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www.thelancet.com Vol 390 September 16, 2017
1345
Global Health Metrics
Global, regional, and national comparative risk assessment
of 84 behavioural, environmental and occupational, and
metabolic risks or clusters of risks, 1990–2016: a systematic
analysis for the Global Burden of Disease Study 2016
GBD 2016 Risk Factors Collaborators*
Summary
Background The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a
comprehensive assessment of risk factor exposure and attributable burden of disease. By providing estimates over a
long time series, this study can monitor risk exposure trends critical to health surveillance and inform policy debates
on the importance of addressing risks in context.
Methods We used the comparative risk assessment framework developed for previous iterations of GBD to estimate
levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group,
sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks
from 1990 to 2016. This study included 481 risk-outcome pairs that met the GBD study criteria for convincing or
probable evidence of causation. We extracted relative risk (RR) and exposure estimates from 22 717 randomised
controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources, according
to the GBD 2016 source counting methods. Using the counterfactual scenario of theoretical minimum risk exposure
level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. Finally, we
explored four drivers of trends in attributable burden: population growth, population ageing, trends in risk exposure,
and all other factors combined.
Findings Since 1990, exposure increased significantly for 30 risks, did not change significantly for four risks, and
decreased significantly for 31 risks. Among risks that are leading causes of burden of disease, child growth failure and
household air pollution showed the most significant declines, while metabolic risks, such as body-mass index and high
fasting plasma glucose, showed significant increases. In 2016, at Level 3 of the hierarchy, the three leading risk factors
in terms of attributable DALYs at the global level for men were smoking (124·1 million DALYs [95% UI 111·2 million to
137·0 million]), high systolic blood pressure (122·2 million DALYs [110·3 million to 133·3 million], and low birthweight
and short gestation (83·0 million DALYs [78·3 million to 87·7 million]), and for women, were high systolic blood
pressure (89·9 million DALYs [80·9 million to 98·2 million]), high body-mass index (64·8 million DALYs [44·4 million
to 87·6 million]), and high fasting plasma glucose (63·8 million DALYs [53·2 million to 76·3 million]). In 2016 in
113 countries, the leading risk factor in terms of attributable DALYs was a metabolic risk factor. Smoking remained
among the leading five risk factors for DALYs for 109 countries, while low birthweight and short gestation was the
leading risk factor for DALYs in 38 countries, particularly in sub-Saharan Africa and South Asia. In terms of important
drivers of change in trends of burden attributable to risk factors, between 2006 and 2016 exposure to risks explains an
9·3% (6·9–11·6) decline in deaths and a 10·8% (8·3–13·1) decrease in DALYs at the global level, while population
ageing accounts for 14·9% (12·7–17·5) of deaths and 6·2% (3·9–8·7) of DALYs, and population growth for 12·4%
(10·1–14·9) of deaths and 12·4% (10·1–14·9) of DALYs. The largest contribution of trends in risk exposure to disease
burden is seen between ages 1 year and 4 years, where a decline of 27·3% (24·9–29·7) of the change in DALYs between
2006 and 2016 can be attributed to declines in exposure to risks.
Interpretation Increasingly detailed understanding of the trends in risk exposure and the RRs for each risk-outcome
pair provide insights into both the magnitude of health loss attributable to risks and how modification of risk exposure
has contributed to health trends. Metabolic risks warrant particular policy attention, due to their large contribution to
global disease burden, increasing trends, and variable patterns across countries at the same level of development.
GBD 2016 findings show that, while it has huge potential to improve health, risk modification has played a relatively
small part in the past decade.
Funding The Bill & Melinda Gates Foundation, Bloomberg Philanthropies.
Copyright © The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
Lancet 2017; 390: 1345–422
*Collaborators listed at the end
of the Article
Correspondence to:
Prof Emmanuela Gakidou,
Institute for Health Metrics and
Evaluation, Seattle, WA 98121,
USA
gakidou@uw.edu
For more on Bloomberg
Philanthropies see
www.bloomberg.org
Global Health Metrics
1346
www.thelancet.com Vol 390 September 16, 2017
Introduction
A core premise of public health is that prevention can
be a powerful instrument for improving human health,
one that is often cost-eective and minimises harm
to individuals from ill health. The core objectives
of prevention include the reduction or modification of
exposure to risks including metabolic, behavioural,
environmental, and occupational factors. Quantifying
risks to health and thus the targets of many public health
actions is an essential prerequisite for eective public
health. The evidence on the relation between risk
exposure and health is constantly evolving: new
information about the relative risks (RRs) associated with
dierent risks for dierent outcomes continues to
emerge from cohort studies, randomised trials, and case-
control studies. These studies can establish evidence for
new risks or risk-outcome pairs or reduce the strength of
evidence for existing risks. New data are also regularly
collected on the levels of exposure in dierent populations
and in dierent settings. Regularly updated monitoring
of the evidence base on risk factors is crucial for public
health and for individual risk modification through
primary care and self-management.
Several studies explore risk-attributable burden for
individual risks1–3 at the global, regional, or national level.
Other studies provide assessments of exposure for selected
risks. However, the Global Burden of Diseases, Injuries,
and Risk Factors Study (GBD) comparative risk assessment
(CRA) is the only comprehensive and comparable
approach to risk factor quantification. The most recent of
these assessments was GBD 2015.4–6 With each cycle of
GBD, scientific discussions have emerged on various
dimensions of risk quantification that have led to
improvements and modifications of GBD. Many of these
are focused on the strength of evidence supporting a causal
connection for specific risk-outcome pairs, while others
relate to measurement challenges.7–9 Further, new risk
factors have been added for important health conditions
included in GBD, such as neonatal outcomes and
Alzheimer’s dementia,10 which have previously not had
associated risk factors. The recent trials on blood pressure
control at lower levels of systolic blood pressure, including
Research in context
Evidence before this study
The Global Burden of Diseases, Injuries, and Risk Factors Study
2016 (GBD 2016) remains the most comprehensive effort to
conduct a population-level comparative risk assessment across
countries and risks. Other sources of population-level estimates
of risk include WHO and UNICEF reports as well as independent
scientific publications. Notable differences in methods and
definitions produce variation in results, although in several
cases there is general agreement in regional or global patterns.
The GBD study remains the only peer-reviewed, comprehensive,
and annual assessment of risk factor burden by age, sex, cause,
and location for a long time series that complies with the
Guidelines for Accurate and Transparent Health Estimates
Reporting (GATHER).
Added value of this study
This study builds upon GBD 2015 and provides several important
improvements as well as the quantification of five new risks.
The innovations and improvements from last year can be
summarised as follows. Across all risk factors, there were
7155 additional data sources, according to the GBD 2016 source
counting methods. For diet, we included data for dietary recall,
household budget, and food frequency questionnaires. We also
incorporated sales data from 170 countries as well as national
accounting of food available to populations in a given year. In
GBD 2016, we are producing estimates for the following
five new risks: smokeless tobacco, low birthweight and short
gestation, low birthweight for gestation, short gestation for
birthweight, and diet low in legumes. We also extended the high
body-mass index (BMI) analysis to include childhood obesity. We
have also added 93 new risk-outcome pairs. Major revisions to
the estimation of the following risk factors were undertaken for
GBD 2016. For second-hand smoke, we changed the estimation
method to ensure consistency with the estimates for smoking
prevalence. For alcohol, we estimated new relative risks (RRs) for
all outcomes, we incorporated more data for exposure and new
adjustments for tourism and unrecorded consumption, and we
redefined the theoretical minimum risk exposure level (TMREL).
For diet, we estimated the disease burden of dietary risks based
on the absolute level of intake rather than the intake
standardised to 2000 kcal per day. We developed an ensemble
model of different parametric distributions to generate better
fits to the distributions of continuous risk factors. Mediation
evidence was reviewed and updated based on an analysis of
ten pooled cohorts. We have expanded the analysis of
geographic and temporal trends in risk exposure and burden by
development, using the Socio-demographic Index (SDI), and
have also explored where countries are in the risk transition. We
also improved and modified our decomposition methods so that
the results shown are additive and can be aggregated to explain
trends in all-cause and cause-specific mortality, as well as trends
across age groups. The decomposition analysis has been
extended to examine how risk factors have contributed to trends
in all-cause mortality by age and sex as well as by cause.
Implications of all the available evidence
Increasingly detailed understanding of the trends in risk
exposure and the RRs for each risk-outcome pair provides
insights into both the magnitude of health loss attributable to
risks and how modification of risk exposure has contributed to
health trends. This analysis shows a mismatch between the
potential for risk modification to improve health and the
relatively modest role that risk modification has played in the
past generation in improving global health.
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1347
the Systolic Blood Pressure Intervention Trial (SPRINT)11
and Heart Outcomes Prevention Evaluation-3 (HOPE-3)
trial,12 have also brought attention to the dierence between
a population health perspective on the quantification of
risks and the clinical question of risk reversibility. The
CRA framework provides an important insight into the
role of dierent risks in contributing to levels of population
health but does not necessarily provide all the information
necessary to guide individual clinical decision making.
The GBD 2016 CRA includes 84 risk factors and an
associated 481 risk-outcome pairs. In addition to new
data and updated methods, we have included five new
risks in the GBD 2016 CRA. The study was undertaken
for 195 countries and territories and provides estimates
of exposure and attributable deaths and disability-
adjusted life-years (DALYs) for 1990 through to 2016. We
explored how risks change with development, measured
by the Socio-demographic Index (SDI), and also
decomposed changes in deaths and DALYs into the
contributions of population ageing, population growth,
trends in risk exposure, and all other factors combined.
As with previous iterations of GBD, the GBD 2016 CRA
results presented here supersede all previously published
GBD CRA estimates.
Methods
Overview
The CRA conceptual framework was developed by Murray
and Lopez,13 who established a causal web of hierarchically
organised risks or causes that contribute to health
outcomes (method appendix; appendix 1 p 432), which
allows quantification of risks or causes at any level in the
framework. In GBD 2016, as in previous iterations of
GBD, we evaluated a set of behavioural, environmental,
and occupational, and metabolic risks, where risk-
outcome pairs were included based on evidence rules
(appendix 1 p 344). These risks were organised into five
hierarchical levels as described in appendix 1 (p 374). At
Level 0, the GBD 2016 provides estimates for all risk
factors combined, at Level 1 the GBD 2016
provides estimates for three groups: environmental and
occupational, metabolic, and behavioral risk factors. At
Level 2, there are 17 risks, at Level 3 there are 50 risks, and
at Level 4 there are 67 risks, for a total of 84 risks or
clusters of risks. To date, we have not quantified the
contribution of other classes of risk factors
(appendix 1 p 376); however, using an analysis of the
relation between risk exposures and socio-demographic
development, measured with the use of SDI, we provide
some insights into the potential magnitude of distal
social, cultural, and economic factors.
Two types of risk assessment are possible within the
CRA framework: attributable burden and avoidable
burden.13 Attributable burden is the reduction in current
disease burden that would have been possible if past
population exposure had shifted to an alternative or
counterfactual distribution of risk exposure. Avoidable
burden is the potential reduction in future disease burden
that could be achieved by changing the current distribution
of exposure to a counterfactual distribution of exposure.
Murray and Lopez13 identified four types of counterfactual
exposure distributions: theoretical, plausible, feasible, and
cost-eective minimum risk. In GBD studies, to date and
in this study, we focus on attributable burden using the
theoretical minimum risk exposure level, which is the
distribution of risk comprising the levels of exposure that
minimise risk for each individual in the population.
Overall, this analysis follows the CRA methods used in
GBD 2015.4 The methods described in this study provide
a high-level overview of the analytical logic, focusing on
areas of notable change from the methods used in GBD
2015, with details provided in appendix 1 (p 10). This
study complies with the Guidelines for Accurate and
Transparent Health Estimates Reporting (GATHER)
statement14 (appendix 1 p 377).
Geographical units of analysis and years for estimation
In GBD 2016, locations are arranged as a set of hierarchical
categories: seven super-regions, 21 regions nested within
the seven super-regions, and 195 countries and territories
nested in the 21 regions. Additionally, we present estimates
at the subnational level for five countries with a population
greater than 200 million in 2016: Brazil, China, India,
Indonesia, and the USA. We produced a complete set of
age-specific, sex-specific, cause-specific, and location-
specific estimates of risk factor exposure and attributable
burden for 1990–2016 for all included risk factors.
Attributable burden estimation
Four key components are included in estimation of the
burden attributable to a given risk factor: the metric of
burden being assessed (number of deaths, years of life lost
[YLLs], years lived with disability [YLDs], or DALYs [the
sum of YLLs and YLDs]), the exposure levels for a risk
factor, the relative risk of a given outcome due to exposure,
and the counterfactual level of risk factor exposure.
Estimates of attributable DALYs for a risk-outcome pair are
equal to DALYs for the outcome multiplied by the
population attributable fraction (PAF) for the risk-outcome
pair for a given age, sex, location, and year. A similar logic
applies for estimation of attributable deaths, YLLs, or
YLDs. Risks are categorised on the basis of how exposure
was measured: dichotomous, polytomous, or continuous.
The PAF represents the proportion of outcome that would
be reduced in a given year if the exposure to a risk factor in
the past were reduced to the counterfactual level of the
theoretical minimum risk exposure level (supplementary
results, appendix 2 p 1).
Causal evidence for risk-outcome pairs
In this study, as in GBD 2015, we have included risk-
outcome pairs that we have assessed as meeting the
World Cancer Research Fund grades of convincing or
probable evidence (see appendix 1 p 10 for definitions of
See Online for appendix 1
See Online for appendix 2
Global Health Metrics
1348
www.thelancet.com Vol 390 September 16, 2017
these grades).15 Table 1 provides a summary of the
evidence supporting a causal relation between a risk and
an outcome for each pair included in GBD 2016. For
each risk-outcome pair, we used recent systematic
reviews to identify independent prospective studies
(randomised controlled trials, non-randomised
interventions, and cohorts) that evaluated the putative
relationship. For risk-outcome pairs with fewer than five
prospective studies, we evaluated evidence from case-
control studies as well (appendix 1 p 344). Table 1
summarises the evidence using multiple dimensions,
which supports our assessment that each included risk-
outcome pair meets the criteria of convincing or
probable evidence (appendix 1 p 10 contains a
justification of the criteria presented to support
causality). In this summary of evidence, we have focused
on randomised controlled trials and prospective
observational studies, along with supporting evidence,
like dose–response relationships and biologically
plausible mechanisms.
Estimation process
Information about the data sources, estimation methods,
computational tools, and statistical analysis used in the
derivation of our estimates are provided in appendix 1
(p 10). The analytical steps for estimation of burden
attributable to single or clusters of risk-outcome pairs are
summarised in appendix 1 (p 10). Table 2 provides
definitions of exposure for each risk factor, the theoretical
minimum risk exposure level (TMREL) used, and metrics
of data availability. For each risk, we estimated eect size
as a function of age and sex and exposure level, mean
exposure, the distribution of exposure across individuals,
and the TMREL. The approach taken is largely similar to
GBD 2015 for each quantity for each risk. Some
methodological improv ements have been implemented
and new data sources incorporated. Appendix 1 (p 34)
provides details of each step by risk. Citation information
for the data sources used for relative risks are provided in
searchable form through an online source tool.
All point estimates are reported with 95% uncertainty
intervals (UIs). UIs include uncertainty from each
relevant component, consisting of exposure, relative
risks, TMREL, and burden rates. Where percentage
change is reported (with 95% UIs), we computed it on
the basis of the point estimates being compared.
In GBD 2015, we produced a summary measure of
exposure for each risk, called the summary exposure
value (SEV), which is a metric that captures risk-weighted
exposure for a population, or risk-weighted prevalence of
an exposure. The scale for SEV spans from 0% to 100%,
such that an SEV of 0% reflects no risk exposure in a
population and 100% indicates that an entire population
is exposure to the maximum possible level for that risk.
In GBD 2016, we show estimates of SEVs for each risk
factor and provide details on how SEVs are computed for
categorical and continuous risks in appendix 1 (p 10).
Fitting a distribution to exposure data
The most informative data describing the distribution of
risk factors within a population come from individual-level
data; additional sources of data include reported means
and variances. In cases when a risk factor also defines a
disease, such as haemoglobin level and anaemia, the
prevalence of disease is also frequently reported. To model
the distribution of any particular risk factor, we seek a
family of probability density functions (PDFs), a fitting
method, and a model selection criterion. To make use of
the most data describing most populations, we used the
method of moments (MoM); the first two empirical
moments from a population, the mean and variance, were
used to determine the PDF describing the distribution of
risk within any population, where exceptions to this rule
are justified by context. We used the Kolmogorov-Smirnov
test to measure the goodness of fit (GoF), but in some
cases, the GoF was based on the prediction error for the
prevalence of disease.
We used an ensemble technique in which a model
selection algorithm is used to choose the best model for
each risk factor.16 We drew the initial set of candidate
models from commonly used PDF families. We fitted each
PDF candidate family to each dataset using the MoM, and
used the Kolmogorov-Smirnov test17 as the measure of GoF.
Preliminary analysis showed that the GoF ranking of PDF
families varied across datasets for any particular risk factor
and that combining the predictions of dierently fitted
PDF families could dramatically improve the GoF for each
dataset. Therefore, we developed a new model for prediction
using the ensemble of candidate models, which is a
weighted linear combination of all candidate models, {f},
where a set of weights {w} is chosen such that it is the sum
of the weights equals to one and the values of the weights
were determined by a second GoF criterion with its own
validation process. Because of basic dierences among risk
factors, their distributions, and the risk attribution process,
the model selection process was often slightly dierent for
each risk factor. The details can be summarised by (1) the
summary statistics for each dataset; (2) a table showing the
Kolmogorov-Smirnov statistic for each candidate model
and URD; (3) the criterion used for determining the overall
GoF; (4) summary results of the validation process; and (5)
the weights defining the final ensemble model for each
dataset.
New risks and risks with significant changes in the
estimation methods compared with GBD 2015
We took several steps to improve the estimation of alcohol
use as a risk factor. First, on the exposure side, we added
26 survey series, which contributed 12 195 datapoints in
our models. Second, we developed and implemented a
method that adjusts total consumption for tourism and
unrecorded consumption for each location-year. Third,
we calculated the TMREL. We chose TMREL as being the
exposure that minimises an individual’s risk of suering
burden from any given cause related to alcohol
For the tool see
http://ghdx.healthdata.org/
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1349
Risk Outcome RCTs
(n)
RCTs with
significant
effect in
the
opposite
direction
(%)
RCTs
with null
findings
(%)
Prospective
observational
studies (n)*
Prospective
observational
studies with
significant
association in
the opposite
direction (%)
Case-control
studies
assessing
the risk-
outcome
pair
relationship
(n)†
Case-control
studies that
show
significant
association
in the
opposite
direction (%)
Lower
limit
of RR
>1·5
Dose–
response
relationship
Biological
plausibility
‡
Analogy§
2 Unsafe water, sanitation, and handwashing
3Unsafe water
source– chlorination
or solar (point of use
treatment)
Diarrhoeal
diseases
24 0 42 6 0 ·· ·· Yes ·· Ye s No
3Unsafe water
source–piped
Diarrhoeal
diseases
1 0 0 9 11 ·· ·· Yes ·· Yes No
3Unsafe water
source–filter
Diarrhoeal
diseases
11 0 45 2 0 ·· ·· Ye s ·· Yes No
3Unsafe water
source– improved
water
Diarrhoeal
diseases
0 ·· ·· 5 0 ·· ·· Ye s ·· Yes No
3 Unsafe sanitation–
piped
Diarrhoeal
diseases
0 ·· ·· 7 0 ·· ·· Yes ·· Yes No
3 Unsafe sanitation–
improved sanitation
Diarrhoeal
diseases
0 ·· ·· 9 0 ·· ·· Yes ·· Ye s No
3No access to
handwashing facility
Diarrhoeal
diseases
19 0 42 0 ·· ·· ·· No ·· Yes No
3No access to
handwashing facility
Lower
respiratory
infections
8 0 50 11 0 ·· ·· No ·· Yes No
2 Air pollution
3 Ambient particulate
matter pollution
Lower
respiratory
infections
0 ·· ·· 19 0 ·· ·· No Ye s Yes No
3 Ambient particulate
matter pollution
Tracheal,
bronchus, and
lung cancer
0 ·· ·· 27 0 ·· ·· No Yes Yes Ye s
3 Ambient particulate
matter pollution
Ischaemic heart
disease
0 ·· ·· 16 0 ·· ·· No Ye s Yes Yes
3 Ambient particulate
matter pollution
Ischaemic stroke 0 ·· ·· 25 0 ·· ·· No Yes Yes Yes
3 Ambient particulate
matter pollution
Haemorrhagic
stroke
0 ·· ·· 25 0 ·· ·· No Yes Yes Yes
3 Ambient particulate
matter pollution
Chronic
obstructive
pulmonary
disease
0 ·· ·· 12 0 ·· ·· No Yes Ye s Yes
3 Household air
pollution from solid
fuels
Lower
respiratory
infections
0 ·· ·· 0 ·· 9 0 No Yes Yes No
3 Household air
pollution from solid
fuels
Tracheal,
bronchus, and
lung cancer
0 ·· ·· 0 ·· 20 0 No Ye s Yes Yes
3 Household air
pollution from solid
fuels
Ischaemic heart
disease
0 ·· ·· 16 0 ·· ·· No Ye s Yes Yes
3 Household air
pollution from solid
fuels
Ischaemic stroke 0 ·· ·· 25 0 ·· ·· No Yes Yes Yes
3 Household air
pollution from solid
fuels
Haemorrhagic
stroke
0 ·· ·· 25 0 ·· ·· No Yes Yes Yes
(Table 1 continues on next page)
Global Health Metrics
1350
www.thelancet.com Vol 390 September 16, 2017
Risk Outcome RCTs
(n)
RCTs with
significant
effect in
the
opposite
direction
(%)
RCTs
with null
findings
(%)
Prospective
observational
studies (n)*
Prospective
observational
studies with
significant
association in
the opposite
direction (%)
Case-control
studies
assessing
the risk-
outcome
pair
relationship
(n)†
Case-control
studies that
show
significant
association
in the
opposite
direction (%)
Lower
limit
of RR
>1·5
Dose–
response
relationship
Biological
plausibility
‡
Analogy§
(Continued from previous page)
3 Household air
pollution from solid
fuels
Chronic
obstructive
pulmonary
disease
0 ·· ·· 0 ·· 2 0 No Yes Ye s Yes
3 Household air
pollution from solid
fuels
Cataract 0 ·· ·· 0 ·· 11 0 No Yes Yes No
3Ambient ozone
pollution
Chronic
obstructive
pulmonary
disease
0 ·· ·· 4 0 0 0 No Yes Ye s No
2 Other environmental risks
3 Residential radon Tracheal,
bronchus, and
lung cancer
0 ·· ·· 1 0 29 0 No Yes Ye s No
3 Lead exposure Idiopathic
developmental
intellectual
disability
0 ·· ·· 8 0 ·· ·· No Ye s Yes No
3 Lead exposure Systolic blood
pressure
0 ·· ·· 3 0 1 0 No Yes Yes No
2 Occupational risks
4 Occupational
exposure to asbestos
Larynx cancer 0 ·· ·· 27 0 ·· ·· No ·· Yes Yes
4 Occupational
exposure to asbestos
Tracheal,
bronchus, and
lung cancer
0 ·· ·· 18 0 ·· ·· Ye s ·· Yes Yes
4 Occupational
exposure to asbestos
Ovarian cancer 0 ·· ·· 15 0 ·· ·· No ·· Yes Yes
4 Occupational
exposure to asbestos
Mesothelioma 0 ·· ·· 5 0 ·· ·· Ye s ·· Yes Yes
4 Occupational
exposure to arsenic
Tracheal,
bronchus, and
lung cancer
0 ·· ·· 9 0 ·· ·· No ·· Yes No
4 Occupational
exposure to benzene
Leukaemia 0 ·· ·· 12 0 ·· ·· Ye s ·· Yes No
4 Occupational
exposure to
beryllium
Tracheal,
bronchus, and
lung cancer
0 ·· ·· 3 0 2 0 No ·· Yes No
4 Occupational
exposure to
cadmium
Tracheal,
bronchus, and
lung cancer
0 ·· ·· 7 0 ·· ·· No ·· Yes No
4 Occupational
exposure to
chromium
Tracheal,
bronchus, and
lung cancer
0 ·· ·· 26 0 ·· ·· No ·· Yes No
4 Occupational
exposure to diesel
engine exhaust
Tracheal,
bronchus, and
lung cancer
0 ·· ·· 17 0 ·· ·· No ·· Yes No
4 Occupational
exposure to second-
hand smoke
Tracheal,
bronchus, and
lung cancer
0 ·· ·· 25 0 ·· ·· No ·· Yes No
4 Occupational
exposure to
formaldehyde
Nasopharynx
cancer
0 ·· ·· 2 0 6 0 No ·· Yes Yes
(Table 1 continues on next page)
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1351
Risk Outcome RCTs
(n)
RCTs with
significant
effect in
the
opposite
direction
(%)
RCTs
with null
findings
(%)
Prospective
observational
studies (n)*
Prospective
observational
studies with
significant
association in
the opposite
direction (%)
Case-control
studies
assessing
the risk-
outcome
pair
relationship
(n)†
Case-control
studies that
show
significant
association
in the
opposite
direction (%)
Lower
limit
of RR
>1·5
Dose–
response
relationship
Biological
plausibility
‡
Analogy§
(Continued from previous page)
4 Occupational
exposure to
formaldehyde
Leukaemia 0 ·· ·· 13 0 ·· ·· No ·· Yes Yes
4 Occupational
exposure to nickel
Tracheal,
bronchus, and
lung cancer
0 ·· ·· 6 0 ·· ·· No ·· Yes No
4 Occupational
exposure to
polycyclic aromatic
hydrocarbons
Tracheal,
bronchus, and
lung cancer
0 ·· ·· 39 0 ·· ·· No ·· Yes No
4 Occupational
exposure to silica
Tracheal,
bronchus, and
lung cancer
0 ·· ·· 17 0 ·· ·· No ·· Yes No
4 Occupational
exposure to sulfuric
acid
Larynx cancer 0 ·· ·· 14 0 ·· ·· Ye s ·· Yes No
4 Occupational
exposure to
trichloroethylene
Kidney cancer 0 ·· ·· 20 0 ·· ·· No ·· Yes No
3 Occupational
asthmagens
Asthma 0 ·· ·· 16 0 ·· ·· No ·· Yes No
3 Occupational
particulate matter,
gases, and fumes
Chronic
obstructive
pulmonary
disease
0 ·· ·· 9 0 ·· ·· No ·· Yes No
3 Occupational noise Age-related and
other hearing
loss
0 ·· ·· 5 0 ·· ·· Ye s ·· Yes No
3 Occupational
ergonomic factors
Low back pain 0 ·· ·· 10 0 ·· ·· No ·· Yes No
2 Child and maternal malnutrition
4 Non-exclusive
breastfeeding
Diarrhoeal
diseases
0 ·· ·· 5 0 ·· ·· Ye s ·· Yes No
4 Non-exclusive
breastfeeding
Lower
respiratory
infections
0 ·· ·· 6 0 ·· ·· Ye s ·· Yes No
4 Discontinued
breastfeeding
Diarrhoeal
diseases
0 ·· ·· 2 0 ·· ·· No ·· Yes No
4Child underweight Diarrhoeal
diseases
0 ·· ·· 7 0 ·· ·· Yes ·· Yes No
4Child underweight Lower
respiratory
infections
0 ·· ·· 7 0 ·· ·· Yes ·· Yes No
4Child underweight Measles 0 ·· ·· 7 0 ·· ·· Yes ·· Yes No
4Child wasting Diarrhoeal
diseases
0 ·· ·· 7 0 ·· ·· Yes ·· Yes No
4Child wasting Lower
respiratory
infections
0 ·· ·· 7 0 ·· ·· Yes ·· Yes No
4Child wasting Measles 0 ·· ·· 7 0 ·· ·· Yes ·· Yes No
4 Child stunting Diarrhoeal
diseases
0 ·· ·· 7 0 ·· ·· No ·· Yes No
(Table 1 continues on next page)
Global Health Metrics
1352
www.thelancet.com Vol 390 September 16, 2017
Risk Outcome RCTs
(n)
RCTs with
significant
effect in
the
opposite
direction
(%)
RCTs
with null
findings
(%)
Prospective
observational
studies (n)*
Prospective
observational
studies with
significant
association in
the opposite
direction (%)
Case-control
studies
assessing
the risk-
outcome
pair
relationship
(n)†
Case-control
studies that
show
significant
association
in the
opposite
direction (%)
Lower
limit
of RR
>1·5
Dose–
response
relationship
Biological
plausibility
‡
Analogy§
(Continued from previous page)
4 Child stunting Lower
respiratory
infections
0 ·· ·· 7 0 ·· ·· No ·· Yes No
4 Child stunting Measles 0 ·· ·· 7 0 ·· ·· No ·· Yes No
4 Short gestation for
birthweight
Diarrhoeal
diseases
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Short gestation for
birthweight
Lower
respiratory
infections
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Short gestation for
birthweight
Upper
respiratory
infections
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Short gestation for
birthweight
Otitis media 0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Short gestation for
birthweight
Pneumococcal
meningitis
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Short gestation for
birthweight
Haemophilus
influenzae type B
meningitis
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Short gestation for
birthweight
Meningococcal
infection
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Short gestation for
birthweight
Other meningitis 0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Short gestation for
birthweight
Encephalitis 0 ·· ·· 20 0 ·· ·· Yes Yes Yes Yes
4 Short gestation for
birthweight
Neonatal
preterm birth
complications
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Short gestation for
birthweight
Neonatal
encephalopathy
due to birth
asphyxia and
trauma
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Short gestation for
birthweight
Neonatal sepsis
and other
neonatal
infections
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Short gestation for
birthweight
Haemolytic
disease and
other neonatal
jaundice
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Short gestation for
birthweight
Other neonatal
disorders
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Short gestation for
birthweight
Sudden infant
death syndrome
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Low birthweight for
gestation
Diarrhoeal
diseases
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Low birthweight for
gestation
Lower
respiratory
infections
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Low birthweight for
gestation
Upper
respiratory
infections
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
(Table 1 continues on next page)
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1353
Risk Outcome RCTs
(n)
RCTs with
significant
effect in
the
opposite
direction
(%)
RCTs
with null
findings
(%)
Prospective
observational
studies (n)*
Prospective
observational
studies with
significant
association in
the opposite
direction (%)
Case-control
studies
assessing
the risk-
outcome
pair
relationship
(n)†
Case-control
studies that
show
significant
association
in the
opposite
direction (%)
Lower
limit
of RR
>1·5
Dose–
response
relationship
Biological
plausibility
‡
Analogy§
(Continued from previous page)
4 Low birthweight for
gestation
Otitis media 0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Low birthweight for
gestation
Pneumococcal
meningitis
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Low birthweight for
gestation
Haemophilus
influenzae type B
meningitis
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Low birthweight for
gestation
Meningococcal
infection
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Low birthweight for
gestation
Other meningitis 0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Low birthweight for
gestation
Encephalitis 0 ·· ·· 20 0 ·· ·· Yes Yes Yes Yes
4 Low birthweight for
gestation
Neonatal
preterm birth
complications
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Low birthweight for
gestation
Neonatal
encephalopathy
due to birth
asphyxia and
trauma
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Low birthweight for
gestation
Neonatal sepsis
and other
neonatal
infections
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Low birthweight for
gestation
Haemolytic
disease and
other neonatal
jaundice
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Low birthweight for
gestation
Other neonatal
disorders
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
4 Low birthweight for
gestation
Sudden infant
death syndrome
0 ·· ·· 20 0 ·· ·· Ye s Yes Yes Yes
3Vitamin A deficiency Diarrhoeal
diseases
19 0 63 0 ·· ·· ·· No ·· Yes No
3Vitamin A deficiency Measles 12 0 83 0 ·· ·· ·· Ye s ·· Yes No
3Zinc deficiency Diarrhoeal
diseases
14 0 29 0 ·· ·· ·· No ·· Yes No
3Zinc deficiency Lower
respiratory
infections
6 0 17 0 ·· ·· ·· No ·· Ye s No
2 Tobacco
3 Smoking Tuberculosis 0 ·· ·· 4 0 10 0 No ·· Ye s Yes
3 Smoking Lip and oral
cavity cancer
