational studies used for respiratory syncytial
virus and infl uenza were based on case series data from
predominantly tertiary-level hospitals, which might not be
representative of the underlying population and are prone
to varying case-defi nitions and diagnostic methods. Finally,
hospital discharge data for the relative diff erences in case-
fatality for respiratory syncytial virus and infl uenza
compared with pneumococcus and H infl uenzae B were
limited to high-income and middle-income countries.
Several of these shortcomings are being addressed by the
Pneumonia Etiological Research for Child Health project.110
Injuries
Most global road traffi c deaths occur in low-income and
middle-income countries and are rapidly increasing
because of the growth in motorisation. Mortality rates
caused by traffi c-related injuries are increasing in
low-income and middle-income countries. Pedestrians
are most often aff ected, followed by car occupants and
motorcyclists. Conversely, traffi c deaths are decreasing in
high-income countries. We noted a similar divergence
between low-income and high-income countries for
occupational injuries: they generally fell in high-income
countries (with the exception of deaths resulting from
asbestos-related mesotheliomas), whereas occupational
injury deaths have increased in low-income countries
(data not shown).
Suicide continues to be a major public health problem
in many regions. Half of all suicide deaths occur in
China and India alone. However, the trends are in
opposite directions, decreasing rapidly in China but
rising in India between 1990 and 2013. Both countries
have undergone economic growth and urbanisation, a
key factor in limiting access to lethal pesticides, a
common method of suicide by poisoning in both
countries.111 Therefore, as yet unexplained reasons must
exist for the divergence between the two countries.
We recorded several sharp increases in mortality
caused by war and disaster. Particularly, the 2010 Haiti
earthquake, confl ict in Syria over the past several years,
the 2011 Tōhoku earthquake and tsunami in Japan, and
confl ict in Libya in 2011 have caused considerable loss
of life. The war in Syria led to an estimated 29 947 deaths
(19 392–54 903) in 2013, and about 10 504 deaths and
21 422 deaths in each of the preceding 2 years.
Uncertainty around these estimates is large because
several diff erent estimates exist. These estimates are of
the direct deaths attributable to armed confl icts and
natural disasters and do not account for the full eff ects
of mechanisms such as the breakdown of health
systems or critical infrastructure. For example, the
confl ict in Syria has had a substantial eff ect on routine
immunisation for polio, with coverage now as low as
50% in some areas.112 The estimation of direct deaths
caused by war and natural disasters is one of the most
challenging components of the GBD measurement. We
depend on the work of various groups to collate
combatant reports, newspaper reports, humanitarian
agency assessments, and other direct accounts to
approximate the number of deaths. Vital registration
systems often do not function in war or confl ict but
might be more useful in countries with natural
disasters as a way of measuring the number of deaths.
More work is needed to better measure shock mortality.
India
India accounts for 19% of the world’s deaths in 2013.
Estimations of cause of death for India are important both
for health policy in India and for global understanding of
causes of death. India has had remarkable progress in
reducing both child and adult mortality over the past
23 years. Average yearly rates of decline were 1·3% per
year for adults and 3·7% for children.
Unfortunately, less cause of death data were available for
2013 than for 1990 or 2000. The Medical Certifi cation of
Causes of Death system provided ongoing information
about patterns of urban mortality with better completeness
in some states than in others. In rural areas, the Survey of
Causes of Death (Rural) routinely reported causes of death
from verbal autopsy from 1980 to 1998. This survey was
replaced with a verbal autopsy sample collected by the
Registrar-General of India based on the ongoing Sample
Registration Scheme. Data for 2002–04 have been reported
but not in full detail—results were released in a series of
articles spanning 2008–14 but even these have not
provided the standard tabulation of deaths by International
Classifi cation of Diseases cause, age, and sex used by
most countries. Verbal autopsies were collected after 2004
but no data have been analysed or released. Attempts to
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41
add verbal autopsy to other major data collection eff orts of
the Government of India, such as the Annual Health
Survey and the latest round of the District Level Household
Survey, have so far been unsuccessful. Small community
studies continue to be published but there is a major gap
in knowledge of rural cause of death.
In view of the rapid change in India, including decreases
in child mortality and adult mortality, simple predictions
based on the 2002–04 data are inadequate. Our modelling
strategy takes into account trends for key covariates that
explain some changes in age-specifi c rates for many
causes; nevertheless, more recent national data would be
helpful to develop more precise estimates of causes of
death for India. Epidemics such as Chikungunya, dengue,
and H1N1 infl uenza also point to the need for better
ongoing surveillance of causes of death in India that does
not suff er from long time lags.113–116
Comparing diff erent global health estimates
Comparison of the GBD 2013 results with GBD 2010 for
1990 or 2010 shows some important diff erences. The
overall correlation coeffi cient of age-sex-country-cause
rates was 0·998 in both 1990 and 2010 but some causes
have changed substantially at the global level. The
ten causes in terms of the largest change in the number
of global deaths were Alzheimer’s disease and other
dementias, ischaemic heart disease, interstitial lung
disease and pulmonary sarcoidosis, cerebrovascular
disease, neonatal encephalopathy caused by birth
asphyxia and trauma, lower respiratory infections, other
cardiovascular and circulatory diseases, cirrhosis,
malaria, and chronic kidney disease. These changes
might be because of new data, modifi cations of garbage
coding algorithms, and revised modelling strategies
(appendix). Generally, the data used has substantially
increased: from 8967 site-years to 14 244 site-years.
Some specifi c changes are worth noting. First, data for
China has greatly increased. Given China’s population, the
incorporation of large amounts of new data for cause of
death led to large changes in China and these aff ected even
global estimates. The fi ve largest changes for China in
2013 compared with the GBD 2010 were ischaemic heart
disease, Alzheimer’s disease and other dementias,
cerebrovascular disease, interstitial lung disease and
pulmonary sarcoidosis, and chronic obstructive pulmonary
disease. Second, more detailed cause of death data covering
189 causes instead of 98 causes were available for Russia
for the GBD 2013. This aff ected several smaller causes,
such as those related to alcohol. Third, we included new
vital registration data for Turkey for 2010–12. Fourth, we
modelled India in two components, urban and rural,
which enabled us to make much more use of some data
sources such as the Survey of Causes of Death (Rural) for
rural India. Because India is large, these changes have a
global eff ect. Fifth, for cancers, we incorporated
1145 registry-years of new data, including 128 from the
Cancer Incidence in Five Continents Volume X.48 Sixth, the
change to use of a Bayesian noise reduction algorithm for
smoothing has reduced the number of outliers, particularly
in small verbal autopsy studies, some of which were
included in the GBD 2010. Seventh, changes to garbage
code redistribution algorithms, particularly the use of
statistically derived algorithms that vary by region and
country, has had eff ects on injuries, cancers, and
cardiovascular diseases. Other changes included treating
unspecifi ed anaemia as a garbage code whereas in the
GBD 2010 it was mapped to iron-defi ciency anaemia,
moving abdominal hernia from other digestive diseases to
hernia, as well as moving deaths related to specifi c
procedures to the category of adverse eff ects of medical
treatment. In the GBD 2010, we included abdominal
hernia, including umbilical hernia, ventral hernia, and
diaphragmatic hernia in the category “other digestive
diseases”. In the GBD 2013, we combined these with
inguinal hernia and femoral hernia into one cause named
“hernia”. Additionally, we moved some ill-defi ned causes
from the other digestive diseases category to more specifi c
causes, thereby reducing the number of deaths in other
digestive and changing the distribution of all digestive
deaths among its more disaggregated causes. Seventh, the
assessment of all-cause mortality in the GBD 2013
benefi ted from both new data and improved approaches
for assessment of the age pattern of mortality in the model
life-table system. Finally, the more detailed analysis of
HIV/AIDS led to major changes both for HIV/AIDS
(particularly in countries with concentrated epidemics)
and for other causes, particularly in the people of
reproductive age and in countries with moderate-to-large
epidemics.
The International Agency for Research on Cancer
produces cancer estimates by country, age, sex, and cancer
site for 2008 and 2012 (GLOBOCAN). Our defi nitions and
the GLOBOCAN defi nitions are compatible for 25 sites.
For these cancer sites, the total estimated prevalence from
GLOBOCAN was 6 848 204 cases in 2008 and
7 483 018 cases in 2012. By comparison, the GBD estimates
were 6 930 377 for 2008, and 7 437 018 for 2012. Worldwide,
the largest variation in estimates occurs for thyroid cancer,
testicular cancer, and other pharynx cancers, with
diff erences of 20–30%. The rough similarity of results at
worldwide masks substantial national variation.
Comparing age-standardised death rates for 2012, the
correlation ranges from 0·94 for tracheal, bronchus, and
lung cancer, to 0·20 for thyroid cancer. Five cancers have
correlations below 0·5 (ovarian, non-Hodgkin lymphoma,
testicular, Hodgkin lymphoma, and thyroid). A further six
cancers have correlations of 0·5–0·7 (uterine,
nasopharynx, lip and oral cavity, breast, leukaemia, and
multiple myeloma).
Because both GLOBOCAN and our estimates used
population-based cancer registry data and vital registration
data as inputs, the wide variation in results requires
explanation. As with all comparisons of global health
estimates, the diff erences stem from data, data processing,
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and model development. We included a wider range of
registries than did GLOBOCAN, particularly in China,
and we used of a broader database of vital registration
data. Our redistribution of cancer of unknown primary
was based on a statistical model. The most important
diff erences, however, probably stem from the modelling
strategy. For all cancer sites in all countries, we used
CODEm. GLOBOCAN used nine diff erent methods to
estimate cancer mortality depending on the country.83,117
The choice of method can lead to surprising diff erences in
estimated rates for neighbouring countries without data.
For example, the age-standardised death rate for male
thyroid cancer in Timor Leste is 250% higher than that for
Indonesia; age-standardised death rates for testicular
cancer diff er by 1300% between Mali and Mauritania. The
GLOBOCAN estimates have a substantial subjective
component in the choice of which modelling strategy to
use and do not provide any estimate of uncertainty.
Empirical assessment of the validity of the GLOBOCAN
methods—for example, through cross-validation—would
help to understand the strength of the approach.
Understanding causes of death begins with
assessment of all-cause mortality. There are some
notable diff erences between our assessment of global
age-specifi c deaths and those produced by the United
Nations Population Division in their World Population
Prospects 2012 revision (WPP2012). For the three
periods (1995–2000, 2000–05, and 2005–10) as defi ned
in WPP2012, the total numbers of deaths were 2·4–3·6%
higher (6·1 million–9·1 million deaths) than estimated
by us. These diff erentials translate into a diff erence of
7·8 million deaths for the 5-year period between 2005
and 2010. The diff erence is greatest for younger age
groups. For 2005–10, estimated under-5 deaths from
WPP2012 are 10·7% higher (3·9 million more deaths)
than for the GBD 2013.