0 ·· ·· 5 0 ·· ·· Ye s ·· Yes Ye s
3 Smoking Nasopharynx
cancer
0 ·· ·· 4 0 28 0 Ye s ·· Yes Yes
3 Smoking Oesophageal
cancer
0 ·· ·· 5 0 ·· ·· Ye s ·· Yes Ye s
3 Smoking Colon and
rectum cancer
0 ·· ·· 19 0 ·· ·· No ·· Ye s Yes
3 Smoking Liver cancer 0 ·· ·· 54 0 ·· ·· Ye s ·· Yes Yes
3 Smoking Gastric cancer 0 ·· ·· 19 0 ·· ·· No ·· Ye s Yes
(Table 1 continues on next page)
Global Health Metrics
1354
www.thelancet.com Vol 390 September 16, 2017
Risk Outcome RCTs
(n)
RCTs with
significant
effect in
the
opposite
direction
(%)
RCTs
with null
findings
(%)
Prospective
observational
studies (n)*
Prospective
observational
studies with
significant
association in
the opposite
direction (%)
Case-control
studies
assessing
the risk-
outcome
pair
relationship
(n)†
Case-control
studies that
show
significant
association
in the
opposite
direction (%)
Lower
limit
of RR
>1·5
Dose–
response
relationship
Biological
plausibility
‡
Analogy§
(Continued from previous page)
3 Smoking Pancreatic
cancer
0 ·· ·· 19 0 ·· ·· Ye s ·· Yes Yes
3 Smoking Larynx cancer 0 ·· ·· 5 0 ·· ·· Yes ·· Yes Ye s
3 Smoking Tracheal,
bronchus, and
lung cancer
0 ·· ·· 38 0 ·· ·· Ye s ·· Yes Yes
3 Smoking Breast cancer 0 ·· ·· 19 0 ·· ·· No ·· Yes Yes
3 Smoking Cervical cancer 0 ·· ·· 15 0 ·· ·· No ·· Yes Yes
3 Smoking Prostate cancer 0 ·· ·· 19 0 ·· ·· No ·· Yes Yes
3 Smoking Kidney cancer 0 ·· ·· 8 0 ·· ·· Yes ·· Yes Yes
3 Smoking Bladder cancer 0 ·· ·· 37 0 ·· ·· Yes ·· Yes Yes
3 Smoking Leukaemia 0 ·· ·· 22 0 ·· ·· No ·· Yes Yes
3 Smoking Ischaemic heart
disease
0 ·· ·· 86 .. ·· ·· No ·· Yes Yes
3 Smoking Ischaemic stroke 0 ·· ·· 60 .. ·· ·· No ·· Yes Yes
3 Smoking Haemorrhagic
stroke
0 ·· ·· 60 .. ·· ·· No ·· Yes Ye s
3 Smoking Atrial fibrillation
and flutter
0 ·· ·· 16 0 ·· ·· No ·· Yes Yes
3 Smoking Peripheral
vascular disease
0 ·· ·· 10 0 ·· ·· No ·· Yes Ye s
3 Smoking Other
cardiovascular
and circulatory
diseases
0 ·· ·· 5 0 ·· ·· No ·· Yes Ye s
3 Smoking Chronic
obstructive
pulmonary
disease
0 ·· ·· 42 0 ·· ·· Yes ·· Yes Yes
3 Smoking Asthma 0 ·· ·· 8 12 ·· ·· No ·· Yes Yes
3 Smoking Other chronic
respiratory
diseases
0 ·· ·· 5 0 ·· ·· Ye s ·· Yes Ye s
3 Smoking Peptic ulcer
disease
0 ·· ·· 7 0 ·· ·· No ·· Yes No
3 Smoking Gallbladder and
biliary diseases
0 ·· ·· 10 0 ·· ·· No ·· Yes Ye s
3 Smoking Alzheimer’s
disease and
other dementias
0 ·· ·· 13 8 ·· ·· No ·· Yes Ye s
3 Smoking Parkinson’s
disease
0 ·· ·· 8 0 ·· ·· Ye s ·· Yes Yes
3 Smoking Multiple
sclerosis
0 ·· ·· 6 0 ·· ·· No ·· Yes Yes
3 Smoking Diabetes
mellitus
0·· ·· 88 0 ·· ·· No ·· Yes No
3 Smoking Rheumatoid
arthritis
0 ·· ·· 5 0 ·· ·· No ·· Yes No
3 Smoking Low back pain 0 ·· ·· 13 0 ·· ·· No ·· Yes Yes
3 Smoking Cataract 0 ·· ·· 13 0 ·· ·· No ·· Yes No
3 Smoking Macular
degeneration
0 ·· ·· 5 0 ·· ·· No ·· Yes No
(Table 1 continues on next page)
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1355
Risk Outcome RCTs
(n)
RCTs with
significant
effect in
the
opposite
direction
(%)
RCTs
with null
findings
(%)
Prospective
observational
studies (n)*
Prospective
observational
studies with
significant
association in
the opposite
direction (%)
Case-control
studies
assessing
the risk-
outcome
pair
relationship
(n)†
Case-control
studies that
show
significant
association
in the
opposite
direction (%)
Lower
limit
of RR
>1·5
Dose–
response
relationship
Biological
plausibility
‡
Analogy§
(Continued from previous page)
3 Smoking Low bone mass-
related fractures
0 ·· ·· 14 14 ·· ·· No ·· Yes Yes
3 Smoking Hip fracture 0 ·· ·· 15 20 ·· ·· No ·· Yes Yes
3 Smoking Abdominal
aortic aneurism
0 ·· ·· 10 0 ·· ·· No ·· Yes Ye s
3Smokeless tobacco Oral cancer 0 ·· ·· 4 0 21 5 Yes ·· Yes Yes
3Smokeless tobacco Oesophageal
cancer
0 ·· ·· 2 0 10 0 Yes ·· Yes Yes
3 Second-hand smoke Lower
respiratory
infections
0 ·· ·· 18 0 ·· ·· No Ye s Yes Yes
3 Second-hand smoke Otitis media 0 ·· ·· 1 0 4 0 No ·· Yes Ye s
3 Second-hand smoke Tracheal,
bronchus, and
lung cancer
0 ·· ·· 13 0 ·· ·· No Yes Yes Ye s
3 Second-hand smoke Breast cancer 0 ·· ·· 21 0 ·· ·· No ·· Yes Ye s
3 Second-hand smoke Ischaemic heart
disease
0 ·· ·· 5 0 ·· ·· No Ye s Yes Yes
3 Second-hand smoke Ischaemic stroke 0 ·· ·· 4 0 3 ·· No Yes Ye s Yes
3 Second-hand smoke Haemorrhagic
stroke
0 ·· ·· 4 0 3 ·· No Yes Yes Yes
3 Second-hand smoke Chronic
obstructive
pulmonary
disease
0 ·· ·· 2 0 1 0 No Yes Yes Ye s
3 Second-hand smoke Diabetes
mellitus
0 ·· ·· 5 0 ·· ·· No ·· Yes Ye s
2Alcohol and drug use
3Alcohol use Tuberculosis 0 ·· ·· 3 0 18 11 Yes Ye s Yes Yes
3Alcohol use Lower
respiratory
infections
0 ·· ·· 2 0 2 0 Yes Yes Ye s Ye s
3Alcohol use Lip and oral
cavity cancer
0 ·· ·· 6 0 ·· ·· Ye s Yes Yes Yes
3Alcohol use Nasopharynx
cancer
0 ·· ·· 6 0 ·· ·· Ye s Yes Yes Yes
3Alcohol use Other pharynx
cancer
0 ·· ·· 6 0 ·· ·· Ye s Yes Yes Yes
3Alcohol use Oesophageal
cancer
0 ·· ·· 10 0 ·· ·· Ye s Yes Ye s Ye s
3Alcohol use Colon and
rectum cancer
0 ·· ·· 15 13 ·· ·· Yes Ye s Yes Yes
3Alcohol use Liver cancer 0 ·· ·· 9 0 ·· ·· Ye s Yes Yes Yes
3Alcohol use Larynx cancer 0 ·· ·· 7 0 ·· ·· Ye s Yes Yes Ye s
3Alcohol use Breast cancer 0 ·· ·· 13 23 ·· ·· Yes Yes Yes Yes
3Alcohol use Ischaemic heart
disease
0 ·· ·· 63 0 ·· ·· Ye s Yes Yes Yes
3Alcohol use Ischaemic stroke 0 ·· ·· 20 0 ·· ·· Yes Ye s Yes Yes
3Alcohol use Haemorrhagic
stroke
0 ·· ·· 16 0 ·· ·· Ye s Yes Yes Yes
3Alcohol use Hypertensive
heart disease
0 ·· ·· 12 0 ·· ·· Yes Yes Ye s Yes
(Table 1 continues on next page)
Global Health Metrics
1356
www.thelancet.com Vol 390 September 16, 2017
Risk Outcome RCTs
(n)
RCTs with
significant
effect in
the
opposite
direction
(%)
RCTs
with null
findings
(%)
Prospective
observational
studies (n)*
Prospective
observational
studies with
significant
association in
the opposite
direction (%)
Case-control
studies
assessing
the risk-
outcome
pair
relationship
(n)†
Case-control
studies that
show
significant
association
in the
opposite
direction (%)
Lower
limit
of RR
>1·5
Dose–
response
relationship
Biological
plausibility
‡
Analogy§
(Continued from previous page)
3Alcohol use Atrial fibrillation
and flutter
0 ·· ·· 10 10 ·· ·· Ye s Yes Yes Ye s
3Alcohol use Cirrhosis 0 ·· ·· 14 0 ·· ·· Ye s Yes Yes Ye s
3Alcohol use Pancreatitis 0 ·· ·· 4 50 3 0 Yes Yes Ye s No
3Alcohol use Epilepsy 0 ·· ·· 1 0 2 0 No Yes Ye s No
3Alcohol use Diabetes
mellitus
0 ·· ·· 37 32 ·· ·· Yes Yes Ye s No
3Alcohol use Motor vehicle
road injuries
0 ·· ·· 3 0 ·· ·· Ye s Yes Ye s Ye s
3Alcohol use Unintentional
injuries
0 ·· ·· 4 0 4 0 Yes Ye s Yes Yes
3Alcohol use Self-harm 0 ·· ·· 0 ·· ·· ·· Yes Ye s Yes Yes
3Alcohol use Interpersonal
violence
0 ·· ·· 2 0 1 0 Yes Yes Yes Yes
3Drug use Hepatitis B 0 ·· ·· 6 0 ·· ·· Yes ·· Yes Yes
3Drug use Hepatitis C 0 ·· ·· 16 0 ·· ·· Ye s ·· Yes Ye s
3Drug use Self-harm 0 ·· ·· 1 0 0 0 No ·· Yes No
2 Dietary risks
3 Diet low in fruits Lip and oral
cavity cancer
0 ·· ·· 2 0 15 0 No Ye s Yes Yes
3 Diet low in fruits Nasopharynx
cancer
0 ·· ·· 2 0 15 0 No Ye s Yes Yes
3 Diet low in fruits Other pharynx
cancer
0 ·· ·· 2 0 15 0 No Ye s Yes Yes
3 Diet low in fruits Oesophageal
cancer
0 ·· ·· 5 0 ·· ·· No Ye s Yes Yes
3 Diet low in fruits Larynx cancer 0 ·· ·· 2 0 15 0 No Ye s Yes Yes
3 Diet low in fruits Tracheal,
bronchus, and
lung cancer
0 ·· ·· 22 0 ·· ·· No Ye s Yes Ye s
3 Diet low in fruits Ischaemic heart
disease
0 ·· ·· 9 0 ·· ·· No Ye s Yes Yes
3 Diet low in fruits Ischaemic stroke 0 ·· ·· 9 0 ·· ·· No Ye s Yes Yes
3 Diet low in fruits Haemorrhagic
stroke
0 ·· ·· 5 0 ·· ·· No Ye s Yes Yes
3 Diet low in fruits Diabetes
mellitus
0 ·· ·· 9 0 ·· ·· No Ye s Yes No
3 Diet low in
vegetables
Oesophageal
cancer
0 ·· ·· 5 0 ·· ·· No Ye s Yes No
3Diet low in
vegetables
Ischaemic heart
disease
0 ·· ·· 9 0 ·· ·· No Ye s Yes Yes
3 Diet low in
vegetables
Ischaemic stroke 0 ·· ·· 8 0 ·· ·· No Ye s Yes Yes
3 Diet low in
vegetables
Haemorrhagic
stroke
0 ·· ·· 5 0 ·· ·· No Ye s Yes Yes
3 Diet low in legumes Ischaemic heart
disease
0 ·· ·· 5 0 ·· ·· No Ye s Yes No
3 Diet low in whole
grains
Ischaemic heart
disease
0 ·· ·· 7 0 ·· ·· No Yes Yes Ye s
3 Diet low in whole
grains
Ischaemic stroke 0 ·· ·· 6 0 ·· ·· No Ye s Yes Yes
(Table 1 continues on next page)
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1357
Risk Outcome RCTs
(n)
RCTs with
significant
effect in
the
opposite
direction
(%)
RCTs
with null
findings
(%)
Prospective
observational
studies (n)*
Prospective
observational
studies with
significant
association in
the opposite
direction (%)
Case-control
studies
assessing
the risk-
outcome
pair
relationship
(n)†
Case-control
studies that
show
significant
association
in the
opposite
direction (%)
Lower
limit
of RR
>1·5
Dose–
response
relationship
Biological
plausibility
‡
Analogy§
(Continued from previous page)
3 Diet low in whole
grains
Haemorrhagic
stroke
0 ·· ·· 6 0 ·· ·· No Ye s Yes Yes
3 Diet low in whole
grains
Diabetes
mellitus
0 ·· ·· 10 0 ·· ·· No Ye s Yes No
3 Diet low in nuts and
seeds
Ischaemic heart
disease
1 0 100 6 0 ·· ·· No Yes Yes No
3 Diet low in nuts and
seeds
Diabetes
mellitus
1 0 100 5 0 ·· ·· No Yes Yes No
3 Diet low in milk Colon and
rectum cancer
0 ·· ·· 7 0 ·· ·· No Yes Yes No
3 Diet high in red
meat
Colon and
rectum cancer
0 ·· ·· 8 0 ·· ·· No Ye s Yes No
3 Diet high in red
meat
Diabetes
mellitus
0 ·· ·· 9 11 ·· ·· No Ye s Yes No
3 Diet high in
processed meat
Colon and
rectum cancer
0 ·· ·· 9 11 ·· ·· No Ye s Yes No
3 Diet high in
processed meat
Ischaemic heart
disease
0 ·· ·· 5 0 ·· ·· No Ye s Yes No
3 Diet high in
processed meat
Diabetes
mellitus
0 ·· ·· 8 0 ·· ·· No Ye s Yes No
3 Diet high in
sugar-sweetened
beverages
Body-mass
index
10 0 60 22 0 ·· ·· Yes Yes Yes No
3 Diet low in fibre Colon and
rectum cancer
0 ·· ·· 15 0 ·· ·· No Yes Yes No
3 Diet low in fibre Ischaemic heart
disease
0 ·· ·· 12 0 ·· ·· No Yes Yes No
3 Diet low in calcium Colon and
rectum cancer
0 ·· ·· 13 0 ·· ·· No Yes Yes No
3 Diet low in seafood
omega 3 fatty acids
Ischaemic heart
disease
17 0 94 16 0 ·· ·· No Yes Ye s No
3 Diet low in
polyunsaturated
fatty acids
Ischaemic heart
disease
8 0 75 11 0 ·· ·· No Ye s Yes No
3 Diet high in trans
fatty acids
Ischaemic heart
disease
0 ·· ·· 13 0 ·· ·· No Yes Yes No
3 Diet high in sodium Stomach cancer 0 ·· ·· 10 0 ·· ·· No Yes Yes No
3 Diet high in sodium Systolic blood
pressure
45 0 73 0 .. ·· ·· No Ye s Yes No
2 Sexual abuse and violence
3 Childhood sexual
abuse
Alcohol use
disorders
0 ·· ·· 2 0 3 0 No .. Yes Ye s
3 Childhood sexual
abuse
Depressive
disorders
0 ·· ·· 7 0 ·· ·· No ·· Yes Yes
3 Intimate partner
violence
HIV/AIDS 0 ·· ·· 2 0 0 0 No ·· Yes No
3 Intimate partner
violence
Maternal
abortion,
miscarriage, and
ectopic
pregnancy
0·· ·· 1 0 3 0 Ye s ·· Yes No
3 Intimate partner
violence
Depressive
disorders
0 ·· ·· 4 0 0 0 No ·· Ye s Yes
(Table 1 continues on next page)
Global Health Metrics
1358
www.thelancet.com Vol 390 September 16, 2017
Risk Outcome RCTs
(n)
RCTs with
significant
effect in
the
opposite
direction
(%)
RCTs
with null
findings
(%)
Prospective
observational
studies (n)*
Prospective
observational
studies with
significant
association in
the opposite
direction (%)
Case-control
studies
assessing
the risk-
outcome
pair
relationship
(n)†
Case-control
studies that
show
significant
association
in the
opposite
direction (%)
Lower
limit
of RR
>1·5
Dose–
response
relationship
Biological
plausibility
‡
Analogy§
(Continued from previous page)
2 Low physical activity
2 Low physical activity Colon and
rectum cancer
0 ·· ·· 20 15 ·· ·· No Ye s Yes Yes
2 Low physical activity Breast cancer 0 ·· ·· 35 0 ·· ·· No Yes Yes Yes
2 Low physical activity Ischaemic heart
disease
0 ·· ·· 45 9 ·· ·· No Yes Yes Yes
2 Low physical activity Ischaemic stroke 0 ·· ·· 27 11 ·· ·· No Yes Ye s Yes
2 Low physical activity Diabetes
mellitus
0 ·· ·· 57 7 ·· ·· No Ye s Yes No
2 High fasting plasma
glucose
Tuberculosis 0 ·· ·· 18 0 ·· ·· Yes Yes Ye s No
2 High fasting plasma
glucose
Colon and
rectum cancer
0 ·· ·· 21 0 ·· ·· No ·· ·· Yes
2 High fasting plasma
glucose
Liver cancer 0 ·· ·· 28 0 ·· ·· Yes ·· ·· No
2 High fasting plasma
glucose
Pancreatic
cancer
0 ·· ·· 35 0 ·· ·· Ye s ·· ·· Ye s
2 High fasting plasma
glucose
Lung cancer 0 ·· ·· 16 6 ·· ·· No ·· ·· Ye s
2 High fasting plasma
glucose
Breast cancer 0 ·· ·· 39 0 ·· ·· No ·· ·· Yes
2 High fasting plasma
glucose
Ovarian cancer 0 ·· ·· 11 0 ·· ·· No ·· ·· Ye s
2 High fasting plasma
glucose
Bladder cancer 0 ·· ·· 14 0 ·· ·· No ·· ·· Ye s
2 High fasting plasma
glucose
Ischaemic heart
disease
8 0 100 150 ·· ·· ·· Ye s Yes Yes Yes
2 High fasting plasma
glucose
Ischaemic stroke 9 0 100 150 ·· ·· ·· Yes Yes Ye s Ye s
2 High fasting plasma
glucose
Haemorrhagic
stroke
9 0 100 150 ·· ·· ·· Ye s Yes Yes Yes
2 High fasting plasma
glucose
Alzheimer’s
disease and
other dementias
0 ·· ·· 17 0 ·· ·· No ·· ·· No
2 High fasting plasma
glucose
Peripheral
vascular disease
14 ·· ·· 4 0 ·· ·· Yes Yes Yes Yes
2 High fasting plasma
glucose
Chronic kidney
disease
5 ·· ·· 32 ·· ·· ·· Ye s Yes Yes No
2 High fasting plasma
glucose
Glaucoma 0 ·· ·· 5 0 ·· ·· No ·· ·· Yes
2 High fasting plasma
glucose
Cataract 0 ·· ·· 1 0 1 0 No ·· ·· Yes
2High total
cholesterol
Ischaemic heart
disease
21 0 57 88 ·· ·· ·· Yes Yes Ye s Ye s
2High total
cholesterol
Ischaemic stroke 21 0 57 88 ·· ·· ·· Yes Yes Ye s Yes
2 High systolic blood
pressure
Rheumatic heart
disease
0 ·· ·· 62 ·· ·· ·· Yes Yes Ye s Ye s
2 High systolic blood
pressure
Ischaemic heart
disease
56 0 ·· 88 ·· ·· ·· Yes Yes Yes Yes
2 High systolic blood
pressure
Ischaemic stroke 54 0 .. 150 ·· ·· ·· Yes Ye s Yes Yes
(Table 1 continues on next page)
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1359
Risk Outcome RCTs
(n)
RCTs with
significant
effect in
the
opposite
direction
(%)
RCTs
with null
findings
(%)
Prospective
observational
studies (n)*
Prospective
observational
studies with
significant
association in
the opposite
direction (%)
Case-control
studies
assessing
the risk-
outcome
pair
relationship
(n)†
Case-control
studies that
show
significant
association
in the
opposite
direction (%)
Lower
limit
of RR
>1·5
Dose–
response
relationship
Biological
plausibility
‡
Analogy§
(Continued from previous page)
2 High systolic blood
pressure
Haemorrhagic
stroke
54 0 ·· 150 ·· ·· ·· Yes Yes Ye s Yes
2 High systolic blood
pressure
Cardiomyopathy
and myocarditis
0 ·· ·· 62 ·· ·· ·· Yes Yes Ye s Ye s
2 High systolic blood
pressure
Other
cardiomyopathy
0 ·· ·· 62 ·· ·· ·· Yes Yes Ye s Ye s
2 High systolic blood
pressure
Atrial fibrillation
and flutter
20 5 60 88 ·· ·· ·· Yes Ye s Yes Yes
2 High systolic blood
pressure
Aortic aneurysm 0 ·· ·· 62 ·· ·· ·· Yes Ye s Yes Yes
2 High systolic blood
pressure
Peripheral
vascular disease
0 ·· ·· 88 ·· ·· ·· Yes Yes Yes Yes
2 High systolic blood
pressure
Endocarditis 0 ·· ·· 62 ·· ·· ·· Yes Yes Yes Yes
2 High systolic blood
pressure
Other
cardiovascular
and circulatory
diseases
0 ·· ·· 88 ·· ·· ·· No Yes Yes Yes
2 High systolic blood
pressure
Chronic kidney
disease
8 ·· ·· 88 ·· ·· ·· Yes Yes Yes No
2 High body-mass
index (adult)
Non-Hodgkin
lymphoma
0 ·· ·· 8 0 ·· ·· No Ye s Yes Yes
2 High body-mass
index (adult)
Oesophageal
cancer
0 ·· ·· 16 0 ·· ·· .. Yes Yes Yes
2 High body-mass
index (adult)
Colon and
rectum cancer
0 ·· ·· 38 0 ·· ·· No Ye s Yes Yes
2 High body-mass
index (adult)
Liver cancer 0 ·· ·· 34 0 ·· ·· No Yes Yes Ye s
2 High body-mass
index (adult)
Gallbladder and
biliary tract
cancer
0 ·· ·· 10 0 ·· ·· No Ye s Yes Yes
2 High body-mass
index (adult)
Pancreatic
cancer
0 ·· ·· 20 0 ·· ·· No Ye s Yes Yes
2 High body-mass
index (adult)
Breast cancer
(post
menopause)
0 ·· ·· 44 2 ·· ·· No Yes Yes Yes
2 High body-mass
index (adult)
Breast cancer
(pre-
menopause)
0 ·· ·· 25 8 ·· ·· No Ye s Yes No
2 High body-mass
index (adult)
Uterine cancer 0 ·· ·· 37 0 ·· ·· No Ye s Yes Yes
2 High body-mass
index (adult)
Ovarian cancer 0 ·· ·· 31 3 ·· ·· No Yes Yes Yes
2 High body-mass
index (adult)
Kidney cancer 0 ·· ·· 28 0 ·· ·· No Yes Yes Ye s
2 High body-mass
index (adult)
Thyroid cancer 0 ·· ·· 16 0 ·· ·· No Ye s Yes Yes
2 High body-mass
index (adult)
Multiple
myeloma
0 ·· ·· 20 ·· ·· ·· ·· Yes Ye s Yes
2 High body-mass
index (adult)
Leukaemia 0 ·· ·· 17 0 ·· ·· No Ye s Yes Yes
2 High body-mass
index (adult)
Ischaemic heart
disease
0 ·· ·· 129 ·· ·· ·· No Yes Yes Ye s
(Table 1 continues on next page)
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(appendix1 p 22 for more detail). Fourth, we performed a
systematic review of all cohort and case-control studies
reporting a RR, hazard ratio, or odds ratio for any risk-
outcome pairs studied in GBD 2016 and then modelled a
dose-response relationship using DisMod ordinary
dierential equations (ODE).18 Fifth, we estimated injury
PAFs from cohort studies and adjusted them to account
for victims.
Risk Outcome RCTs
(n)
RCTs with
significant
effect in
the
opposite
direction
(%)
RCTs
with null
findings
(%)
Prospective
observational
studies (n)*
Prospective
observational
studies with
significant
association in
the opposite
direction (%)
Case-control
studies
assessing
the risk-
outcome
pair
relationship
(n)†
Case-control
studies that
show
significant
association
in the
opposite
direction (%)
Lower
limit
of RR
>1·5
Dose–
response
relationship
Biological
plausibility
‡
Analogy§
(Continued from previous page)
2 High body-mass
index (adult)
Ischaemic stroke 0 ·· ·· 102 ·· ·· ·· No Yes Yes Ye s
2 High body-mass
index (adult)
Haemorrhagic
stroke
0 ·· ·· 129 ·· ·· ·· No Yes Yes Ye s
2 High body-mass
index (adult)
Hypertensive
heart disease
0 ·· ·· 85 ·· ·· ·· No Yes Yes Ye s
2 High body-mass
index (adult)
Atrial fibrillation
and flutter
0 ·· ·· 5 0 ·· ·· ·· No Ye s Yes
2 High body-mass
index (adult)
Asthma 0 ·· ·· 7 0 ·· ·· ·· Yes Yes No
2 High body-mass
index (adult)
Alzheimer’s
disease and
other dementias
0 ·· ·· 6 0 ·· ·· ·· No Ye s No
2 High body-mass
index (adult)
Gallbladder
disease
0 ·· ·· 16 0 ·· ·· ·· Yes Yes Yes
2 High body-mass
index (adult)
Diabetes
mellitus
0 ·· ·· 85 .. ·· ·· Yes Yes Ye s No
2 High body-mass
index (adult)
Chronic kidney
disease
0 ·· ·· 57 ·· ·· ·· No Yes Yes No
2 High body-mass
index (adult)
Osteoarthritis 0 ·· ·· 32 0 ·· ·· No Ye s Yes Ye s
2 High body-mass
index (adult)
Low back pain 0 ·· ·· 5 0 ·· ·· No Ye s Yes Yes
2 High body-mass
index (adult)
Gout 0 ·· ·· 10 0 ·· ·· .. Yes Yes No
2 High body-mass
index (adult)
Cataract 0 ·· ·· 17 0 ·· ·· .. Yes Ye s No
2 High body-mass
index (child)
Asthma 0 ·· ·· 5 0 ·· ·· No Ye s Yes No
2 Low bone mineral
density
Injuries 0 ·· ·· 12 .. ·· ·· No Yes Yes Ye s
2 Impaired kidney
function
Ischaemic heart
disease
0 ·· ·· 6 0 ·· ·· Ye s ·· Yes Yes
2 Impaired kidney
function
Ischaemic stroke 0 ·· ·· 6 0 ·· ·· Ye s ·· Yes Yes
2 Impaired kidney
function
Haemorrhagic
stroke
0 ·· ·· 8 0 ·· ·· Ye s ·· Yes Yes
2 Impaired kidney
function
Peripheral
vascular disease
0 ·· ·· 5 0 ·· ·· Ye s ·· Yes Ye s
2 Impaired kidney
function
Gout 0 ·· ·· 3 0 0 0 Yes ·· Yes No
If multiple reports existed from the same study, we counted them as one study. We only assessed the dose–response relationship for continuous risks. To evaluate the magnitude of the effect size for continuous
risks, we evaluated the relative risk comparing the 75th percentile with the 25th percentile of the exposure distribution at the global level. RCT=randomised controlled trial. RR=relative risk. *Prospective cohort
studies or non-randomised interventions. †Case-control studies were included for those risk-outcome pairs where the sum of RCT and prospective observational studies included was less than five (where
applicable). ‡Whether or not any biological or mechanistic pathway exists that could potentially explain the relationship of the risk-outcome pair. §Whether or not the risk is associated with another outcome
from the same category and whether or not any evidence exists that it can cause the current outcome through the same pathway.
Table 1: Descriptive cataloguing of the epidemiological evidence used to assess whether each risk-outcome paper meets the causal criteria for inclusion in the Global Burden of Disease
Study 2016 by risk level
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1361
Risk factors Exposure definition Theoretical minimum risk exposure
level
Data representativeness index
<2006 2006–16 Total
0 All ·· ·· 100·0% 100·0% 100·0%
1 Environmental and
occupational risks
·· ·· 100·0% 100·0% 100·0%
2Unsafe water, sanitation,
and handwashing
·· ·· 58·0% 75·4% 70·0%
3Unsafe water source Proportion of households with access to different water sources
(unimproved, improved except piped, piped water supply) and
reported use of household water treatment methods (boiling or
filtering, chlorinating or solar filtering, no treatment)
All households have access to water from
a piped water supply that is also boiled or
filtered before drinking
70·1% 88·4% 83·5%
3 Unsafe sanitation Proportion of households with access to different sanitation
facilities (unimproved, improved except sewer, sewer connection)
All households have access to toilets with
sewer connection
69·5% 88·4% 83·5%
3 No access to handwashing
facility
Proportion of households with access to handwashing facility with
soap, water, and wash station
All households have access to
handwashing facility with soap, water,
and wash station
10·3% 33·3% 35·4%
2 Air pollution ·· ·· 100·0% 100·0% 100·0%
3 Ambient particulate matter
pollution
Annual average daily exposure to outdoor air concentrations of
PM2·5
Uniform distribution between 2·4 µg/m³
and 5·9 µg/m³
23·1% 56·9% 78·0%
3 Household air pollution from
solid fuels
Individual exposure to PM2·5 due to use of solid cooking fuels No households are exposed to excess
indoor concentration of particles from
solid fuel use (assuming PM2·5 in no fuel
use is consistent with a TMREL of 2·4–5·9)
72·8% 59·5% 76·4%
3Ambient ozone pollution Seasonal (3 month) hourly maximum ozone concentrations,
measured in ppb
Uniform distribution between 33·3 µg/m³
and 41·9 µg/m³, according to
minimum/5th percent concentrations
100·0% 100·0% 100·0%
2 Other environmental risks ·· ·· 48·7% 26·2% 51·8%
3 Residential radon Average daily exposure to indoor air radon levels measured in
becquerels (radon disintegrations per second) per cubic metre (Bq/
m³)
10 Bq/m³, corresponding to the outdoor
concentration of radon
39·0% 0·0% 39·0%
3 Lead exposure Blood lead levels in µg/dL of blood, bone lead levels in µg/g of
bone
2 ug/dL, corresponding to lead levels in
pre-industrial humans as natural sources
of lead prevent the feasibility of zero
exposure
37·4% 26·2% 43·6%
2 Occupational risks ·· ·· 92·3% 90·8% 100·0%
3 Occupational carcinogens ·· ·· 86·7% 85·6% 92·8%
4Occupational exposure to
asbestos
Proportion of the population with cumulative exposure to
asbestos
No occupational exposure to asbestos 82·6% 74·9% 87·2%
4Occupational exposure to
arsenic
Proportion of the population ever exposed to arsenic at work or
through their occupation
No occupational exposure to arsenic 82·6% 74·9% 87·2%
4Occupational exposure to
benzene
Proportion of the population ever exposed to benzene at work or
through their occupation
No occupational exposure to benzene 82·6% 74·9% 87·2%
4Occupational exposure to
beryllium
Proportion of the population ever exposed to beryllium at work or
through their occupation
No occupational exposure to beryllium 82·6% 74·9% 87·2%
4Occupational exposure to
cadmium
Proportion of the population ever exposed to cadmium at work or
through their occupation
No occupational exposure to cadmium 82·6% 74·9% 87·2%
4Occupational exposure to
chromium
Proportion of the population ever exposed to chromium at work
or through their occupation
No occupational exposure to chromium 82·6% 74·9% 87·2%
4Occupational exposure to
diesel engine exhaust
Proportion of the population ever exposed to diesel engine
exhaust at work or through their occupation
No occupational exposure to diesel
engine exhaust
82·6% 74·9% 87·2%
4Occupational exposure to
second-hand smoke
Proportion of the population ever exposed to second-hand smoke
at work or through their occupation
No occupational exposure to second-
hand smoke
82·6% 74·9% 87·2%
4Occupational exposure to
formaldehyde
Proportion of the population ever exposed to formaldehyde at
work or through their occupation
No occupational exposure to
formaldehyde
82·6% 74·9% 87·2%
4Occupational exposure to
nickel
Proportion of the population ever exposed to nickel at work or
through their occupation
No occupational exposure to nickel 82·6% 74·9% 87·2%
4Occupational exposure to
polycyclic aromatic
hydrocarbons
Proportion of the population ever exposed to polycyclic aromatic
hydrocarbons at work or through their occupation
No occupational exposure to polycyclic
aromatic hydrocarbons
82·6% 74·9% 87·2%
(Table 2 continues on next page)
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Risk factors Exposure definition Theoretical minimum risk exposure
level
Data representativeness index
<2006 2006–16 Total
(Continued from previous page)
4Occupational exposure to
silica
Proportion of the population ever exposed to silica at work or
through their occupation
No occupational exposure to silica 82·6% 74·9% 87·2%
4Occupational exposure to
sulfuric acid
Proportion of the population ever exposed to sulfuric acid at work
or through their occupation
No occupational exposure to sulfuric acid 80·5% 73·3% 85·1%
4Occupational exposure to
trichloroethylene
Proportion of the population ever exposed to trichlorethylene at
work or through their occupation
No occupational exposure to
trichloroethylene
80·5% 73·3% 85·1%
3 Occupational asthmagens Proportion of the population currently exposed to asthmagens at
work or through their occupation
Background asthmagen exposures 82·6% 74·9% 87·2%
3 Occupational particulate
matter, gases, and fumes
Proportion of the population ever exposed to particulates, gases,
or fumes at work or through their occupation
No occupational exposure to particulates,
gases, or fumes
83·6% 75·9% 88·2%
3 Occupational noise Proportion of the population ever exposed to noise greater than
85 dB at work or through their occupation
Background noise exposure 83·6% 75·9% 88·2%
3 Occupational injuries Proportion of the population at risk to injuries related to work or
through their occupation
The rate of injury deaths per
100 000 person-years is zero
82·6% 75·4% 87·2%
3 Occupational ergonomic
factors
Proportion of the population who are exposed to ergonomic risk
factors for low back pain at work or through their occupation
All individuals have the ergonomic
factors of clerical and related workers
82·6% 74·9% 87·2%
1 Behavioural risks ·· ·· 100·0% 100·0% 100·0%
2 Child and maternal
malnutrition
·· ·· 100·0% 100·0% 100·0%
3 Suboptimal breastfeeding ·· ·· 67·1% 54·6% 73·9%
4 Non-exclusive breastfeeding Proportion of children younger than 6 months who receive
predominant, partial, or no breastfeeding
All children are exclusively breastfed for
first 6 months of life
67·1% 54·6% 73·9%
4 Discontinued breastfeeding Proportion of children aged 6–23 months who do not receive any
breastmilk
All children continue to receive
breastmilk until 2 years of age
68·1% 65·3% 79·2%
3 Child growth failure ·· ·· 5·6% 0·0% 5·6%
4Child underweight Proportion of children less than –3 SD, –3 to –2 SD, and –2 to –1 SDs
of the WHO 2006 standard weight-for-age curve
All children are above –1 SD of WHO 2006
standard weight-for-age curve
77·4% 65·1% 81·0%
4Child wasting Proportion of children less than –3 SD, –3 to –2 SDs, and –2 to
–1 SD of the WHO 2006 standard weight-for-length curve
All children are above –1 SD of WHO 2006
standard weight-for-height curve
78·0% 66·2% 82·1%
4 Child stunting Proportion of children less than –3 SD, –3 to –2 SD, and –2 to –1 SD
of the WHO 2006 standard height-for-age curve
All children are above –1 SD of WHO 2006
standard height-for-age curve
78·0% 66·2% 82·1%
3 Low birthweight and short
gestation
·· ·· 3·6% 16·4% 18·0%
4 Short gestation for
birthweight
Proportion of births occurring in 2 week bands starting from
<24 weeks to 39–40 weeks
40–41 weeks gestation 3·6% 16·4% 18·0%
4 Low birthweight for
gestation
Proportion of births occurring in 500 g categories starting from
<500 g to 4000–4499 g
4500–4999 g birthweight 3·6% 16·4% 18·0%
3Iron deficiency Peripheral blood haemoglobin concentration in g/L Counterfactual haemoglobin
concentration in the abscence of iron
deficiency in g/L
81·5% 44·1% 85·1%
3Vitamin A deficiency Proportion of children aged 0–5 years with serum retinol
concentration <0·7 µmol/L
No childhood vitamin A deficiency 54·9% 44·1% 56·4%
3Zinc deficiency Proportion of the population with inadequate zinc intake versus
loss
No inadequate zinc intake 94·9% 93·3% 94·9%
2Tobacco ·· ·· 98·0% 100·0% 100·0%
3 Smoking Smoking Impact Ratio method: cumulative exposure to smoked
tobacco products, proxied by excess lung cancer mortality; direct
smoking: 5 year lagged proportion of the population who
currently smoke daily
All individuals are lifelong non-smokers 92·8% 96·9% 99·0%
3Smokeless tobacco Current use of any smokeless tobacco product All individuals are lifelong non-users of
smokeless tobacco products
34·4% 70·8% 73·3%
3 Second-hand smoke Average daily exposure to air particulate matter in the home from
second-hand smoke with an aerodynamic diameter smaller than
2·5 µg, measured in µg/m3, among non-smokers living with a
current daily smoker
No second-hand smoke exposure 73·9% 67·7% 90·8%
2Alcohol and drug use ·· ·· 54·9% 62·6% 79·0%
(Table 2 continues on next page)
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Risk factors Exposure definition Theoretical minimum risk exposure
level
Data representativeness index
<2006 2006–16 Total
(Continued from previous page)
3Alcohol use Average daily alcohol consumption of pure alcohol (measured in g
per day) in current drinkers who had consumed alcohol during the
past 12 months; binge drinking: proportion of the population
reporting binge consumption of at least 60 g for males and 48 g
for females of pure alcohol on a single occasion
No alcohol consumption 52·3% 45·6% 69·7%
3Drug use Proportion of the population dependent upon opioids, cannabis,
cocaine, or amphetamines; proportion of the population who
have ever injected drugs
No drug use 20·5% 37·4% 43·1%
2 Dietary risks ·· ·· 100·0% 100·0% 100·0%
3 Diet low in fruits Average daily consumption of fruits (fresh, frozen, cooked,
canned, or dried fruits, excluding fruit juices and salted or pickled
fruits)
Consumption of fruit between 200 g and
300 g per day
94·9% 94·9% 94·9%
3 Diet low in vegetables Average daily consumption of vegetables (fresh, frozen, cooked,
canned, or dried vegetables, excluding legumes and salted or
pickled vegetables, juices, nuts, and seeds, and starchy vegetables
such as potatoes or corn)
Consumption of vegetables between
290 g and 430 g per day
100·0% 100·0% 100·0%
3 Diet low in legumes Average daily consumption of legumes (fresh, frozen, cooked,
canned, or dried legumes)
Consumption of legumes between 50 g
and 70 g per day
100·0% 100·0% 100·0%
3 Diet low in whole grains Average daily consumption of whole grains (bran, germ, and
endosperm in their natural proportion) from breakfast cereals,
bread, rice, pasta, biscuits, muffins, tortillas, pancakes, and other
sources
Consumption of whole grains between
100 g and 150 g per day
15·9% 13·9% 20·0%
3 Diet low in nuts and seeds Average daily consumption of nut and seed foods Consumption of nuts and seeds between
16 g and 25 g per day
100·0% 100·0% 100·0%
3 Diet low in milk Average daily consumption of milk including non-fat, low-fat, and
full-fat milk, excluding soy milk and other plant derivatives
Consumption of milk between 350 g and
520 g per day
100·0% 100·0% 100·0%
3 Diet high in red meat Average daily consumption of red meat (beef, pork, lamb, and
goat but excluding poultry, fish, eggs, and all processed meats)
Consumption of red meat between 18 g
and 27 g per day
100·0% 100·0% 100·0%
3 Diet high in processed meat Average daily consumption of meat preserved by smoking, curing,
salting, or addition of chemical preservatives
Consumption of processed meat between
0 g and 4 g per day
100·0% 100·0% 100·0%
3 Diet high in sugar-sweetened
beverages
Average daily consumption of beverages with ≥50 kcal per 226·8 g
serving, including carbonated beverages, sodas, energy drinks,
fruit drinks, but excluding 100% fruit and vegetable juices
Consumption of sugar-sweetened
beverages between 0 g and 5 g per day
34·9% 30·3% 36·9%
3 Diet low in fibre Average daily intake of fibre from all sources including fruits,
vegetables, grains, legumes, and pulses
Consumption of fibre between 19 g and
28 g per day
100·0% 100·0% 100·0%
3 Diet low in calcium Average daily intake of calcium from all sources, including milk,
yogurt, and cheese
Consumption of calcium between 1·00 g
and 1·50 g per day
100·0% 100·0% 100·0%
3 Diet low in seafood omega 3
fatty acids
Average daily intake of eicosapentaenoic acid and
docosahexaenoic acid
Consumption of seafood omega 3 fatty
acids between 200 mg and 300 mg per day
100·0% 100·0% 100·0%
3 Diet low in polyunsaturated
fatty acids
Average daily intake of omega 6 fatty acids from all sources,
mainly liquid vegetable oils, including soybean oil, corn oil, and
safflower oil
Consumption of polyunsaturated fatty
acids between 9% and 13% of total daily
energy
96·9% 94·9% 96·9%
3 Diet high in transfatty acids Average daily intake of transfat from all sources, mainly partially
hydrogenated vegetable oils and ruminant products
Consumption of transfatty acids between
0% and 1% of total daily energy
37·4% 38·5% 38·5%
3 Diet high in sodium 24 h urinary sodium measured in g per day 24 h urinary sodium between 1 g and 5 g
per day
15·9% 21·5% 26·2%
2 Sexual abuse and violence ·· ·· 68·2% 78·0% 87·2%
3 Childhood sexual abuse Proportion of the population ever having had the experience of
intercourse or other contact abuse (ie, fondling and other sexual
touching) when aged 15 years or younger, and the perpetrator or
partner was more than 5 years older than the victim
No childhood sexual abuse 31·8% 18·5% 38·0%
3 Intimate partner violence Proportion of the population who have ever experienced one or
more acts of physical or sexual violence by a present or former
intimate partner since age 15 years
No intimate partner violence 67·2% 76·4% 86·2%
2 Unsafe sex Proportion of the population with exposure to sexual encounters
that convey the risk of disease
No exposure to a disease agent through
sex
14·9% 51·3% 51·8%
2 Low physical activity Average weekly physical activity at work, home, transport-related,
and recreational measured by MET min per week
All adults experience 3000–4500 MET
min per week
52·3% 35·9% 67·2%
(Table 2 continues on next page)
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We made several improvements in the process of
estimating the burden of disease attributable to dietary
risks. To improve the quality and coverage of our dietary
estimates, we systematically searched literature for
nationally or subnationally representative studies pro-
viding information on consumption of each dietary factor.
We also made a systematic eort to obtain individual-level
data for consumption of dietary factors; re-extracted data
from all available sources; and standardised the definition
of dietary factors across dierent sources. To capture
recent trends in consumption, we used data on sales of
dierent fresh and packaged foods to inform our estimates.
To address the concerns over within-person variation in
intake, we estimated usual intake of each dietary factor and
used that to estimate the attributable disease burden. To
make the current and optimal levels of intake more
comparable, we used absolute intake of each dietary factor
(rather than intake standardised to 2000 kcal per day). For
more detail, see appendix 1 (p 117).
There were two substantial changes in the estimation of
second-hand smoke compared with GBD 2015. First, we
estimated the proportion of a population exposed to second-
hand smoke using information about household
composition and smoking status from household surveys
and censuses, rather than using questions that ask directly
about exposure to second-hand smoke in surveys. Second,
we modelled exposure using spatiotemporal Gaussian
process regression (ST-GPR), borrowing strength across
sexes and all ages, whereas in GBD 2015 we ran a DisMod
model separately by sex and age. Further, we found
significant evidence of associations between second-hand
smoke exposure and two additional outcomes: breast cancer
and diabetes, which were added to the list of risk-outcome
pairs for second-hand smoke. More details on the esti-
mation approach are presented in appendix 1 (p 98).
For the first time in the GBD study, we estimated
exposure to and burden attributable to smokeless
tobacco, defined as current use of any smokeless tobacco
product. RR estimates were derived from prospective
cohort studies and case-control studies and vary
depending on the type of product used. Based on
available evidence, for chewing tobacco RRs were
significantly higher than one for oral cancer and
oesophageal cancer, while for snus or snu we did not
find sucient evidence of a RR greater than one for any
health outcome. Additional details on the estimation
methods and RRs are presented in appendix 1 (p 11, p 181).
Low birthweight for gestation and short gestation for
birthweight are included as new risk factors for GBD 2016.
The estimation has been parameterised to be polytomous
by 500 g and 2 week categories. Low birthweight and
gestational age are highly correlated risks and they are
estimated in a completely interdependent manner. For
each univariate analysis, identification of TMREL and
calculation of PAFs is contingent on the other dimension.
In other words, we found the lowest risk birthweight
category for each 2 week gestational age band and,
correspondingly, the lowest risk gestational age for each
500 g birthweight band. RRs were then estimated for each
500 g per 2 week bin. Exposure for each bin was estimated
in three steps. First, we estimated by generating ensemble
distribution estimates using modelled mean and
categorical prevalence estimates for each of birthweight
(mean, % <2500 g) and gestational age (mean, % <37 weeks,
% <28 weeks) for each location, year, and sex. Second, we
evaluated all microdata where both gestational age and
birthweight were available and found a high degree of
consistency in the correlation between them. Third, we
took the pooled correlation coecient from step 2
combined with univariate ensemble distributions from
step 1 and used a copula linking function to simulate the
joint distribution which was then summarised into each
500 g per 2 week category. Joint PAF calculation used a
TMREL defined as the lowest overall risk of the entire
matrix of birthweight and gestational age (see appendix1
p 77 for more details).
Risk factors Exposure definition Theoretical minimum risk exposure
level
Data representativeness index
<2006 2006–16 Total
(Continued from previous page)
1 Metabolic risks ·· ·· 100·0% 100·0% 100·0%
2 High fasting plasma glucose Serum fasting plasma glucose measured in mmol/L 4·8–5·4 mmol/L 51·8% 53·3% 69·7%
2High total cholesterol Serum total cholesterol, measured in mmol/L 2·78–3·38 mmol/L 59·0% 48·2% 78·0%
2 High systolic blood pressure Systolic blood pressure, measured in mmHg 110–115 mm Hg 64·1% 65·1% 83·6%
2 High body-mass index Body-mass index, measured in kg/m² 25 kg/m² 91·3% 100·0% 100·0%
2 Low bone mineral density Standardised mean bone mineral density values measured by dual
x-ray absorptiometry at the femoral neck in g/cm²
99th percentile of NHANES 2005–14 by
age and sex
33·3% 12·3% 35·9%
2 Impaired kidney function Proportion of the population with ACR >30 mg/g and/or GFR
<60 mL/min per 1·73m², excluding end-stage renal disease
ACR <30 mg/g and GFR >60 mL/min per
1·73m²
10·3% 0·0% 10·3%
GBD=Global Burden of Disease. MET=metabolic equivalent. NHANES=National Health and Nutrition Examination Survey. PM2.5=particulate matter with an aerodynamic diameter smaller than 2·5 µm, measured
in μg/m³. TMREL=theoretical minimum risk exposure level. ppb=parts per billion. ACR=albumin-to-creatine ratio. GFR=glomerular filtration rate.
Table 2: GBD 2016 risk factor hierarchy and accompanying exposure definitions, theoretical minimum risk exposure level, and data representativeness index for each risk factor,
pre-2006, 2006–16, and total (across all years)
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1365
Mediation
In GBD 2016, we updated our approach for estimation of
the joint eects of combinations of risk factors
(appendix 1 p 23). Using individual-level data from
prospective cohort studies, we estimated the proportion
of the eect of behavioural risks on cardiometabolic
outcomes mediated through metabolic risk factors. We
also estimated the proportion of the eect of each
metabolic risk factor on cardiometabolic outcomes
mediated through other metabolic risks. For each
mediation pathway, we only included the mediators for
which sucient evidence existed for their causal
relationship with the disease endpoint.
Explaining the drivers of trends in deaths and DALYs
As in GBD 2015, we undertook a decomposition analysis
of changes in DALYs over the time period into four main
components, namely, changes in DALYs due to changes
in: (1) population growth; (2) population age structure;
(3) exposure to all risks for a disease; and (4) all other
factors combined, approximated as the risk-deleted death
and DALY rates. Risk-deleted rates refers to death and
DALY rates that would be observed if we removed all risk
factors included in GBD 2016, estimated as DALY rates
multiplied by one minus the PAF for the set of risks. We
used methods developed by Das Gupta,19 but as
the methods presented there do not result in the
decomposition results being linear aggregates over time
or risk, we adapted these methods further in GBD 2016.
Our decomposition analysis was undertaken for each
5 year time period, at the all-risk level, taking into account
risk mediation at the most detailed cause level. The
contribution of changes in exposure for the individual
risks was scaled to the all-risk eect at the most detailed
outcome level. The contribution of risk exposures over
longer time periods—eg, 2000–16—or at higher cause
aggregates—eg, all cause—were calculated as the linear
aggregate of the eect of individual risks at the most
detailed cause level and time period.
Risk transition with development
We explored how exposure to risks varies across levels of
development using the SDI, a composite indicator of
development status constructed for GBD 2015 whose
components are strongly correlated with health outcomes.
It is the geometric mean of 0 to 1 for indices of total
fertility rate, mean education for those aged 15 years and
older, and lag-distributed income per capita. More details
on the estimation of SDI can be found in appendix 1
(p 32).
Role of the funding source
The funders of the study had no role in the study design,
data collection, data analysis, data interpretation, or
writing of the report. The authors had full access to all
data in the study and had final responsibility for the
decision to submit for publication.
Results
Global exposure to risks
From 1990 to 2016, trends in SEVs varied across the set of
risk factors included in GBD 2016. Of note, SEVs decreased
by more than 40% for three risks: diet high in transfatty
acids (51·3% [95% UI 34·1–70·1]), household air pollution
from solid fuels (43·1% [40·7–45·6]), and unsafe sanitation
(40·3% [35·5–44·7]; table 3, appendix 2 p 1399). During
the same period, SEVs increased by more than 40% for
high body-mass index (BMI; 60·2% [45·1–79·1]), diet high
in sugar-sweetened beverages (44·7% [36·1–52·7]), occu-
pational exposure to diesel engine exhaust (41·8%
[41·3–42·2]), and occupational exposure to trichloro-
ethylene (40·6% [40·2–41·1]).
Across countries there is substantial variation in risk
exposure by level of SDI. Some risk factors, such as high
fasting plasma glucose (FPG) and high systolic blood
pressure, show similar SEVs across levels of SDI, while
others, including household air pollution and unsafe
water source, show marked trends with sociodemographic
development. Figure 1 shows the relationship between
SEVs and SDI for the leading three metabolic, behavioural,
and environmental and occupational risk factors and how
that changed between 1990 and 2016. Within leading
metabolic risks (high BMI, high FPG, and high systolic
blood pressure [SBP]), risk-weighted exposure shows an
increasing trend with increasing SDI for only high BMI.
Overall, the SEV for high BMI has increased during the
time period. Looking at the leading three environmental
risk factors (ambient air pollution, household air pollution,
and unsafe water), figure 2 shows an inverse relationship
with SDI for household air pollution and unsafe water,
with SEVs approaching zero at high levels of SDI, while
the relationship is less consistent with ambient air
pollution. Finally, the relationship between SDI and the
leading behavioural risk factors is more heterogeneous,
with smoking and alcohol use having a positive correlation
with SDI, and short gestation for birthweight having a
negative correlation with SDI.
Global attributable burden for all risk factors combined
and their overlap
Globally, 59·9% (58·4–61·3) of deaths and 45·2%
(43·2–47·3) of DALYs could be attributed to the risk factors
assessed in GBD 2016. For deaths, non-communicable
diseases (NCDs) show the largest proportion attributable
to measured risk factors, at 64·4% (62·6–66·2), with
communicable, maternal, neonatal, and nutritional
(CMNN) causes at 57·9% (55·4–61·0), and injuries at
25·8% (23·7–27·8). The picture was dierent for DALYs,
however, where we observed that 58·2% (56·4–60·3) of
DALYs in CMNN causes are attributable to risk factors,
compared with 43·5% (40·7–46·7) in NCDs and 21·0%
(19·3–22·7) for injuries. Leading causes of DALYs in
CMNN causes, such as diarrhoea and lower respiratory
infections (LRI), also showed more than 80% of DALYs can
be attributed to risk factors (appendix 2 p 1).
Global Health Metrics
1366
www.thelancet.com Vol 390 September 16, 2017
Risk Male Female Combined
percent
change
1990–2016
1990 2006 2016 Percent
change
1990–2006
Percent
change
2006–16
Percent
change
1990–2016
1990 2006 2016 Percent
change
1990–2006
Percent
change
2006–16
Percent
change
1990–2016
1 Environmental and occupational risks
2 Unsafe water, sanitation, and handwashing
3Unsafe water
source
23·27
(15·57 to
26·55)
21·27
(14·30 to
24·21)
20·08
(13·47 to
22·83)
–8·61
(–10·54 to
–6·62)*
–5·61
(–7·29 to
–3·75)*
–13·74
(–15·78 to
–11·37)*
22·94
(15·32 to
26·19)
21·12
(14·19 to
24·03)
20·04
(13·45 to
22·79)
–7·96
(–9·87 to
–6·01)*
–5·08
(–6·76 to
–3·25)*
–12·64
(–14·67 to
–10·28)*
–13·14
(–15·18 to
–10·78)*
3 Unsafe sanitation 56·46
(53·29 to
60·79)
42·29
(38·91 to
46·94)
33·26
(29·47 to
38·52)
–25·10
(–28·04 to
–22·10)*
–21·36
(–25·52 to
–17·53)*
–41·10
(–45·39 to
–36·37)*
55·13
(51·99 to
59·44)
41·91
(38·53 to
46·70)
33·34
(29·49 to
38·67)
–23·97
(–27·05 to
–20·77)*
–20·45
(–24·51
to
–16·63)*
–39·51
(–44·05 to
–34·59)*
–40·28
(–44·73 to
–35·47)*
3No access to
handwashing
facility
36·22
(35·56 to
36·95)
34·57
(34·00 to
35·14)
33·13
(32·66 to
33·62)
–4·57
(–6·31 to
–2·72)*
–4·15
(–5·29 to
–2·89)*
–8·53
(–10·53 to
–6·29)*
35·82
(35·15 to
36·53)
34·54
(33·98 to
35·11)
33·34
(32·87 to
33·83)
–3·57
(–5·34 to
–1·69)*
–3·46
(–4·64 to
–2·22)*
–6·91
(–8·94 to
–4·61)*
–7·67
(–9·69 to
–5·40)*
2 Air pollution
3 Ambient
particulate
matter pollution
44·42
(37·19 to
53·39)
45·74
(38·10 to
54·89)
49·56
(41·42 to
58·71)
2·96
(1·88 to
3·97)*
8·37
(6·79 to
9·43)*
11·57
(9·47 to
13·51)*
43·79
(36·57 to
52·71)
45·00
(37·47 to
54·11)
48·87
(40·79 to
58·02)
2·76
(1·69 to
3·76)*
8·58
(6·98 to
9·69)*
11·58
(9·44 to
13·55)*
11·60
(9·48 to
13·56)*
3 Household air
pollution from
solid fuels
34·05
(27·33 to
41·50)
25·65
(20·30 to
31·36)
18·95
(14·97 to
23·48)
–24·66
(–27·07 to
–22·55)*
–26·12
(–28·68
to
–23·75)*
–44·33
(–47·18 to
–41·84)*
35·67
(30·59 to
40·81)
27·57
(23·56 to
31·88)
20·69
(17·54 to
24·11)
–22·71
(–24·92 to
–20·85)*
–24·95
(–27·24
to
–22·68)*
–42·00
(–44·45 to
–39·54)*
–43·14
(–45·63 to
–40·73)*
3Ambient ozone
pollution
38·49
(13·87 to
68·02)
43·30
(15·71 to
74·16)
48·75
(18·05 to
78·30)
12·50
(8·84 to
14·03)*
12·57
(5·92 to
15·54)*
26·63
(15·49 to
31·29)*
38·22
(13·78 to
67·36)
42·66
(15·45 to
73·25)
47·94
(17·71 to
77·40)
11·61
(8·35 to
12·94)*
12·39
(5·99 to
15·22)*
25·44
(15·05 to
29·65)*
26·03
(15·27 to
30·47)*
2 Other environmental risks
3 Residential radon 26·12
(22·17 to
30·31)
26·08
(22·09 to
30·34)
26·17
(22·17 to
30·54)
–0·12
(–1·27 to
1·03)
0·34
(–0·24 to
0·98)
0·22
(–1·41 to
1·95)
26·27
(22·33 to
30·45)
26·23
(22·25 to
30·48)
26·34
(22·32 to
30·69)
–0·12
(–1·32 to
1·09)
0·41
(–0·22 to
1·10)
0·29
(–1·45 to
2·12)
0·25
(–1·43 to
2·04)
3 Lead exposure 20·01
(8·93 to
33·97)
18·57
(8·35 to
31·87)
15·01
(6·28 to
27·06)
–7·19
(–10·97 to
–4·90)*
–19·20
(–25·88 to
–14·37)*
–25·01
(–32·80 to
–18·88)*
10·27
(2·82 to
21·64)
10·18
(3·19 to
21·15)
8·37
(2·47 to
18·17)
–0·80
(–4·67 to
13·60)
–17·86
(–24·00
to
–13·50)*
–18·52
(–24·51 to
–10·29)*
–22·68
(–30·06 to
–17·08)*
2 Occupational risks
3 Occupational carcinogens
4 Occupational
exposure to
asbestos
4·11
(3·85 to
4·44)
4·00
(3·76 to
4·30)
3·90
(3·65 to
4·21)
–2·68
(–5·60 to
–0·17)*
–2·41
(–3·52 to
–1·46)*
–5·03
(–7·49 to
–2·85)*
1·47
(1·36 to
1·68)
1·25
(1·17 to
1·40)
1·19
(1·11 to
1·32)
–14·74
(–16·66 to
–13·21)*
–4·97
(–6·27 to
–3·53)*
–18·98
(–21·23 to
–17·53)*
–6·91
(–8·97 to
–5·07)*
4 Occupational
exposure to
arsenic
0·91
(0·00 to
3·12)
0·96
(0·00 to
3·46)
1·02
(0·00 to
3·75)
6·31
(0·18 to
10·99)*
6·23
(3·89 to
8·29)*
12·94
(4·14 to
20·24)*
0·72
(0·00 to
2·37)
0·81
(0·00 to
2·84)
0·88
(0·00 to
3·16)
11·83
(2·14 to
19·69)*
8·82
(5·35 to
11·33)*
21·70
(8·33 to
33·09)*
16·81
(6·00 to
25·80)*
4 Occupational
exposure to
benzene
0·77
(0·36 to
1·59)
0·87
(0·44 to
1·74)
0·96
(0·51 to
1·88)
12·93
(9·26 to
21·67)*
10·25
(8·22 to
14·21)*
24·50
(18·21 to
38·83)*
0·65
(0·27 to
1·43)
0·80
(0·37 to
1·68)
0·94
(0·46 to
1·91)
22·92
(17·94 to
37·88)*
17·04
(13·69 to
24·74)*
43·86
(34·03 to
71·79)*
33·27
(25·56 to
52·63)*
4 Occupational
exposure to
beryllium
0·09
(0·09 to
0·09)
0·10
(0·10 to
0·10)
0·11
(0·11 to
0·11)
10·33
(10·18 to
10·46)*
6·40
(6·30 to
6·51)*
17·39
(17·17 to
17·62)*
0·07
(0·07 to
0·07)
0·08
(0·08 to
0·08)
0·09
(0·09 to
0·09)
23·36
(23·14 to
23·58)*
13·48
(13·35 to
13·61)*
39·99
(39·65 to
40·30)*
26·78
(26·60 to
26·96)*
4 Occupational
exposure to
cadmium
0·18
(0·18 to
0·18)
0·20
(0·20 to
0·20)
0·22
(0·22 to
0·22)
13·27
(12·96 to
13·59)*
9·35
(9·15 to
9·58)*
23·86
(23·39 to
24·33)*
0·13
(0·13 to
0·14)
0·16
(0·16 to
0·17)
0·19
(0·18 to
0·19)
22·86
(22·14 to
23·54)*
13·76
(13·16 to
14·47)*
39·76
(38·93 to
40·61)*
30·69
(30·23 to
31·19)*
4 Occupational
exposure to
chromium
0·38
(0·38 to
0·39)
0·45
(0·44 to
0·45)
0·50
(0·49 to
0·51)
17·37
(17·03 to
17·72)*
11·82
(11·59 to
12·06)*
31·24
(30·77 to
31·73)*
0·28
(0·28 to
0·29)
0·36
(0·35 to
0·37)
0·42
(0·41 to
0·43)
26·61
(25·62 to
27·46)*
16·00
(15·33 to
16·77)*
46·86
(45·96 to
47·74)*
37·94
(37·46 to
38·43)*
4Occupational
exposure to diesel
engine exhaust
2·29
(2·26 to
2·32)
2·78
(2·74 to
2·81)
3·11
(3·07 to
3·14)
21·41
(20·94 to
21·81)*
11·86
(11·60 to
12·11)*
35·80
(35·28 to
36·30)*
1·22
(1·20 to
1·23)
1·61
(1·59 to
1·64)
1·86
(1·83 to
1·89)
32·59
(32·10 to
33·07)*
15·19
(14·80 to
15·65)*
52·73
(51·97 to
53·59)*
41·78
(41·29 to
42·22)*
(Table 3 continues on next page)
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1367
Risk Male Female Combined
percent
change
1990–2016
1990 2006 2016 Percent
change
1990–2006
Percent
change
2006–16
Percent
change
1990–2016
1990 2006 2016 Percent
change
1990–2006
Percent
change
2006–16
Percent
change
1990–2016
(Continued from previous page)
4 Occupational
exposure to
second-hand
smoke
12·58
(5·66 to
21·95)
12·96
(5·67 to
22·91)
13·77
(6·00 to
24·44)
3·02
(0·61 to
4·26)*
6·23
(5·39 to
6·83)*
9·44
(6·25 to
11·25)*
10·65
(4·83 to
18·85)
11·47
(5·11 to
20·49)
12·18
(5·39 to
21·86)
7·69
(5·75 to
8·77)*
6·17
(5·33 to
6·69)*
14·33
(11·51 to
15·87)*
11·63
(8·77 to
13·29)*
4 Occupational
exposure to
formaldehyde
0·79
(0·77 to
0·81)
0·91
(0·88 to
0·93)
1·01
(0·98 to
1·03)
14·91
(14·45 to
15·39)*
10·67
(10·41 to
10·94)*
27·17
(26·49 to
27·88)*
0·57
(0·55 to
0·58)
0·70
(0·67 to
0·72)
0·80
(0·77 to
0·82)
22·93
(21·84 to
23·99)*
14·63
(14·06 to
15·25)*
40·92
(39·83 to
42·05)*
32·87
(32·25 to
33·48)*
4 Occupational
exposure to
nickel
1·60
(0·00 to
7·78)
1·67
(0·00 to
8·37)
1·75
(0·00 to
8·89)
4·62
(–3·15 to
7·54)
4·52
(1·26 to
6·14)*
9·36
(–1·85 to
14·13)
1·07
(0·00 to
4·86)
1·18
(0·00 to
5·65)
1·27
(0·00 to
6·20)
10·38
(–1·64 to
15·93)
7·75
(3·07 to
10·22)*
18·93
(1·74 to
27·60)*
13·25
(–0·43 to
19·36)
4 Occupational
exposure to
polycyclic
aromatic
hydrocarbons
0·80
(0·79 to
0·81)
0·93
(0·92 to
0·94)
1·05
(1·03 to
1·06)
17·04
(16·72 to
17·34)*
11·89
(11·71 to
12·09)*
30·96
(30·53 to
31·40)*
0·58
(0·58 to
0·59)
0·74
(0·73 to
0·75)
0·86
(0·85 to
0·88)
26·50
(25·77 to
27·17)*
16·77
(16·27 to
17·29)*
47·71
(46·91 to
48·49)*
38·08
(37·65 to
38·52)*
4 Occupational
exposure to silica
5·76
(2·34 to
14·58)
5·97
(2·59 to
14·73)
6·21
(2·78 to
15·06)
3·67
(0·97 to
10·72)*
4·05
(2·23 to
7·26)*
7·87
(3·25 to
18·79)*
3·11
(1·19 to
7·93)
3·16
(1·34 to
7·75)
3·29
(1·45 to
7·87)
1·62
(–2·33 to
12·08)
3·98
(1·53 to
8·05)*
5·66
(–0·80 to
21·02)
7·16
(1·90 to
19·77)*
4 Occupational
exposure to
sulfuric acid
0·93
(0·56 to
1·94)
0·98
(0·63 to
1·95)
1·03
(0·67 to
2·00)
5·70
(0·98 to
11·50)*
4·63
(2·40 to
7·07)*
10·58
(3·34 to
19·35)*
0·68
(0·39 to
1·49)
0·77
(0·48 to
1·57)
0·83
(0·54 to
1·64)
12·54
(5·65 to
22·38)*
7·87
(4·54 to
11·75)*
21·39
(10·34 to
36·35)*
15·18
(6·51 to
26·46)*
4 Occupational
exposure to
trichloroethylene
0·22
(0·22 to
0·22)
0·26
(0·26 to
0·27)
0·30
(0·29 to
0·30)
19·43
(19·07 to
19·87)*
11·90
(11·59 to
12·26)*
33·64
(33·15 to
34·20)*
0·16
(0·16 to
0·16)
0·21
(0·21 to
0·21)
0·24
(0·24 to
0·25)
29·39
(28·28 to
30·19)*
16·04
(15·52 to
16·69)*
50·15
(49·33 to
50·89)*
40·64
(40·23 to
41·07)*
3 Occupational
asthmagens
23·14
(19·26 to
27·93)
23·44
(19·61 to
28·24)
23·97
(20·12 to
28·88)
1·30
(0·28 to
2·38)*
2·28
(1·75 to
2·92)*
3·61
(2·17 to
5·17)*
10·70
(8·71 to
13·08)
12·42
(10·13 to
15·13)
13·39
(10·96 to
16·30)
16·04
(14·27 to
17·83)*
7·80
(7·01 to
8·74)*
25·09
(22·75 to
27·86)*
10·50
(8·62 to
12·32)*
3 Occupational
particulate
matter, gases,
and fumes
12·28
(9·40 to
16·46)
12·53
(9·64 to
16·72)
12·60
(9·72 to
16·79)
1·99
(1·40 to
2·55)*
0·58
(0·19 to
0·97)*
2·59
(1·76 to
3·42)*
5·59
(4·29 to
7·66)
6·15
(4·78 to
8·35)
6·49
(5·05 to
8·81)
10·01
(8·28 to
11·78)*
5·44
(4·56 to
6·36)*
16·00
(13·53 to
18·35)*
7·30
(5·61 to
8·97)*
3 Occupational
noise
16·38
(13·89 to
19·41)
16·38
(14·00 to
19·31)
16·21
(13·92 to
18·94)
–0·01
(–0·92 to
0·79)
–1·06
(–2·04 to
–0·40)*
–1·07
(–2·82 to
0·37)
7·11
(6·22 to
8·05)
7·94
(6·98 to
8·97)
8·45
(7·45 to
9·52)
11·69
(11·00 to
12·54)*
6·47
(6·09 to
6·87)*
18·92
(17·89 to
20·19)*
5·41
(3·83 to
6·85)*
3 Occupational
ergonomic
factors
24·56
(23·13 to
26·22)
24·62
(23·05 to
26·43)
23·44
(22·01 to
25·12)
0·27
(–0·72 to
1·19)
–4·79
(–5·10 to
–4·46)*
–4·54
(–5·47 to
–3·62)*
12·46
(11·73 to
13·36)
14·70
(13·80 to
15·80)
15·15
(14·21 to
16·25)
17·95
(16·56 to
19·42)*
3·06
(2·72 to
3·41)*
21·56
(19·97 to
23·25)*
4·31
(3·39 to
5·27)*
1 Behavioural risks
2 Child and maternal malnutrition
3 Suboptimal breastfeeding
4 Non-exclusive
breastfeeding
24·03
(17·85 to
32·14)
22·62
(16·92 to
29·93)
22·72
(17·08 to
29·99)
–5·85
(–7·72 to
–3·89)*
0·43
(–1·59 to
2·60)
–5·45
(–7·72 to
–2·66)*
23·99
(17·82 to
32·11)
22·60
(16·88 to
29·95)
22·70
(17·05 to
30·00)
–5·79
(–7·61 to
–3·86)*
0·42
(–1·54 to
2·61)
–5·39
(–7·63 to
–2·63)*
–5·42
(–7·68 to
–2·65)*
4 Discontinued
breastfeeding
12·15
(12·04 to
12·30)
11·93
(11·84 to
12·07)
12·75
(12·60 to
12·93)
–1·80
(–3·03 to
–0·46)*
6·86
(5·55 to
8·28)*
4·94
(3·15 to
6·70)*
12·15
(12·04 to
12·30)
11·89
(11·80 to
12·03)
12·69
(12·53 to
12·86)
–2·12
(–3·35 to
–0·80)*
6·71
(5·40 to
8·14)*
4·45
(2·68 to
6·18)*
4·70
(2·92 to
6·45)*
3 Child growth failure
4 Child
underweight
14·90
(13·04 to
16·56)
12·52
(10·85 to
14·08)
9·19
(7·59 to
10·69)
–15·99
(–19·77 to
–12·69)*
–26·61
(–30·36 to
–23·72)*
–38·34
(–43·20 to
–34·46)*
14·04
(12·19 to
15·73)
11·14
(9·38 to
12·65)
8·41
(6·81 to
9·85)
–20·62
(–24·29 to
–17·36)*
–24·50
(–28·14
to
–21·50)*
–40·06
(–44·66 to
–35·98)*
–39·14
(–43·61 to
–35·50)*
4Child wasting 8·46
(7·11 to
9·72)
8·39
(7·08 to
9·60)
7·11
(5·84 to
8·33)
–0·85
(–3·11 to
1·33)
–15·26
(–18·20 to
–12·78)*
–15·98
(–19·31 to
–13·01)*
8·24
(6·88 to
9·49)
7·57
(6·30 to
8·78)
6·54
(5·36 to
7·69)
–8·16
(–10·86 to
–5·59)*
–13·56
(–15·99
to
–11·39)*
–20·61
(–24·15 to
–17·18)*
–18·19
(–21·38 to
–15·64)*
(Table 3 continues on next page)
Global Health Metrics
1368
www.thelancet.com Vol 390 September 16, 2017
Risk Male Female Combined
percent
change
1990–2016
1990 2006 2016 Percent
change
1990–2006
Percent
change
2006–16
Percent
change
1990–2016
1990 2006 2016 Percent
change
1990–2006
Percent
change
2006–16
Percent
change
1990–2016
(Continued from previous page)
4 Child stunting 24·71
(17·03 to
27·50)
21·31
(14·80 to
23·95)
17·07
(11·93 to
19·73)
–13·77
(–16·16 to
–11·88)*
–19·86
(–23·55 to
–17·01)*
–30·90
(–35·38 to
–27·56)*
23·49
(16·28 to
26·33)
19·53
(13·62 to
22·16)
15·48
(10·78 to
18·07)
–16·82
(–19·62 to
–14·76)*
–20·77
(–24·47
to
–17·65)*
–34·10
(–38·80 to
–30·53)*
–32·40
(–36·66 to
–29·30)*
3 Low birthweight and short gestation
4 Short gestation
for birthweight
10·22
(9·52 to
11·09)
10·55
(9·81 to
11·50)
10·78
(10·00 to
11·81)
3·28
(2·71 to
3·92)*
2·16
(1·46 to
3·30)*
5·51
(4·37 to
7·01)*
10·26
(9·44 to
11·25)
10·67
(9·76 to
11·76)
10·92
(9·95 to
12·11)
3·95
(3·26 to
4·71)*
2·34
(1·67 to
3·41)*
6·39
(5·17 to
7·92)*
5·94
(4·95 to
7·21)*
4 Low birthweight
for gestation
8·91
(7·92 to
10·06)
8·73
(7·80 to
9·80)
8·61
(7·71 to
9·64)
–2·04
(–3·00 to
–1·29)*
–1·34
(–1·93 to
–0·81)*
–3·36
(–4·65 to
–2·25)*
9·23
(8·23 to
10·59)
8·93
(8·04 to
10·16)
8·83
(7·96 to
10·03)
–3·22
(–4·53 to
–2·16)*
–1·14
(–1·68 to
–0·61)*
–4·32
(–5·85 to
–2·94)*
–3·83
(–5·10 to
–2·79)*
3Iron deficiency ·· ·· ·· ·· ·· ·· 8·36