The WPP2012 global under-5 death estimates were also
higher than those of UNICEF; part of this diff erence
might be a result of the agencies releasing their estimates
at diff erent times. The biggest relative diff erence was for
the adolescent age group (age 5–14 years). For 2005–10,
the estimated deaths in adolescents from WPP2012 were
45·1% higher than in the GBD 2013, even though the
absolute diff erence was about 2·2 million for a 5-year
period, less than 1% (2·17 million of 264·7 million) of the
total deaths for the same period. The diff erences are even
greater at the GBD regional level. For 2005–10, the
relative diff erence between WPP2012 and GBD 2013
ranged from 26·7% (122 800) lower in WPP2012 in
Oceania, to 36·0% (1·9 million) higher in WPP2012
in central sub-Saharan Africa. WPP2012 tends to have
high estimates of adolescent mortality compared with
the GBD 2013 for all regions in sub-Saharan Africa,
Andean Latin America, north Africa and Middle East,
and southeast Asia. Overall, we fi nd more diff erences in
estimates for sub-Saharan Africa across all age group in
both relative and absolute terms.
Such discrepancy originates from diff erent assessments
of child mortality rates and the diff erence in model
life-table systems, both of which used child mortality rate
to generate age-specifi c mortality rates. Estimating
mortality for the adolescent age group is important.118–120
As part of the background research for the GBD 2013, we
assessed the Demographic Health Surveys complete
birth history data for age groups 5–9 years and 10–14 years
and compared this data in countries with almost
complete vital registration or sample registration
systems, such as India. We also systematically assessed
estimates of adolescent mortality from sites of the health
and demographic surveillance systems, a network known
as INDEPTH. When we assessed the ratio of 5q5
(probability of death from age 5 years to age 10 years) and
5q10 (probability of death from age 10 years to age 15 years)
to under-5 mortality, confl icting pictures arise: our GBD
2013 estimates are sometimes higher than one source
and lower than the other. Further analysis is warranted to
validate our approaches for estimating adolescent
mortality in low-income and middle-income countries
without working vital registration systems. In addition,
eff orts are needed to improve both data collection and
method development to better estimate mortality for
adolescents.
As in the GBD 2010, we noted diff erences for causes of
child death compared with those produced by the Child
Health Epidemiology Reference Group (CHERG;
table 5). Given the complexity of both approaches, it is
CHERG GBD 2013
Neonates aged 0–27 days
Congenital abnormalities 270 (207–366) 251 (221–291)
Diarrhoea 50 (17–151) 52 (44–61)
Pneumonia 325 (209–470) 213 (186–242)
Intrapartum-related complications* 717 (610–876) 657 (532–770)
Sepsis or meningitis 393 (252–552) 369 (237–504)
Tetanus 58 (20–276) 34 (16–48)
Other neonatal disorders 181 (115–284) 470 (411–557)
All causes 3072† 2807 (2719–2898)
Children aged 1–59 months
Injury 354 (274–429) 350 (310–394)
Diarrhoea 751 (538–1031) 536 (461–607)
AIDS 159 (131–185) 102 (95–111)
Pneumonia 1071 (977–1176) 772 (693–850)
Malaria 564 (432–709) 699 (576–855)
Measles 114 (92–176) 95 (52–166)
Meningitis 180 (136–237) 129 (98–163)
Other disorders 1356 (1112–1581) 1355 (1211–1524)
All causes 4550† 4039 (3883–4207)
Data are thousands of deaths (95% uncertainty interval). GBD=Global Burden of Disease Study. CHERG=Child Health
Epidemiology Reference Group. *Compares GBD cause “Neonatal encephalopathy (birth asphyxia/trauma)” with
CHERG cause “intrapartum-related complications”. †CHERG did not report uncertainty estimates for all-cause
mortality in children.
Table 5: Comparison of GBD and CHERG estimated child deaths for select causes in 2010
For the INDEPTH Network see
http:// www.indepth-network.
org/
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43
diffi cult to isolate the reason for the diff erences.
One reason might be the diff erent studies used: we
included 5039 site-years of vital registration and 358 of
verbal autopsy data compared with 578 and 192 for
CHERG. Our modelling strategy was founded on
modelling each individual cause separately, using the
most appropriate method for each cause, and then
combining the diff erent cause estimates into an overall
assessment consistent with all-cause mortality using
CoDCorrect. CHERG used separate modelling strategies
for HIV/AIDS, measles, pertussis, and malaria outside
of Africa, and four diff erent models for the remainder of
the child causes. Separate logistic models, each with
subtly incomparable cause lists, were used for neonates
and children older than 1 month, for low mortality
countries excluding China, for high mortality countries
excluding India, for India alone, and for China alone.
This partition of the world into separate models was not
justifi ed statistically—for example, they have not shown
statistically diff erent relationships with covariates for
their four sets of models. Additionally, post-estimation
adjustments were applied to pneumonia, meningitis,
and malaria to account for intervention eff ectiveness;
pneumonia, sepsis, meningitis, and tetanus to account
for the reliance on a combined severe infection cause in
the primary model; and diarrhoea, neonatal sepsis, and
sudden infant death syndrome in China to account for
studies that report few causes.
We used a more empirical approach. We quantifi ed
both the root-mean squared error and validity of the UIs
through cross-validation; CHERG has not to date reported
any cross-validation results. Given the possibility that
diff erent relationships might exist between covariates
such as access to clean water or sanitation and diarrhoeal
mortality in diff erent parts of the world, we undertook a
sensitivity analysis in which we excluded vital registration
data from high-income regions from the models for lower
respiratory infections and diarrhoea. We detected no
substantial diff erences for estimated global cause of death
patterns in these cases. Furthermore, in CHERG,
neonatal causes were assumed to not cause deaths after
1 month although high quality vital registration systems
routinely report deaths from these causes that extend into
the second month of life.
Challenges and limitations
In the GBD 2013, we did not include several clinical
pathways to death on the cause list, such as heart failure,
sepsis, fungal infection, and acute kidney injury. These
clinical entities following the underlying cause construct
of the International Classifi cation of Disease are treated as
garbage codes and redistributed to the likely underlying
cause. Although this approach is consistent with the idea
of assigning each death uniquely to the underlying cause,
it masks endpoints for clinical service delivery. For
example, most fungal infections are relatively minor, but
potentially millions of people contract invasive fungal
diseases121 that can be important pathways to death.
Similar assessments can be made for sepsis, acute kidney
injury, and heart failure. In future iterations of the GBD,
we will aim to quantify mortality that occurs through
these intermediate causes. Such intermediate cause
estimation cannot be presented in the same causes lists as
underlying causes of death but can provide supplemental
and important information that would otherwise go
unrecognised in global epidemiology.
Even in high-income countries with complete vital
registration systems, our results diff er from offi cial
statistics.122 This diff erence is largely caused by the
emphasis in the GBD on enhancing comparability through
redistribution of deaths assigned to garbage codes.
Country-specifi c data for cause of death show substantial
national variation in coding practices. Generally, we used
global or regional algorithms to redistribute deaths
assigned to garbage codes. This approach is fairly coarse
and does not capture local variation in certifi cation practice
or timing of implementation of coding rules. The GBD
2013 is the most detailed eff ort to date to try and
systematically deal with garbage code redistribution. Some
changes, such as the treatment of ill-defi ned cancers or
heart failure using statistical approaches, altered the GBD
2013 results compared with the GBD 2010. We believe that
the GBD results including the fraction of deaths assigned
to diff erent types of garbage codes can be useful for
national statistical authorities’ eff orts to improve medical
certifi cation of causes of death. We also believe that
through the extensive network of GBD collaborators, we
can move in future research to more country-specifi c
redistribution algorithms. To ensure comparability,
however, these national variations will have to be grounded
in a sound statistical approach and theory of measurement.
A study of this scope has many limitations. First is
the quality of the underlying medical certifi cation of
causes of death and verbal autopsy data. Even medical
certifi cation of causes of death has limitations, which is
shown by the need for garbage code redistribution.106,123,124
Moreover, verbal autopsy data vary substantially in
terms of the instrument used and the training given to
physicians assigning causes of death. These
shortcomings might reduce the comparability of cause
of death data between countries and of our estimates
based on these data.
Second, we did not incorporate uncertainty from garbage
code redistribution into our estimation of UIs. Propagating
such uncertainty into the CODEm models will require
revision of the modelling strategy or an enormous increase
in computational time. As evidenced by the change for
some causes compared with the GBD 2010 as a result of
changes in redistribution derived from statistical methods,
this is an important area for future research.
Third, the major expansion of data for China and the
associated changes in the estimates for some but not all
causes, shows that UIs cannot take into account data that
have not been included in the analysis.
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Fourth, for some causes, CODEm produces larger UIs
in high-income countries than might be expected. This
diff erence is largely the result of heterogeneity across
high-income countries for a cause that cannot be explained
by the models. This eff ect is more notable for causes such
as diabetes, for which there are reasons to believe that
large variation in certifi cation practice remains in high-
income countries.125 Because diabetes or increased fasting
plasma glucose is a risk factor for macrovascular
outcomes, diff erences in how physicians interpret the
meaning of underlying cause could explain such national
variation in practice. In the GBD, the full consequences of
high fasting plasma glucose are captured in the risk factor
assessment;8 deaths caused by diabetes in our analysis
were only those that were recorded on the death certifi cate
to be the underlying cause.
Fifth, although we tried to improve the comparability of
cause of death data over time through mapping variants of
the International Classifi cation of Diseases and
garbage-code redistribution, some time trends might be
aff ected by changes in diagnostic technology. Some causes,
such as cancers, might have been less likely to have been
diagnosed in the 1980s and 1990s, when imaging and other
diagnostic techniques were not widespread.
Sixth, for chronic kidney disease, the breakdown
into deaths from diabetes, hypertension, acute
glomerulonephritis, and other depends on both detailed
cause of death data and renal registry data. In clinical
practice, assigning chronic kidney disease to a particular
cause might be diffi cult for patients with both hypertension
and diabetes.
Seventh, in some unusual cases such as chronic
respiratory diseases in India, the sum of modelled
estimates for CoDCorrect level 2 causes are much smaller
than the level 1 modelled estimate leading to very large
corrections for the CoDCorrect step. Very large
corrections for CoDCorrect suggests that the component
models for these causes can be improved in the future
with better data or methods.
Eighth, for natural history models, most notably for
HIV/AIDS, changes in parameter assumptions such as
the death rate on or off antiretroviral therapy, can have a
large eff ect on estimated mortality. We believe that
progressive revision of these models improves the
estimates but nevertheless, validation of natural history
models is diffi cult. For CODEm, we were able to quantify
with the cross-validation strategy model performance but
this is not possible with the natural history models.
Ninth, a strength of the GBD approach is that all
estimates of cause-specifi c mortality must sum to
all-cause mortality in a country-age-sex-year group.
However, this means that estimates for a specifi c cause
are aff ected by the estimates for all other causes. Causes
of death such as malaria, that have very wide UIs are
particularly aff ected by the estimates of other causes.