(6·25 to
10·98)
8·49
(6·35 to
11·11)
8·52
(6·38 to
11·16)
1·46
(1·27 to
1·69)*
0·39
(0·28 to
0·50)*
1·86
(1·65 to
2·09)*
1·87
(1·67 to
2·11)*
3Vitamin A
deficiency
20·37
(16·63 to
24·22)
16·91
(13·58 to
20·19)
15·30
(12·25 to
18·41)
–16·99
(–18·83 to
–15·00)*
–9·55
(–11·66 to
–7·57)*
–24·92
(–27·67 to
–22·08)*
19·12
(15·60 to
22·93)
16·03
(13·04 to
19·14)
14·44
(11·43 to
17·54)
–16·16
(–17·99 to
–13·29)*
–9·90
(–12·40
to
–7·72)*
–24·46
(–27·62 to
–21·13)*
–24·69
(–27·48 to
–21·70)*
3Zinc deficiency 11·26
(3·33 to
21·64)
9·36
(2·93 to
18·11)
7·96
(2·45 to
15·87)
–16·89
(–20·14 to
–10·22)*
–14·99
(–17·90 to
–11·14)*
–29·35
(–33·23 to
–21·63)*
11·31
(3·29 to
21·72)
9·35
(2·90 to
18·12)
7·96
(2·46 to
15·92)
–17·29
(–20·61 to
–11·41)*
–14·85
(–17·96
to
–10·84)*
–29·57
(–33·72 to
–22·15)*
–29·46
(–32·76 to
–23·49)*
2 Tobacco
3 Smoking 35·72
(32·76 to
39·76)
30·16
(27·23 to
34·44)
25·14
(22·69 to
28·74)
–15·57
(–18·63 to
–12·33)*
–16·63
(–20·29 to
–12·87)*
–29·61
(–33·96 to
–24·13)*
11·11
(9·22 to
14·19)
9·65
(7·88 to
12·63)
7·93
(6·49 to
10·55)
–13·15
(–16·68 to
–7·93)*
–17·83
(–23·41
to
–12·38)*
–28·63
(–34·48 to
–20·87)*
–28·99
(–33·00 to
–24·33)*
3 Smokeless
tobacco
13·39
(12·68 to
14·11)
15·58
(15·10 to
16·07)
15·04
(14·34 to
15·80)
16·36
(9·65 to
23·26)*
–3·46
(–8·38 to
2·17)
12·33
(4·70 to
20·89)*
8·34
(7·65 to
9·00)
9·31
(8·82 to
9·80)
8·61
(7·88 to
9·37)
11·55
(1·46 to
23·48)*
–7·44
(–16·18
to 2·32)
3·25
(–8·03 to
16·93)
9·11
(2·16 to
16·49)*
3 Second-hand
smoke
23·11
(22·52 to
23·63)
19·73
(19·48 to
19·96)
18·96
(18·60 to
19·28)
–14·62
(–16·80 to
–12·40)*
–3·91
(–4·99 to
–2·82)*
–17·96
(–20·68 to
–15·11)*
43·29
(42·00 to
44·40)
35·87
(35·10 to
36·55)
33·32
(32·49 to
33·97)
–17·13
(–18·78 to
–15·25)*
–7·11
(–8·22 to
–6·01)*
–23·03
(–25·21 to
–20·59)*
–21·39
(–23·64 to
–18·82)*
2Alcohol and drug use
3Alcohol use 13·82
(11·94 to
15·70)
14·27
(12·36 to
16·27)
14·05
(12·28 to
15·85)
3·26
(–1·07 to
8·20)
–1·54
(–5·49 to
2·86)
1·68
(–3·97 to
8·77)
5·68
(4·57 to
6·78)
4·90
(3·97 to
5·88)
4·83
(3·93 to
5·78)
–13·70
(–17·59 to
–9·62)*
–1·49
(–6·97 to
4·13)
–14·98
(–20·47 to
–8·98)*
–2·84
(–8·09 to
3·36)
3Drug use 0·63
(0·32 to
1·13)
0·61
(0·31 to
1·09)
0·61
(0·31 to
1·10)
–2·98
(–3·70 to
–2·26)*
0·41
(–0·76 to
1·52)
–2·58
(–4·08 to
–0·98)*
0·38
(0·19 to
0·68)
0·35
(0·18 to
0·64)
0·36
(0·18 to
0·65)
–7·43
(–8·40 to
–6·57)*
1·19
(–0·04 to
2·29)
–6·33
(–7·94 to
–4·82)*
–3·84
(–5·28 to
–2·42)*
2 Dietary risks
3 Diet low in fruits 74·84
(54·78 to
91·07)
67·49
(47·24 to
86·59)
61·77
(41·88 to
80·91)
–9·81
(–13·94 to
–4·92)*
–8·49
(–11·37 to
–6·45)*
–17·47
(–23·57 to
–11·01)*
72·47
(52·33 to
89·49)
63·10
(43·24 to
82·40)
56·95
(37·44 to
75·97)
–12·92
(–17·54 to
–7·87)*
–9·75
(–12·98
to
–7·61)*
–21·41
(–28·12 to
–15·01)*
–19·41
(–25·83 to
–13·02)*
3 Diet low in
vegetables
54·19
(36·90 to
71·51)
45·87
(29·62 to
62·63)
41·55
(26·41 to
57·44)
–15·36
(–19·49 to
–12·55)*
–9·41
(–11·75 to
–7·67)*
–23·32
(–28·38 to
–19·69)*
56·42
(38·74 to
74·29)
48·11
(31·55 to
64·93)
43·79
(28·06 to
60·10)
–14·74
(–18·82 to
–12·04)*
–8·96
(–11·33
to
–7·32)*
–22·38
(–27·62 to
–18·87)*
–22·83
(–27·86 to
–19·31)*
3 Diet low in
legumes
41·50
(28·42 to
54·90)
45·97
(33·03 to
58·84)
45·13
(32·40 to
57·73)
10·78
(6·42 to
17·21)*
–1·83
(–3·69 to
0·03)
8·75
(4·50 to
14·83)*
47·78
(33·68 to
61·80)
52·23
(38·14 to
65·93)
51·61
(37·74 to
65·20)
9·33
(6·14 to
13·95)*
–1·18
(–2·64 to
0·16)
8·04
(4·89 to
12·67)*
8·18
(4·89 to
13·01)*
3 Diet low in whole
grains
64·83
(45·85 to
83·18)
58·75
(41·16 to
76·64)
58·64
(41·07 to
76·49)
–9·37
(–10·53 to
–7·87)*
–0·20
(–0·52 to
0·11)
–9·55
(–10·79 to
–8·01)*
65·47
(46·12 to
83·94)
59·44
(41·30 to
77·68)
60·66
(42·41 to
78·76)
–9·21
(–10·80 to
–7·50)*
2·06
(1·26 to
2·80)*
–7·34
(–8·50 to
–6·19)*
–8·44
(–9·57 to
–7·11)*
(Table 3 continues on next page)
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1369
Risk Male Female Combined
percent
change
1990–2016
1990 2006 2016 Percent
change
1990–2006
Percent
change
2006–16
Percent
change
1990–2016
1990 2006 2016 Percent
change
1990–2006
Percent
change
2006–16
Percent
change
1990–2016
(Continued from previous page)
3 Diet low in nuts
and seeds
88·83
(68·81 to
99·29)
83·75
(63·76 to
95·14)
81·39
(60·77 to
94·85)
–5·72
(–7·32 to
–4·13)*
–2·82
(–4·85 to
–0·31)*
–8·38
(–11·67 to
–4·50)*
89·01
(68·93 to
99·33)
84·32
(64·20 to
95·78)
81·94
(61·27 to
95·19)
–5·27
(–6·94 to
–3·53)*
–2·82
(–4·65 to
–0·54)*
–7·94
(–11·18 to
–4·16)*
–8·16
(–11·38 to
–4·33)*
3 Diet low in milk 81·31
(63·75 to
93·81)
83·31
(65·78 to
95·69)
83·48
(65·88 to
95·94)
2·47
(1·94 to
3·09)*
0·20
(–0·23 to
0·59)
2·67
(2·11 to
3·25)*
81·43
(63·96 to
94·04)
83·40
(65·84 to
95·88)
83·62
(65·94 to
96·11)
2·42
(1·83 to
3·19)*
0·27
(–0·14 to
0·64)
2·70
(2·11 to
3·43)*
2·71
(2·19 to
3·22)*
3 Diet high in red
meat
19·44
(16·17 to
22·95)
21·77
(18·13 to
25·66)
24·66
(21·03 to
28·64)
11·97
(6·60 to
17·58)*
13·28
(8·34 to
19·80)*
26·84
(20·66 to
34·20)*
8·50
(6·08 to
11·17)
8·96
(6·42 to
11·86)
10·84
(7·89 to
13·98)
5·35
(–2·82 to
16·00)
21·05
(10·94 to
34·59)*
27·53
(17·12 to
41·17)*
27·57
(21·74 to
34·70)*
3 Diet high in
processed meat
7·84
(6·20 to
10·03)
7·62
(6·12 to
9·88)
6·19
(4·58 to
8·67)
–2·79
(–8·00 to
1·77)
–18·81
(–26·23 to
–11·45)*
–21·08
(–29·56 to
–12·44)*
5·38
(3·82 to
7·35)
5·07
(3·69 to
7·09)
4·32
(2·95 to
6·49)
–5·92
(–11·73 to
0·09)
–14·62
(–22·25
to
–7·62)*
–19·68
(–27·67 to
–10·91)*
–20·45
(–27·41 to
–12·36)*
3 Diet high in
sugar-sweetened
beverages
12·19
(11·19 to
13·25)
15·70
(14·48 to
16·87)
17·90
(16·52 to
19·25)
28·79
(22·74 to
35·00)*
14·06
(11·40 to
16·81)*
46·89
(38·49 to
55·16)*
9·47
(8·47 to
10·53)
11·79
(10·79 to
12·82)
13·45
(12·28 to
14·59)
24·47
(17·25 to
31·99)*
14·10
(10·72 to
17·61)*
42·02
(31·42 to
52·44)*
44·73
(36·08 to
52·69)*
3 Diet low in fibre 59·23
(38·47 to
80·17)
56·12
(35·61 to
76·88)
53·27
(33·15 to
73·95)
–5·24
(–7·57 to
–3·74)*
–5·08
(–7·43 to
–3·57)*
–10·06
(–13·95 to
–7·46)*
66·96
(45·54 to
87·29)
64·02
(42·94 to
84·48)
61·76
(40·67 to
82·64)
–4·39
(–6·33 to
–3·04)*
–3·53
(–5·53 to
–2·10)*
–7·77
(–11·04 to
–5·33)*
–8·89
(–12·30 to
–6·46)*
3 Diet low in
calcium
63·99
(44·32 to
82·99)
60·79
(41·37 to
80·23)
57·11
(37·87 to
76·76)
–5·01
(–6·88 to
–3·24)*
–6·05
(–8·50 to
–4·33)*
–10·75
(–14·66 to
–7·46)*
66·45
(46·60
to 85·13)
63·73
(43·96 to
83·09)
60·70
(41·09 to
80·49)
–4·09
(–5·70 to
–2·36)*
–4·76
(–6·69 to
–3·15)*
–8·66
(–11·87 to
–5·44)*
–9·67
(–13·21 to
–6·43)*
3 Diet low in
seafood omega 3
fatty acids
80·95
(62·89 to
93·16)
78·84
(60·21 to
92·11)
76·66
(57·55 to
91·30)
–2·61
(–4·24 to
–1·11)*
–2·76
(–4·41 to
–0·86)*
–5·31
(–8·41 to
–1·95)*
82·50
(64·41 to
94·45)
81·01
(62·43 to
93·88)
79·29
(60·18 to
93·34)
–1·81
(–3·21 to
–0·60)*
–2·12
(–3·59 to
–0·53)*
–3·89
(–6·61 to
–1·16)*
–4·59
(–7·46 to
–1·59)*
3 Diet low in
polyunsaturated
fatty acids
45·06
(43·63 to
46·41)
42·75
(41·51 to
43·97)
39·59
(38·34 to
40·95)
–5·12
(–8·80 to
–1·13)*
–7·39
(–10·43 to
–4·14)*
–12·13
(–15·52 to
–8·13)*
42·88
(41·39 to
44·43)
42·13
(40·86 to
43·41)
39·01
(37·71 to
40·27)
–1·75
(–6·01 to
2·86)
–7·42
(–10·78
to
–3·74)*
–9·03
(–13·10 to
–4·82)*
–10·66
(–13·33 to
–7·81)*
3 Diet high in
transfatty acids
7·64
(3·38 to
13·77)
4·95
(1·69 to
10·43)
3·65
(0·96 to
8·75)
–35·13
(–52·63 to
–22·20)*
–26·21
(–45·06
to
–14·67)*
–52·13
(–72·97 to
–33·77)*
10·53
(5·22 to
17·83)
7·03
(2·87 to
13·30)
5·21
(1·73 to
11·02)
–33·22
(–46·91 to
–21·80)*
–25·92
(–42·55
to
–15·38)*
–50·53
(–68·52 to
–34·00)*
–51·32
(–70·08 to
–34·10)*
3 Diet high in
sodium
44·14
(19·26 to
76·14)
40·66
(12·48 to
76·89)
39·77
(11·77 to
76·42)
–7·89
(–35·22 to
1·07)
–2·18
(–9·26 to
–0·40)*
–9·89
(–40·40 to
0·12)
43·80
(18·77 to
76·78)
37·98
(11·76 to
74·27)
36·22
(10·50 to
72·98)
–13·29
(–38·80 to
–2·86)*
–4·64
(–12·09
to
–1·57)*
–17·32
(–44·79 to
–4·79)*
–13·63
(–42·22 to
–2·33)*
2 Sexual abuse and violence
3 Childhood sexual
abuse
6·78
(5·66 to
8·02)
6·81
(5·72 to
7·98)
7·09
(5·94 to
8·33)
0·46
(–0·56 to
1·57)
4·10
(3·41 to
4·77)*
4·58
(3·92 to
5·29)*
7·78
(6·57 to
9·20)
7·51
(6·39 to
8·83)
7·68
(6·46 to
9·06)
–3·44
(–4·40 to
–2·36)*
2·23
(1·23 to
3·13)*
–1·29
(–2·41 to
0·17)
1·46
(0·76 to
2·27)*
3 Intimate partner
violence
·· ·· ·· ·· ·· ·· 11·80
(10·05 to
13·42)
10·90
(9·40 to
12·26)
10·32
(8·88 to
11·63)
–7·62
(–8·80 to
–6·28)*
–5·33
(–5·90 to
–4·79)*
–12·55
(–13·59 to
–11·39)*
–12·80
(–13·82 to
–11·65)*
2 Low physical
activity
18·02
(9·66 to
28·42)
18·16
(9·81 to
28·87)
18·25
(9·81 to
28·80)
0·78
(–28·27 to
39·60)
0·51
(–27·42 to
41·11)
1·30
(0·94 to
1·72)*
15·32
(8·52 to
23·82)
15·12
(8·42 to
23·35)
15·05
(8·33 to
23·45)
–1·28
(–30·22 to
37·56)
–0·49
(–28·76
to 41·07)
–1·77
(–2·31 to
–1·28)*
0·07
(–0·32 to
0·38)
1 Metabolic risks
2 High fasting
plasma glucose
2·87
(1·79 to
4·14)
3·68
(2·37 to
5·23)
3·70
(2·36 to
5·22)
28·39
(19·48 to
41·24)*
0·60
(–7·76 to
8·12)
29·17
(21·33 to
40·03)*
2·46
(1·48 to
3·73)
3·34
(2·14 to
4·76)
3·14
(1·97 to
4·55)
35·71
(20·73 to
59·93)*
–5·93
(–15·95
to 1·66)
27·66
(18·53 to
42·31)*
28·77
(21·32 to
39·87)*
2High total
cholesterol
17·43
(13·61 to
21·82)
16·87
(13·07 to
21·24)
16·75
(12·96 to
21·12)
–3·20
(–4·08 to
–2·45)*
–0·74
(–1·28 to
–0·21)*
–3·91
(–4·91 to
–2·98)*
20·14
(16·16 to
24·66)
19·30
(15·40 to
23·74)
19·05
(15·10 to
23·49)
–4·17
(–5·13 to
–3·34)*
–1·29
(–1·91 to
–0·68)*
–5·41
(–6·65 to
–4·34)*
–5·12
(–6·23 to
–4·19)*
(Table 3 continues on next page)
Global Health Metrics
1370
www.thelancet.com Vol 390 September 16, 2017
Within NCDs, three of the leading causes of deaths
and DALYs, ischaemic heart disease (IHD; 93·3%
[90·3–95·7] of deaths and 94·4% [92·6–95·8] of DALYs),
haemorrhagic stroke (88·2% [84·3–91·8] of deaths and
89·5% [87·1–91·6] of DALYS), and chronic obstructive
pulmonary disorder (COPD; 76·6% [69·9–82·9] of deaths
and 73·8% [67·4–80·2] DALYs) all have high proportions
attributable to measured risk factors. Lung cancer, a leading
cause of death but not DALYs, also has a large proportion of
total deaths and DALYs attributed to measured risk factors
(84·1% [78·9–88·3] and 83·2% [78·0–87·6] respectively),
while for Alzheimer’s disease only 21·4% (11·2–34·0) of
total deaths and 22·3% (11·8–35·1) of DALYs can be
attributed to measured risk factors. For leading causes of
DALYs that do not cause death, such as low back pain and
sense organ diseases, less than a third of their total burden
can be attributable to measured risk factors (23·0%
[20·1–25·9] for low back and neck pain and 13·8% [12·4–
15·4] for sense organ diseases). Across all cancers, 42·1%
(38·9–45·3) of deaths and 39·8% (36·8–42·8) of DALYs are
attributable to measured risk factors; however, there is a
very large range within cancers, from cervical cancer at
100% of deaths attributable to risk factors and lung cancer
at 84·08% (78·9–88·3) of deaths attributable to risk factors
to several cancers at nearly zero, such as brain cancer.
Across types of risk factors, behavioural risk factors
accounted for 32·7% (30·7–34·8) of attributable DALYs,
followed by metabolic risk factors at 16·8% (15·7–18·0),
and environmental and occupational at 13·1% (12·1–14·2).
This pattern was seen in middle SDI, middle-high SDI,
and high SDI locations, while in low SDI and low-middle
SDI locations environmental risk factors accounted for a
larger proportion of attributable DALYs than metabolic
risk factors. This is a pattern that has persisted since 1990;
notably, however, the importance of metabolic risk factors
is growing steadily in low SDI and low-middle SDI
locations, while that of environmental and occupational
risks has decreased during this time period. More detail
can be found in appendix 2 (p 1399).
Levels and trends in the burden attributable to risk factors
Table 4 reports all-cause deaths and DALYs attributable to
all risk factors considered here from 2006 to 2016,
including detail on attributable deaths and DALYs by
risk-outcome pair (appendix 2 p 1865) contains results
for every location. Globally, 32·8 million (31·9 million to
33·7 million) deaths were attributable to all risk factors
in 2016, a significant increase since 2006 of 2·9%
(1·1–4·8); however, age-standardised attributable death
rate declined from 2006 to 2016 by 18·7% (17·3–20·0). By
contrast, total DALYs attributable to all risks decreased by
8·6% (6·6–10·7) since 2006, and age-standardised DALY
rate attributable to all risks decreased by 21·7%
(20·0–23·3). Among Level 1 risks, the largest decreases
in age-standardised death rates were observed for
environmental and occupational risks (24·3%
[22·5–26·0]), followed by behavioural risks (21·5%
[19·8–23·3]), and metabolic risks (11·9% [9·9–13·5]).
Similarly, there were significant decreases in age-
standardised DALY rates for all three Level 1 risk factors,
although the magnitude of decrease was larger for DALY
rates than death rates. In the year 2016, behavioural risk
factors accounted for the largest number of deaths
(21·8 million [20·5 million to 23·3 million]) and DALYs
(781·1 million [737·1 million to 830·1 million]). While
there were decreases in both deaths and DALYs
attributable to behavioural risk factors since 2006, these
decreases were significant for deaths (2·5% [0·1–4·9])
Risk Male Female Combined
percent
change
1990–2016
1990 2006 2016 Percent
change
1990–2006
Percent
change
2006–16
Percent
change
1990–2016
1990 2006 2016 Percent
change
1990–2006
Percent
change
2006–16
Percent
change
1990–2016
(Continued from previous page)
2 High systolic
blood pressure
25·23
(23·60 to
27·13)
25·37
(23·68 to
27·32)
25·69
(23·99 to
27·69)
0·54
(0·18 to
0·91)*
1·29
(0·96 to
1·62)*
1·83
(1·40 to
2·31)*
26·03
(24·45 to
27·80)
25·03
(23·53 to
26·77)
24·69
(23·20 to
26·38)
–3·82
(–4·22 to
–3·47)*
–1·35
(–1·70 to
–0·98)*
–5·12
(–5·55 to
–4·68)*
–1·95
(–2·28 to
–1·61)*
2 High body-mass
index
5·91
(3·95 to
8·57)
7·93
(5·41 to
11·27)
9·50
(6·52 to
13·51)
34·16
(25·41 to
45·65)*
19·82
(15·03 to
25·28)*
60·75
(45·26 to
81·32)*
6·62
(4·51 to
9·52)
8·89
(6·21 to
12·30)
10·64
(7·51 to
14·57)
34·22
(25·81 to
44·44)*
19·69
(15·07 to
24·73)*
60·65
(45·85 to
79·65)*
60·25
(45·14 to
79·11)*
2Low bone mineral
density
11·49
(10·42 to
12·67)
11·40
(10·34 to
12·58)
11·33
(10·29 to
12·51)
–0·75
(–1·08 to
–0·46)*
–0·60
(–0·84 to
–0·36)*
–1·34
(–1·79 to
–0·91)*
12·59
(11·47 to
13·76)
12·59
(11·48 to
13·78)
12·65
(11·51 to
13·82)
0·06
(–0·16 to
0·27)
0·41
(0·16 to
0·65)*
0·46
(0·21 to
0·69)*
–0·51
(–0·75 to
–0·26)*
2 Impaired kidney
function
4·78
(2·94 to
9·15)
4·84
(2·98 to
9·27)
4·90
(3·01 to
9·36)
1·16
(0·41 to
1·83)*
1·23
(0·61 to
1·76)*
2·41
(1·11 to
3·34)*
5·46
(3·42 to
10·22)
5·48
(3·41 to
10·27)
5·54
(3·44 to
10·37)
0·35
(–0·80 to
1·78)
1·06
(0·47 to
1·73)*
1·41
(–0·05 to
3·33)
1·63
(0·44 to
3·05)*
Data in parentheses are 95% uncertainty intervals. SEVs=summary exposure values. *Statistically significant increase or decrease.
Table 3: Global age-standardised SEVs for all risk factors, 1990, 2006, and 2016, with mean percent change for three time periods, between 1990 and 2006, 2006 and 2016, and 1990
and 2016, by risk level
Global Health Metrics
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1371
and DALYs (14·3% [11·8–16·6]). There was a significant,
17·9% (15·7–20·6), increase in number of deaths
attributable to metabolic risk factors, from 14·8 million
deaths (14·0 million to 15·7 million) in 2006 to
17·5 million deaths (16·4 million to 18·5 million) in
2016, with similar increases observed for DALYs.
Environmental and occupational risk factors accounted
for the fewest number of deaths and DALYs, and there
was a significant decline in both measures since 2006.
Global patterns of burden attributable to risk factors
across quintiles of SDI
Figure 2 shows that in 2016, the leading Level 2 risk factors
in terms of attributable DALYs at the global level for both
sexes combined were malnutrition (11·5% [10·8–12·3] of
DALYs), diet (9·6% [8·2–11·1] of DALYs), high blood
pressure (8·9% [7·9–9·9] of DALYs), tobacco (7·4%
[6·7–8·3] of DALYs), and air pollution (6·8% [6·1–7·6] of
DALYs). The list at this level of aggregation is similar for
Metabolic risk factorsEnvironmental or occupational risk factors
SEVs
Behavioural risk factors
0·175
0·200
0·2 50
0·300
0·350
0·225
0·275
0·325
0·375
High systolic blood pressure
1990 correlation 0·33
2016 correlation –0·01
0
0·2
0·4
0·6
0·8
1·0
Ambient particulate matter pollution
1990 correlation –0·46
2016 correlation –0·49
0 0·2 0·4 0·6 0·8 1·0
0
0·1
0·2
0·3
0·5
0·7
0·4
0·6
SDI
Smoking
1990 correlation 0·66
2016 correlation 0·45
0
0·05
0·10
0·15
0·20
0·25
High fasting plasma glucose
1990 correlation –0·02
2016 correlation –0·04
0
0·2
0·4
0·6
0·8
Household air pollution from solid fuels
1990 correlation –0·89
2016 correlation –0·90
0 0·2 0·4 0·6 0·8 1·0
0
0·08
0·09
0·10
0·12
0·11
0·13
SDI
Short gestation for birthweight
1990 correlation –0·61
2016 correlation –0·49
0
0·1
0·4
0·2
0·3
0·5
High body-mass index
1990 correlation 0·49
2016 correlation 0·52
0
0·1
0·4
0·2
0·3
0·5
Unsafe water source
1990 correlation –0·93
2016 correlation –0·91
0 0·2 0·4
SDI
0·6 0·8 1·0
0
0·05
0·15
0·25
0·10
0·20
0·30
0·35
Alcohol use
1990 correlation 0·52
2016 correlation 0·52
Figure 1: Relationship between SEVs and SDI for the three metabolic, behavioural, and environmental or occupational risk factors that are responsible for the
largest number of attributable DALYs globally
Each point corresponds to a country in either 1990 (red) or 2016 (blue). Pearson correlation coefficients have been estimated to summarise the relationship between
SEVs and SDI in 1990 and in 2016. SEVs=summary exposure values. SDI=Socio-demographic Index. DALYs=disability-adjusted life-years.
Global Health Metrics
1372
www.thelancet.com Vol 390 September 16, 2017
Child and maternal malnutrition
Dietary risks
High systolic blood pressure
Tobacco
Air pollution
High fasting plasma glucose
High body-mass index
Alcohol and drug use
High total cholesterol
Occupational risks
Unsafe water, sanitation, and handwashing
Impaired kidney function
Unsafe sex
Low physical activity
Other environmental risks
Low bone mineral density
Sexual abuse and violence
Child and maternal malnutrition
Air pollution
Unsafe water, sanitation, and handwashing
Unsafe sex
Dietary risks
High systolic blood pressure
Alcohol and drug use
High fasting plasma glucose
Tobacco
High body-mass index
Occupational risks
Impaired kidney function
High total cholesterol
Sexual abuse and violence
Other environmental risks
Low physical activity
Low bone mineral density
Child and maternal malnutrition
Air pollution
Dietary risks
High systolic blood pressure
Tobacco
Unsafe water, sanitation, and handwashing
High fasting plasma glucose
Alcohol and drug use
High body-mass index
High total cholesterol
Occupational risks
Unsafe sex
Impaired kidney function
Low physical activity
Other environmental risks
Low bone mineral density
Sexual abuse and violence
Dietary risks
High systolic blood pressure
Tobacco
High fasting plasma glucose
High body-mass index
Air pollution
Alcohol and drug use
Child and maternal malnutrition
High total cholesterol
Occupational risks
Impaired kidney function
Unsafe sex
Low physical activity
Unsafe water, sanitation, and handwashing
Other environmental risks
Low bone mineral density
Sexual abuse and violence
Dietary risks
High systolic blood pressure
Tobacco
Alcohol and drug use
High body-mass index
High fasting plasma glucose
High total cholesterol
Air pollution
Occupational risks
Impaired kidney function
Child and maternal malnutrition
Low physical activity
Unsafe sex
Low bone mineral density
Other environmental risks
Sexual abuse and violence
Unsafe water, sanitation, and handwashing
Tobacco
Dietary risks
High systolic blood pressure
High body-mass index
Alcohol and drug use
High fasting plasma glucose
High total cholesterol
Occupational risks
Impaired kidney function
Air pollution
Low physical activity
Low bone mineral density
Child and maternal malnutrition
Unsafe sex
Other environmental risks
Sexual abuse and violence
Unsafe water, sanitation, and handwashing
AGlobal BLow SDI countries
DMiddle SDI countriesCLow-middle SDI countries
02·5 5·0 7·510·0 12·5 15·0 17·5 20·0 22·5 25·027·5 30·0
DALYs (%)
FHigh SDI countries
02·5 5·0 7·510·0 12·5 15·0 17·5 20·0 22·5 25·027·5 30·0
DALYs (%)
EMiddle-high SDI countries
HIV/AIDS and tuberculosis Maternal disorders Neonatal disorders
Nutritional deficiencies
Chronic respiratory diseases Cirrhosis and other chronic liver diseases Digestive diseases Neurological disorders
Mental and substance use disorders
Transport injuries Unintentional injuries Self-harm and interpersonal violence
Musculoskeletal disorders Other non-communicable diseasesDiabetes, urogenital, blood, and endocrine diseases
Neoplasms Cardiovascular diseasesOther communicable, maternal, neonatal, and nutritional diseases
Diarrhoea, lower respiratory infections, and other common infectious diseases
Global Health Metrics
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1373
both sexes combined, with a notable dierence being that
alcohol and drug use is the fifth-leading risk factor for men,
with 7·9% (7·2–8·6) of DALYs, but is at eleventh place for
women (2·6% [2·3–2·9] of DALYs). More detail can be
found in appendix 2 (p 1399). The patterns of risks vary by
development, as seen across the panels of figure 3. At the
lowest level of SDI, the leading risk is malnutrition with
25·0% (23·2–26·6) of DALYs, followed by air pollution
(8·0% [7·1–9·0] of DALYs), unsafe water, sanitation, and
handwashing (7·8% [6·6–9·4] of DALYs), and unsafe sex
(4·7% [4·3–5·2] of DALYs). While malnutrition remains
the leading risk factor at the low-middle level of SDI, diet
(7·8% [6·8–9·0] of DALYs), high systolic blood pressure
(7·2% [6·8–8·1] of DALYs), and tobacco use (5·9% [5·3–6·6]
of DALYs) get included among the leading five causes as
well. At the middle SDI level, diet is among the leading five
risks with 12·5% (10·6–14·6) of DALYs while high systolic
blood pressure and tobacco follow in importance. At the top
three levels of SDI, high BMI increases in importance and
makes it to the leading five risks, with 7·2% (4·7–10·0) of
DALYs in middle SDI locations, with 9·8% (6·5–13·2) of
DALYs in high-middle SDI locations, and 8·7% (5·9–11·7)
of DALYs in high SDI locations. The panels in figure 3
clearly show the risk transition across levels of development.
Changes in leading risk factors in 1990, 2006, and 2016
Figure 3 shows the leading 30 risk factors at Level 3 of the
hierarchy and the median change in DALYs between 1990,
2006, and 2016. In terms of rates, among the top ten leading
risks in 1990, child growth failure, unsafe sanitation, and
unsafe water have experienced the largest declines over the
period of 1990–2016. While these three risks have remained
in the top 30 in 2016 for men, their ranks have fallen by
several places to 9th (child growth failure), 21st (unsafe
sanitation), and 16th (unsafe water). For women, their
ranks have fallen to 5th (child growth failure), 16th (unsafe
sanitation), and 13th (unsafe water). Between 1990 and
2006, median age-standardised DALY rates decreased by
46·7% (42·1–51·1) for men and 49·0% (45·0–53·0) for
women, and in the most recent period child growth failure
demonstrated further declines by 43·8% (36·9–49·8) for
men and 48·7% (42·3–54·6) for women.
The risk factor of low birthweight for gestation and
short gestation for birthweight remains among the
leading risks (second position in 1990 for both sexes;
third position in 2016 for men and fourth position for
women), despite declines in both the number of DALYs
and the age-standardised DALY rates since 1990. Smoking
is another risk where there has been a consistent decline
since 1990 in both SEVs and age-standardised DALY
rates, yet it has consistently been ranked among the
leading three risk factors for men in DALYs since 1990.
The trend in unsafe sex coincides with the HIV/AIDS
epidemic. Figure 3 shows that unsafe sex experienced
large increases between 1990 and 2006, by 198·8%
(170·45–228·2) for men and 204·0% (170·0–236·4) for
women, resulting in a higher rank in 2006, followed by
declines of 43·8% (41·7–45·7) for men and 46·7%
(44·1–49·0) for women since 2006 resulting in a lower rank
in 2016. On the other hand, drug use follows a dierent
trend, and increased for men by 17·6% (13·0–25·5)
between 1990 and 2006 and resulted in a higher rank in
2006, and decreased 5·7% (2·2–9·0) since 2006. Despite
declines, drug use rose from the 25th leading risk to the
18th leading risk for men between 1990 and 2016.
Air pollution, both household air pollution and ambient
particulate matter, were among the leading ten risk factors
for men and women in 1990 and have remained important
in 2016. The median percent change in age-standardised
DALY rates showed important declines in both time
periods for men and women. Specifically, in the most
recent time period household air pollution declined by
38·3% (35·3–41·4) for men and 41·1% (37·8–44·2) for
women, and ambient air pollution decreased by 14·2%
(11·5–17·1) for men and 21·3% (17·8–24·5) for women, in
terms of median age-standardised DALY rates.
The metabolic risk factors have increased in both rank
and in the absolute number of DALYs between 1990 and
2016 for both men and women. High blood pressure was
the fourth-leading risk factor for both men and women in
1990 and had risen to be the second leading risk factor for
men and the leading risk factor for women by 2016. In
terms of the number of DALYs, men showed an increase
of 16·2% (13·1–19·4) since 2006, while for women the
increase was less steep at 7·7% (4·5–11·7). In terms of the
median change in age-standardised DALY rates since
2006, both sexes showed a decline, 10·5% (8·2–12·7) for
men and 16·8% (13·7–19·3) for women. Other leading
metabolic risk factors, including high BMI, high FPG,
and high total cholesterol, exhibited similar trends to high
blood pressure over this time period. All four of these
metabolic risk factors are within the leading ten risk
factors globally for men and women in 2016.
Among the leading risk factors in terms of DALYs, high
BMI and high FPG have the fastest increases in SEVs with
annualised rates of change of 1·7% (1·5–1·9) and 0·9%
(0·6–1·3), respectively, since 1990 (figure 4). On the other
hand, other leading risk factors in 2016 such as smoking
and household air pollution exhibited significant and fast
declines in SEVs, with a –1·3% (–1·6 to –1·1) annualised
rate of change for smoking and –2·3% (–2·5 to –2·2) for
household air pollution between 1990 and 2016 (figure 4).
Drivers of changes in risk-attributable deaths and DALYs
Figure 5 shows the relative contributions to changes in
deaths and DALYs of important drivers grouped into four
Figure 2: DALYs attributable to all Level 2 risk factors apportioned by Level 2
cause for each risk, both sexes combined, 2016, at the global level (A); for low
SDI countries (B); for low-middle SDI countries (C); for middle SDI countries
(D); for middle-high SDI countries (E); and for high SDI countries (F)
DALYs from causes attributable to each risk factor are shown in different colours.
Cutoffs on the SDI scale for the quintiles were selected based on examining the
entire distribution of locations between 1980 and 2016.
DALYs=disability-adjusted life-years. SDI=Socio-demographic Index.
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1374
www.thelancet.com Vol 390 September 16, 2017
Risk 2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
0 All risk factors: all
causes
31 848·45
(31 122·54 to
32 552·54)
32 756·24
(31 855·63 to
33 694·29)
2·85
(1·12 to 4·76)*
–18·73
(–20·03 to
–17·34)*
1 182 311·16
(1 130 619·01 to
1 237 965·11)
1 080 115·72
(1 017 412·55 to
1 149 380·02)
–8·64
(–10·66 to
–6·56)*
–21·71
(–23·29 to
–20·02)*
1 Environmental and
occupational risks: all
causes
9751·57
(9103·89 to
10 482·39)
9293·43
(8663·33 to
9987·21)
–4·70
(–7·01 to –2·37)*
–24·30
(–26·04 to
–22·49)*
367 198·64
(341 616·82 to
392 521·03)
311 970·97
(290 297·06 to
335 402·79)
–15·04
(–18·03 to
–12·10)*
–27·23
(–29·61 to
–24·99)*
2Unsafe water,
sanitation, and
handwashing: all
causes
2231·21
(1736·49 to
3001·11)
1660·77
(1253·69 to
2312·04)
–25·57
(–32·82 to
–16·38)*
–36·78
(–42·78 to
–29·05)*
118 178·24
(99 042·42 to
141 176·50)
75 796·04
(61 906·38 to
93 460·54)
–35·86
(–41·64 to
–29·70)*
–40·76
(–45·87 to
–35·67)*
3 Unsafe water source: all
causes
1570·53
(716·65 to
2364·77)
1160·16
(515·93 to
1858·37)
–26·13
(–34·62 to
–15·83)*
–37·49
(–44·89 to
–28·82)*
82 040·06
(38 265·29 to
110 406·22)
52 440·65
(23 552·84 to
73 900·44)
–36·08
(–42·68 to
–29·41)*
–41·12
(–46·79 to –35·48)*
·· Diarrhoeal diseases 1570·53
(716·65 to
2364·77)
1160·16
(515·93 to
1858·37)
–26·13
(–34·62 to
–15·83)*
–37·49
(–44·89 to
–28·82)*
82 040·06
(38 265·29 to
110 406·22)
52 440·65
(23 552·84 to
73 900·44)
–36·08
(–42·68 to
–29·41)*
–41·12
(–46·79 to –35·48)*
3 Unsafe sanitation: all
causes
1323·65
(1010·23 to
1827·53)
898·24
(662·82 to
1307·68)
–32·14
(–39·14 to
–23·02)*
–42·64
(–48·36 to –35·22)*
68 961·68
(56 942·58 to
84 299·83)
40 746·60
(32 803·83 to
52 138·77)
–40·91
(–46·85 to
–34·53)*
–45·60
(–50·61 to –40·01)*
·· Diarrhoeal diseases 1323·65
(1010·23 to
1827·53)
898·24
(662·82 to
1307·68)
–32·14
(–39·14 to
–23·02)*
–42·64
(–48·36 to –35·22)*
68 961·68
(56 942·58 to
84 299·83)
40 746·60
(32 803·83 to
52 138·77)
–40·91
(–46·85 to
–34·53)*
–45·60
(–50·61 to –40·01)*
3No access to
handwashing facility: all
causes
1015·06
(577·66 to
1507·15)
750·34
(432·56 to
1131·56)
–26·08
(–32·18 to
–18·98)*
–36·78
(–41·76 to –30·81)*
55 096·20
(32 668·57 to
75 567·19)
35 254·90
(20 869·21 to
49 149·44)
–36·01
(–41·20 to
–30·50)*
–40·63
(–45·29 to –35·52)*
·· Diarrhoeal diseases 792·95
(360·19 to
1257·46)
570·85
(258·99 to
952·81)
–28·01
(–35·54 to
–18·65)*
–38·97
(–45·15 to –31·40)*
41 827·94
(20 281·34 to
62 434·28)
26 425·31
(12 807·57 to
39 599·17)
–36·82
(–43·37 to
–30·06)*
–41·76
(–47·28 to –35·97)*
·· Lower respiratory
infections
222·11
(145·63 to
295·64)
179·49
(115·61 to
242·67)
–19·19
(–24·55 to
–13·84)*
–28·58
(–33·02 to –24·25)*
13 268·26
(8655·13 to
17 504·35)
8829·59
(5765·49 to
11 701·18)
–33·45
(–38·72 to
–27·80)*
–37·00
(–41·97 to –31·71)*
2 Air pollution: all causes 6219·85
(5700·42 to
6672·51)
6116·40
(5631·62 to
6602·60)
–1·66
(–4·14 to 0·71)
–23·23
(–25·07 to
–21·50)*
186 446·12
(170 917·71 to
200 934·77)
162 795·90
(150 578·26 to
175 615·70)
–12·68
(–15·73 to
–9·60)*
–26·91
(–29·13 to –24·61)*
3 Ambient particulate
matter pollution: all
causes
3687·20
(3239·45 to
4139·59)
4092·69
(3624·44 to
4575·02)
11·00
(8·47 to 13·49)*
–13·89
(–15·70 to –12·08)*
105 732·08
(93 627·48 to
118 532·10)
105 674·02
(94 523·78 to
117 808·56)
–0·05
(–3·82 to 3·79)
–17·06
(–19·47 to –14·61)*
·· Lower respiratory
infections
689·26
(521·80 to
875·27)
653·41
(493·27 to
826·93)
–5·20
(–10·38 to 0·26)
–18·07
(–22·22 to –13·78)*
37 842·21
(29 069·73 to
47 285·96)
28 517·03
(22 127·01 to
35 104·21)
–24·64
(–29·89 to
–18·73)*
–29·02
(–33·86 to –23·49)*
·· Tracheal, bronchus,
and lung cancer
223·57
(138·58 to
320·46)
279·72
(176·22 to
394·23)
25·11
(20·65 to 29·89)*
–3·86
(–7·22 to –0·26)*
5144·29
(3212·18 to
7331·40)
6200·23
(3930·38 to
8667·86)
20·53
(15·80 to 25·30)*
–6·26
(–9·86 to –2·55)*
·· Ischaemic heart
disease
1291·11
(1080·95 to
1483·53)
1576·10
(1329·73 to
1802·54)
22·07
(18·51 to 25·96)*
–7·08
(–9·39 to –4·51)*
29 520·10
(25 239·88 to
33 875·88)
34 934·16
(29 929·72 to
40 054·61)
18·34
(14·80 to
21·99)*
–7·14
(–9·65 to –4·51)*
·· Ischaemic stroke 309·39
(245·84 to
383·15)
348·33
(280·51 to
427·60)
12·59
(8·45 to 17·60)*
–15·31
(–17·89 to –12·61)*
6437·02
(5283·80 to
7652·84)
7386·59
(6061·34 to
8749·55)
14·75
(10·75 to 19·29)*
–11·99
(–14·73 to –9·11)*
·· Haemorrhagic stroke 435·48
(366·35 to
511·88)
448·19
(377·96 to
523·91)
2·92
(0·02 to 6·19)*
–20·85
(–22·67 to –18·80)*
11 173·69
(9404·22 to
13 008·51)
11 480·35
(9697·30 to
13 306·88)
2·74
(–0·09 to 5·90)
–19·12
(–21·06 to –16·92)*
·· Chronic obstructive
pulmonary disease
738·38
(436·10 to
1068·58)
786·94
(470·94 to
1144·45)
6·58
(2·97 to 11·35)*
–20·29
(–22·92 to –16·77)*
15 614·77
(9275·94 to
22 808·67)
17 155·66
(10 435·61 to
24 906·98)
9·87
(6·63 to 14·26)*
–15·81
(–18·23 to –12·39)*
3 Household air pollution
from solid fuels: all
causes
3260·73
(2828·54 to
3717·84)
2576·36
(2215·95 to
2968·89)
–20·99
(–23·97 to
–18·17)*
–37·55
(–39·90 to –35·29)*
108 733·32
(93 447·82 to
123 249·34)
77 161·35
(66 086·37 to
88 048·87)
–29·04
(–32·28 to
–25·64)*
–39·54
(–42·12 to –36·98)*
(Table 4 continues on next page)
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1375
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Lower respiratory
infections
883·96
(676·61 to
1,091·63)
626·13
(474·40 to
784·88)
–29·17
(–34·70 to
–24·35)*
–37·62
(–42·19 to –33·56)*
52 410·01
(39 779·96 to
64 065·04)
30 860·63
(23 269·88 to
38 522·92)
–41·12
(–46·18 to
–36·08)*
–44·24
(–48·85 to –39·52)*
·· Tracheal, bronchus,
and lung cancer
189·07
(129·80 to
251·56)
158·38
(104·77 to
215·35)
–16·23
(–21·84 to
–10·64)*
–35·50
(–39·78 to –31·18)*
4518·13
(3103·06 to
5979·21)
3664·00
(2429·30 to
4955·47)
–18·90
(–24·24 to
–13·66)*
–36·75
(–40·93 to –32·60)*
·· Ischaemic heart
disease
813·36
(710·55 to
942·98)
738·11
(636·96 to
862·96)
–9·25
(–12·66 to
–5·82)*
–30·16
(–32·76 to –27·49)*
20 235·31
(17 542·11 to
23 458·32)
17 906·39
(15 397·09 to
20 977·14)
–11·51
(–15·14 to
–8·13)*
–30·11
(–32·73 to –27·46)*
·· Ischaemic stroke 229·91
(190·08 to
274·53)
186·00
(152·23 to
223·85)
–19·10
(–22·59 to
–15·45)*
–38·70
(–41·31 to –36·03)*
5044·48
(4209·50 to
5962·85)
4157·95
(3400·32 to
4956·92)
–17·57
(–21·21 to
–14·05)*
–36·61
(–39·32 to –33·88)*
·· Haemorrhagic stroke 392·09
(333·05 to
457·68)
289·08
(242·86 to
341·24)
–26·27
(–29·22 to
–23·19)*
–43·24
(–45·50 to –40·97)*
10 360·06
(8799·85 to
12 047·27)
7733·95
(6482·40 to
9074·27)
–25·35
(–28·20 to
–22·44)*
–41·23
(–43·47 to –38·95)*
·· Chronic obstructive
pulmonary disease
752·34
(505·06 to
1102·38)
578·68
(372·08 to
886·32)
–23·08
(–28·16 to
–17·61)*
–42·31
(–46·13 to –38·28)*
15 181·60
(10 127·03 to
22 826·12)
11 804·50
(7559·95 to
18 339·49)
–22·24
(–27·19 to
–16·95)*
–40·36
(–44·18 to –36·26)*
·· Cataract ·· ·· ·· ·· 983·74
(689·54 to
1354·17)
1033·93
(713·92 to
1415·58)
5·10
(2·29 to 7·74)*
–19·66
(–21·81 to –17·54)*
3Ambient ozone
pollution: all causes
187·61
(71·39 to
318·15)
233·64
(90·11 to
385·30)
24·53
(20·20 to 30·74)*
–6·98
(–10·15 to –2·37)*
3159·44
(1197·45 to
5338·23)
3796·83
(1463·89 to
6257·23)
20·17
(15·72 to 26·93)*
–7·97
(–11·32 to –2·93)*
·· Chronic obstructive
pulmonary disease
187·61
(71·39 to
318·15)
233·64
(90·11 to
385·30)
24·53
(20·20 to 30·74)*
–6·98
(–10·15 to –2·37)*
3159·44
(1197·45 to
5338·23)
3796·83
(1463·89 to
6257·23)
20·17
(15·72 to 26·93)*
–7·97
(–11·32 to –2·93)*
2 Other environmental
risks: all causes
518·27
(290·36 to
800·27)
597·74
(328·83 to
923·47)
15·33
(11·40 to
19·50)*
–12·38
(–14·88 to –9·65)*
14 319·52
(8496·18 to
21 426·17)
15 128·92
(8891·77 to
22 939·09)
5·65
(2·11 to 8·68)*
–15·31
(–17·72 to –13·41)*
3 Residential radon: all
causes
49·87
(33·90 to
66·96)
57·69
(38·12 to
77·92)
15·68
(9·62 to 21·68)*
–11·27
(–15·06 to –7·72)*
1126·44
(773·82 to
1494·91)
1255·37
(847·29 to
1677·75)
11·45
(5·90 to 16·90)*
–13·56
(–17·10 to –10·13)*
·· Tracheal, bronchus,
and lung cancer
49·87
(33·90 to
66·96)
57·69
(38·12 to
77·92)
15·68
(9·62 to 21·68)*
–11·27
(–15·06 to –7·72)*
1126·44
(773·82 to
1494·91)
1255·37
(847·29 to
1677·75)
11·45
(5·90 to 16·90)*
–13·56
(–17·10 to –10·13)*
3 Lead exposure: all
causes
468·39
(239·69 to
749·97)
540·04
(269·07 to
868·97)
15·30
(10·68 to 19·90)*
–12·50
(–15·42 to –9·62)*
13 193·09
(7393·18 to
20 140·03)
13 873·55
(7578·92 to
21 565·04)
5·16
(1·14 to 8·25)*
–15·47
(–18·17 to –13·45)*
·· Rheumatic heart
disease
3·54
(0·91 to 8·13)
3·05
(0·68 to 7·44)
–13·92
(–31·65 to 3·67)
–31·70
(–45·03 to –20·41)*
108·53
(26·02 to 246·12)
81·79
(17·81 to 203·32)
–24·64
(–43·10 to
–10·85)*
–38·40
(–52·34 to –28·96)*
·· Ischaemic heart
disease
227·95
(111·53 to
383·20)
276·33
(133·04 to
465·14)
21·23
(15·40 to 26·04)*
–8·33
(–11·26 to –5·43)*
4760·44
(2328·25 to
7983·16)
5298·38
(2534·71 to
8932·87)
11·30
(6·67 to 14·95)*
–13·26
(–16·77 to –10·72)*
·· Ischaemic stroke 60·00
(28·38 to
104·80)
66·73
(30·98 to
116·68)
11·21
(5·97 to 16·91)*
–15·93
(–19·04 to –13·00)*
1285·87
(621·62 to
2199·93)
1412·53
(664·14 to
2446·24)
9·85
(4·23 to 13·89)*
–15·71
(–19·81 to –12·81)*
·· Haemorrhagic stroke 98·90
(44·03 to
168·33)
95·67
(41·01 to
165·67)
–3·26
(–9·56 to 0·71)
–25·84
(–30·31 to –23·22)*
2371·40
(1022·88 to
4002·19)
2183·12
(881·23 to
3800·89)
–7·94
(–14·84 to
–3·76)*
–28·02
(–33·48 to –24·76)*
·· Hypertensive heart
disease
43·68
(11·57 to
104·93)
56·00
(12·63 to
140·58)
28·21
(5·57 to 44·11)*
–4·14
(–19·03 to 6·47)
868·54
(309·66 to
1899·80)
992·22
(318·80 to
2259·29)
14·24
(–2·90 to 27·83)
–11·18
(–24·36 to –1·13)*
·· Other
cardiomyopathy
1·48
(0·41 to 3·27)
1·54
(0·38 to 3·49)
3·73
(–14·57 to 16·87)
–20·52
(–32·44 to –11·60)*
35·62
(9·50 to 79·57)
33·18
(7·77 to 79·05)
–6·84
(–23·21 to 4·21)
–25·95
(–38·79 to –17·48)*
(Table 4 continues on next page)
Global Health Metrics
1376
www.thelancet.com Vol 390 September 16, 2017
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Atrial fibrillation and
flutter
1·76
(0·65 to 3·49)
2·45
(0·88 to 4·91)
39·22
(29·86 to 46·04)*
0·69
(–2·60 to 4·79)
69·13
(28·74 to 129·13)
83·64
(32·94 to 159·75)
20·99
(12·73 to 25·56)*
–6·93
(–12·49 to –4·15)*
·· Aortic aneurysm 1·52
(0·54 to 2·93)
1·63
(0·55 to 3·25)
7·18
(–2·91 to 14·30)
–17·85
(–24·53 to –13·48)*
32·04
(11·44 to 61·16)
32·04
(10·70 to 62·83)
0·01
(–9·81 to 6·58)
–21·90
(–29·35 to –16·99)*
·· Peripheral vascular
disease
0·16
(0·03 to 0·41)
0·20
(0·03 to 0·53)
19·84
(–2·64 to 35·45)
–11·54
(–24·29 to –3·03)*
5·60
(1·52 to 12·98)
6·34
(1·62 to 15·16)
13·07
(2·26 to 20·57)*
–13·53
(–21·26 to –9·06)*
·· Endocarditis 0·83
(0·29 to 1·74)
0·97
(0·32 to 2·09)
16·60
(4·99 to 26·41)*
–9·45
(–17·23 to –4·25)*
20·69
(6·81 to 43·39)
21·53
(6·70 to 47·48)
4·04
(–6·57 to 13·03)
–16·42
(–25·07 to –10·13)*
·· Other cardiovascular
and circulatory
diseases
5·41
(1·95 to 10·17)
5·94
(1·95 to 11·38)
9·71
(–0·49 to 16·74)
–16·12
(–23·01 to –11·62)*
152·85
(52·63 to 310·64)
153·78
(48·05 to 324·49)
0·60
(–9·13 to 6·44)
–20·84
(–28·73 to –16·05)*
·· Idiopathic
developmental
intellectual disability
·· ·· ·· ·· 2916·48
(1228·14 to
5089·94)
2920·47
(1234·48 to
5155·20)
0·14
(–3·18 to 2·41)
–8·99
(–12·03 to –6·90)*
·· Chronic kidney
disease due to
diabetes mellitus
10·10
(4·15 to 18·35)
12·65
(5·09 to 23·20)
25·28
(19·04 to 29·44)*
–4·60
(–8·89 to –1·68)*
260·59
(102·04 to 495·91)
302·16
(113·45 to 584·15)
15·96
(9·37 to 20·09)*
–10·04
(–15·36 to –6·87)*
·· Chronic kidney disease
due to hypertension
6·22
(2·72 to 11·32)
8·27
(3·62 to 15·14)
33·02
(27·46 to 37·34)*
–2·02
(–5·80 to 0·78)
128·17
(54·29 to 242·87)
155·62
(64·33 to 297·32)
21·42
(15·72 to 25·19)*
–6·97
(–11·22 to –4·21)*
·· Chronic kidney
disease due to
glomerulonephritis
2·42
(0·93 to 4·54)
2·92
(1·08 to 5·50)
20·90
(15·70 to 26·39)*
–7·48
(–11·06 to –4·04)*
65·59
(21·46 to 133·89)
70·02
(22·73 to 143·93)
6·76
(0·61 to 11·59)*
–15·00
(–20·26 to –11·29)*
·· Chronic kidney disease
due to other causes
4·42
(1·88 to 8·20)
5·69
(2·39 to 10·62)
28·68
(22·76 to 34·24)*
–2·47
(–6·62 to 1·13)
111·53
(43·93 to 213·01)
126·74
(48·88 to 248·43)
13·63
(7·40 to 18·08)*
–10·64
(–15·64 to –6·92)*
2 Occupational risks: all
causes
1409·60
(1288·25 to
1539·63)
1528·02
(1383·55 to
1680·97)
8·40
(6·20 to 10·41)*
–14·80
(–16·48 to
–13·37)*
68 543·89
(60 461·38 to
77 147·09)
75 925·43
(66 060·97 to
86 257·10)
10·77
(8·84 to 12·62)*
–8·98
(–10·61 to –7·49)*
3 Occupational
carcinogens: all causes
628·39
(529·77 to
733·38)
746·54
(624·13 to
874·38)
18·80
(16·21 to 21·35)*
–8·62
(–10·42 to –6·83)*
17 462·68
(14 595·36 to
20 617·18)
20 682·73
(17 015·37 to
24 682·77)
18·44
(15·67 to 21·04)*
–7·56
(–9·50 to –5·67)*
4 Occupational exposure
to asbestos: all causes
187·83
(142·94 to
233·46)
222·32
(168·96 to
277·92)
18·36
(15·32 to 21·47)*
–10·30
(–12·67 to –7·98)*
3197·37
(2410·48 to
4019·53)
3640·71
(2743·34 to
4594·60)
13·87
(11·05 to 16·82)*
–12·65
(–14·84 to –10·38)*
·· Larynx cancer 3·25
(1·80 to 4·82)
3·74
(2·02 to 5·53)
15·08
(11·70 to 18·64)*
–13·01
(–15·58 to –10·35)*
59·03
(32·22 to 89·00)
65·51
(35·04 to 99·12)
10·97
(7·31 to 14·61)*
–15·26
(–18·03 to –12·51)*
·· Tracheal, bronchus,
and lung cancer
155·24
(111·10 to
201·47)
181·45
(128·29 to
236·62)
16·88
(13·29 to 20·48)*
–11·40
(–14·19 to –8·74)*
2539·55
(1770·09 to
3359·44)
2844·28
(1957·87 to
3803·22)
12·00
(8·53 to 15·59)*
–14·15
(–16·73 to –11·42)*
·· Ovarian cancer 5·16
(2·58 to 7·94)
6·02
(2·98 to 9·40)
16·73
(9·65 to 23·13)*
–13·33
(–18·64 to –8·71)*
82·25
(40·54 to 128·84)
93·12
(45·80 to 149·95)
13·21
(5·49 to 20·04)*
–13·97
(–19·78 to –8·87)*
·· Mesothelioma 21·29
(20·16 to
22·57)
27·61
(25·56 to
29·34)
29·68
(23·73 to 34·79)*
–1·06
(–5·59 to 2·90)
443·53
(413·23 to 481·26)
553·97
(507·29 to 597·78)
24·90
(19·28 to
29·80)*
–3·39
(–7·70 to 0·41)
·· Asbestosis 2·89
(1·92 to 3·56)
3·49
(2·43 to 4·06)
21·00
(13·33 to 30·87)*
–7·91
(–13·67 to –0·40)*
73·00
(57·24 to 86·90)
83·83
(67·86 to 97·43)
14·83
(9·18 to 21·83)*
–9·23
(–13·68 to –3·33)*
4 Occupational exposure
to arsenic: all causes
6·55
(1·52 to 11·97)
8·07
(2·05 to 14·63)
23·27
(18·26 to 35·03)*
–5·27
(–9·14 to 4·00)
182·17
(43·97 to 330·51)
219·22
(57·76 to 395·48)
20·34
(15·23 to 31·54)*
–6·95
(–10·91 to 2·16)
·· Tracheal, bronchus,
and lung cancer
6·55
(1·52 to 11·97)
8·07
(2·05 to 14·63)
23·27
(18·26 to 35·03)*
–5·27
(–9·14 to 4·00)
182·17
(43·97 to 330·51)
219·22
(57·76 to 395·48)
20·34
(15·23 to 31·54)*
–6·95
(–10·91 to 2·16)
4 Occupational exposure
to benzene: all causes
1·63
(0·52 to 2·67)
1·90
(0·60 to 3·12)
16·21
(11·28 to 21·54)*
–1·47
(–6·03 to 3·50)
74·24
(23·12 to 121·81)
83·87
(25·51 to 138·49)
12·97
(8·01 to 18·09)*
–2·29
(–6·92 to 2·56)
·· Leukaemia 1·63
(0·52 to 2·67)
1·90
(0·60 to 3·12)
16·21
(11·28 to 21·54)*
–1·47
(–6·03 to 3·50)
74·24
(23·12 to 121·81)
83·87
(25·51 to 138·49)
12·97
(8·01 to 18·09)*
–2·29
(–6·92 to 2·56)
·· Acute lymphoid
leukaemia
0·28
(0·09 to 0·46)
0·37
(0·11 to 0·62)
32·70
(19·53 to 41·21)*
14·39
(3·02 to 21·87)*
13·95
(4·29 to 22·85)
18·08
(5·39 to 29·97)
29·59
(16·43 to 37·84)*
13·73
(2·19 to 21·17)*
(Table 4 continues on next page)
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1377
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Chronic lymphoid
leukaemia
0·09
(0·03 to 0·15)
0·11
(0·04 to 0·19)
24·21
(16·84 to 35·39)*
0·43
(–6·33 to 10·47)
3·30
(1·12 to 5·43)
4·07
(1·35 to 6·69)
23·32
(15·50 to 33·93)*
1·98
(–5·31 to 11·94)
·· Acute myeloid
leukaemia
0·41
(0·14 to 0·67)
0·54
(0·18 to 0·90)
32·16
(24·33 to 41·56)*
11·07
(3·73 to 20·10)*
17·90
(6·04 to 29·37)
23·24
(7·47 to 38·33)
29·83
(21·43 to 39·32)*
11·51
(3·58 to 20·44)*
·· Chronic myeloid
leukaemia
0·15
(0·05 to 0·24)
0·15
(0·05 to 0·25)
4·79
(–3·59 to 14·00)
–11·79
(–18·96 to –3·60)*
6·60
(2·12 to 10·80)
6·86
(2·14 to 11·39)
4·04
(–4·07 to 13·79)
–10·84
(–18·16 to –2·42)*
·· Other leukaemia 0·70
(0·21 to 1·16)
0·71
(0·21 to 1·19)
1·61
(–4·21 to 7·56)
–13·30
(–18·31 to –8·34)*
32·49
(9·76 to 53·35)
31·62
(9·61 to 52·92)
–2·70
(–8·57 to 3·32)
–15·49
(–20·60 to –10·44)*
4 Occupational exposure
to beryllium: all causes
0·20
(0·17 to 0·24)
0·26
(0·21 to 0·31)
28·93
(22·38 to 34·98)*
–0·80
(–4·98 to 2·96)
5·76
(4·76 to 6·81)
7·22
(5·89 to 8·59)
25·48
(18·89 to 31·61)*
–2·63
(–6·90 to 1·11)
·· Tracheal, bronchus,
and lung cancer
0·20
(0·17 to 0·24)
0·26
(0·21 to 0·31)
28·93
(22·38 to 34·98)*
–0·80
(–4·98 to 2·96)
5·76
(4·76 to 6·81)
7·22
(5·89 to 8·59)
25·48
(18·89 to 31·61)*
–2·63
(–6·90 to 1·11)
4 Occupational exposure
to cadmium: all causes
0·46
(0·39 to 0·53)
0·61
(0·50 to 0·71)
31·38
(24·62 to 37·82)*
1·00
(–3·45 to 5·10)
13·15
(11·14 to 15·16)
16·83
(14·14 to 19·64)
28·00
(21·21 to 34·47)*
–0·75
(–5·34 to 3·51)
·· Tracheal, bronchus,
and lung cancer
0·46
(0·39 to 0·53)
0·61
(0·50 to 0·71)
31·38
(24·62 to 37·82)*
1·00
(–3·45 to 5·10)
13·15
(11·14 to 15·16)
16·83
(14·14 to 19·64)
28·00
(21·21 to 34·47)*
–0·75
(–5·34 to 3·51)
4 Occupational exposure
to chromium: all causes
0·96
(0·86 to 1·07)
1·28
(1·13 to 1·44)
33·02
(27·40 to 38·50)*
2·28
(–1·68 to 6·05)
27·33
(24·34 to 30·24)
35·45
(31·40 to 40·17)
29·71
(24·03 to 35·28)*
0·57
(–3·49 to 4·55)
·· Tracheal, bronchus,
and lung cancer
0·96
(0·86 to 1·07)
1·28
(1·13 to 1·44)
33·02
(27·40 to 38·50)*
2·28
(–1·68 to 6·05)
27·33
(24·34 to 30·24)
35·45
(31·40 to 40·17)
29·71
(24·03 to 35·28)*
0·57
(–3·49 to 4·55)
4 Occupational exposure
to diesel engine
exhaust: all causes
13·41
(11·85 to
15·17)
17·50
(15·20 to
20·06)
30·45
(24·63 to 35·78)*
0·26
(–3·89 to 3·78)
381·69
(337·43 to 428·72)
485·69
(426·18 to 553·93)
27·25
(21·26 to 32·75)*
–1·40
(–5·65 to 2·19)
·· Tracheal, bronchus,
and lung cancer
13·41
(11·85 to
15·17)
17·50
(15·20 to
20·06)
30·45
(24·63 to 35·78)*
0·26
(–3·89 to 3·78)
381·69
(337·43 to 428·72)
485·69
(426·18 to 553·93)
27·25
(21·26 to 32·75)*
–1·40
(–5·65 to 2·19)
4 Occupational exposure
to second-hand smoke:
all causes
364·05
(275·49 to
465·66)
433·15
(326·16 to
554·32)
18·98
(15·73 to 22·42)*
–7·67
(–9·82 to –5·44)*
12 060·36
(9008·45 to
15 202·22)
14 474·34
(10 754·05 to
18 289·00)