Tenth, models used to generate estimates of all-cause
mortality and cause-specifi c mortality make use of a long
list of covariates. Uncertainty in these covariates, such as
GDP per head, was not routinely quantifi ed but
nevertheless might be substantial. We were not able to
propagate uncertainty in the independent variables used
in the modelling stages into the fi nal results. 95% UIs
might therefore be under-estimated. However, when we
have tested in a few cases the eff ect of propagating
uncertainty in the independent variables in the case of
the HIV crude death rate, the changes to UIs, were
minor (data not shown).
Eleventh, we made extraordinary eff orts to propagate
uncertainty throughout our all-cause mortality estimation
process, which is not yet common practice in modern
demographic research. However, uncertainty in covariates
used in the fi rst stage model of child and adult mortality
rate was not included because of the complexity of added
computation and the fact that these covariates have little
eff ect on our fi nal estimates, as indicated by our
preliminary testing.
Lastly, empirical age patterns of mortality, which are
vital for the estimation of mortality for many low-income
and middle-income countries, mostly come from
high-income countries with great vital registration
systems and some low-income and middle-income
countries in the most recent period. Countries in the
sub-Saharan African region are least represented in our
empirical database of age pattern of mortality (appendix
pp 81–89). Propagating uncertainty from both under-5
and adult mortality rates (two key entry parameters for
our new model life-table system), and from the standard
life-table generation process has given our death
estimates in sub-Saharan African countries substantial
uncertainty; accurate documentation of age pattern of
mortality in these countries are key for producing best
all-cause mortality estimates in the future.
Conclusion
Global public policy to reduce premature death needs a
detailed, up-to-date, and accurate understanding of
progress (or lack thereof) of disease and injury control
strategies. This understanding applies not just to
diseases that have been the focus of global public health
eff orts for the past few decades, but increasingly, as we
have shown, for newly recognised contributors to global
health trends. Through the process of providing yearly
updates, the GBD is transforming into a collective
approach to global health surveillance. Ideally, it will
aggregate data from all available sources and provide a
coherent view of health levels and trends that is timely,
valid, and local. To fully achieve a collective process of
global health surveillance, the time lag will need to be
shortened between data collection, reporting, and
inclusion in the GBD. Public policy in countries will be
much better informed if more frequent assessments
are accompanied by less uncertainty around the
estimates. Uncertainty will decrease not so much as a
result of further methodological advances in disease
Articles
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45
modelling and data synthesis, but much more as a
result of greater investment and awareness among
countries and donors alike of the need to strengthen
vital registration systems.
Global collective action to reduce mortality from major
communicable diseases such as diarrhoea, measles,
tetanus, tuberculosis, and, more recently, HIV/AIDS and
malaria, is working, but will require continued
intervention eff orts and resources and will probably be
even more responsive if periodic assessments such as
that reported here are available and used. While progress
is being made to control several major non-communicable
diseases of global concern, others have been largely
neglected but are rising in importance, particularly drug
use disorders, cirrhosis, diabetes, and chronic kidney
disease. Greater prominence to reducing disease burden
from these diseases, as well as continuing priority for
injury control, is strongly suggested by our analysis. The
fi ndings on global, regional, and national trends in
mortality from diseases should provide an important
baseline for discussions about the next generation of
health goals and targets after the Millennium
Development Goals.
GBD 2013 Mortality and Causes of Death Collaborators
Mohsen Naghavi, Haidong Wang, Rafael Lozano, Adrian Davis,
Xiaofeng Liang, Maigeng Zhou, Stein Emil Vollset,
Ayse Abbasoglu Ozgoren*, Safa Abdalla*, Foad Abd-Allah*,
Muna I Abdel Aziz*, Semaw Ferede Abera*, Victor Aboyans*,
Biju Abraham*, Jerry P Abraham*, Katrina E Abuabara*,
Ibrahim Abubakar*, Laith J Abu-Raddad*, Niveen ME Abu-Rmeileh*,
Tom Achoki*, Ademola Adelekan*, Zanfi na Ademi*, Koranteng Adofo*,
Arséne Kouablan Adou*, José C Adsuar*, Johan Ärnlov*,
Emilie Elisabet Agardh*, Dickens Akena*, Mazin J Al Khabouri*,
Deena Alasfoor*, Mohammed Albittar*, Miguel Angel Alegretti*,
Alicia V Aleman*, Zewdie Aderaw Alemu*, Rafael Alfonso-Cristancho*,
Samia Alhabib*, Mohammed K Ali*, Raghib Ali*, Francois Alla*,
Faris Al Lami*, Peter Allebeck*, Mohammad A AlMazroa*,
Rustam Al-Shahi Salman*, Ubai Alsharif*, Elena Alvarez*,
Nelson Alviz-Guzman*, Adansi A Amankwaa*, Azmeraw T Amare*,
Omid Ameli*, Hassan Amini*, Walid Ammar*, H Ross Anderson*,
Benjamin O Anderson*, Carl Abelardo T Antonio*, Palwasha Anwari*,
Henry Apfel*, Solveig Argeseanu Cunningham*,
Valentina S Arsic Arsenijevic*, Al Artaman*, Majed Masoud Asad*,
Rana J Asghar*, Reza Assadi*, Lydia S Atkins*, Charles Atkinson*,
Alaa Badawi*, Maria C Bahit*, Talal Bakfalouni*,
Kalpana Balakrishnan*, Shivanthi Balalla*, Amitava Banerjee*,
Ryan M Barber*, Suzanne L Barker-Collo*, Simon Barquera*,
Lars Barregard*, Lope H Barrero*, Tonatiuh Barrientos-Gutierrez*,
Arindam Basu*, Sanjay Basu*, Mohammed Omar Basulaiman*,
Justin Beardsley*, Neeraj Bedi*, Ettore Beghi*, Tolesa Bekele*,
Michelle L Bell*, Corina Benjet*, Derrick A Bennett*,
Isabela M Bensenor*, Habib Benzian*, Amelia Bertozzi-Villa*,
Tariku Jibat Beyene*, Neeraj Bhala*, Ashish Bhalla*, Zulfi qar A Bhutta*,
Boris Bikbov*, Aref Bin Abdulhak*, Stan Biryukov*, Jed D Blore*,
Fiona M Blyth*, Megan A Bohensky*, Guilherme Borges*, Dipan Bose*,
Soufi ane Boufous*, Rupert R Bourne*, Lindsay N Boyers*,
Michael Brainin*, Michael Brauer*, Carol E G Brayne*,
Alexandra Brazinova*, Nicholas Breitborde*, Hermann Brenner*,
Adam D M Briggs*, Jonathan C Brown*, Traolach S Brugha*,
Geoff rey C Buckle*, Linh Ngoc Bui*, Gene Bukhman*, Michael Burch*,
Ismael Ricardo Campos Nonato*, Hélène Carabin*, Rosario Cárdenas*,
Jonathan Carapetis*, David O Carpenter*, Valeria Caso*,
Carlos A Castañeda-Orjuela*, Ruben Estanislao Castro*,
Ferrán Catalá-López*, Fiorella Cavalleri*, Jung-Chen Chang*,
Fiona C Charlson*, Xuan Che*, Honglei Chen*, Yingyao Chen*,
Jian Sheng Chen*, Zhengming Chen*, Peggy Pei-Chia Chiang*,
Odgerel Chimed-Ochir*, Rajiv Chowdhury*, Hanne Christensen*,
Costas A Christophi*, Ting-Wu Chuang*, Sumeet S Chugh*,
Massimo Cirillo*, Matthew M Coates*, Luc Edgar Coff eng*,
Megan S Coggeshall*, Aaron Cohen*, Valentina Colistro*,
Samantha M Colquhoun*, Mercedes Colomar*,
Leslie Trumbull Cooper*, Cyrus Cooper*, Luis M Coppola*,
Monica Cortinovis*, Karen Courville*, Benjamin C Cowie*,
Michael H Criqui*, John A Crump*, Lucia Cuevas-Nasu*,
Iuri da Costa Leite*, Kaustubh C Dabhadkar*, Lalit Dandona*,
Rakhi Dandona*, Emily Dansereau*, Paul I Dargan*, Anand Dayama*,
Vanessa De la Cruz-Góngora*, Shelley F de la Vega*, Diego De Leo*,
Louisa Degenhardt*, Borja del Pozo-Cruz*, Robert P Dellavalle*,
Kebede Deribe*, Don C Des Jarlais*, Muluken Dessalegn*,
Gabrielle A deVeber*, Samath D Dharmaratne*, Mukesh Dherani*,
Jose-Luis Diaz-Ortega*, Cesar Diaz-Torne*, Daniel Dicker*,
Eric L Ding*, Klara Dokova*, E Ray Dorsey*, Tim R Driscoll*,
Leilei Duan*, Herbert C Duber*, Adnan M Durrani*, Beth E Ebel*,
Karen M Edmond*, Richard G Ellenbogen*, Yousef Elshrek*,
Sergey Petrovich Ermakov*, Holly E Erskine*, Babak Eshrati*,
Alireza Esteghamati*, Kara Estep*, Thomas Fürst*, Saman Fahimi*,
Anna S Fahrion*, Emerito Jose A Faraon*, Farshad Farzadfar*,
Derek FJ Fay*, Andrea B Feigl*, Valery L Feigin*,
Manuela Mendonca Felicio*, Seyed-Mohammad Fereshtehnejad*,
Jeff erson G Fernandes*, Alize J Ferrari*, Thomas D Fleming*,
Nataliya Foigt*, Kyle Foreman*, Mohammad H Forouzanfar*,
F Gerry R Fowkes*, Urbano Fra Paleo*, Richard C Franklin*,
Neal D Futran*, Lynne Gaffi kin*, Ketevan Gambashidze*,
Fortuné Gbètoho Gankpé*, Francisco Armando García-Guerra*,
Ana Cristina Garcia*, Johanna M Geleijnse*, Bradford D Gessner*,
Katherine B Gibney*, Richard F Gillum*, Stuart Gilmour*,
Ibrahim Abdelmageem Mohamed Ginawi*, Maurice Giroud*,
Elizabeth L Glaser*, Shifalika Goenka*, Hector Gomez Dantes*,
Philimon Gona*, Diego Gonzalez-Medina*, Caterina Guinovart*,
Rahul Gupta*, Rajeev Gupta*, Richard A Gosselin*, Carolyn C Gotay*,