20·02
(16·70 to 23·11)*
–5·73
(–7·98 to –3·57)*
·· Lower respiratory
infections
25·22
(11·95 to
41·26)
31·03
(14·71 to
51·31)
23·07
(18·77 to 27·30)*
–3·05
(–6·52 to 0·24)
754·30
(355·32 to
1235·87)
901·83
(424·90 to
1491·75)
19·56
(15·20 to 24·09)*
–4·55
(–7·93 to –1·02)*
·· Otitis media 0·00
(0·00 to 0·00)
0·00
(0·00 to 0·00)
–51·34
(–68·17 to
–26·94)*
–53·19
(–69·51 to –29·77)*
0·00
(0·00 to 0·00)
0·00
(0·00 to 0·00)
–0·26
(–3·00 to 2·10)
–4·95
(–7·62 to –2·74)*
·· Tracheal, bronchus,
and lung cancer
36·79
(17·19 to
62·63)
44·38
(20·66 to
75·46)
20·63
(16·93 to 23·85)*
–7·23
(–10·03 to –4·78)*
1009·34
(472·19 to
1717·66)
1185·42
(551·75 to
2013·66)
17·45
(13·68 to 20·74)*
–9·21
(–12·08 to –6·69)*
·· Breast cancer 3·93
(0·93 to 6·85)
4·86
(1·19 to 8·40)
23·68
(16·23 to 31·66)*
–3·23
(–9·07 to 2·90)
131·38
(30·86 to 228·23)
160·49
(39·88 to 276·83)
22·16
(14·48 to
30·64)*
–3·10
(–9·13 to 3·41)
·· Ischaemic heart
disease
145·11
(108·16 to
184·75)
177·23
(131·12 to
226·23)
22·13
(16·84 to 27·55)*
–4·86
(–7·90 to –1·79)*
4427·58
(3270·63 to
5659·16)
5337·92
(3904·37 to
6856·49)
20·56
(15·58 to 25·71)*
–4·76
(–7·77 to –1·66)*
·· Ischaemic stroke 24·76
(17·40 to
32·82)
28·32
(19·67 to
38·37)
14·40
(7·34 to 21·60)*
–12·26
(–16·32 to –8·12)*
749·82
(529·06 to
995·24)
892·52
(616·96 to
1211·73)
19·03
(12·07 to 26·19)*
–8·13
(–12·07 to –4·39)*
·· Haemorrhagic stroke 52·38
(37·29 to
69·30)
56·78
(39·66 to
75·11)
8·39
(3·67 to 13·19)*
–15·19
(–17·69 to –12·61)*
1679·51
(1187·37 to
2237·60)
1799·87
(1247·86 to
2400·70)
7·17
(2·54 to 11·92)*
–14·80
(–17·22 to –12·28)*
·· Chronic obstructive
pulmonary disease
48·15
(22·29 to
85·80)
51·90
(23·79 to
91·43)
7·78
(4·30 to 11·52)*
–17·01
(–19·72 to –14·14)*
1570·14
(727·09 to
2820·77)
1819·99
(831·20 to
3260·92)
15·91
(12·59 to 19·00)*
–10·63
(–13·17 to –8·26)*
·· Diabetes mellitus 27·71
(10·20 to
43·82)
38·64
(14·31 to
60·82)
39·45
(37·04 to 42·10)*
7·38
(5·52 to 9·33)*
1738·30
(616·68 to
2847·07)
2376·30
(847·55 to
3851·52)
36·70
(34·59 to
39·02)*
7·55
(5·95 to 9·37)*
(Table 4 continues on next page)
Global Health Metrics
1378
www.thelancet.com Vol 390 September 16, 2017
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
4 Occupational exposure
to formaldehyde: all
causes
0·95
(0·78 to 1·15)
1·09
(0·90 to 1·32)
13·89
(5·40 to 22·94)*
–4·03
(–10·29 to 2·45)
42·70
(35·09 to 51·51)
46·93
(38·81 to 56·99)
9·90
(1·78 to 18·05)*
–5·67
(–12·09 to 0·77)
·· Nasopharynx cancer 0·42
(0·29 to 0·58)
0·48
(0·33 to 0·68)
13·08
(–1·13 to 29·19)
–6·37
(–16·69 to 4·83)
17·57
(11·88 to 24·10)
19·02
(12·99 to 27·09)
8·25
(–5·73 to 24·38)
–8·78
(–19·03 to 3·13)
·· Acute lymphoid
leukaemia
0·09
(0·08 to 0·11)
0·13
(0·11 to 0·15)
34·88
(18·69 to 42·16)*
16·60
(3·46 to 22·11)*
4·72
(3·86 to 5·83)
6·21
(5·02 to 7·60)
31·43
(16·61 to 38·79)*
15·52
(2·59 to 21·35)*
·· Chronic lymphoid
leukaemia
0·02
(0·02 to 0·03)
0·03
(0·03 to 0·04)
30·39
(20·80 to 38·61)*
7·25
(0·20 to 13·50)*
0·95
(0·79 to 1·17)
1·22
(1·02 to 1·44)
27·96
(16·27 to 37·10)*
7·86
(–0·97 to 15·12)
·· Acute myeloid
leukaemia
0·11
(0·09 to 0·13)
0·15
(0·12 to 0·18)
35·38
(28·55 to 41·33)*
15·11
(9·74 to 19·69)*
5·06
(4·14 to 6·15)
6·70
(5·54 to 8·16)
32·57
(25·83 to 39·02)*
14·91
(9·32 to 20·00)*
·· Chronic myeloid
leukaemia
0·04
(0·04 to 0·05)
0·05
(0·04 to 0·06)
6·23
(0·31 to 13·49)*
–9·89
(–14·91 to –4·16)*
2·05
(1·66 to 2·53)
2·15
(1·73 to 2·65)
4·77
(–1·61 to 12·57)
–9·72
(–15·13 to –3·27)*
·· Other leukaemia 0·26
(0·21 to 0·31)
0·26
(0·21 to 0·31)
–1·61
(–9·46 to 6·91)
–15·64
(–21·92 to –9·05)*
12·35
(9·66 to 14·91)
11·64
(9·27 to 14·19)
–5·80
(–13·73 to 2·60)
–17·95
(–24·52 to –11·12)*
4 Occupational exposure
to nickel: all causes
6·68
(0·95 to 17·47)
8·10
(1·24 to 20·81)
21·35
(15·63 to 32·69)*
–6·73
(–11·14 to 2·03)
187·01
(27·49 to 483·87)
221·35
(34·93 to 563·34)
18·37
(12·66 to
29·20)*
–8·40
(–12·92 to 0·27)
·· Tracheal, bronchus,
and lung cancer
6·68
(0·95 to 17·47)
8·10
(1·24 to 20·81)
21·35
(15·63 to 32·69)*
–6·73
(–11·14 to 2·03)
187·01
(27·49 to 483·87)
221·35
(34·93 to 563·34)
18·37
(12·66 to
29·20)*
–8·40
(–12·92 to 0·27)
4 Occupational exposure
to polycyclic aromatic
hydrocarbons: all causes
3·41
(2·89 to 3·92)
4·53
(3·83 to 5·29)
32·92
(26·40 to 39·18)*
2·21
(–2·06 to 6·07)
97·03
(82·37 to 111·83)
125·78
(105·37 to 145·87)
29·63
(22·78 to 35·89)*
0·51
(–4·05 to 4·55)
·· Tracheal, bronchus,
and lung cancer
3·41
(2·89 to 3·92)
4·53
(3·83 to 5·29)
32·92
(26·40 to 39·18)*
2·21
(–2·06 to 6·07)
97·03
(82·37 to 111·83)
125·78
(105·37 to 145·87)
29·63
(22·78 to 35·89)*
0·51
(–4·05 to 4·55)
4 Occupational exposure
to silica: all causes
50·95
(28·57 to
73·67)
58·40
(31·42 to
86·00)
14·63
(8·41 to 19·51)*
–12·09
(–16·90 to –8·34)*
1396·95
(774·36 to
2030·14)
1574·57
(860·21 to
2314·76)
12·71
(6·89 to 17·49)*
–12·64
(–17·10 to –8·94)*
·· Tracheal, bronchus,
and lung cancer
40·38
(17·91 to
63·22)
48·00
(21·24 to
75·45)
18·88
(13·37 to 24·69)*
–8·64
(–12·83 to –4·21)*
1123·77
(503·32 to
1756·69)
1303·95
(576·29 to
2042·00)
16·03
(10·58 to 21·66)*
–10·26
(–14·61 to –5·78)*
·· Silicosis 10·57
(9·77 to 12·23)
10·40
(9·57 to 11·68)
–1·60
(–14·72 to 5·54)
–24·35
(–34·05 to –18·99)*
273·19
(247·13 to 310·72)
270·62
(243·58 to 301·41)
–0·94
(–14·17 to 5·65)
–22·15
(–32·16 to –17·05)*
4 Occupational exposure
to sulfuric acid: all
causes
2·96
(1·27 to 5·35)
3·54
(1·52 to 6·49)
19·47
(13·15 to 26·36)*
–8·14
(–12·96 to –2·92)*
89·85
(38·68 to 161·94)
105·23
(45·84 to 192·42)
17·12
(10·83 to 23·79)*
–9·04
(–13·97 to –3·84)*
·· Larynx cancer 2·96
(1·27 to 5·35)
3·54
(1·52 to 6·49)
19·47
(13·15 to 26·36)*
–8·14
(–12·96 to –2·92)*
89·85
(38·68 to 161·94)
105·23
(45·84 to 192·42)
17·12
(10·83 to 23·79)*
–9·04
(–13·97 to –3·84)*
4 Occupational exposure
to trichloroethylene: all
causes
0·04
(0·01 to 0·07)
0·06
(0·01 to 0·11)
48·91
(43·08 to 53·27)*
14·75
(10·28 to 18·08)*
1·17
(0·26 to 2·16)
1·72
(0·38 to 3·23)
47·21
(41·37 to 51·60)*
14·65
(10·09 to 17·96)*
·· Kidney cancer 0·04
(0·01 to 0·07)
0·06
(0·01 to 0·11)
48·91
(43·08 to 53·27)*
14·75
(10·28 to 18·08)*
1·17
(0·26 to 2·16)
1·72
(0·38 to 3·23)
47·21
(41·37 to 51·60)*
14·65
(10·09 to 17·96)*
3 Occupational
asthmagens: all causes
36·83
(26·75 to
47·73)
37·57
(28·36 to
47·94)
2·02
(–6·22 to 10·50)
–19·40
(–25·79 to –12·65)*
2122·64
(1699·18 to
2619·54)
2339·48
(1860·90 to
2923·32)
10·22
(4·21 to 15·66)*
–8·91
(–14·66 to –3·71)*
·· Asthma 36·83
(26·75 to
47·73)
37·57
(28·36 to
47·94)
2·02
(–6·22 to 10·50)
–19·40
(–25·79 to –12·65)*
2122·64
(1699·18 to
2619·54)
2339·48
(1860·90 to
2923·32)
10·22
(4·21 to 15·66)*
–8·91
(–14·66 to –3·71)*
3 Occupational particulate
matter, gases, and
fumes: all causes
407·53
(338·66 to
479·12)
424·27
(349·98 to
507·55)
4·11
(–0·16 to 8·15)
–21·37
(–23·97 to –18·61)*
8771·11
(7497·47 to
10 068·75)
9377·10
(7972·61 to
10 789·56)
6·91
(3·78 to 10·55)*
–17·84
(–19·96 to –15·42)*
·· Chronic obstructive
pulmonary disease
399·93
(331·13 to
472·15)
416·68
(342·87 to
499·76)
4·19
(–0·04 to 8·27)
–21·32
(–23·94 to –18·63)*
8557·06
(7276·89 to
9859·70)
9154·55
(7771·09 to
10 539·37)
6·98
(3·85 to 10·70)*
–17·82
(–19·97 to –15·30)*
(Table 4 continues on next page)
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1379
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Coal workers
pneumoconiosis
3·03
(1·91 to 3·49)
2·68
(1·79 to 3·07)
–11·29
(–19·54 to 0·39)
–32·63
(–38·84 to –23·91)*
87·45
(65·52 to 104·62)
89·05
(70·16 to 108·86)
1·83
(–6·81 to 12·73)
–21·65
(–28·21 to –13·50)*
·· Other
pneumoconiosis
4·57
(3·71 to 6·32)
4·91
(4·16 to 6·56)
7·27
(–1·26 to 15·86)
–17·49
(–24·05 to –11·05)*
126·59
(104·34 to 161·76)
133·51
(112·07 to 165·88)
5·46
(–2·23 to 13·23)
–16·68
(–22·73 to –10·63)*
3 Occupational noise: all
causes
·· ·· ·· ·· 5865·39
(4107·31 to
8092·94)
7108·28
(4978·56 to
9802·69)
21·19
(19·01 to 22·96)*
–0·74
(–2·21 to 0·56)
·· Age-related and other
hearing loss
·· ·· ·· ·· 5865·39
(4107·31 to
8092·94)
7108·28
(4978·56 to
9802·69)
21·19
(19·01 to 22·96)*
–0·74
(–2·21 to 0·56)
3 Occupational injuries: all
causes
352·96
(344·63 to
360·98)
335·71
(328·64 to
343·27)
–4·89
(–7·71 to –1·89)*
–17·78
(–20·22 to –15·20)*
21 906·21
(20 353·14 to
23 776·95)
21 774·60
(19 810·66 to
24 090·16)
–0·60
(–4·19 to 2·98)
–12·95
(–15·95 to –9·94)*
·· Pedestrian road
injuries
67·01
(62·03 to
73·85)
63·97
(59·35 to
69·72)
–4·53
(–10·63 to 0·01)
–18·09
(–23·28 to –14·24)*
3434·81
(3182·07 to
3771·04)
3278·98
(3037·91 to
3549·98)
–4·54
(–10·46 to
–0·04)*
–16·52
(–21·66 to –12·63)*
·· Cyclist road injuries 10·32
(9·27 to 11·62)
9·99
(8·96 to 11·51)
–3·16
(–8·97 to 5·60)
–17·77
(–22·81 to –10·17)*
673·49
(580·35 to 787·54)
707·70
(596·26 to 850·17)
5·08
(–0·72 to 11·51)
–9·29
(–14·16 to –3·79)*
·· Motorcyclist road
injuries
44·53
(40·17 to
49·15)
42·56
(38·79 to
46·82)
–4·41
(–9·93 to 0·93)
–15·65
(–20·46 to
–10·89)*
2623·88
(2388·56 to
2894·57)
2549·86
(2330·54 to
2817·91)
–2·82
(–8·29 to 2·44)
–13·57
(–18·33 to –9·00)*
·· Motor vehicle road
injuries
74·30
(65·78 to
85·66)
73·17
(67·36 to
83·11)
–1·51
(–6·37 to 7·20)
–13·94
(–18·25 to –6·31)*
4091·59
(3653·03 to
4667·92)
4058·76
(3712·02 to
4590·12)
–0·80
(–5·44 to 7·38)
–12·17
(–16·25 to –4·94)*
·· Other road injuries 1·98
(1·74 to 2·43)
1·88
(1·68 to 2·28)
–5·05
(–12·93 to 5·85)
–18·33
(–25·07 to –8·69)*
167·13
(137·60 to 207·71)
198·25
(158·96 to 254·29)
18·62
(9·95 to 27·66)*
2·77
(–4·57 to 10·39)
·· Other transport
injuries
14·25
(12·59 to
15·77)
13·71
(12·58 to
14·97)
–3·74
(–11·07 to 5·18)
–16·70
(–22·82 to –8·99)*
970·91
(855·69 to
1109·73)
969·10
(847·30 to
1119·40)
–0·19
(–6·23 to 6·80)
–12·66
(–17·77 to –6·62)*
·· Falls 38·58
(34·48 to
40·43)
39·52
(36·06 to
41·41)
2·42
(–3·75 to 8·20)
–14·40
(–19·49 to –9·55)*
3253·95
(2718·82 to
3890·24)
3637·49
(3004·39 to
4424·49)
11·79
(6·86 to 16·48)*
–4·69
(–8·77 to –0·73)*
·· Drowning 29·91
(28·60 to
31·52)
26·74
(25·32 to
28·13)
–10·60
(–14·16 to
–6·83)*
–21·41
(–24·53 to –18·10)*
1558·83
(1491·01 to
1643·06)
1365·42
(1294·39 to
1433·95)
–12·41
(–15·98 to
–8·51)*
–21·39
(–24·55 to –17·92)*
.. Fire, heat, and hot
substances
10·40
(9·05 to 11·29)
9·42
(8·02 to 10·46)
–9·40
(–14·52 to –4·17)*
–22·76
(–27·12 to –18·35)*
749·03
(646·32 to
879·73)
758·15
(626·89 to 922·03)
1·22
(–4·90 to 6·74)
–11·99
(–17·27 to –7·18)*
.. Poisonings 6·69
(5·21 to 7·55)
5·85
(4·37 to 6·54)
–12·57
(–23·95 to 3·86)
–24·93
(–34·60 to –11·05)*
351·08
(280·67 to 395·21)
313·70
(244·88 to 347·77)
–10·65
(–20·53 to 3·82)
–21·69
(–30·25 to –9·04)*
.. Unintentional firearm
injuries
4·19
(3·23 to 4·60)
3·83
(2·80 to 4·20)
–8·61
(–17·66 to 0·01)
–19·38
(–27·38 to –11·76)*
253·51
(198·88 to
282·79)
240·12
(181·74 to 271·14)
–5·28
(–13·02 to 2·95)
–15·61
(–22·69 to –8·27)*
·· Unintentional
suffocation
0·77
(0·68 to 0·90)
0·92
(0·67 to 1·04)
19·08
(–7·56 to 34·67)
4·58
(–18·86 to 18·24)
69·06
(56·28 to 86·36)
80·23
(63·30 to 101·14)
16·18
(1·23 to 25·85)*
2·43
(–10·54 to 10·82)
·· Other exposure to
mechanical forces
17·58
(13·83 to
18·79)
15·29
(11·75 to
16·30)
–13·04
(–17·93 to
–8·65)*
–24·91
(–29·14 to –21·17)*
1292·58
(1072·42 to
1512·50)
1290·07
(1044·64 to
1570·19)
–0·19
(–5·99 to 5·81)
–13·26
(–18·20 to –8·29)*
·· Venomous animal
contact
6·92
(6·26 to 7·56)
5·66
(5·21 to 6·27)
–18·20
(–25·20 to
–6·78)*
–30·45
(–36·32 to –20·64)*
446·07
(390·64 to
500·68)
389·47
(338·03 to 449·59)
–12·69
(–19·31 to
–3·42)*
–23·84
(–29·63 to –15·81)*
·· Non-venomous
animal contact
1·47
(1·09 to 1·80)
1·32
(0·99 to 1·71)
–9·58
(–17·89 to 0·09)
–23·14
(–30·13 to –14·72)*
122·37
(93·44 to 157·70)
116·27
(88·44 to 149·33)
–4·98
(–11·38 to 1·81)
–17·63
(–22·98 to –11·96)*
·· Pulmonary aspiration
and foreign body in
airway
5·70
(5·08 to 6·60)
6·15
(5·50 to 7·31)
8·00
(0·69 to 17·21)*
–8·88
(–15·06 to –1·29)*
368·39
(314·09 to 433·78)
400·88
(341·30 to 484·16)
8·82
(2·49 to 15·63)*
–5·71
(–11·16 to 0·22)
(Table 4 continues on next page)
Global Health Metrics
1380
www.thelancet.com Vol 390 September 16, 2017
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Foreign body in other
body part
1·03
(0·70 to 1·32)
1·05
(0·76 to 1·32)
1·96
(–7·01 to 15·08)
–11·39
(–19·07 to –0·51)*
123·60
(91·48 to 162·21)
136·41
(101·25 to 180·00)
10·37
(3·84 to 16·97)*
–3·80
(–9·33 to 1·81)
·· Other unintentional
injuries
17·35
(15·52 to
18·15)
14·67
(12·87 to
15·41)
–15·45
(–19·85 to
–11·30)*
–26·15
(–30·00 to –22·53)*
1355·94
(1171·12 to
1583·40)
1283·74
(1074·02 to
1552·79)
–5·32
(–10·48 to
–0·38)*
–17·08
(–21·56 to –12·93)*
3Occupational ergonomic
factors: all causes
·· ·· ·· ·· 13 229·58
(9255·44 to
17 770·82)
15 479·93
(10 733·37 to
20 772·45)
17·01
(14·86 to 19·35)*
–1·74
(–3·26 to –0·45)*
·· Low back pain ·· ·· ·· ·· 13 229·58
(9255·44 to
17 770·82)
15 479·93
(10 733·37 to
20 772·45)
17·01
(14·86 to 19·35)*
–1·74
(–3·26 to –0·45)*
1 Behavioural risks: all
causes
22 393·17
(21 227·31 to
23 619·19)
21 830·19
(20 450·24 to
23 314·12)
–2·51
(–4·89 to –0·13)*
–21·55
(–23·25 to
–19·81)*
910 996·12
(869 496·72 to
953 010·97)
781 103·69
(737 052·73 to
830 058·54)
–14·26
(–16·59 to
–11·83)*
–25·18
(–27·08 to –23·22)*
2 Child and maternal
malnutrition: all causes
4301·09
(4107·68 to
4499·13)
2736·96
(2573·81 to
2904·34)
–36·37
(–39·81 to
–32·52)*
–36·99
(–40·42 to
–33·17)*
406 715·03
(385 244·16 to
429 424·87)
275 068·98
(255 117·96 to
296 600·82)
–32·37
(–36·04 to
–28·67)*
–33·64
(–37·18 to –30·01)*
3 Suboptimal
breastfeeding: all causes
278·09
(223·03 to
332·55)
152·48
(124·06 to
183·65)
–45·17
(–50·75 to
–38·89)*
–45·59
(–51·13 to –39·40)*
24 214·14
(19 400·12 to
28 949·80)
13 373·25
(10 878·18 to
16 087·13)
–44·77
(–50·34 to
–38·57)*
–45·20
(–50·73 to –39·07)*
4 Non-exclusive
breastfeeding: all causes
264·19
(210·54 to
318·37)
144·11
(116·21 to
173·92)
–45·45
(–51·08 to
–39·34)*
–45·79
(–51·39 to –39·70)*
22 971·14
(18 284·60 to
27 651·42)
12 598·41
(10 160·91 to
15 194·04)
–45·16
(–50·76 to
–39·06)*
–45·49
(–51·07 to –39·43)*
·· Diarrhoeal diseases 169·62
(132·18 to
206·77)
88·76
(68·74 to
111·24)
–47·67
(–54·86 to
–39·28)*
–48·02
(–55·16 to –39·68)*
14 810·81
(11 518·55 to
18 077·32)
7821·54
(6057·18 to
9801·86)
–47·19
(–54·37 to
–38·91)*
–47·54
(–54·67 to –39·31)*
·· Lower respiratory
infections
94·57
(62·35 to
130·16)
55·35
(35·96 to
75·66)
–41·47
(–46·49 to
–35·45)*
–41·79
(–46·78 to
–35·80)*
8160·33
(5381·55 to
11 233·53)
4776·87
(3103·20 to
6528·56)
–41·46
(–46·48 to
–35·45)*
–41·78
(–46·77 to –35·80)*
4 Discontinued
breastfeeding: all causes
16·70
(5·98 to 29·32)
10·04
(3·49 to 17·76)
–39·90
(–48·41 to
–29·27)*
–41·77
(–50·01 to –31·40)*
1490·66
(534·34 to
2615·99)
924·29
(322·52 to
1634·92)
–37·99
(–46·40 to
–27·87)*
–39·95
(–48·10 to –30·10)*
·· Diarrhoeal diseases 16·70
(5·98 to 29·32)
10·04
(3·49 to 17·76)
–39·90
(–48·41 to
–29·27)*
–41·77
(–50·01 to –31·40)*
1490·66
(534·34 to
2615·99)
924·29
(322·52 to
1634·92)
–37·99
(–46·40 to
–27·87)*
–39·95
(–48·10 to –30·10)*
3 Child growth failure: all
causes
1874·90
(1718·60 to
2023·15)
1010·58
(908·98 to
1119·90)
–46·10
(–51·03 to
–40·34)*
–47·58
(–52·39 to –42·00)*
164 876·44
(151 738·69 to
177 603·01)
91 199·77
(82 272·24 to
100 948·47)
–44·69
(–49·42 to
–39·13)*
–46·23
(–50·84 to –40·81)*
4Child underweight: all
causes
615·18
(515·40 to
776·56)
312·61
(266·20 to
389·00)
–49·18
(–55·81 to
–41·66)*
–50·76
(–57·23 to –43·44)*
55 627·11
(46 807·75 to
69 301·37)
30 009·75
(25 768·76 to
36 212·38)
–46·05
(–52·86 to
–37·94)*
–47·77
(–54·35 to –39·88)*
·· Diarrhoeal diseases 127·09
(100·36 to
161·58)
52·67
(40·79 to
66·71)
–58·56
(–64·86 to
–51·39)*
–59·80
(–65·94 to –52·78)*
11 105·27
(8743·61 to
14 096·57)
4690·97
(3642·30 to
5935·54)
–57·76
(–64·06 to
–50·68)*
–59·03
(–65·13 to –52·13)*
·· Lower respiratory
infections
163·67
(110·46 to
282·27)
74·94
(50·68 to
134·75)
–54·21
(–59·84 to
–48·26)*
–55·36
(–60·85 to
–49·50)*
14 008·04
(9452·95 to
24 153·07)
6422·64
(4342·77 to
11 542·06)
–54·15
(–59·76 to
–48·19)*
–55·29
(–60·77 to –49·43)*
·· Measles 90·51
(19·16 to
218·56)
18·86
(3·38 to 49·55)
–79·17
(–85·30 to
–74·27)*
–80·02
(–85·90 to –75·33)*
7705·27
(1633·01 to
18 583·17)
1607·04
(288·75 to
4213·11)
–79·14
(–85·23 to
–74·24)*
–79·99
(–85·84 to –75·30)*
·· Protein-energy
malnutrition
233·90
(206·48 to
265·01)
166·14
(141·84 to
197·87)
–28·97
(–41·25 to
–12·92)*
–31·28
(–43·19 to –15·72)*
22 808·52
(20 316·73 to
25 669·57)
17 289·10
(14 869·06 to
20 449·39)
–24·20
(–35·67 to
–10·17)*
–26·77
(–37·78 to –13·18)*
4 Child wasting: all causes 1734·23
(1516·43 to
1927·79)
952·40
(813·72 to
1,078·99)
–45·08
(–50·29 to
–39·28)*
–46·57
(–51·63 to –40·95)*
152 812·32
(134 145·84 to
169 500·58)
86 165·42
(74 409·95 to
97 423·29)
–43·61
(–48·72 to
–37·94)*
–45·17
(–50·15 to –39·64)*
(Table 4 continues on next page)
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1381
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Diarrhoeal diseases 681·26
(562·68 to
775·51)
358·50
(291·19 to
415·43)
–47·38
(–54·62 to
–38·82)*
–48·84
(–55·87 to –40·50)*
59 883·01
(49 482·08 to
68 365·76)
32 202·36
(26 200·00 to
37 322·59)
–46·22
(–53·39 to
–38·04)*
–47·73
(–54·71 to –39·75)*
·· Lower respiratory
infections
710·19
(548·14 to
830·75)
400·91
(298·47 to
479·06)
–43·55
(–49·82 to
–37·17)*
–44·86
(–51·02 to –38·64)*
60 842·27
(46 968·94 to
71 152·73)
34 383·68
(25 595·42 to
41 070·89)
–43·49
(–49·75 to
–37·14)*
–44·79
(–50·93 to –38·62)*
·· Measles 108·87
(22·88 to
295·48)
26·85
(4·48 to 77·75)
–75·34
(–83·92 to
–69·78)*
–76·29
(–84·44 to
–70·99)*
9278·52
(1953·33 to
25 167·82)
2290·28
(383·79 to
6620·03)
–75·32
(–83·90 to
–69·75)*
–76·27
(–84·43 to –70·96)*
·· Protein-energy
malnutrition
233·90
(206·48 to
265·01)
166·14
(141·84 to
197·87)
–28·97
(–41·25 to
–12·92)*
–31·28
(–43·19 to –15·72)*
22 808·52
(20 316·73 to
25 669·57)
17 289·10
(14 869·06 to
20 449·39)
–24·20
(–35·67 to
–10·17)*
–26·77
(–37·78 to –13·18)*
4 Child stunting: all causes 366·43
(184·02 to
613·94)
162·19
(74·85 to
301·18)
–55·74
(–63·28 to
–48·78)*
–57·10
(–64·53 to –50·28)*
31 579·40
(15 947·91 to
52 776·94)
14 114·74
(6609·85 to
26 162·13)
–55·30
(–62·90 to
–48·48)*
–56·68
(–64·19 to –50·03)*
·· Diarrhoeal diseases 133·15
(51·03 to
233·07)
60·15
(21·84 to
112·30)
–54·83
(–61·70 to
–45·79)*
–56·28
(–62·91 to –47·53)*
11 661·60
(4481·93 to
20 495·06)
5381·00
(2025·40 to
10 118·70)
–53·86
(–60·50 to
–44·93)*
–55·35
(–61·80 to –46·71)*
·· Lower respiratory
infections
173·89
(17·78 to
415·12)
88·31
(7·20 to
226·69)
–49·22
(–56·08 to
–37·47)*
–50·59
(–57·26 to –39·05)*
14 868·01
(1516·40 to
35 518·80)
7564·20
(616·04 to
19 419·68)
–49·12
(–55·96 to
–37·42)*
–50·49
(–57·14 to –38·99)*
·· Measles 59·38
(5·88 to
164·35)
13·73
(1·21 to 40·90)
–76·87
(–82·40 to
–71·74)*
–77·86
(–83·16 to –72·91)*
5049·80
(506·57 to
13 966·03)
1169·54
(103·24 to
3473·72)
–76·84
(–82·34 to
–71·73)*
–77·82
(–83·10 to –72·90)*
3 Low birthweight and
short gestation: all
causes
2341·51
(2264·77 to
2427·94)
1673·60
(1589·23 to
1758·45)
–28·52
(–31·98 to
–24·88)*
–28·19
(–31·66 to –24·53)*
202 783·89
(196 133·92 to
210 268·23)
144 947·75
(137 645·54 to
152 301·77)
–28·52
(–31·97 to
–24·88)*
–28·19
(–31·66 to –24·53)*
4 Short gestation for
birthweight: all causes
2064·01
(1949·93 to
2171·84)
1485·61
(1392·05 to
1580·00)
–28·02
(–31·59 to
–24·49)*
–27·69
(–31·27 to –24·14)*
178 754·75
(168 864·65 to
188 091·13)
128 668·91
(120 565·96 to
136 862·69)
–28·02
(–31·58 to
–24·49)*
–27·69
(–31·27 to –24·14)*
·· Diarrhoeal diseases 55·68
(50·20 to
61·51)
23·63
(20·92 to
26·58)
–57·57
(–62·84 to
–51·19)*
–57·43
(–62·71 to –51·02)*
4820·28
(4345·76 to
5325·35)
2045·43
(1811·41 to
2301·28)
–57·57
(–62·84 to
–51·19)*
–57·43
(–62·71 to –51·02)*
·· Lower respiratory
infections
183·79
(162·94 to
202·96)
104·40
(89·31 to
119·24)
–43·20
(–48·74 to
–37·39)*
–42·98
(–48·54 to –37·15)*
15 913·47
(14 107·71 to
17 572·78)
9039·55
(7732·87 to
10 324·80)
–43·20
(–48·74 to
–37·39)*
–42·97
(–48·54 to –37·15)*
.. Upper respiratory
infections
0·09
(0·06 to 0·12)
0·05
(0·04 to 0·07)
–41·66
(–60·35 to
–14·21)*
–41·40
(–60·16 to –13·83)*
7·54
(5·36 to 10·34)
4·40
(3·13 to 6·32)
–41·66
(–60·35 to
–14·21)*
–41·40
(–60·16 to –13·82)*
·· Otitis media 0·01
(0·01 to 0·02)
0·01
(0·00 to 0·01)
–55·23
(–72·96 to
–21·17)*
–55·11
(–72·91 to –20·94)*
1·13
(0·78 to 1·72)
0·51
(0·33 to 0·83)
–55·23
(–72·96 to
–21·18)*
–55·11
(–72·91 to –20·95)*
·· Pneumococcal
meningitis
0·74
(0·51 to 1·02)
0·62
(0·40 to 0·93)
–15·86
(–31·66 to 6·43)
–15·56
(–31·41 to 6·80)
63·93
(43·85 to 87·89)
53·80
(35·00 to 80·84)
–15·85
(–31·66 to 6·43)
–15·56
(–31·41 to 6·80)
·· H influenzae type B
meningitis
2·05
(1·48 to 2·68)
1·71
(1·22 to 2·40)
–16·55
(–32·62 to 6·20)
–16·25
(–32·37 to 6·59)
177·11
(127·74 to 231·69)
147·80
(105·37 to 207·64)
–16·55
(–32·62 to 6·20)
–16·25
(–32·37 to 6·59)
·· Meningococcal
infection
7·44
(5·63 to 9·42)
4·67
(3·45 to 6·41)
–37·20
(–47·71 to
–22·35)*
–36·98
(–47·52 to –22·08)*
643·78
(487·60 to
815·98)
404·31
(298·70 to 555·31)
–37·20
(–47·71 to
–22·35)*
–36·98
(–47·52 to –22·08)*
·· Other meningitis 5·57
(4·16 to 7·06)
5·57
(4·05 to 8·31)
0·08
(–17·80 to 26·45)
0·43
(–17·54 to 26·88)
481·88
(360·51 to 611·08)
482·25
(350·92 to 719·69)
0·08
(–17·80 to 26·45)
0·43
(–17·53 to 26·88)
·· Encephalitis 1·34
(1·00 to 1·56)
1·00
(0·79 to 1·24)
–24·98
(–43·32 to
–2·82)*
–24·73
(–43·13 to –2·52)*
115·89
(86·59 to 134·97)
86·95
(68·43 to 107·11)
–24·98
(–43·32 to
–2·82)*
–24·73
(–43·13 to –2·52)*
·· Neonatal preterm
birth complications
819·36
(770·29 to
909·83)
590·38
(541·05 to
643·11)
–27·95
(–33·72 to
–22·15)*
–27·60
(–33·41 to –21·78)*
70 980·50
(66 730·62 to
78 805·17)
51 151·21
(46 878·45 to
55 713·15)
–27·94
(–33·70 to
–22·14)*
–27·59
(–33·39 to –21·77)*
(Table 4 continues on next page)
Global Health Metrics
1382
www.thelancet.com Vol 390 September 16, 2017
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Neonatal
encephalopathy due
to birth asphyxia and
trauma
477·77
(426·69 to
525·03)
370·94
(322·96 to
419·15)
–22·36
(–29·79 to
–14·36)*
–21·97
(–29·43 to –13·93)*
41 371·74
(36 949·03 to
45 464·08)
32 120·93
(27 966·29 to
36 295·65)
–22·36
(–29·79 to
–14·36)*
–21·97
(–29·43 to –13·93)*
·· Neonatal sepsis and
other neonatal
infections
170·34
(138·61 to
217·43)
151·23
(126·64 to
206·06)
–11·22
(–21·77 to 2·78)
–10·86
(–21·43 to 3·20)
14 749·24
(12 001·55 to
18 826·29)
13 094·15
(10 964·91 to
17 842·59)
–11·22
(–21·77 to 2·78)
–10·86
(–21·44 to 3·20)
·· Haemolytic disease
and other neonatal
jaundice
55·72
(48·90 to
64·54)
32·45
(27·90 to
38·04)
–41·77
(–49·82 to
–32·96)*
–41·52
(–49·61 to
–32·68)*
4824·42
(4234·14 to
5587·98)
2809·45
(2416·02 to
3293·80)
–41·77
(–49·82 to
–32·96)*
–41·52
(–49·61 to –32·68)*
·· Other neonatal
disorders
282·16
(250·52 to
317·48)
197·44
(173·31 to
220·92)
–30·03
(–37·01 to
–21·73)*
–29·69
(–36·71 to –21·36)*
24 432·57
(21 692·72 to
27 491·34)
17 096·35
(15 006·94 to
19 130·02)
–30·03
(–37·01 to
–21·73)*
–29·69
(–36·71 to –21·36)*
·· Sudden infant death
syndrome
1·98
(1·47 to 2·54)
1·52
(1·16 to 1·87)
–23·02
(–39·72 to –8·37)*
–22·88
(–39·61 to –8·20)*
171·26
(127·36 to 219·51)
131·83
(100·77 to 161·72)
–23·02
(–39·72 to
–8·37)*
–22·88
(–39·61 to –8·20)*
4 Low birthweight for
gestation: all causes
1096·85
(1005·37 to
1207·52)
778·37
(705·63 to
864·12)
–29·04
(–33·70 to
–24·31)*
–28·69
(–33·38 to –23·94)*
95 009·64
(87 086·08 to
104 596·97)
67 430·06
(61 121·27 to
74 855·14)
–29·03
(–33·69 to
–24·30)*
–28·69
(–33·37 to –23·93)*
·· Diarrhoeal diseases 8·81
(6·21 to 11·88)
3·44
(2·38 to 4·75)
–60·94
(–66·12 to
–55·24)*
–60·78
(–65·99 to –55·07)*
762·62
(537·68 to
1028·73)
297·89
(206·10 to 411·47)
–60·94
(–66·12 to
–55·24)*
–60·78
(–65·99 to –55·07)*
·· Lower respiratory
infections
35·74
(25·03 to
48·36)
19·19
(12·83 to
26·70)
–46·30
(–52·21 to
–39·99)*
–46·06
(–51·99 to –39·72)*
3094·33
(2167·54 to
4187·61)
1661·65
(1111·03 to
2311·96)
–46·30
(–52·21 to
–39·99)*
–46·06
(–51·99 to –39·71)*
·· Upper respiratory
infections
0·02
(0·01 to 0·03)
0·01
(0·01 to 0·02)
–44·27
(–64·75 to
–11·69)*
–44·00
(–64·57 to –11·28)*
1·71
(1·05 to 2·67)
0·95
(0·56 to 1·55)
–44·27
(–64·75 to
–11·69)*
–44·00
(–64·57 to –11·27)*
·· Otitis media 0·00
(0·00 to 0·00)
0·00
(0·00 to 0·00)
–55·15
(–71·15 to
–26·78)*
–55·00
(–71·08 to –26·52)*
0·16
(0·09 to 0·25)
0·07
(0·04 to 0·12)
–55·15
(–71·15 to
–26·78)*
–55·00
(–71·08 to –26·52)*
·· Pneumococcal
meningitis
0·13
(0·08 to 0·20)
0·11
(0·06 to 0·17)
–19·39
(–35·79 to 2·80)
–19·04
(–35·49 to 3·25)
11·42
(6·55 to 17·49)
9·20
(5·13 to 15·15)
–19·39
(–35·79 to 2·80)
–19·04
(–35·49 to 3·25)
·· H influenzae type B
meningitis
0·36
(0·22 to 0·53)
0·29
(0·17 to 0·45)
–20·05
(–36·17 to 2·27)
–19·71
(–35·89 to 2·71)
31·35
(19·05 to 45·47)
25·07
(14·61 to 38·71)
–20·05
(–36·16 to 2·27)
–19·71
(–35·89 to 2·71)
·· Meningococcal
infection
1·30
(0·81 to 1·86)
0·80
(0·46 to 1·25)
–38·84
(–50·93 to
–23·22)*
–38·58
(–50·72 to –22·88)*
112·69
(69·75 to 161·06)
68·92
(39·87 to 108·45)
–38·84
(–50·93 to
–23·22)*
–38·58
(–50·72 to –22·88)*
·· Other meningitis 1·00
(0·62 to 1·44)
0·94
(0·55 to 1·48)
–5·58
(–23·60 to 18·81)
–5·19
(–23·30 to 19·32)
86·49
(53·37 to 124·38)
81·66
(47·91 to 127·74)
–5·58
(–23·59 to 18·81)
–5·18
(–23·29 to 19·32)
·· Encephalitis 0·20
(0·13 to 0·28)
0·14
(0·10 to 0·21)
–29·88
(–45·21 to
–11·08)*
–29·59
(–44·97 to –10·70)*
17·74
(11·22 to 24·41)
12·44
(8·28 to 17·83)
–29·88
(–45·21 to
–11·08)*
–29·59
(–44·97 to –10·70)*
·· Neonatal preterm
birth complications
819·36
(770·29 to
909·83)
590·38
(541·05 to
643·11)
–27·95
(–33·72 to
–22·15)*
–27·60
(–33·41 to –21·78)*
70 980·50
(66 730·62 to
78 805·17)
51 151·21
(46 878·45 to
55 713·15)
–27·94
(–33·70 to
–22·14)*
–27·59
(–33·39 to –21·77)*
·· Neonatal
encephalopathy due
to birth asphyxia and
trauma
117·60
(83·00 to
156·33)
84·68
(58·86 to
116·05)
–27·99
(–35·44 to
–20·42)*
–27·62
(–35·10 to –20·01)*
10 183·45
(7187·60 to
13 537·45)
7332·98
(5096·63 to
10 049·16)
–27·99
(–35·44 to
–20·42)*
–27·62
(–35·10 to –20·01)*
·· Neonatal sepsis and
other neonatal
infections
35·50
(23·71 to
50·81)
29·56
(19·65 to
43·87)
–16·73
(–28·32 to –2·51)*
–16·34
(–27·98 to –2·04)*
3073·99
(2053·27 to
4400·09)
2559·70
(1701·26 to
3798·68)
–16·73
(–28·32 to
–2·51)*
–16·34
(–27·98 to –2·04)*
·· Haemolytic disease
and other neonatal
jaundice
11·40
(7·99 to 15·92)
6·32
(4·34 to 9·07)
–44·53
(–53·03 to
–34·47)*
–44·26
(–52·80 to –34·17)*
987·26
(692·04 to
1378·49)
547·68
(375·55 to 785·14)
–44·52
(–53·03 to
–34·47)*
–44·26
(–52·80 to –34·17)*
(Table 4 continues on next page)
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1383
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Other neonatal
disorders
65·24
(45·86 to
88·09)
42·37
(28·62 to
57·94)
–35·06
(–42·81 to
–26·85)*
–34·73
(–42·52 to –26·48)*
5649·31
(3971·62 to
7628·08)
3668·67
(2478·27 to
5016·91)
–35·06
(–42·81 to
–26·85)*
–34·73
(–42·52 to –26·48)*
·· Sudden infant death
syndrome
0·19
(0·11 to 0·30)
0·14
(0·08 to 0·21)
–28·09
(–43·32 to
–14·05)*
–27·96
(–43·22 to –13·89)*
16·64
(9·23 to 26·37)
11·97
(6·94 to 18·10)
–28·09
(–43·32 to
–14·05)*
–27·96
(–43·22 to –13·89)*
3Iron deficiency: all
causes
27·52
(12·14 to
43·69)
20·95
(9·79 to 33·31)
–23·88
(–30·25 to
–15·97)*
–31·14
(–36·96 to –24·17)*
33 835·12
(22 660·82 to
48 281·14)
35 849·87
(24 052·89 to
50 796·92)
5·95
(4·22 to 7·72)*
–3·09
(–4·68 to –1·56)*
·· Maternal
haemorrhage
19·26
(7·33 to 32·35)
14·10
(5·33 to 24·29)
–26·80
(–34·82 to
–17·79)*
–33·34
(–40·64 to –25·27)*
1105·24
(420·28 to
1854·49)
798·94
(301·29 to
1366·56)
–27·71
(–35·87 to
–18·55)*
–33·84
(–41·20 to –25·64)*
·· Maternal sepsis and
other pregnancy
related infections
5·54
(2·03 to 9·37)
3·89
(1·38 to 6·67)
–29·86
(–38·95 to
–20·14)*
–35·83
(–44·10 to
–26·86)*
325·54
(119·05 to 544·08)
227·22
(80·46 to 386·37)
–30·20
(–38·96 to
–20·38)*
–35·70
(–43·84 to –27·13)*
·· Iron-deficiency
anaemia
2·72
(2·35 to 3·89)
2·96
(2·52 to 3·75)
8·94
(–9·11 to 27·26)
–11·59
(–27·78 to 4·94)
32 404·33
(21 523·57 to
46 641·55)
34 823·71
(23 073·25 to
49 667·43)
7·47
(6·17 to 8·89)*
–1·78
(–2·96 to –0·51)*
3Vitamin A deficiency: all
causes
108·40
(62·61 to
166·64)
42·18
(24·16 to
65·39)
–61·08
(–66·90 to
–53·83)*
–62·78
(–68·35 to –55·79)*
9600·08
(5604·44 to
14 578·34)
3979·05
(2357·30 to
6000·15)
–58·55
(–64·51 to
–51·19)*
–60·47
(–66·15 to –53·43)*
·· Diarrhoeal diseases 64·17
(32·47 to
96·79)
30·04
(14·54 to
46·71)
–53·18
(–60·01 to
–44·61)*
–55·12
(–61·70 to –46·81)*
5620·97
(2833·29 to
8506·22)
2695·50
(1309·94 to
4149·27)
–52·05
(–58·86 to
–43·62)*
–54·05
(–60·63 to –45·90)*
·· Measles 44·23
(13·84 to
96·01)
12·14
(3·73 to 28·13)
–72·55
(–77·30 to
–67·69)*
–73·85
(–78·35 to –69·07)*
3753·95
(1185·75 to
8149·96)
1031·27
(318·97 to
2391·75)
–72·53
(–77·21 to
–67·68)*
–73·83
(–78·30 to –69·05)*
·· Vitamin A deficiency ·· ·· ·· ·· 225·16
(139·62 to 348·12)
252·29
(158·71 to 388·09)
12·05
(8·70 to 15·49)*
2·64
(–0·33 to 5·60)
3Zinc deficiency: all
causes
53·32
(2·85 to
141·73)
25·09
(1·32 to 69·47)
–52·95
(–60·88 to
–43·61)*
–55·43
(–62·95 to
–46·59)*
4651·43
(359·57 to
12 155·34)
2245·65
(213·63 to
5993·34)
–51·72
(–59·17 to
–38·63)*
–54·27
(–61·32 to –41·88)*
·· Diarrhoeal diseases 31·31
(0·00 to 87·89)
14·67
(0·00 to 42·50)
·· ·· 2785·75
(132·69 to
7615·90)
1359·88
(108·32 to
3778·60)
–51·18
(–58·99 to
–14·97)*
–53·76
(–61·16 to –19·47)*
.. Lower respiratory
infections
22·01
(0·00 to 86·72)
10·42
(0·00 to 42·20)
.. .. 1865·68
(2·95 to 7331·05)
885·77
(2·26 to 3568·53)
–52·52
(–60·86 to
–18·33)*
–55·03
(–62·93 to –22·65)*
2Tobacco: all causes 6853·45
(6227·56 to
7447·85)
7131·38
(6503·23 to
7780·89)
4·06
(1·29 to 6·96)*
–20·37
(–22·48 to
–18·30)*
178 305·14
(163 133·82 to
194 298·17)
177 302·31
(162 327·84 to
194 250·39)
–0·56
(–3·34 to 2·52)
–21·31
(–23·35 to –19·05)*
3 Smoking: all causes 6081·95
(5443·81 to
6681·35)
6321·10
(5673·66 to
6962·35)
3·93
(0·87 to 7·06)*
–20·68
(–22·98 to –18·31)*
153 365·37
(138 408·89 to
167 887·88)
155 065·75
(140 025·42 to
170 602·15)
1·11
(–1·79 to 4·20)
–20·83
(–23·12 to –18·45)*
·· Drug-susceptible
tuberculosis
129·07
(66·60 to
195·37)
90·24
(44·98 to
139·18)
–30·08
(–34·42 to
–26·43)*
–44·49
(–48·00 to –41·50)*
4240·53
(2168·23 to
6389·84)
2934·12
(1440·35 to
4528·89)
–30·81
(–34·75 to
–27·37)*
–43·68
(–47·17 to –40·93)*
·· Multidrug-resistant
tuberculosis without
extensive drug
resistance
14·07
(7·18 to 21·44)
8·19
(4·00 to 12·82)
–41·80
(–48·21 to
–35·18)*
–53·46
(–58·72 to –48·18)*
458·79
(234·16 to
698·87)
257·57
(126·48 to 404·28)
–43·86
(–50·23 to
–37·69)*
–54·10
(–59·43 to –49·05)*
·· Extensively drug-
resistant tuberculosis
0·92
(0·48 to 1·40)
1·33
(0·66 to 2·11)
44·76
(19·92 to 72·63)*
16·94
(–2·69 to 38·90)
31·47
(16·42 to 48·08)
43·78
(21·82 to 68·67)
39·10
(14·10 to 68·50)*
14·83
(–5·56 to 38·75)
·· Lower respiratory
infections
326·00
(257·89 to
397·51)
345·94
(270·42 to
426·72)
6·12
(0·26 to 10·80)*
–19·55
(–23·91 to –16·10)*
7002·64
(5630·01 to
8415·41)
7022·96
(5529·91 to
8607·77)
0·29
(–5·31 to 5·35)
–20·81
(–25·17 to –16·90)*
(Table 4 continues on next page)
Global Health Metrics
1384
www.thelancet.com Vol 390 September 16, 2017
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Lip and oral cavity
cancer
51·72
(44·13 to
60·07)
64·11
(53·04 to
77·13)
23·95
(15·31 to 32·49)*
–4·81
(–11·29 to 1·58)
1393·05
(1176·02 to
1627·58)
1658·72
(1362·64 to
2015·39)
19·07
(10·04 to
28·65)*
–6·71
(–13·94 to 0·57)
·· Nasopharynx cancer 21·46
(15·13 to
28·44)
22·33
(16·02 to
30·03)
4·05
(–6·14 to 14·01)
–18·25
(–25·96 to –10·94)*
635·38
(432·94 to
851·08)
618·24
(441·07 to 838·14)
–2·70
(–13·59 to 9·00)
–22·14
(–30·47 to –13·55)*
·· Oesophageal cancer 144·04
(90·12 to
204·21)
144·40
(93·42 to
205·40)
0·25
(–5·55 to 8·89)
–23·33
(–27·74 to –17·01)*
3249·88
(2072·43 to
4607·13)
3104·99
(2025·56 to
4431·70)
–4·46
(–10·71 to 4·48)
–25·91
(–30·69 to –19·11)*
·· Stomach cancer 86·47
(50·10 to
134·64)
78·50
(45·61 to
124·13)
–9·21
(–15·82 to –2·65)*
–30·05
(–34·90 to –25·17)*
1985·62
(1151·90 to
3061·29)
1668·26
(961·69 to
2631·19)
–15·98
(–23·04 to
–8·83)*
–34·01
(–39·20 to –28·67)*
·· Colon and rectum
cancer
46·29
(32·90 to
59·71)
49·01
(33·90 to
64·52)
5·88
(–0·09 to 11·90)
–19·96
(–24·42 to –15·55)*
973·58
(681·64 to
1272·87)
963·80
(667·18 to
1291·65)
–1·00
(–7·15 to 5·34)
–23·19
(–27·69 to –18·37)*
·· Liver cancer due to
hepatitis B
41·56
(18·21 to
77·66)
43·39
(19·66 to
81·96)
4·41
(–6·80 to 17·06)
–17·17
(–24·98 to –8·46)*
1222·27
(522·08 to
2265·72)
1164·10
(521·30 to
2235·84)
–4·76
(–17·66 to 10·74)
–22·84
(–32·30 to –11·64)*
·· Liver cancer due to
hepatitis C
19·24
(10·53 to
28·42)
22·01
(12·01 to
32·79)
14·35
(7·81 to 21·02)*
–12·81
(–17·64 to –8·10)*
414·97
(225·94 to 628·23)
448·80
(238·88 to 687·22)
8·15
(0·75 to 16·28)*
–16·20
(–21·61 to –10·61)*
·· Liver cancer due to
alcohol use
14·69
(8·71 to 21·74)
16·71
(9·61 to 25·36)
13·74
(5·31 to 21·66)*
–12·48
(–18·79 to –6·56)*
343·61
(201·26 to
506·60)
377·43
(217·22 to 566·00)
9·84
(0·81 to 18·67)*
–14·44
(–20·97 to –7·97)*
·· Liver cancer due to
other causes
24·71
(11·36 to
45·81)
26·42
(12·15 to
49·30)
6·90
(–4·05 to 18·94)
–15·62
(–23·12 to –7·34)*
683·22
(286·75 to
1300·01)
662·38
(294·93 to
1262·06)
–3·05
(–15·91 to 13·35)
–21·80
(–31·15 to –10·21)*
·· Pancreatic cancer 61·47
(49·77 to
74·37)
70·90
(56·11 to
87·71)
15·34
(9·71 to 21·46)*
–12·20
(–16·27 to –7·87)*
1315·34
(1059·48 to
1601·29)
1431·89
(1125·82 to
1797·72)
8·86
(2·52 to 15·73)*
–15·66
(–20·48 to –10·57)*
·· Larynx cancer 60·04
(50·50 to
68·78)
64·92
(53·57 to
76·19)
8·14
(2·92 to 13·33)*
–17·06
(–21·01 to –13·10)*
1524·37
(1284·41 to
1751·12)
1596·46
(1320·70 to
1877·67)
4·73
(–0·81 to 10·00)
–18·86
(–23·02 to –14·71)*
·· Tracheal, bronchus,
and lung cancer
1014·39
(875·09 to
1123·75)
1144·75
(973·82 to
1299·87)
12·85
(7·75 to 17·44)*
–13·53
(–17·47 to –10·01)*
22 094·05
(18 775·21 to
24 684·60)
23 701·45
(19 814·76 to
27 245·91)
7·28
(1·69 to 12·27)*
–16·85
(–21·09 to –13·12)*
·· Breast cancer 16·88
(5·04 to 30·02)
17·91
(5·25 to 31·90)
6·11
(–0·34 to 13·11)
–18·14
(–22·76 to –13·09)*
457·80
(129·80 to 835·33)
452·41
(126·69 to 827·14)
–1·18
(–8·24 to 6·76)
–21·43
(–26·56 to –15·69)*
·· Cervical cancer 11·03
(3·91 to 19·39)
10·85
(3·81 to 18·98)
–1·66
(–10·26 to 7·78)
–22·54
(–28·68 to –15·43)*
331·91
(114·69 to 595·93)
306·32
(105·80 to 541·94)
–7·71
(–17·35 to 2·77)
–25·19
(–32·55 to –16·79)*
·· Prostate cancer 15·29
(11·00 to
19·90)
16·68
(11·72 to
22·10)
9·09
(2·16 to 18·00)*
–19·29
(–24·05 to –12·77)*
257·95
(186·43 to 331·39)
268·27
(190·32 to 355·74)
4·00
(–3·76 to 12·92)
–21·25
(–26·88 to –14·50)*
·· Kidney cancer 20·10
(13·46 to
26·02)
22·07
(14·53 to
29·44)
9·79
(1·71 to 18·14)*
–16·07
(–21·91 to –9·92)*
464·77
(311·24 to 604·80)
480·06
(316·13 to 639·24)
3·29
(–5·41 to 12·59)
–19·84
(–26·34 to –12·84)*
·· Bladder cancer 44·33
(33·18 to
55·10)
49·84
(36·98 to
63·01)
12·42
(6·43 to 18·18)*
–15·76
(–20·05 to –11·55)*
820·50
(616·52 to
1016·68)
867·04
(639·82 to
1098·93)
5·67
(–0·75 to 11·94)
–19·01
(–23·71 to –14·36)*
·· Acute lymphoid
leukaemia
2·45
(1·19 to 3·88)
2·65
(1·26 to 4·39)
8·51
(–1·79 to 18·19)
–14·18
(–21·95 to –6·75)*
74·37
(35·76 to 121·34)
77·25
(35·75 to 131·34)
3·87
(–8·05 to 15·59)
–16·04
(–25·20 to –6·72)*
·· Chronic lymphoid
leukaemia
4·13
(2·06 to 6·30)
4·32
(2·09 to 6·76)
4·69
(–3·63 to 13·22)
–21·24
(–27·22 to –15·09)*
81·77
(41·39 to 125·41)
81·18
(39·93 to 126·38)
–0·73
(–9·78 to 8·35)
–23·72
(–30·52 to –17·08)*
·· Acute myeloid
leukaemia
7·27
(3·54 to 11·03)
8·00
(3·82 to 12·46)
10·07
(2·74 to 16·47)*
–14·79
(–20·27 to –10·13)*
174·17
(88·65 to 265·91)
182·46
(89·93 to 285·57)
4·76
(–3·79 to 12·31)
–17·20
(–23·71 to –11·49)*
(Table 4 continues on next page)
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1385
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Chronic myeloid
leukaemia
2·27
(1·10 to 3·48)
1·92
(0·89 to 3·02)
–15·62
(–22·48 to
–8·88)*
–34·65
(–39·76 to
–29·68)*
56·81
(27·53 to 88·46)
45·30
(21·19 to 72·34)
–20·26
(–27·97 to
–13·34)*
–36·45
(–42·56 to –30·76)*
·· Other leukaemia 8·73
(4·12 to 14·42)
8·74
(4·11 to 14·48)
0·05
(–8·87 to 8·12)
–21·85
(–28·03 to –16·79)*
220·47
(100·95 to 372·96)
198·49
(92·02 to 332·58)
–9·97
(–21·62 to 1·39)
–27·29
(–35·24 to –19·78)*
·· Ischaemic heart
disease
1346·04
(1125·86 to
1572·65)
1391·74
(1144·96 to
1649·56)
3·40
(–0·14 to 7·31)
–20·16
(–22·96 to –17·24)*
36 051·24
(30 135·29 to
41 836·78)
36 302·60
(29 797·02 to
42 911·24)
0·70
(–2·92 to 4·82)
–20·54
(–23·37 to –17·43)*
·· Ischaemic stroke 350·47
(292·23 to
407·20)
347·05
(290·43 to
408·73)
–0·98
(–5·13 to 3·31)
–24·72
(–27·99 to –21·50)*
8972·51
(7487·52 to
10 550·77)
9235·11
(7655·96 to
10 990·85)
2·93
(–1·37 to 6·94)
–20·73
(–24·06 to –17·62)*
·· Haemorrhagic stroke 574·87
(485·95 to
664·83)
535·26
(448·47 to
627·04)
–6·89
(–10·16 to
–3·66)*
–27·91
(–30·41 to –25·36)*
16 024·57
(13 595·74 to
18 501·64)
14 873·84
(12 549·42 to
17 354·01)
–7·18
(–10·27 to
–4·08)*
–26·69
(–29·11 to –24·28)*
·· Hypertensive heart
disease
92·58
(69·07 to
115·52)
104·36
(75·52 to
129·77)
12·72
(–0·81 to 23·61)
–13·06
(–23·84 to –4·65)*
2418·95
(1818·67 to
3023·75)
2611·14
(1927·81 to
3211·98)
7·95
(–2·72 to 17·76)
–14·97
(–23·65 to –7·28)*
·· Atrial fibrillation and
flutter
11·78
(8·34 to 15·89)
14·23
(10·02 to
19·31)
20·80
(16·34 to 24·92)*
–11·40
(–14·55 to –8·44)*
616·54
(429·29 to
846·49)
710·44
(488·75 to 984·65)
15·23
(12·91 to 17·40)*
–10·95
(–12·66 to –9·34)*
·· Aortic aneurysm 22·06
(17·20 to
26·40)
22·71
(17·69 to
27·64)
2·92
(–2·08 to 9·42)
–20·66
(–24·43 to –15·86)*
554·61
(435·81 to 658·02)
560·44
(442·97 to 678·22)
1·05
(–4·19 to 8·10)
–20·47
(–24·50 to –15·10)*
·· Peripheral vascular
disease
4·59
(3·26 to 5·98)
5·12
(3·60 to 6·91)
11·65
(–0·50 to 26·59)
–15·97
(–24·80 to –5·05)*
148·14
(100·88 to 203·55)
163·17
(110·40 to 225·34)
10·14
(0·93 to 20·74)*
–16·24
(–22·89 to –8·54)*
·· Other cardiovascular
and circulatory
diseases
53·33
(40·77 to
70·74)
55·64
(41·83 to
73·98)
4·33
(–0·04 to 9·45)
–19·15
(–22·65 to –15·27)*
1998·90
(1531·92 to
2560·68)
2084·86
(1574·17 to
2694·49)
4·30
(0·52 to 8·39)*
–16·90
(–19·76 to –13·76)*
·· Chronic obstructive
pulmonary disease
1190·52
(889·10 to
1462·49)
1253·30
(989·51 to
1520·42)
5·27
(–0·57 to 14·11)
–22·12
(–26·38 to –15·51)*
23 659·75
(18 550·88 to
28 461·63)
25 038·91
(20 395·51 to
29 918·00)
5·83
(–0·06 to 13·85)
–19·26
(–23·63 to –13·01)*
·· Asthma 65·36
(44·88 to
91·56)
56·81
(39·28 to
78·57)
–13·08
(–20·43 to
–5·24)*
–32·94
(–38·71 to –26·92)*
2444·85
(1802·94 to
3242·52)
2291·51
(1694·45 to
2999·22)
–6·27
(–12·71 to –0·11)*
–25·66
(–30·95 to –20·73)*
·· Other chronic
respiratory diseases
3·07
(2·06 to 4·10)
3·77
(2·53 to 5·06)
22·95
(13·58 to 33·14)*
–5·85
(–12·82 to 1·88)
103·01
(73·48 to 140·16)
126·04
(88·32 to 176·23)
22·36
(12·41 to 32·01)*
–1·70
(–9·84 to 6·67)
·· Peptic ulcer disease 43·27
(31·46 to
55·50)
36·14
(26·26 to
47·09)
–16·48
(–21·32 to
–12·20)*
–35·23
(–39·05 to –31·93)*
1202·60
(882·23 to
1539·63)
1008·27
(740·13 to
1308·30)
–16·16
(–20·61 to
–11·83)*
–33·35
(–36·87 to –30·04)*
·· Gallbladder and biliary
diseases
2·22
(1·49 to 2·91)
2·32
(1·55 to 3·09)
4·66
(–1·51 to 10·83)
–20·29
(–25·11 to –15·43)*
54·26
(36·94 to 71·95)
55·54
(36·83 to 74·21)
2·36
(–2·84 to 7·48)
–19·57
(–23·79 to –15·50)*
·· Alzheimer’s disease
and other dementias
67·57
(33·10 to
106·19)
82·80
(39·50 to
132·02)
22·55
(15·91 to 27·45)*
–11·65
(–17·20 to –7·77)*
1062·73
(491·18 to
1670·22)
1256·05
(555·38 to
1982·80)
18·19
(12·39 to 22·10)*
–11·38
(–16·39 to –8·22)*
·· Parkinson’s disease –20·15
(–26·54 to
–14·37)
–23·16
(–30·39 to
–16·44)
14·93
(10·72 to 19·10)*
–12·99
(–16·05 to –9·98)*
–403·98
(–525·52 to
–283·21)
–461·19
(–599·84 to
–324·73)
14·16
(10·52 to 17·80)*
–12·22
(–14·86 to –9·59)*
·· Multiple sclerosis 1·70
(1·11 to 2·36)
1·68
(1·09 to 2·33)
–0·89
(–9·77 to 5·59)
–21·16
(–28·03 to –16·17)*
98·79
(62·61 to 138·27)
99·08
(62·16 to 140·65)
0·29
(–5·43 to 4·75)
–18·13
(–22·71 to –14·62)*
·· Diabetes mellitus 56·47
(17·07 to
99·11)
66·30
(19·12 to
117·72)
17·40
(11·86 to 21·43)*
–9·78
(–14·35 to –6·53)*
2881·41
(847·72 to
5096·99)
3192·65
(911·95 to
5662·02)
10·80
(7·07 to 13·63)*
–12·37
(–15·58 to –10·13)*
·· Rheumatoid arthritis 1·45
(0·58 to 2·38)
1·37
(0·54 to 2·27)
–6·01
(–10·76 to –0·77)*
–28·01
(–31·59 to –24·03)*
224·49
(88·79 to 401·57)
241·06
(94·19 to 434·89)
7·38
(4·34 to 9·93)*
–14·89
(–17·34 to –12·89)*
(Table 4 continues on next page)
Global Health Metrics
1386
www.thelancet.com Vol 390 September 16, 2017
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Low back pain ·· ·· ·· ·· 2459·39
(1047·30 to
4016·91)
2567·74
(1082·41 to
4200·46)
4·41
(1·74 to 7·02)*
–14·98
(–17·00 to –13·33)*
·· Cataract ·· ·· ·· ·· 404·92
(261·96 to
595·50)
457·74
(295·38 to 678·21)
13·05
(9·89 to 16·35)*
–12·81
(–15·31 to –10·33)*
·· Macular degeneration ·· ·· ·· ·· 35·10
(10·74 to 63·30)
43·59
(13·27 to 79·98)
24·18
(19·16 to
28·49)*
–6·46
(–10·16 to –3·27)*
·· Pedestrian road
injuries
4·26
(3·12 to 5·55)
4·27
(3·07 to 5·52)
0·19
(–6·60 to 4·72)
–22·32
(–27·66 to
–18·89)*
192·70
(132·72 to 266·50)
203·56
(140·13 to 285·42)
5·64
(0·50 to 9·31)*
–15·61
(–19·72 to –12·60)*
·· Cyclist road injuries 0·66
(0·46 to 0·87)
0·65
(0·46 to 0·88)
–0·71
(–7·23 to 8·03)
–22·08
(–27·08 to –15·18)*
88·95
(56·29 to 134·78)
101·22
(63·20 to 155·51)
13·79
(10·68 to
16·34)*
–8·26
(–10·82 to –6·20)*
·· Motorcyclist road
injuries
1·42
(0·98 to 1·92)
1·41
(0·95 to 1·89)
–0·73
(–7·31 to 5·07)
–20·26
(–25·42 to –15·61)*
146·73
(93·93 to 217·75)
154·92
(99·00 to 232·89)
5·58
(2·29 to 8·25)*
–13·79
(–16·50 to –11·69)*
·· Motor vehicle road
injuries
3·42
(2·40 to 4·58)
3·27
(2·30 to 4·35)
–4·43
(–9·10 to 3·95)
–24·68
(–28·27 to –18·07)*
220·30
(149·90 to 312·87)
222·17
(149·26 to 318·67)
0·85
(–2·50 to 5·15)
–18·53
(–21·35 to –15·12)*
·· Other road injuries 0·11
(0·08 to 0·15)
0·11
(0·08 to 0·16)
1·37
(–8·56 to 15·72)
–22·45
(–30·44 to –11·01)*
24·54
(14·89 to 38·62)
34·33
(20·60 to 54·65)
39·88
(36·47 to 42·74)*
12·99
(9·94 to 15·33)*
·· Other transport
injuries
0·96
(0·71 to 1·24)
0·95
(0·69 to 1·23)
–1·50
(–7·54 to 7·47)
–22·86
(–27·55 to –15·90)*
98·02
(64·22 to 142·79)
97·82
(63·60 to 143·42)
–0·21
(–3·18 to 2·92)
–19·19
(–21·61 to –16·77)*
·· Falls 14·00
(9·94 to 18·05)
15·72
(11·19 to
20·28)
12·30
(4·47 to 19·50)*
–17·04
(–22·83 to –11·46)*
834·32
(556·29 to
1207·21)
927·35
(615·93 to
1342·67)
11·15
(8·56 to 13·31)*
–12·04
(–14·43 to –10·17)*
·· Other exposure to
mechanical forces
0·72
(0·50 to 0·93)
0·68
(0·45 to 0·90)
–4·72
(–16·10 to 1·04)
–25·28
(–34·05 to –20·84)*
143·72
(85·67 to 229·57)
159·21
(93·69 to 258·36)
10·78
(8·20 to 12·76)*
–10·26
(–12·38 to –8·55)*
·· Non-venomous
animal contact
0·07
(0·05 to 0·09)
0·06
(0·04 to 0·08)
–14·75
(–23·01 to
–4·84)*
–33·85
(–40·10 to –26·00)*
7·11
(4·27 to 11·66)
6·44
(3·79 to 10·64)
–9·45
(–12·88 to
–6·48)*
–26·94
(–29·78 to –24·44)*
·· Assault by other
means
0·49
(0·30 to 0·70)
0·41
(0·27 to 0·61)
–15·12
(–27·68 to 5·98)
–32·36
(–42·05 to –16·11)*
79·58
(48·44 to 125·25)
77·53
(46·64 to 123·06)
–2·58
(–6·78 to 1·58)
–20·52
(–23·97 to –17·23)*
·· Forces of nature,
conflict and terrorism,
and state actor
violence
0·05
(0·03 to 0·07)
0·02
(0·01 to 0·03)
–61·88
(–74·78 to
–49·82)*
–68·62
(–79·42 to –58·85)*
7·32
(2·99 to 15·95)
8·92
(2·89 to 21·37)
21·82
(–10·52 to 35·55)
1·45
(–25·71 to 12·96)
3Smokeless tobacco: all
causes
39·05
(32·22 to
45·82)
48·24
(39·35 to
56·91)
23·52
(14·92 to 31·94)*
–4·58
(–11·36 to 1·82)
1063·08
(872·62 to
1258·43)
1262·17
(1016·17 to
1498·73)
18·73
(10·62 to
26·38)*
–6·49
(–12·92 to –0·38)*
·· Lip and oral cavity
cancer
25·14
(19·77 to
30·36)
32·14
(24·93 to
39·24)
27·85
(17·75 to 37·18)*
–1·25
(–9·09 to 6·14)
697·47
(540·47 to
849·10)
854·15
(658·17 to
1052·56)
22·46
(12·41 to 31·49)*
–3·28
(–11·05 to 3·85)
·· Oesophageal cancer 13·91
(10·12 to
17·58)
16·10
(11·51 to
20·45)
15·71
(8·62 to 23·45)*
–10·58
(–16·11 to –4·51)*
365·61
(264·21 to
464·10)
408·02
(289·36 to 522·28)
11·60
(4·38 to 19·33)*
–12·54
(–17·96 to –6·30)*
3 Second-hand smoke: all
causes
848·70
(674·54 to
1044·47)
883·93
(715·08 to
1085·10)
4·15
(0·25 to 8·62)*
–18·91
(–21·61 to –16·21)*
26 546·21
(19 817·27 to
34 362·69)
23 761·45
(18 439·15 to
29 543·70)
–10·49
(–16·84 to
–2·78)*
–24·59
(–28·47 to –20·00)*
·· Lower respiratory
infections