Atsushi Goto*, Hebe N Gouda*, Nicholas Graetz*, K Fern Greenwell*,
Harish Chander Gugnani*, David Gunnell*, Reyna A Gutiérrez*,
Juanita Haagsma*, Nima Hafezi-Nejad*, Holly Hagan*,
Maria Hagstromer*, Yara A Halasa*, Randah Ribhi Hamadeh*,
Hannah Hamavid*, Mouhanad Hammami*, Jamie Hancock*,
Graeme J Hankey*, Gillian M Hansen*, Hilda L Harb*,
Heather Harewood*, Josep Maria Haro*, Rasmus Havmoeller*,
Roderick J Hay*, Simon I Hay*, Mohammad T Hedayati*,
Ileana B Heredia Pi*, Kyle R Heuton*, Pouria Heydarpour*,
Hideki Higashi*, Martha Hijar*, Hans W Hoek*, Howard J Hoff man*,
John C Hornberger*, H Dean Hosgood*, Mazeda Hossain*,
Peter J Hotez*, Damian G Hoy*, Mohamed Hsairi*, Guoqing Hu*,
John J Huang*, Mark D Huff man*, Andrew J Hughes*,
Abdullatif Husseini*, Chantal Huynh*, Marissa Iannarone*,
Kim M Iburg*, Bulat T Idrisov*, Nayu Ikeda*, Kaire Innos*,
Manami Inoue*, Farhad Islami*, Samaya Ismayilova*,
Kathryn H Jacobsen*, Simerjot Jassal*, Sudha P Jayaraman*,
Paul N Jensen*, Vivekanand Jha*, Guohong Jiang*, Ying Jiang*,
Jost B Jonas*, Jonathan Joseph*, Knud Juel*,
Edmond Kato Kabagambe*, Haidong Kan*, André Karch*,
Chante Karimkhani*, Ganesan Karthikeyan*, Nicholas Kassebaum*,
Anil Kaul*, Norito Kawakami*, Konstantin Kazanjan*, Dhruv S Kazi*,
Andrew H Kemp*, Andre Pascal Kengne*, Andre Keren*,
Maia Kereselidze*, Yousef Saleh Khader*,
Shams Eldin Ali Hassan Khalifa*, Ejaz Ahmed Khan*, Gulfaraz Khan*,
Young-Ho Khang*, Christian Kieling*, Yohannes Kinfu*,
Jonas M Kinge*, Daniel Kim*, Sungroul Kim*, Miia Kivipelto*,
Luke Knibbs*, Ann Kristin Knudsen*, Yoshihiro Kokubo*,
Sowarta Kosen*, Meera Kotagal*, Michael A Kravchenko*,
Sanjay Krishnaswami*, Hans Krueger*, Barthelemy Kuate Defo*,
Ernst J Kuipers*, Burcu Kucuk Bicer*, Chanda Kulkarni*,
Veena S Kulkarni*, Kaushalendra Kumar*, Ravi B Kumar*,
Gene F Kwan*, Hmwe Kyu*, Taavi Lai*, Arjun Lakshmana Balaji*,
Ratilal Lalloo*, Tea Lallukka*, Hilton Lam*, Qing Lan*,
Van C Lansingh*, Heidi J Larson*, Anders Larsson*, Pablo M Lavados*,
Alicia EB Lawrynowicz*, Janet L Leasher*, Jong-Tae Lee*, James Leigh*,
Articles
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Mall Leinsalu*, Ricky Leung*, Carly Levitz*, Bin Li*, Yichong Li*,
Yongmei Li*, Chelsea Liddell*, Stephen S Lim*,
Graça Maria Ferreira de Lima*, Maggie L Lind*, Steven E Lipshultz*,
Shiwei Liu*, Yang Liu*, Belinda K Lloyd*, Katherine T Lofgren*,
Giancarlo Logroscino*, Stephanie J London*, Joannie Lortet-Tieulent*,
Paulo A Lotufo*, Robyn M Lucas*, Raimundas Lunevicius*,
Ronan Anthony Lyons*, Stefan Ma*, Vasco Manuel Pedro Machado*,
Michael F MacIntyre*, Mark T Mackay*, Jennifer H MacLachlan*,
Carlos Magis-Rodriguez*, Abbas A Mahdi*, Marek Majdan*,
Reza Malekzadeh*, Srikanth Mangalam*,
Christopher Chabila Mapoma*, Marape Marape*, Wagner Marcenes*,
Christopher Margono*, Guy B Marks*, Melvin Barrientos Marzan*,
Joseph R Masci*, Mohammad Taufi q Mashal*, Felix Masiye*,
Amanda J Mason-Jones*, Richard Matzopolous*, Bongani M Mayosi*,
Tasara T Mazorodze*, John J McGrath*, Abigail C McKay*,
Martin McKee*, Abigail McLain*, Peter A Meaney*,
Man Mohan Mehndiratta*, Fabiola Mejia-Rodriguez*,
Yohannes Adama Melaku*, Michele Meltzer*, Ziad A Memish*,
Walter Mendoza*, George A Mensah*, Atte Meretoja*,
Francis A Mhimbira*, Ted R Miller*, Edward J Mills*,
Awoke Misganaw*, Santosh K Mishra*, Charles N Mock*,
Terrie E Moffi tt*, Norlinah Mohamed Ibrahim*,
Karzan Abdulmuhsin Mohammad*, Ali H Mokdad*,
Glen Liddell Mola*, Lorenzo Monasta*, Jonathan de la Cruz Monis*,
Julio C Montañez Hernandez*, Marcella Montico*, Thomas J Montine*,
Meghan D Mooney*, Ami R Moore*, Maziar Moradi-Lakeh*,
Andrew E Moran*, Rintaro Mori*, Joanna Moschandreas*,
Wilkister Nyaora Moturi*, Madeline L Moyer*, Dariush Mozaff arian*,
Ulrich O Mueller*, Mitsuru Mukaigawara*, Erin C Mullany*,
Joseph Murray*, Adetoun Mustapha*, Paria Naghavi*, Aliya Naheed*,
Kovin S Naidoo*, Luigi Naldi*, Devina Nand*, Vinay Nangia*,
KM Venkat Narayan*, Denis Nash*, Jamal Nasher*, Chakib Nejjari*,
Robert G Nelson*, Marian Neuhouser*, Sudan Prasad Neupane*,
Polly A Newcomb*, Lori Newman*, Charles R Newton*, Marie Ng*,
Frida Namnyak Ngalesoni*, Grant Nguyen*, Nhung thi Trang Nguyen*,
Muhammad Imran Nisar*, Sandra Nolte*, Ole F Norheim*,
Rosana E Norman*, Bo Norrving*, Luke Nyakarahuka*, Shaun Odell*,
Martin O’Donnell*, Takayoshi Ohkubo*, Summer Lockett Ohno*,
Bolajoko O Olusanya*, Saad B Omer*, John Nelson Opio*,
Orish Ebere Orisakwe*, Katrina F Ortblad*, Alberto Ortiz*,
Maria Lourdes K Otayza*, Amanda W Pain*, Jeyaraj D Pandian*,
Carlo Irwin Panelo*, Jeemon Panniyammakal*, Christina Papachristou*,
Angel J Paternina Caicedo*, Scott B Patten*, George C Patton*,
Vinod K Paul*, Boris Pavlin*, Neil Pearce*, Carlos A Pellegrini*,
David M Pereira*, Sophie C Peresson*, Rogelio Perez-Padilla*,
Fernando P Perez-Ruiz*, Norberto Perico*, Aslam Pervaiz*,
Konrad Pesudovs*, Carrie B Peterson*, Max Petzold*, Bryan K Phillips*,
David E Phillips*, Michael R Phillips*, Dietrich Plass*,
Frédéric Bernard Piel*, Dan Poenaru*, Suzanne Polinder*,
Svetlana Popova*, Richie G Poulton*, Farshad Pourmalek*,
Dorairaj Prabhakaran*, Dima Qato*, Amado D Quezada*,
D Alex Quistberg*, Felicia Rabito*, Anwar Rafay*, Kazem Rahimi*,
Vafa Rahimi-Movaghar*, Sajjad UR Rahman*, Murugesan Raju*,
Ivo Rakovac*, Saleem M Rana*, Amany Refaat*, Giuseppe Remuzzi*,
Antonio L Ribeiro*, Stefano Ricci*, Patricia M Riccio*, Lee Richardson*,
Jan Hendrik Richardus*, Bayard Roberts*, D Allen Roberts*,
Margaret Robinson*, Anna Roca*, Alina Rodriguez*,
David Rojas-Rueda*, Luca Ronfani*, Robin Room*, Gregory A Roth*,
Dietrich Rothenbacher*, David H Rothstein*, Jane TF Rowley*,
Nobhojit Roy*, George M Ruhago*, Lesley Rushton*,
Sankar Sambandam*, Kjetil Søreide*, Mohammad Yahya Saeedi*,
Sukanta Saha*, Ramesh Sahathevan*, Mohammad Ali Sahraian*,
Berhe Weldearegawi Sahle*, Joshua A Salomon*, Deborah Salvo*,
Genesis May J Samonte*, Uchechukwu Sampson*,
Juan Ramon Sanabria*, Logan Sandar*, Itamar S Santos*,
Maheswar Satpathy*, Monika Sawhney*, Mete Saylan*,
Peter Scarborough*, Ben Schöttker*, Jürgen C Schmidt*,
Ione JC Schneider*, Austin E Schumacher*, David C Schwebel*,
James G Scott*, Sadaf G Sepanlou*, Edson E Servan-Mori*,
Katya Shackelford*, Amira Shaheen*, Saeid Shahraz*,
Marina Shakh-Nazarova*, Siyi Shangguan*, Jun She*,
Sara Sheikhbahaei*, Donald S Shepard*, Kenji Shibuya*,
Yukito Shinohara*, Kawkab Shishani*, Ivy Shiue*, Rupak Shivakoti*,
Mark G Shrime*, Inga Dora Sigfusdottir*, Donald H Silberberg*,
Andrea P Silva*, Edgar P Simard*, Shireen Sindi*, Jasvinder A Singh*,
Lavanya Singh*, Edgar Sioson*, Vegard Skirbekk*, Karen Sliwa*,
Samuel So*, Michael Soljak*, Samir Soneji*, Sergey S Soshnikov*,
Luciano A Sposato*, Chandrashekhar T Sreeramareddy*,
Jeff rey D Stanaway*, Vasiliki Kalliopi Stathopoulou*, Kyle Steenland*,
Claudia Stein*, Caitlyn Steiner*, Antony Stevens*, Heidi Stöckl*,
Kurt Straif*, Konstantinos Stroumpoulis*, Lela Sturua*,
Bruno F Sunguya*, Soumya Swaminathan*, Mamta Swaroop*,
Bryan L Sykes*, Karen M Tabb*, Ken Takahashi*,
Roberto Tchio Talongwa*, Feng Tan*, David Tanne*, Marcel Tanner*,
Mohammad Tavakkoli*, Braden Te Ao*, Carolina Maria Teixeira*,
Tara Templin*, Eric Yeboah Tenkorang*, Abdullah Sulieman Terkawi*,
Bernadette A Thomas*, Andrew L Thorne-Lyman*, Amanda G Thrift*,
George D Thurston*, Taavi Tillmann*, David L Tirschwell*,
Imad M Tleyjeh*, Marcello Tonelli*, Fotis Topouzis*, Jeff rey A Towbin*,
Hideaki Toyoshima*, Jeff erson Traebert*, Bach X Tran*,
Thomas Truelsen*, Ulises Trujillo*, Matias Trillini*,
Zacharie Tsala Dimbuene*, Miltiadis Tsilimbaris*, E Murat Tuzcu*,
Clotilde Ubeda*, Uche S Uchendu*, Kingsley N Ukwaja*,
Eduardo A Undurraga*, Andrew J Vallely*, Steven van de Vijver*,
Coen H van Gool*, Yuri Y Varakin*, Tommi J Vasankari*,
Ana Maria Nogales Vasconcelos*, Monica S Vavilala*,
N Venketasubramanian*, Lakshmi Vijayakumar*, Salvador Villalpando*,
Francesco S Violante*, Vasiliy Victorovich Vlassov*, Gregory R Wagner*,
Stephen G Waller*, JianLi Wang*, Linhong Wang*, XiaoRong Wang*,
Yanping Wang*, Tati Suryati Warouw*, Scott Weichenthal*,
Elisabete Weiderpass*, Robert G Weintraub*, Wang Wenzhi*,
Andrea Werdecker*, K Ryan R Wessells*, Ronny Westerman*,
Harvey A Whiteford*, James D Wilkinson*, Thomas Neil Williams*,
Solomon Meseret Woldeyohannes*, Charles DA Wolfe*,
Timothy M Wolock*, Anthony D Woolf *, John Q Wong*,
Jonathan L Wright*, Sarah Wulf*, Brittany Wurtz*, Gelin Xu*,
Yang C Yang*, Yuichiro Yano*, Hiroshi Yatsuya*, Paul Yip*,
Naohiro Yonemoto*, Seok-Jun Yoon*, Mustafa Younis*, Chuanhua Yu*,
Kim Yun Jin*, Maysaa El Sayed Zaki*,
Mohammed Fouad Zamakhshary*, Hajo Zeeb*, Yong Zhang*,
Yong Zhao*, Yingfeng Zheng*, Jun Zhu*, Shankuan
Zhu*, David Zonies*, Xiao Nong Zou*, Joseph R Zunt*, Theo Vos†,
Alan D Lopez†, Christopher JL Murray†. *Authors listed alphabetically.
†Joint senior authors.
Affi liations
Institute for Health Metrics and Evaluation (M Naghavi PhD,
Wang H PhD, R Lozano PhD, S E Vollset MD, T Achoki MD,
H Apfel BA, C Atkinson BS, R M Barber BS, A Bertozzi-Villa BS,
S Biryukov BS, J C Brown MAIS, M M Coates BS, L E Coff eng PhD,
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K R Heuton BS, H Higashi PhD, C Huynh BA, M Iannarone MSc,
J Joseph BS, N Kassebaum MD, H H Kyu PhD, C Levitz MPH,
C Liddell BE, M L Lind BS, K T Lofgren MPH, M F MacIntyre MEd,
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M Moradi-Lakeh PhD, M Moyer BA, E C Mullany BA, P Naghavi BESc,
M Ng PhD, G Nguyen BA, S Odell MPP, S L Ohno BA,
K F Ortblad MPH, A W Pain MPH, D E Phillips BS, B K Phillips BA,
L Richardson BS, D A Roberts BS, M Robinson BA, G A Roth MD,
L Sandar BS, A E Schumacher BS, K Shackelford BA, L Singh BS,
E Sioson MS, J Stanaway PhD, C Steiner MPH, A Stevens PhD,
T Templin BA, B A Thomas MD, T M Wolock BA, S Wulf MPH,
B Wurtz MPH, T Vos PhD, Prof A D Lopez PhD,
Prof C J L Murray DPhil), School of Medicine (Prof R G Ellenbogen MD,
J L Wright MD), Children’s Hospital (N Kassebaum MD), Department of
Neurology (D L Tirschwell MD), Harborview Injury Prevention and
Research Center (B E Ebel MD), University of Washington, Seattle, WA,
USA (R Alfonso-Cristancho PhD, Prof B O Anderson MD,
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University of Auckland, Auckland, New Zealand (B del Pozo-Cruz PhD);
University of Birmingham, Birmingham, UK (A Banerjee MA);
Department of Occupational and Environmental Health, University of
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Children, London, UK (M Burch MD); Universidad Autonoma
Metropolitana, Mexico, DF, Mexico (Prof R Cárdenas Sc.D.);
Department of Biostatistics and Epidemiology, University of Oklahoma
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CTSU, Nuffi eld Dept. of Population Health, Oxford, UK (Prof Z Chen);
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Pau, Barcelona, Spain (C Diaz-Torne MD); Department of Social
Medicine, Faculty of Public Health, Medical University-Varna, Varna,
Bulgaria (K Dokova PhD); University of Rochester Medical Center,
Rochester, NY, USA (Prof E R Dorsey MD); National Institutes of Health,
Montgomery Village, MD, USA (A M Durrani MD); School of Medicine
and Pharmacology (Prof G J Hankey MD), University of Western
Australia, Perth, WA, Australia (Prof K M Edmond PhD); Food Science
Department, Faculty of Agriculture, University of Tripoli, Tripoli, Libya
(Prof Y M Elshrek PhD); The Institute of Social and Economic Studies of
Population at the Russian Academy of Sciences, Moscow, Russia
(Prof S P Ermakov DSc); Arak University of Medical Sciences & Health
Aff airs, Arak, Markazi, Iran (B Eshrati PhD); Endocrinology and
Metabolism Research Center (Prof A Esteghamati MD,N Hafezi-Nejad
MD, S Sheikhbahaei MD); Non-Communicable Diseasese Reesearch
Center (F Farzadfar MD), Digestive Diseases Research Center
(Prof R Malekzadeh MD, S G Sepanlou MD), Sina Trauma and Surgery
Research Center (Prof V Rahimi-Movaghar MD), MS Research Center
(M A Sahraian MD), Department of Community Medicine
(M Moradi- Lakeh), Tehran University of Medical Sciences, Tehran, Iran
(P Heydarpour MD); Centre for Health Policy & Department of
Infectious Disease Epidemiology (T Fürst PhD), MRC-PHE Centre for
Health and Environment (A B Mustapha PhD), Imperial College
London, London, UK (K Foreman MPH, Prof A Rodriguez PhD,
L Rushton PhD, M Soljak PhilD, Prof T N Williams MD); Division of
Information, Evidence, Research and Innovation (C Stein PHD), World
Health Organization, Regional Offi ce for Europe, Copenhagen,
Denmark (A S Fahrion Dr med vet, I Rakovac PhD), WHO, Geneva,
Switzerland (L Newman MD); ARS Norte (C M Teixeira MD), I.P.—
Departamento Saúde Pública, Porto, Portugal (M M Felicio MD,
G M F d Lima BSC, V M P Machado MSc); German Hospital Oswaldo
Cruz, Institute of Education and Sciences, São Paulo, São Paulo, Brazil
(Prof J G Fernandes PhD); Institute of Gerontology, Acad Med Sci, Kyiv,
Ukraine (N Foigt PhD); James Cook University, Townsville, QLD,
Australia (R C Franklin PhD); National Center for Disease Control &
Public Health, Tbilisi, Georgia (K Gambashidze MS, K Kazanjan MS,
M Kereselidze PhD, M Shakh-Nazarova MS, L Sturua PhD); Clinique
Coopérative de Parakou, Parakou, Borgou, Benin (F G Gankpé MD);
Public Health Unit of Primary Health Care Group of Almada-Seixal,
Almada, Setúbal, Portugal (A C Garcia MPH); Wageningen University,
Division of Human Nutrition, Wageningen, Netherlands
(J M Geleijnse PhD); Agence de Medecine Preventive, Paris, France
(B D Gessner MD); Eastern Health Clinical School (B K Lloyd PhD),
Monash University, Melbourne, VIC, Australia (K B Gibney MPH,
Prof A G Thrift PHD); Howard University, Washington DC, DC, USA
(Prof R F Gillum MD); Graduate School of Medicine (M Inoue PhD),
School of Public Health (Prof N Kawakami MD), University of Tokyo,
Tokyo, Japan (S Gilmour MPH, Prof K Shibuya DrPH); University of
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49
Hail, College of Medicine, Hail, Kingdom of Saudi Arabia
(I A M Ginawi MD); University Hospital of Dijon, Dijon France, Region
of Burgondy, France (Prof M Giroud MD); Brandeis University,
Waltham, MA (E L Glaser MA, Y A Halasa MS, B T Idrisov MD,
S Shahraz MD, Prof D S Shepard PhD, E A Undurraga PhD); University
of California San Francisco, San Francisco, CA, USA
(R A Gosslin MD D S Kazi MD); Department of Diabetes Research,
National Center for Global Health and Medicine, Tokyo, Japan
(A Goto PhD); Saint James School of Medicine, Kralendijk, Bonaire,
Netherlands Antilles (Prof H C Gugnani PhD); PATH, Seattle, WA, USA
(C Guinovart PhD); University of Bristol, Bristol, UK
(Prof D Gunnell DSc); Fortis Escorts Hospital, Jaipur, Rajasthan, India
(R Gupta MD PhD); Kanawha Charleston Health Department,
Charleston, WV, USA (R Gupta MD); Nelson Institute of Environmental
Medicine, New York University School of Medicine, Tuxedo
(Prof G D Thurston ScD), New York University, New York, NY, USA
(Prof H Hagan PhD); Arabian Gulf University, Manama, Bahrain
(Prof R R Hamadeh Dphil); Wayne County Department of Health and
Human Services, Detroit, MI, USA (M Hammami MD); Eunice Gibson
Polyclinic, Bridgetown, St. Michael, Barbados (H C Harewood MPH);
Parc Sanitari Sant Joan de Déu, CIBERSAM, University of Barcelona,
Sant Boi de Llobregat, Barcelona, Spain (Prof J M Haro MD);
International Foundation for Dermatology, London, UK
(Prof R J Hay DM); Mazandaran University of Medical Sciences, Sari,
Mazandaran, Iran (Prof M T Hedayati PhD); Fundacion Entornos AC,
Cuernavaca, Morelos, Mexico (Prof M Hijar PhD); Parnassia Psychiatric
Institute, The Hague, Netherlands (Prof H W Hoek MD); National
Institute on Deafness and Other Communication Disorders, National
Institutes of Health, Bethesda, MD, USA (H J Hoff man MA); Cedar
Associates, Menlo Park, CA, USA (Prof J C Hornberger MD); Albert
Einstein College of Medicine, Bronx, NY, USA (Prof H D Hosgood PhD);
London School of Hygiene and Tropical Medicine, London, UK
(M Hossain MSc, H J Larson PhD, Prof M McKee DSc,
Prof N Pearce PhD, B Roberts PhD, H Stöckl Dphil, T Tillmann MPH);
Baylor College of Medicine, Houston, TX, USA (Prof P J Hotez PhD);
Public Health Division, Secretariat of the Pacifi c Community, Noumea,
New Caledonia, Herston, Queensland, Australia (D G Hoy); National
Institute of Public Health (MOH), Tunis, Tunisia (Prof M Hsairi MD);
University of Toronto, Toronto, Ontario, Canada (Prof H Hu MD);
Central South University, Changsha, China (Prof G Hu PhD); Feinberg
School of Medicine (M D Huff man MD), Northwestern University,
Chicago, IL, USA (M Swaroop MD); Public Health Program, Qatar
University, Doha, Qatar, Birzeit, Ramallah, Palestine (A Husseini PhD);
Aarhus University, Aarhus, Denmark (K M Iburg PhD); National
Institute of Health and Nutrition, Bunkyo, Tokyo, Japan (N Ikeda PhD);
National Institute for Health Development, Tallinn, Estonia
(K Innos PhD, M Leinsalu PhD); American Cancer Society, New York,
NY (F Islami PhD), Atlanta, GA, USA (J Lortet-Tieulent MSc); Self-
employed, Baku, Azerbaijan (S Ismayilova MPH); George Mason
University, Fairfax, VA, USA (K H Jacobsen PhD); VA San Diego, San
Diego, CA, USA (Prof S K Jassal); Virginia Commonwealth University,
Richmond, VA, USA (S P Jayaraman MD); Postgraduate Institute of
Medical Education and Research, Chandigarh, India (Prof V Jha DM);
Tianjin Centers for Diseases Control and Prevention, Tianjin, China
(Prof G Jiang MD); Department of Health Development, Institute of
Industrial Ecological Sciences, Department of Environmental
Epidemiology, University of Occupational and Environmental Health,
Japan, Kitakyushu, Fukuoka, Japan (Y Jiang PhD); Department of
Ophthalmology, Medical Faculty Mannheim of the University of
Heidelberg, Mannheim, Germany (Prof J B Jonas MD); The National
Institute of Public Health, Copenhagen, Denmark (Prof K Juel PhD);
Vanderbilt University, Nashville, TN, USA (E K Kabagambe PhD,
U Sampson MD); University of Balamand, Beirut, Lebanon
(Prof N Karam MD); Helmholtz Centre for Infection Research,
Braunschweig, Germany (A Karch MD); German Center for Infection
Research (DZIF), Hannover-Braunschweig site, Braunschweig, Germany
(A Karch); College of Physicians and Surgeons (C Karimkhani BA),
Columbia University, New York, NY, USA (A E Moran MD); All India
Institute of Medical Sciences, New Delhi, India (Prof G Karthikeyan
DM, Prof V K Paul MD); Oklahoma State University, Tulsa, OK, USA
(A Kaul MD); South African Medical Research Council, Cape Town,
Western Cape, South Africa (A P Kengne PhD, R Matzopolous PhD);
Cardiology, Hadassah Ein Kerem University Hospital, Jerusalem, Israel
(Prof A Keren MD); Jordan University of Science and Technology,
AlRamtha, Irbid, Jordan (Prof Y S Khader ScD); Supreme Council of
Health, Doha, Qatar (S E A H Khalifa MSc.); Health Services Academy,
Islamabad, Punjab, Pakistan (E A Khan MPH); UAE University, Al Ain,
Abu Dhabi, United Arab Emirates (G Khan PhD); Institute of Health
Policy and Management, Seoul National University College of Medicine,
Seoul, South Korea (Prof Y-H Khang MD PhD); Federal University of
Rio Grande do Sul, Porto Alegre, RS, Brazil (C Kieling PhD);
Northeastern University, Boston, MA, USA (Prof D Kim DrPH);
Soonchunhyang University, Asan, South Korea (Prof S Kim PhD);
University of Canberra, Canberra, ACT, Australia (Y Kinfu PhD);
Department of Preventive Cardiology, National Cerebral and
Cardiovascular Center, Suita, Osaka, Japan (Y Kokubo PhD); Center for
Community Empowerment, Health Policy & Humanities (S Kosen MD),
NIHRD, Jakarta, Special Province of Jakarta, Indonesia
(T S Warouw PhD); Research Center of Neurology, Moscow, Russia
(M Kravchenko PhD, Prof Y Y Varakin MD); Oregon Health and Science
University, Portland, OR, USA (S Krishnaswami MD); Oregon Health
and Science University, Portland, OR, USA (S Krishnaswami MD);
University of Montreal, Montreal, Quebec, Canada
(Prof B Kuate Defo PhD); Department of Public Health
(S Polinder PhD), Erasmus MC University Medical Center, Rotterdam,
The Netherlands (Prof E J Kuipers PhD, Prof J H Richardus PhD);
Rajrajeshwari Medical College & Hospital, Bangalore, Karnataka, India
(Prof C Kulkarni PhD); Arkansas State University, AR, USA
(V S Kulkarni PhD); International Institute for Population Sciences,
Mumbai, Maharashtra, India (K Kumar MPS); Boston Medical Center,
Boston, MA, USA (G F Kwan MD); Fourth View Consulting, Tallinn,
Estonia (T Lai PhD); Australian Research Centre for Population Oral
Health (ARCPOH), Gold Coast, QLD, Australia (Prof R Lalloo PhD);
School of Dentistry, The University of Adelaide, Gold Coast, QLD,
Australia (Prof R Lalloo); Finnish Institute of Occupational Health,
Topeliuksenkatu, Helsinki, Finland (Prof T Lallukka PhD); Medical
Faculty, Hjelt Institute, University of Helsinki, Finland (Prof T Lallukka);
National Cancer Institute, Rockville, MD, USA (Q Lan PhD); IAPB and
Vision 2020 LA, Weston, FL, USA (V C Lansingh PhD); Servicio de
Neurologí¬a, Clinica Alemana, Universidad del Desarrollo, Santiago,
RM, Chile (P M Lavados MD); Instituto Nacional de Epidemiologia
“Dr. Juan H Jara,” Mar del Plata, Buenos Aires, Argentina
(A E B Lawrynowicz MD, A P Silva MgSc, C Ubeda MPH); Nova
Southeastern University, Fort Lauderdale, FL, USA (J L Leasher OD.);
Korea University, Seoul, South Korea (Prof J-T Lee PhD,
Prof S-J Yoon PhD); SUNY-Albany, Rensselaer, NY, USA (R Leung PhD);
Department of Gerontology (Y Zhang PhD), Jinan Central Hospital,
Jinan, Shandong, China (B Li PhD); Genentech, Inc, South San
Francisco, CA, USA (Y Li PhD); Wayne State University, Miami, FL,
USA (S E Lipshultz MD); Turning Point Alcohol & Drug Centre, Eastern
Health, Fitzroy, Victoria, Australia (B K Lloyd, R Room PhD); University
of Bari, Bari, Italy (Prof G Logroscino MD PhD); The Australian National
University, Canberra, ACT, Australia (Prof R M Lucas PhD); Aintree
University Hospital NHS Foundation Trust, Liverpool, UK
(R Lunevicius PhD); Swansea University, Swansea, UK
(Prof R A Lyons MD); Ministry of Health Singapore, Singapore,
Singapore (S Ma PhD); Royal Children’s Hospital Melbourne, Victoria,
VIC, Australia (M T Mackay MBBS); Centro para la prevención y el
control del VIH /SIDA, México City, Distrito Federal, Mexico
(C Magis-Rodriguez PhD); King George’s Medical University, Lucknow,
Uttar Pradesh, India (Prof A A Mahdi PhD); Technical Standards and
Safety Authority, Toronto, Ontario, Canada (S Mangalam MS); University
of Zambia, Lusaka, Zambia (C C Mapoma PhD, F Masiye PhD);
Botswana-Baylor Children’s Clinical Centre of Excellence, Gaborone,
Botswana (M Marape PhD); Queen Mary, University of London, London,
UK (Prof W Marcenes PhD); University of the East Ramon Magsaysay
Medical Center, Quezon City, Metro Manila, Philippines
(M B Marzan MSc); Elmhurst Hospital Center, Mount Sinai Services,
Elmhurst, NY, USA (Prof J R Masci MD); Ministry of Public Health,
Kabul, Afghanistan (M T Mashal PhD); University of York, York, UK
(A J Mason-Jones PhD); Faculty of Health Sciences, Hatter Institute for
Cardiovascular Research in Africa (Prof K Sliwa PhD), University of
Articles
50
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Cape Town, Cape Town, Western Cape, South Africa
(Prof B M Mayosi Dphil); AIDC EC, Port Elizabeth, Eastern Cape, South
Africa (T T Mazorodze MA); EmergentCorp, Belize City, Belize District,
Belize (A C McKay PhD); Janakpuri Superspecialty Hospital, New Delhi,
Delhi, India (Prof M M Mehndiratta MD); Thomas Jeff erson University,
Philadelphia, PA, USA (M Meltzer MD); UNFPA, Lima, Peru
(W Mendoza MD); Center for Translation Research and Implementation
Science (CTRIS), National Heart, Lung, and Blood Institute, National
Institutes of Health, Bethesda, MD, USA (G A Mensah); Ifakara Health
Institute, Dar es Salaam, Tanzania (F A Mhimbira MSc); Pacifi c Institute
for Research & Evaluation, Calverton MD, USA (T R Miller PhD); Centre
for Population Health Research, Curtin University, Perth, Australia
(T R Miller); University of Ottawa, Ottawa, Ontario, Canada
(E J Mills PhD); Population Education Resource Centre (PERC),
Department of Continuing and Adult Education and Extension Work, S.
N. D. T. Women’s University, Mumbai, Maharashtra, India
(S Mishra PhD); Duke University, Durham, NC, USA
(Prof T E Moffi tt PhD); Department of Medicine, Universiti Kebangsaan
Malaysia Medical Center, Bandar Tun Razak, Kuala Lumpur, Malaysia
(Prof N Mohamed Ibrahim MRCP); University of Salahaddin, Erbil, Iraq
(K A Mohammad PhD); University of Papua New Guinea, Port Moresby,
NCD, Papua New Guinea (Prof G L Mola MD); Institute for Maternal
and Child Health—IRCCS “Burlo Garofolo,” Trieste, Italy
(L Monasta DSc, M Montico MSc, L Ronfani PhD); Bureau of
International Health Cooperation, Manila City, Philippines
(Prof J D L C Monis MSc Epi); University of North Texas, Denton, TX,
USA (Prof A R Moore PhD); National Center for Child Health and
Development, Setagaya, Tokyo, Japan (R Mori PhD); Department of
Medicine, Heraklion (Prof M Tsilimbaris PhD), University of Crete,
Crete, Greece (J Moschandreas PhD); Egerton University, Egerton, Rift
Valley, Kenya (W N Moturi PhD); Friedman School of Nutrition Science
& Policy, Tufts University, Boston, MA, USA (D Mozaff arian DrPH);
Philipps-University Marburg, Marburg, Germany
(Prof U O Mueller PhD); Tokyo Medical and Dental University,
Bunkyo-ku, Tokyo, Japan (M Mukaigawara MD); International Centre
for Diarrhoeal Diseases Research, Bangladesh, Dhaka, Bangladesh
(A Naheed PhD); University of KwaZulu-Natal, Durban, KwaZulu-Natal,
South Africa (Prof K S Naidoo PhD); Azienda Ospedaliera papa Giovanni
XXIII, Bergamo, Italy, Bergamo, Italy (Prof L Naldi MD); Ministry of
Health Fiji, Suva, Republic of Fiji (D Nand MPH); Suraj Eye Institute,
Nagpur, Maharashtra, India (Prof V Nangia MD); School of Public
Health, City University of New York, New York, NY, USA
(Prof D Nash PhD); Ministry of Public Health & Population, Sana’a,
Yemen (J Nasher MSc); Faculty of Medicine, Fez, Morocco
(Prof C Nejjari PhD); National Institute of Diabetes and Digestive and
Kidney Diseases, Phoenix, AZ, USA (R G Nelson PhD);
Fred Hutchinson Cancer Research Center, Seattle, WA, USA
(M L Neuhouser PhD, P A Newcomb PhD); Norwegian Center for
Addiction Research (SERAF), University of Oslo, Norway
(S P Neupane Mphil); Ministry of Health and Social Welfare,
Dar Es Salaam, Tanzania (F N Ngalesoni MSc); Department of Clinical
Sciences, Medical Faculty, Lund University, Lund, Sweden
(Prof B Norrving PhD); National University of Ireland Galway, Galway,
Ireland (M J O’Donnell PhD); Teikyo University School of Medicine,
Tokyo, Japan (Prof T Ohkubo MD); Center for Healthy Start Initiative,
Ikoyi, Lagos, Nigeria (B O Olusanya PhD); Lira District Local
Government, Lira Municipal Council, Northern Uganda, Uganda
(J N OpioMPH); Toxicology Unit, Faculty of Pharmacy, University of
Port Harcourt, Nigeria, Port Harcourt, Rivers State, Nigeria
(Prof O E Orisakwe PhD); IIS-Fundacion Jimenez Diaz, Madrid, Spain
(Prof A Ortiz PhD); Mariano Marcos Memorial Hospital & Medical
Center, City of Batac, Ilocos Norte, Philippines (M L K Otayza MD);
Christian Medical College Ludhiana, Ludhiana, India
(Prof J D Pandian MD); Centre for Chronic Disease Control, New Delhi,
Delhi, India (J Panniyammakal PhD); University of Calgary, Calgary,
Alberta, Canada (Prof S B Patten PhD, J L Wang PhD); Independent
Researcher, Port Moresby, Waigani, NCD, Papua New Guinea
(B I Pavlin MD); REQUIMTE/Laboratório de Farmacognosia,
Departamento de Quí¬mica, Faculdade de Farmácia, Universidade do
Porto, Portugal, Porto, Portugal (Prof D M Pereira PhD); International
Diabetes Federation, International Diabetes Federation, Belgium
(S C Peresson MA); Hospital Universitario Cruces, Baracaldo, Spain
(F P Perez-Ruiz PhD); Postgraduate Medical Institute, Lahore, Punjab,
Pakistan (A Pervaiz MHA); Flinders University, Adelaide, SA, Australia
(Prof K Pesudovs PhD); Aalborg University, Aalborg Øst, Denmark
(C B Peterson PhD); Centre for Applied Biostatistics, Sahlgrenska
Academy, University of Gothenburg, Sweden, Gothenburg, Sweden
(Prof M Petzold PhD); Shanghai Jiao Tong University, Shanghai, China
(Prof M R Phillips); Exposure Assessment and Environmental Health
Indicators, Federal Environment Agency, Bielefeld, North Rhine-
Westphalia, Germany (D Plass MPH); McMaster University, Hamilton,
ON, Canada (Prof D Poenaru MD); Centre for Addiction and Mental
Health, Toronto, Ontario, Canada (S Popova PhD); Centre for Chronic
Disease Control, New Delhi, Delhi, India (Prof D Prabhakaran MD);
College of Pharmacy (Prof D Qato PhD), University of Illinois, Chicago,
IL, USA (K M Tabb PhD); Tulane University School of Public Health and
Tropical Medicine, New Orleans, LA, USA (F Rabito PhD); Contech Intl.,
Lahore, Punjab, Pakistan (A Rafay MS); Hamad Medical Corporation,
Doha, Qatar (S U R Rahman FCPS); University of Missouri, Columbia,
MS, USA (M Raju PhD); Department of Public Health, University of the
Punjab, Lahore, Pakistan, Punjab, India (S M Rana PhD); Walden
University, Minneapolis, MN, USA (Prof A Refaat PhD); Hospital das
Clínica da Universidade Federal de Minas Gerais, Belo Horizonte, Minas
Gerais, Brazil (Prof A L Ribeiro PhD); UO Neurologia USL Umbria 1,
Cittá di Castello, Perugia, Italy (S Ricci MD); Department of Clinical
Neurological Sciences, London Health Sciences Centre, University of
Western Ontario, London, Ontario, Canada (P M Riccio MD); MRC Unit,
Fajara, The Gambia (A Roca PhD); Centre of Research in Environmental
Epidemiology (CREAL), Barcelona, Catalonia, Spain
(D Rojas-Rueda PhD); Institute of Epidemiology and Medical Biometry,
Ulm University, Ulm, Germany (Prof D Rothenbacher MD); Ann &
Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL, USA
(D H Rothstein MD); BARC Hospital, Mumbai, Maharashtra, India
(Prof N Roy MD); Muhimbili University of Health and Allied Sciences,
Dar es Salaam, Tanzania (G M Ruhago MA, B F Sunguya MSc); Rwanda
Bio-Medical Center, Kigali, Rwanda, Rwanda (N Sabin MD); Stavanger
University Hospital, Stavanger, Norway (Prof K Søreide PhD);
Queensland Centre for Mental Health Research, Brisbane, QLD,
Australia (S Saha PhD); UKM Medical Centre, Kuala Lumpur, Malaysia
(R Sahathevan PhD); National HIV/AIDS & STI Surveillance and
Strategic Information Unit, National Epidemiology Center, Department
of Health, Manila, National Capital Region, Philippines
(G M J Samonte MD); Case Western Reserve University, Cleveland, OH,
USA (J R Sanabria MD); Marshall University, Huntington, WV, USA
(M Sawhney PhD); Novartis, Istanbul, Istanbul, Turkey (M I Saylan MD);
Division of Clinical Epidemiology and Aging Research, German Cancer
Research Center, Heidelberg, Baden-Württemberg, Germany
(B Schöttker MPH); Federal University of Santa Catarina, Florianópolis,
SC, Brazil (I J C Schneider PhD); University of Alabama at Birmingham,
Birmingham, AL, USA (Prof D C Schwebel PhD, J A Singh MD);
An-Najah University, Nablus, Palestine (A Shaheen PhD); Tachikawa
Hospital, Tokyo, Japan (Prof Y Shinohara PhD); Washington State
University, Spokane, WA, USA (K Shishani PhD); Heriot-Watt
University, Edinburgh, Scotland, UK (I Shiue PhD); Center for Clinical
Global Health Education (R Shivakoti PhD), Bloomberg School of Public
Health, Johns Hopkins University, Baltimore, MD, USA (B X Tran PhD);
Reykjavik University, Reykjavik, Iceland (Prof I D Sigfusdottir PhD);
Dartmouth College, Lebanon, NH, USA (S Soneji PhD); Federal
Research Institute for Health Organization and Informatics of Ministry
of Health of the Russian Federation, Moscow, Russia
(S S Soshnikov PhD); Department of Clinical Neurological Sciences,
Western University, London, ON, Canada (L A Sposato MD); Faculty of
Medicine and Health Sciences, University Tunku Abdul Rahman,
Selangor, Malaysia, Kajang, Selangor, Malaysia (C T Sreeramareddy MD);
Centre Hospitalier Nord Deux-Sevres, Bressuire, France
(V K Stathopoulou MD); IARC/WHO, Lyon, France (K Straif MD PhD);
KEELPNO, Centre for Disease Control, Greece, dispatched to
“Alexandra” General Hospital of Athens, Athens, Greece
(K Stroumpoulis PhD); National Institute for Research in Tuberculosis,
Chennai, Tamil Nadu, India (S Swaminathan MD); Department of
Criminology, Law and Society (and Sociology), University of California-
Irvine, Irvine, CA, USA (B L Sykes PhD); Institute of Industrial
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51
Ecological Sciences, Department of Environmental Epidemiology,
University of Occupational and Environmental Health, Kitakyushu,
Fukuoka Prefecture, Japan (Prof K Takahashi MD); Ministry of Health—
MINSANTE, Yaounde, Centre, Cameroon (R T Talongwa MD);
Chaim Sheba Medical Center and Tel Aviv University, Tel Hashomer,
Israel (Prof D Tanne MD); Westchester Medical Center, Valhalla, NY,
USA (M Tavakkoli MD); Auckland University of Technology, Auckland,
New Zealand (B J Te Ao MPH); Jhpiego, Addis Ababa, Ethiopia
(A M Temesgen PhD); Memorial University, St John’s, Newfoundland,
Canada (E Y Tenkorang PhD); Department of Anesthesiology, University
of Virginia, Charlottesville, VA, USA (A S Terkawi MD); Department of
Anesthesiology, King Fahad Medical City, Riyadh, Saudi Arabia
(A S Terkawi, Prof I M Tleyjeh); WorldFish, Penang, Malaysia, New York,
NY, USA (A L Thorne-Lyman ScD); Alfasial University, College Of
Medicine, Riyadh, Saudi Arabia (Prof I M Tleyjeh); University of Alberta,
Edmonton, AB, Canada (Prof M Tonelli MD); Aristotle University of
Thessaloniki, Thessaloniki, Greece (Prof F Topouzis PhD); Cincinnati
Children’s Hospital Medical Center, Cincinnati, OH, USA
(Prof J A Towbin MD); Health Care Center of Anjo Kosei Hospital, Anjo
City, Aichi Prefecture, Japan (Prof H Toyoshima MD); University of
Southern Santa Catarina, Palhoça, Santa Catarina, Brazil
(Prof J Traebert PhD); Department of Neurology, Copenhagen University
Hospital Herlev, Herlev, Copenhagen, Denmark (T Truelsen PhD);
Servicio Canario de Salud, Santa Cruz de Tenerife, Tenerife, Spain
(U Trujillo MD); Department of Population Sciences and Development,
Faculty of Economics and Management, University of Kinshasa,
Kinshasa, Democratic Republic of the Congo (Z Tsala Dimbuene PhD);
Department of Internal Medicine, Federal Teaching Hospital Abakaliki,
Abakailiki, Ebonyi State, Nigeria (K N Ukwaja MD); African Population
and Health Research Center, Nairobi, Kenya (S van de Vijver MD);
National Institute for Public Health and the Environment, Bilthoven,
The Netherlands (C H van Gool PhD); UKK Institute for Health
Promotion Research, Tampere, Finland (Prof T J Vasankari MD PhD);
Universidade de Brasília, Brasília, Distrito Federal—DF, Brazil
(Prof A M N Vasconcelos PhD); Neuroscience Centre, Raffl es Hospital,
Singapore, Singapore (N Venketasubramanian MD); Voluntary Health
Services, Sneha, Chennai, Tamil Nadu, India (Prof L Vijayakumar PhD);
University of Bologna, Bologna, Italy (Prof F S Violante MD); Higher
School of Economics, Moscow, Russia (Prof V V Vlassov MD); National
Institute for Occupational Safety and Health, Washington, DC, USA
(G R Wagner MD); Uniformed Services University of Health Sciences,
Bethesda, MD, USA (S Waller MD); National Offi ce for Maternal and
Child’s Health Surveillance, Chengdu, China (Prof Y Wang BS,
Prof J Zhu MD); Health Canada, Ottawa, Ontario, Canada
(S Weichenthal PhD); Murdoch Children’s Research Institute, Royal
Children’s Hospital, Melbourne, VIC, Australia (R G Weintraub MB);
Beijing Neurosurgical Institute, Beijing, China (Prof W Wenzhi MD);
Institute of Medical Sociology and Social Medicine
(A Werdecker Dipl.oec.troph), Marburg, Hessen, Germany
(R Westerman PhD); University of California, Davis, Davis, CA, USA
(K R R Wessells PhD); University of Miami, Miami, FL, USA
(J D Wilkinson MD); Institute of Public Health, University of Gondar,
Gondar, Amhara, Ethiopia (S M Woldeyohannes MPH); Ateneo School
of Medicine and Public Health, Pasig City, Metro Manila, Philippines
(J Q Wong MD); Royal Cornwall Hospital, Truro, Cornwall, UK
(Prof A D Woolf FRCP); Nanjing University School of Medicine, Jinling
Hospital, Nanjing, China (Prof G Xu PhD); University of North Carolina
at Chapel Hill, Chapel Hill, NC, USA (Y C Yang PhD); Division of
Cardiovascular Medicine, Jichi Medical University School of Medicine,
Shimotsuke, Tochigi, Japan (Y Yano MD); Fujita Health Univeristy,
Toyoake, Aichi, Japan (Prof H Yatsuya PhD); The University of Hong
Kong, Hong Kong, China (Prof P Yip PhD); National Center of
Neurology and Psychiatry, Kodira, Tokyo, Japan (N Yonemoto MPH);
Jackson State University, Jackson, MS, USA (Prof M Younis PhD);
Department of Epidemiology and Biostatistics, School of Public Health,
Wuhan University Global Health Institute, Wuhan University, Wuhan,
Hubei, China (Prof C Yu PhD); TCM Medical TK SDN BHD, Nusajaya,
Johor Bahru, Malaysia (K Yun Jin PhD); Mansoura Faculty of Medicine,
Mansoura, Mansoura, Egypt (Prof M E S Zaki MD); Ministry for
Planning and Training, Riyadh, Kingdom of Saudi Arabia
(M Zamakhshary MD); Leibniz Institute for Prevention Research and
Epidemiology—BIPS, Bremen, Germany (Prof H Zeeb PhD);
Chongqing Medical University, Chongqing, China (Prof Y Zhao MD);
Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou,
Guangdong, China (Y Zheng PhD); Zhejiang University School of
Public Health, Hangzhou, Zhejiang, China (Prof S Zhu PhD); Landstuhl
Regional Medical Center, Landstuhl, USA (D Zonies MD); and Cancer
Institute/Hospital, Chinese Academy of Medical Sciences, Beijing,
Beijing, China (Prof X N Zou MD)
Contributors
ADL and CJLM conceived the study and provided overall guidance.
CJLM, ADL, MN, and HW prepared the fi rst draft. All other authors
provided data, developed models, reviewed results, initiated modelling
infrastructure, or reviewed and contributed to the report.
Declaration of interests
BDG works for AMP, which receives grant support for vaccine and
immunisation related work from Crucell, GlaxoSmithKline, Merck,
Novartis, Pfi zer, and Sanofi Pasteur; however, none of this support is for
work related to the present report. KJ reports has consulted for
GlaxoSmithKline on projects outside the submitted work. WM is
program analyst at the UNFPA country offi ce in Peru, which does not
necessarily endorse the study. JAS has received research grants from
Takeda and Savient and consultant fees from Savient, Takeda, Regeneron,
and Allergan. JAS is a member of the executive of OMERACT, which
receives funding from 36 companies; a member of the American College
of Rheumatology’s Guidelines Subcommittee of the Quality of Care
Committee; and a member of the Veterans Aff airs Rheumatology Field
Advisory Committee. RFG is associate editor of Annals of Epidemiology for
which he receives a stipend. CK receives research grants from Brazilian
public funding agencies Conselho Nacional de Desenvolvimento
Científi co e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de
Pessoal de Nível Superior (CAPES), and Fundação de Amparo à Pesquisa
do Estado do Rio Grande do Sul (FAPERGS). He has also received
authorship royalties from publishers Artmed and Manole. GR has
consultancy agreements with Alexion Pharmaceuticals, Reata
Pharmaceuticals, Bayer Healthcare, and Novartis Pharma, and is a
member of the Abbvie Atrasentan Steering Committee; GR does not
accept personal remuneration, compensations are paid to his institution
for research and educational activities. MDH has received research
support from the National Heart, Lung, and Blood Institute and World
Heart Federation for its Emerging Leaders program, which is supported
by unrestricted educational grants from AstraZeneca and Boehringer
Ingelheim. FP-R has received investigation grants from Ministerio de
Sanidad, Gobierno de España, Asociación de Reumatólogos del Hospital
de Cruces, Fundación Española de Reumatología; has been a consultant
(with or without payment) for Astra-Zeneca, Menarini, Metabolex, Ardea
Biosciences, SOBI, Novartis, and Pfi zer; and has been a speaker for
AstraZeneca and Menarini. KBG received the NHMRC-Gustav Nossai
scholarship sponsored by CSL Behring in 2013. MGS has previously
served as consultant for Ethicon on global surgery. PJ is supported by a
career development fellowship from the Wellcome Trust, Public Health
Foundation of India, and a consortium of UK universities. DAQ was
supported by The Eunice Kennedy Shriver National Institute of Child
Health and Human Development of the National Institutes of Health
(number 5T32HD057822). AK has received institutional support
(intramural funding) from the Oklahoma State University Center for
Health Sciences. RAL receives funding through the Farr Institute of
Health Informatics Research. The Farr Institute is supported by Arthritis
Research UK, British Heart Foundation, Cancer Research UK, Economic
and Social Research Council, Engineering and Physical Sciences
Research Council, Medical Research Council, National Institute of Health
Research, National Institute for Social Care and Health Research (Welsh
Government), and the Chief Scientist Offi ce (Scottish Government
Health Directorates), (MRC grant MR/K006525/1). DM reports ad hoc
honoraria from Bunge, Pollock Institute, and Quaker Oats; ad hoc
consulting for Foodminds, Nutrition Impact, Amarin, AstraZeneca,
Winston and Strawn LLP, and Life Sciences Research Organization;
membership of Unilever North America Scientifi c Advisory Board; and
chapter royalties from UpToDate. RD and LB are employed by the US
Department of Veterans Aff airs. VC is on the speaker bureau for
Boehringer Ingelheim Baker. MS is an employee of Novartis Pharma. All
Articles
52
www.thelancet.com Published online December 18, 2014 http://dx.doi.org/10.1016/S0140-6736(14)61682-2
other authors declare no competing interests. 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 affi liated.
Acknowledgments
We thank the countless individuals who have contributed to the Global
Burden of Disease Study 2013 in various capacities. We acknowledge the
extensive support from all staff members at the Institute for Health
Metrics and Evaluation and specifi cally thank: Kelsey Pierce for her
valuable guidance; James Bullard, Serkan Yalcin, Evan Laurie, and
Andrew Ernst for their tireless support of the computational
infrastructure required to produce the results; Linda A Ettinger for her
expert administrative support; and Peter Speyer and Eden Stork for their
persistent and invaluable work to gain access to and catalogue as much
data as possible to inform the estimates. We also acknowledge the
support of the Rwandan Ministry of Health’s GBD Team, led by
Agnes Binagwaho, for their collaboration and for reviewing the
manuscript: Uwaliraye Parfait, Karema Corine, Jean Pierre Nyemazi,
Sabin Nsanzimana, Yvonne Kayiteshonga, Marie Aimee Muhimpundu,
Jean de Dieu Ngirabega, Ida Kankindi, Sayinzoga Felix, and
Gasana Evariste. The following individuals acknowledge various forms
of institutional support. RA-SS was funded by a UK MRC senior clinical
fellowship. SB acknowledges additional funding or institutional support
from International Development Research Center of Canada, Stanford
University, and Rosenkranz Price for health-care research in developing
countries. AR was supported by research grants from Brazilian research
agencies CNPq and FAPEMIG. MK was supported by a NIDDK T32
grant through June 2014. RGN acknowledges that this work was
supported in part by the Intramural Research Program of the National
Institute of Diabetes and Digestive and Kidney Diseases. KK
acknowledges the Government of India for giving him a University
Grant Commission Junior Research Fellowship. GDT acknowledges
support from NYU’s US National Institute of Environmental Health
Sciences Center grant (number ES00260). HC and SJL are supported by
the intramural programme of NIH, the National Institute of
Environmental Health Sciences. KD is supported by a Wellcome Trust
Fellowship in Public Health and Tropical Medicine (grant number
099876). HW, AF, HE, and FC are affi liated with the Queensland Centre
for Mental Health Research, which receives funding from the
Queensland Department of Health. LAR acknowledges the support of
Qatar National Research Fund (04-924-3-251). TF is grateful to the Swiss
National Science Foundation for an Early and an Advanced Postdoc
Mobility fellowship (project number PBBSP3-146869 and
P300P3-154634). IA acknowledges the UK National Institute for Health
Research and the Medical Research Council for funding. HWH
acknowledges support from Parnassia Psychiatric Institute, The Hague,
Netherlands; the Department of Psychiatry, University of Groningen,
University Medical Center Groningen, Netherlands; and the Department
of Epidemiology, Columbia University, New York, NY, USA. JM has
received support from the National Health and Medical Research
Council John Cade Fellowship APP1056929. UM acknowledges funding
from the German National Cohort Consortium. BOA acknowledges a
Susan G. Komen for the Cure Research Program – Leadership Grant
(number SAC110001). AJC’s work on GBD was funded by Health Eff ects
Institute and the William and Flora Hewlett Foundation. RD
acknowledges that funding from the US Deptartment of Veterans Aff airs
supports his salary. RD acknowledges funding from the American
Parkinson’s Disease Association for support of this work. MK receives
research support from the Academy of Finland, the Swedish Research
Council, Alzheimer Association, and AXA Research Fund. SS receives
postdoctoral funding from the Fonds de la recherche en santé du
Québec. GA-C acknowledges funding and support from Health Sciences
and Neurosciences (CISNEURO) Research Group, Cartagena de Indias,
Colombia. No authors received additional compensation for their eff orts.
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