178·55
(92·47 to
275·01)
138·56
(72·78 to
213·49)
–22·40
(–26·76 to
–18·06)*
–31·09
(–34·53 to –27·75)*
10 839·93
(5534·74 to
16 883·74)
6407·40
(3311·02 to
10 061·54)
–40·89
(–44·86 to
–36·99)*
–43·39
(–47·17 to –39·63)*
·· Otitis media 0·09
(0·05 to 0·14)
0·04
(0·02 to 0·07)
–56·93
(–72·65 to
–33·56)*
–58·53
(–73·68 to –36·05)*
219·81
(122·39 to 348·54)
205·95
(110·96 to 328·12)
–6·31
(–9·28 to –3·89)*
–10·69
(–13·55 to –8·35)*
(Table 4 continues on next page)
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1387
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Tracheal, bronchus,
and lung cancer
22·02
(10·49 to
38·89)
27·35
(13·15 to
48·05)
24·20
(18·36 to 27·75)*
–4·80
(–8·93 to –2·34)*
508·53
(246·20 to
907·38)
615·93
(298·94 to
1093·44)
21·12
(14·10 to 25·46)*
–5·79
(–10·86 to –2·78)*
·· Breast cancer 9·27
(2·20 to 15·98)
10·30
(2·57 to 17·72)
11·10
(1·92 to 20·34)*
–13·39
(–20·31 to –6·37)*
287·48
(68·13 to 495·07)
313·04
(77·84 to 535·75)
8·89
(–0·35 to 19·03)
–12·87
(–20·20 to –4·98)*
·· Ischaemic heart
disease
280·21
(219·44 to
344·16)
327·35
(257·71 to
402·31)
16·82
(12·22 to 21·36)*
–12·04
(–14·93 to –9·10)*
5727·36
(4517·00 to
7044·72)
6503·01
(5174·29 to
7952·09)
13·54
(9·44 to 17·40)*
–11·86
(–14·86 to –9·09)*
·· Ischaemic stroke 73·09
(53·89 to
96·52)
75·15
(54·65 to
99·65)
2·82
(–2·13 to 8·25)
–23·12
(–26·26 to –19·82)*
1420·64
(1071·30 to
1818·42)
1493·54
(1101·46 to
1909·51)
5·13
(0·25 to 10·05)*
–19·63
(–22·90 to –16·38)*
·· Haemorrhagic stroke 95·38
(73·00 to
120·43)
90·24
(68·61 to
114·07)
–5·39
(–8·89 to –1·60)*
–27·71
(–30·19 to –25·26)*
2278·60
(1754·81 to
2848·13)
2144·62
(1620·43 to
2692·11)
–5·88
(–9·72 to –2·17)*
–26·29
(–28·90 to –23·84)*
·· Chronic obstructive
pulmonary disease
117·45
(54·19 to
204·35)
119·62
(57·14 to
206·04)
1·85
(–7·05 to 12·62)
–23·85
(–30·75 to –15·70)*
2373·48
(1129·66 to
4027·33)
2496·53
(1213·18 to
4231·63)
5·18
(–3·18 to 15·55)
–19·35
(–26·03 to –11·31)*
·· Diabetes mellitus 72·64
(27·70 to
111·58)
95·33
(36·10 to
146·43)
31·23
(27·87 to 34·52)*
–0·97
(–3·45 to 1·50)
2890·38
(1067·29 to
4591·29)
3581·43
(1319·94 to
5698·92)
23·91
(21·66 to 26·22)*
–3·11
(–4·92 to –1·38)*
2Alcohol and drug use:
all causes
3001·71
(2622·51 to
3396·75)
3257·20
(2820·87 to
3733·04)
8·51
(3·51 to 14·09)*
–13·22
(–17·30 to –8·58)*
125 134·50
(113 568·68 to
136 796·97)
130 597·46
(117 360·41 to
144 336·48)
4·37
(0·66 to 8·63)*
–13·06
(–16·29 to –9·26)*
3 Alcohol use: all causes 2605·72
(2228·59 to
3011·17)
2814·64
(2371·24 to
3292·68)
8·02
(2·40 to 14·06)*
–14·15
(–18·64 to –9·18)*
96 193·70
(86 180·20 to
106 743·49)
99 204·89
(88 310·44 to
111 168·34)
3·13
(–1·28 to 8·17)
–15·00
(–18·77 to –10·84)*
·· Drug-susceptible
tuberculosis
304·97
(234·07 to
375·65)
253·07
(191·46 to
317·27)
–17·02
(–24·00 to
–9·94)*
–33·11
(–38·92 to –27·55)*
11 260·98
(8804·92 to
13 625·09)
9208·69
(7176·84 to
11 319·15)
–18·22
(–24·75 to
–11·48)*
–31·92
(–37·40 to –26·22)*
·· Multidrug-resistant
tuberculosis without
extensive drug
resistance
33·42
(24·76 to
43·26)
23·46
(17·16 to
30·39)
–29·81
(–40·10 to
–19·57)*
–42·99
(–51·14 to –34·77)*
1217·72
(916·99 to
1544·74)
820·14
(608·48 to
1048·27)
–32·65
(–41·96 to
–23·08)*
–43·80
(–51·57 to –35·73)*
·· Extensively drug-
resistant tuberculosis
2·44
(1·87 to 3·04)
3·98
(2·92 to 5·15)
63·02
(31·53 to 98·56)*
33·78
(8·90 to 62·02)*
91·46
(70·76 to 113·12)
140·71
(104·83 to 181·23)
53·84
(23·67 to
88·60)*
28·98
(3·78 to 57·67)*
·· Lower respiratory
infections
97·10
(41·59 to
144·40)
113·58
(47·40 to
175·26)
16·97
(4·11 to 29·42)*
–9·81
(–18·30 to 1·14)
2531·19
(1331·95 to
3554·75)
2699·40
(1437·61 to
3944·68)
6·65
(–5·58 to 18·58)
–13·62
(–23·15 to –3·86)*
·· Lip and oral cavity
cancer
49·44
(41·03 to
57·44)
66·24
(54·69 to
77·03)
33·98
(27·04 to 41·61)*
3·26
(–1·91 to 8·84)
1375·21
(1162·65 to
1574·04)
1769·38
(1482·47 to
2028·79)
28·66
(21·40 to 36·63)*
1·34
(–4·08 to 7·41)
·· Nasopharynx cancer 24·19
(22·28 to
26·03)
28·38
(25·63 to
31·15)
17·33
(7·75 to 26·54)*
–7·62
(–15·07 to –0·25)*
758·75
(706·19 to 809·31)
843·69
(766·09 to 922·53)
11·19
(1·77 to 20·77)*
–10·61
(–18·09 to –3·03)*
·· Other pharynx cancer 33·86
(27·61 to
39·91)
46·29
(37·28 to
55·41)
36·70
(24·71 to 47·23)*
5·77
(–3·34 to 13·91)
970·39
(799·90 to
1139·01)
1285·14
(1045·87 to
1530·24)
32·43
(20·29 to
42·99)*
3·89
(–5·37 to 12·21)
·· Oesophageal cancer 116·52
(92·63 to
140·34)
130·55
(104·87 to
157·78)
12·05
(6·25 to 19·44)*
–14·03
(–18·42 to –8·23)*
2838·08
(2305·86 to
3387·67)
3052·59
(2475·16 to
3672·35)
7·56
(1·96 to 14·53)*
–16·61
(–20·92 to –11·10)*
·· Colon and rectum
cancer
97·08
(77·64 to
116·68)
116·81
(92·14 to
141·81)
20·33
(13·27 to 28·53)*
–8·92
(–14·35 to –2·95)*
2172·68
(1757·07 to
2580·89)
2544·90
(2029·33 to
3047·11)
17·13
(10·21 to 25·62)*
–9·24
(–14·62 to –2·83)*
·· Liver cancer due to
alcohol use
99·05
(83·17 to
116·11)
129·18
(109·73 to
150·41)
30·41
(22·61 to 40·28)*
–0·01
(–6·03 to 7·33)
2281·36
(1911·37 to
2709·93)
2924·48
(2462·18 to
3399·46)
28·19
(20·40 to
38·54)*
–0·56
(–6·67 to 7·20)
(Table 4 continues on next page)
Global Health Metrics
1388
www.thelancet.com Vol 390 September 16, 2017
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Larynx cancer 26·71
(17·87 to
34·27)
29·80
(19·32 to
38·59)
11·58
(5·49 to 18·14)*
–14·18
(–18·81 to –9·19)*
709·81
(474·31 to 897·37)
764·38
(497·31 to 976·37)
7·69
(1·59 to 14·01)*
–16·32
(–20·98 to –11·38)*
·· Breast cancer 52·75
(43·43 to
63·43)
59·24
(47·43 to
72·53)
12·31
(5·75 to 20·05)*
–14·02
(–18·98 to –8·03)*
1443·76
(1178·62 to
1753·45)
1565·91
(1245·66 to
1961·00)
8·46
(1·75 to 16·23)*
–14·40
(–19·77 to –8·23)*
·· Ischaemic heart
disease
–30·76
(–211·14 to
168·37)
–24·23
(–241·48 to
206·18)
–21·24
(–371·35 to
331·24)
–41·97
(–257·08 to
194·50)
767·37
(–2850·37 to
4692·06)
1084·03
(–3136·85 to
5556·97)
41·27
(–352·40 to
255·58)
35·77
(–286·02 to 364·77)
·· Ischaemic stroke 106·64
(44·65 to
173·13)
124·22
(55·72 to
200·79)
16·49
(0·38 to 42·96)*
–11·74
(–23·99 to 13·60)
2508·68
(1269·21 to
3765·46)
2930·95
(1519·90 to
4397·58)
16·83
(3·14 to 35·51)*
–10·07
(–20·90 to 5·69)
·· Haemorrhagic stroke 418·95
(317·05 to
526·94)
457·66
(345·46 to
572·96)
9·24
(3·10 to 17·61)*
–16·31
(–21·14 to –9·54)*
10 365·31
(7962·87 to
12 814·02)
10 957·49
(8335·55 to
13 516·75)
5·71
(0·00 to 13·30)
–16·87
(–21·50 to –10·55)*
·· Hypertensive heart
disease
96·07
(66·61 to
126·07)
131·89
(86·83 to
176·95)
37·28
(18·72 to 50·94)*
1·92
(–11·24 to 12·00)
1987·17
(1428·04 to
2584·36)
2547·33
(1757·70 to
3394·79)
28·19
(13·24 to 39·90)*
–0·64
(–12·18 to 8·58)
·· Alcoholic
cardiomyopathy
87·36
(72·97 to
97·29)
83·31
(67·17 to
102·89)
–4·64
(–21·27 to 17·01)
–24·04
(–36·79 to –7·53)*
2877·83
(2413·88 to
3220·47)
2590·34
(2055·10 to
3239·64)
–9·99
(–27·64 to 14·31)
–26·31
(–40·43 to –7·57)*
·· Atrial fibrillation and
flutter
17·55
(11·75 to
24·14)
25·02
(16·86 to
35·53)
42·50
(32·14 to 53·42)*
–0·75
(–7·75 to 7·02)
542·25
(373·15 to 751·73)
722·89
(496·23 to
1010·00)
33·31
(25·87 to 42·12)*
0·33
(–5·56 to 6·94)
·· Cirrhosis and other
chronic liver diseases
due to alcohol use
294·43
(271·56 to
321·29)
334·68
(306·28 to
371·66)
13·67
(8·76 to 19·55)*
–10·98
(–14·62 to –6·48)*
8874·48
(8108·96 to
9683·50)
9748·69
(8868·52 to
10 855·84)
9·85
(4·80 to 15·93)*
–11·76
(–15·67 to –6·87)*
·· Pancreatitis 31·98
(25·54 to
39·43)
37·26
(28·83 to
47·01)
16·51
(5·09 to 29·89)*
–6·79
(–15·73 to 3·63)
1075·26
(892·23 to
1306·25)
1196·59
(955·03 to
1487·13)
11·28
(–1·10 to 26·14)
–8·13
(–18·00 to 3·87)
·· Epilepsy 20·88
(16·15 to
25·49)
22·02
(16·75 to
27·46)
5·48
(–0·54 to 13·84)
–11·53
(–16·52 to –4·66)*
1810·40
(1311·06 to
2355·11)
1903·17
(1362·83 to
2511·36)
5·12
(–3·66 to 14·38)
–9·55
(–17·15 to –1·60)*
·· Alcohol use disorders 171·96
(150·67 to
183·41)
173·82
(145·45 to
190·83)
1·08
(–7·15 to 10·46)
–17·62
(–24·31 to –10·21)*
15 555·03
(12 602·23 to
19 092·15)
16 237·15
(12 996·82 to
19 945·76)
4·39
(0·39 to 8·57)*
–10·98
(–14·60 to –7·48)*
·· Diabetes mellitus 6·96
(–20·42 to
34·90)
10·11
(–24·38 to
45·22)
45·21
(–231·27 to
359·63)
28·01
(–228·39 to
231·53)
529·10
(–727·58 to
1861·28)
712·23
(–881·16 to
2351·99)
34·61
(–144·45 to
218·89)
9·02
(–194·30 to 145·24)
·· Pedestrian road
injuries
64·98
(37·59 to
97·10)
66·16
(38·29 to
99·80)
1·80
(–6·33 to 10·56)
–15·65
(–22·20 to –8·54)*
2844·80
(1646·88 to
4243·67)
2791·74
(1612·72 to
4240·54)
–1·87
(–9·54 to 6·31)
–16·21
(–22·63 to –9·41)*
·· Cyclist road injuries 9·71
(5·62 to 14·58)
10·09
(5·76 to 15·01)
3·95
(–4·99 to 14·63)
–14·35
(–21·62 to –5·98)*
595·27
(342·91 to
899·58)
647·09
(367·80 to 990·06)
8·71
(0·54 to 17·14)*
–8·96
(–15·42 to –1·95)*
·· Motorcyclist road
injuries
32·74
(18·48 to
49·86)
32·45
(18·55 to
49·44)
–0·89
(–8·65 to 8·24)
–13·90
(–20·39 to –6·20)*
1872·33
(1066·29 to
2824·07)
1857·69
(1065·70 to
2828·52)
–0·78
(–8·22 to 7·77)
–13·07
(–19·49 to –5·77)*
·· Motor vehicle road
injuries
62·76
(36·61 to
92·36)
60·04
(34·52 to
88·42)
–4·34
(–10·50 to 3·86)
–18·19
(–23·45 to –11·39)*
3304·16
(1937·56 to
4824·23)
3120·49
(1833·43 to
4562·31)
–5·56
(–11·62 to 2·27)
–17·65
(–22·82 to –11·04)*
·· Other road injuries 1·65
(0·97 to 2·49)
1·61
(0·94 to 2·45)
–2·42
(–9·91 to 8·37)
–18·54
(–24·61 to –9·67)*
131·93
(76·34 to 202·62)
160·16
(89·43 to 251·23)
21·40
(11·19 to 32·12)*
2·40
(–5·75 to 10·70)
·· Other transport
injuries
11·79
(6·92 to 17·27)
11·88
(6·96 to 17·74)
0·76
(–6·67 to 10·60)
–15·17
(–21·42 to –7·08)*
696·53
(403·45 to
1034·74)
717·10
(414·64 to
1080·93)
2·95
(–3·94 to 11·46)
–11·88
(–17·72 to –5·08)*
(Table 4 continues on next page)
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1389
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Drowning 16·47
(7·29 to 27·24)
15·16
(6·74 to 24·74)
–7·94
(–14·17 to 0·87)
–21·73
(–26·65 to –14·33)*
732·89
(326·96 to
1216·93)
632·25
(274·66 to
1023·81)
–13·73
(–20·25 to
–4·80)*
–24·55
(–29·67 to –17·49)*
·· Fire, heat, and hot
substances
7·88
(3·53 to 12·89)
7·03
(3·24 to 11·36)
–10·85
(–19·34 to
–0·76)*
–27·82
(–34·28 to –19·79)*
412·06
(184·41 to 675·60)
386·39
(172·68 to 635·92)
–6·23
(–14·00 to 2·92)
–21·30
(–27·76 to –13·73)*
·· Poisonings 3·67
(1·62 to 6·18)
3·36
(1·50 to 5·53)
–8·39
(–23·07 to 7·90)
–23·79
(–36·16 to –10·59)*
166·84
(74·30 to 278·34)
150·08
(67·35 to 244·42)
–10·05
(–22·18 to 4·15)
–23·08
(–33·23 to –11·32)*
·· Unintentional firearm
injuries
1·74
(0·76 to 2·96)
1·56
(0·70 to 2·66)
–10·07
(–17·83 to –2·15)*
–23·30
(–30·13 to –16·67)*
94·71
(41·83 to 158·27)
86·98
(38·37 to 146·51)
–8·16
(–15·61 to
–0·64)*
–19·85
(–26·43 to –13·45)*
·· Other unintentional
injuries
7·73
(3·29 to 12·94)
6·82
(2·88 to 11·15)
–11·82
(–20·38 to
–1·00)*
–24·85
(–31·85 to –16·19)*
576·20
(256·50 to
988·39)
568·51
(246·31 to 967·31)
–1·33
(–8·94 to 7·65)
–15·84
(–21·88 to –8·52)*
·· Self-harm by firearm 15·07
(8·50 to 21·95)
15·54
(8·69 to 22·61)
3·13
(–9·93 to 18·73)
–13·75
(–24·66 to –0·52)*
641·41
(369·21 to 940·23)
638·64
(368·84 to 936·40)
–0·43
(–12·27 to 14·69)
–14·04
(–24·48 to –0·99)*
·· Self-harm by other
specified means
145·40
(85·30 to
202·28)
145·14
(87·17 to
202·07)
–0·18
(–9·24 to 11·19)
–16·89
(–24·27 to –7·37)*
6077·14
(3594·27 to
8488·31)
5860·44
(3530·20 to
8127·86)
–3·57
(–12·54 to 8·28)
–17·50
(–25·19 to –7·78)*
·· Assault by firearm 27·84
(14·26 to
41·44)
28·44
(14·59 to
43·20)
2·15
(–5·82 to 10·65)
–9·49
(–16·80 to –2·40)*
1493·88
(775·66 to
2210·77)
1501·73
(766·64 to
2276·30)
0·53
(–7·60 to 8·69)
–9·64
(–17·10 to –2·33)*
·· Assault by sharp
object
18·56
(10·94 to
28·21)
15·99
(9·34 to 25·76)
–13·83
(–22·48 to –1·01)*
–25·17
(–32·85 to –13·74)*
975·52
(582·21 to
1474·15)
839·22
(492·92 to
1317·26)
–13·97
(–22·24 to
–2·48)*
–24·22
(–31·44 to –13·76)*
·· Assault by other
means
18·18
(10·48 to
27·32)
17·03
(9·98 to
26·30)
–6·33
(–19·70 to 12·35)
–20·14
(–31·48 to –4·27)*
1033·77
(611·33 to
1542·23)
996·09
(595·09 to
1505·66)
–3·64
(–15·24 to 10·31)
–16·82
(–26·73 to –4·29)*
3 Drug use: all causes 405·49
(376·08 to
438·09)
451·82
(420·40 to
486·77)
11·42
(6·47 to 17·02)*
–6·66
(–10·58 to –2·17)*
29 405·94
(25 497·29 to
33 535·95)
31 836·26
(27 445·88 to
36 580·02)
8·26
(5·09 to 11·72)*
–6·25
(–9·21 to –3·39)*
·· Drug-susceptible HIV/
AIDS - Tuberculosis
17·88
(11·55 to
26·51)
9·10
(5·87 to 13·66)
–49·13
(–53·01 to
–44·80)*
–56·62
(–59·99 to
–52·92)*
845·64
(545·53 to
1245·37)
452·57
(301·06 to 663·93)
–46·48
(–50·70 to
–41·82)*
–53·69
(–57·35 to –49·74)*
·· Multidrug-resistant
HIV/AIDS -
Tuberculosis without
extensive drug
resistance
2·54
(1·51 to 4·11)
1·20
(0·70 to 1·95)
–52·97
(–60·55 to
–44·20)*
–59·62
(–66·05 to
–52·09)*
118·88
(70·41 to 191·24)
56·61
(33·39 to 90·84)
–52·38
(–60·30 to
–43·58)*
–58·61
(–65·43 to –50·96)*
·· Extensively drug-
resistant HIV/AIDS -
Tuberculosis
0·20
(0·12 to 0·33)
0·29
(0·16 to 0·47)
44·03
(18·74 to 75·38)*
25·61
(3·55 to 52·91)*
9·74
(5·65 to 16·23)
13·91
(7·92 to 22·69)
42·89
(17·38 to 74·95)*
25·59
(3·29 to 53·36)*
·· HIV/AIDS resulting in
other diseases
74·37
(58·68 to
92·92)
53·21
(43·04 to
65·66)
–28·45
(–33·86 to
–21·66)*
–38·29
(–42·87 to –32·58)*
3651·47
(2871·87 to
4584·05)
2670·75
(2142·39 to
3303·74)
–26·86
(–31·94 to
–20·21)*
–36·29
(–40·83 to –30·65)*
·· Hepatitis B 0·32
(0·25 to 0·41)
0·31
(0·24 to 0·40)
–1·97
(–9·09 to 5·66)
–20·37
(–26·08 to –14·19)*
12·08
(9·29 to 15·29)
11·54
(8·88 to 14·71)
–4·47
(–12·19 to 3·69)
–20·80
(–27·31 to –14·08)*
·· Hepatitis C 0·42
(0·32 to 0·54)
0·48
(0·37 to 0·62)
14·60
(–1·60 to 33·24)
–6·68
(–19·61 to 8·34)
16·51
(12·58 to 21·05)
18·25
(13·84 to 23·78)
10·51
(–3·77 to 27·77)
–6·31
(–18·68 to 7·88)
·· Liver cancer due to
hepatitis B
1·52
(1·16 to 1·99)
2·56
(1·97 to 3·32)
68·29
(57·54 to 79·01)*
33·74
(25·83 to 41·46)*
49·42
(37·44 to 64·43)
78·10
(59·59 to 101·47)
58·03
(46·77 to
68·88)*
28·05
(19·75 to 36·41)*
·· Liver cancer due to
hepatitis C
37·35
(31·94 to
43·13)
62·46
(54·75 to
70·85)
67·23
(59·37 to 75·65)*
30·03
(23·93 to 36·56)*
998·00
(853·25 to
1148·69)
1558·54
(1357·79 to
1774·17)
56·17
(48·56 to
63·89)*
23·45
(17·44 to 29·28)*
(Table 4 continues on next page)
Global Health Metrics
1390
www.thelancet.com Vol 390 September 16, 2017
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Cirrhosis and other
chronic liver diseases
due to hepatitis B
1·54
(1·17 to 2·00)
2·44
(1·84 to 3·15)
58·98
(49·73 to 70·92)*
27·20
(20·24 to 36·57)*
53·65
(40·20 to 71·54)
82·78
(61·00 to 109·04)
54·31
(44·79 to
66·22)*
26·26
(18·84 to 35·57)*
·· Cirrhosis and other
chronic liver diseases
due to hepatitis C
106·76
(93·44 to
121·61)
138·75
(122·40 to
157·72)
29·96
(23·69 to 37·62)*
4·48
(–0·44 to 10·18)
3778·13
(3302·73 to
4345·35)
4702·15
(4121·52 to
5404·71)
24·46
(18·21 to 31·86)*
2·04
(–2·98 to 7·86)
·· Opioid use disorders 74·85
(60·54 to
81·38)
86·20
(72·66 to
94·65)
15·18
(2·17 to 30·71)*
–1·48
(–12·84 to 11·27)
12 811·42
(10 013·73 to
15 686·88)
14 781·97
(11 375·36 to
18 250·91)
15·38
(11·23 to 19·48)*
0·65
(–2·95 to 4·18)
·· Cocaine use disorders 8·24
(6·34 to 10·60)
8·80
(7·06 to 11·27)
6·82
(–1·13 to 16·93)
–10·56
(–17·12 to –1·95)*
1060·25
(779·98 to
1375·09)
1153·57
(846·82 to
1511·30)
8·80
(5·14 to 12·25)*
–4·91
(–8·34 to –1·70)*
·· Amphetamine use
disorders
4·47
(3·56 to 5·51)
5·22
(4·30 to 6·85)
16·67
(5·26 to 32·32)*
–1·15
(–10·71 to 12·39)
833·85
(566·91 to
1189·89)
881·40
(599·29 to
1242·60)
5·70
(1·83 to 10·23)*
–3·68
(–7·47 to 0·32)
·· Cannabis use
disorders
·· ·· ·· ·· 623·53
(388·95 to
904·77)
646·48
(400·64 to 944·87)
3·68
(1·23 to 5·98)*
–4·19
(–5·93 to –2·35)*
·· Other drug use
disorders
37·24
(33·94 to
44·21)
43·52
(39·36 to
52·88)
16·89
(6·86 to 25·63)*
–3·22
(–11·47 to 3·81)
2671·45
(2234·42 to
3175·76)
2921·41
(2424·28 to
3502·95)
9·36
(3·77 to 14·54)*
–4·27
(–9·28 to 0·23)
·· Self-harm by firearm 4·91
(3·32 to 7·06)
5·40
(3·70 to 7·78)
10·05
(2·38 to 17·43)*
–5·02
(–11·27 to 1·34)
240·29
(162·77 to 343·62)
257·23
(178·15 to 366·05)
7·05
(–0·19 to 14·74)
–5·28
(–11·59 to 1·34)
·· Self-harm by other
specified means
32·90
(22·24 to
46·44)
31·89
(21·41 to
46·12)
–3·07
(–9·44 to 4·69)
–16·34
(–21·84 to –9·54)*
1631·61
(1100·19 to
2333·27)
1549·00
(1040·55 to
2246·29)
–5·06
(–11·34 to 2·40)
–16·13
(–21·64 to –9·35)*
2 Dietary risks: all causes 9263·92
(7965·82 to
10 628·04)
10 301·54
(8795·36 to
11 912·63)
11·20
(8·54 to 13·87)*
–16·37
(–18·22 to
–14·45)*
210 958·84
(184 793·68 to
239 486·60)
229 065·54
(197 533·69 to
262 533·95)
8·58
(6·07 to 11·05)*
–15·52
(–17·39 to –13·64)*
3 Diet low in fruits: all
causes
2338·84
(1488·15 to
3345·70)
2361·20
(1446·10 to
3447·83)
0·96
(–4·64 to 5·30)
–22·86
(–26·87 to –19·70)*
61 173·38
(40 395·88 to
84 837·17)
60 982·39
(38 806·06 to
87 349·09)
–0·31
(–5·47 to 3·47)
–21·65
(–25·65 to –18·74)*
·· Lip and oral cavity
cancer
8·96
(0·00 to 20·23)
10·98
(0·00 to 25·14)
22·53
(13·43 to
148·39)*
–5·53
(–12·25 to 73·45)
247·05
(0·01 to 557·47)
293·30
(0·01 to 670·94)
18·72
(–7·12 to 144·18)
–6·38
(–20·76 to 61·24)
·· Nasopharynx cancer 3·69
(0·00 to 8·07)
3·64
(0·00 to 8·23)
–1·26
(–12·22 to 78·86)
–22·37
(–30·72 to 41·90)
114·17
(0·00 to 249·36)
107·70
(0·00 to 242·52)
–5·67
(–16·31 to 62·03)
–24·38
(–32·90 to 46·23)
·· Other pharynx cancer 6·21
(0·00 to 13·94)
7·56
(0·00 to 16·62)
21·82
(9·01 to 33·94)*
–5·76
(–15·57 to 3·43)
173·32
(0·01 to 391·06)
205·53
(0·02 to 451·27)
18·58
(5·42 to 30·03)*
–6·90
(–17·08 to 2·37)
·· Oesophageal cancer 81·00
(18·89 to
146·29)
73·59
(16·58 to
138·27)
–9·14
(–15·59 to –4·33)*
–30·50
(–35·29 to –26·84)*
1881·66
(442·78 to
3381·15)
1670·32
(375·53 to
3114·88)
–11·23
(–17·41 to
–6·63)*
–31·32
(–36·09 to –27·72)*
·· Larynx cancer 6·35
(0·00 to 13·79)
6·66
(0·00 to 14·69)
4·72
(–2·22 to 16·96)
–19·44
(–24·54 to –4·98)*
164·55
(0·01 to 358·39)
167·33
(0·01 to 367·29)
1·69
(–5·54 to 15·18)
–20·88
(–26·54 to –12·09)*
·· Tracheal, bronchus,
and lung cancer
147·27
(57·31 to
247·93)
159·12
(61·41 to
273·72)
8·04
(2·20 to 12·40)*
–16·97
(–21·38 to –13·70)*
3344·09
(1317·12 to
5603·00)
3448·19
(1332·88 to
5928·34)
3·11
(–2·71 to 7·59)
–19·76
(–24·22 to –16·39)*
·· Ischaemic heart
disease
892·64
(340·23 to
1554·99)
966·03
(348·99 to
1694·22)
8·22
(3·48 to 12·22)*
–18·16
(–21·67 to –15·34)*
20 722·33
(8039·33 to
35 506·28)
21 579·73
(7912·78 to
37 461·71)
4·14
(–0·42 to 7·96)
–18·07
(–21·50 to –15·19)*
·· Ischaemic stroke 425·78
(220·41 to
640·03)
409·14
(209·36 to
633·75)
–3·91
(–9·56 to 0·56)
–27·14
(–31·31 to –23·83)*
10 807·82
(5946·98 to
15 936·93)
10 769·53
(5810·94 to
16 420·82)
–0·35
(–6·08 to 3·97)
–23·01
(–27·39 to –19·74)*
·· Haemorrhagic stroke 669·49
(357·45 to
1028·72)
607·16
(318·94 to
943·73)
–9·31
(–13·97 to
–5·88)*
–29·80
(–33·33 to –27·21)*
18 624·46
(10 254·35 to
28 041·51)
16 953·42
(9075·66 to
25 909·90)
–8·97
(–13·26 to
–5·62)*
–27·94
(–31·29 to –25·40)*
(Table 4 continues on next page)
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1391
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Diabetes mellitus 97·44
(21·64 to
185·22)
117·31
(26·05 to
226·54)
20·39
(16·39 to 23·96)*
–7·47
(–10·40 to –4·80)*
5093·93
(1087·22 to
9951·32)
5787·36
(1191·75 to
11 487·93)
13·61
(9·08 to 16·83)*
–9·24
(–12·61 to –6·89)*
3 Diet low in vegetables:
all causes
1473·57
(722·80 to
2392·73)
1519·65
(717·79 to
2507·05)
3·13
(–2·27 to 7·50)
–21·93
(–25·70 to –18·91)*
35 185·99
(17 828·73 to
55 574·27)
35 489·09
(17 454·48 to
57 174·79)
0·86
(–3·84 to 4·70)
–20·82
(–24·32 to –17·95)*
·· Ischaemic heart
disease
1040·01
(407·37 to
1823·09)
1121·42
(431·72 to
2003·83)
7·83
(3·88 to 11·53)*
–18·99
(–21·88 to –16·21)*
23 371·67
(9330·74 to
40 305·40)
24 519·18
(9683·15 to
42 823·73)
4·91
(1·30 to 8·34)*
–17·79
(–20·53 to –15·19)*
·· Ischaemic stroke 166·07
(37·85 to
314·01)
158·64
(36·08 to
301·92)
–4·47
(–9·07 to 0·03)
–27·64
(–31·09 to –24·15)*
4235·33
(981·83 to
7798·25)
4145·55
(970·51 to
7782·19)
–2·12
(–6·67 to 2·32)
–24·26
(–27·67 to –20·70)*
·· Haemorrhagic stroke 267·49
(76·09 to
493·58)
239·58
(68·31 to
446·94)
–10·43
(–14·24 to –7·21)*
–30·59
(–33·61 to –28·07)*
7578·98
(2194·05 to
13 820·71)
6824·37
(1980·63 to
12 581·13)
–9·96
(–13·45 to
–6·82)*
–28·54
(–31·26 to –26·01)*
3 Diet low in legumes: all
causes
594·09
(262·56 to
988·59)
672·47
(288·67 to
1113·67)
13·19
(9·22 to 17·17)*
–15·35
(–18·30 to –12·39)*
13 316·19
(5884·98 to
22 031·26)
14 214·45
(6113·49 to
23 571·09)
6·75
(2·54 to 10·70)*
–16·05
(–19·24 to –12·96)*
·· Ischaemic heart
disease
594·09
(262·56 to
988·59)
672·47
(288·67 to
1113·67)
13·19
(9·22 to 17·17)*
–15·35
(–18·30 to –12·39)*
13 316·19
(5884·98 to
22 031·26)
14 214·45
(6113·49 to
23 571·09)
6·75
(2·54 to 10·70)*
–16·05
(–19·24 to –12·96)*
3 Diet low in whole grains:
all causes
2253·17
(1501·70 to
3156·55)
2498·69
(1662·92 to
3507·35)
10·90
(7·75 to 14·17)*
–16·06
(–18·29 to –13·58)*
57 301·21
(38 974·48 to
78 891·27)
62 596·11
(42 330·99 to
86 426·66)
9·24
(6·33 to 12·18)*
–14·31
(–16·55 to –12·03)*
·· Ischaemic heart
disease
1270·67
(755·80 to
1894·10)
1457·40
(862·42 to
2171·59)
14·70
(11·16 to 18·49)*
–14·16
(–16·69 to –11·37)*
27 241·14
(16 286·00 to
40 251·87)
29 799·09
(17 817·53 to
44 415·59)
9·39
(5·80 to 13·05)*
–14·50
(–17·27 to –11·67)*
·· Ischaemic stroke 333·45
(212·24 to
475·32)
348·95
(218·28 to
502·60)
4·65
(0·84 to 8·85)*
–20·70
(–23·49 to –17·61)*
8714·95
(5616·91 to
12 308·00)
9522·32
(6076·81 to
13 506·64)
9·26
(5·36 to 13·06)*
–15·41
(–18·38 to –12·37)*
·· Haemorrhagic stroke 500·51
(320·02 to
708·04)
505·42
(325·05 to
712·14)
0·98
(–1·36 to 3·60)
–21·76
(–23·58 to –19·73)*
13 827·83
(8981·48 to
19 377·52)
13 897·80
(9065·15 to
19 424·10)
0·51
(–1·88 to 3·19)
–20·32
(–22·21 to –18·22)*
·· Diabetes mellitus 148·55
(79·68 to
232·77)
186·92
(100·27 to
288·86)
25·83
(23·33 to 28·40)*
–3·98
(–5·83 to –2·10)*
7517·29
(3992·35 to
11 904·80)
9376·90
(4975·03 to
14 870·23)
24·74
(22·77 to 26·95)*
–1·18
(–2·79 to 0·60)
3 Diet low in nuts and
seeds: all causes
1879·32
(1192·82 to
2585·76)
2155·04
(1349·07 to
2965·36)
14·67
(11·69 to 17·85)*
–13·64
(–15·74 to –11·28)*
44 820·23
(29 633·64 to
60 259·65)
49 492·97
(32 430·01 to
66 636·04)
10·43
(7·52 to 13·44)*
–13·32
(–15·60 to –10·94)*
·· Ischaemic heart
disease
1764·49
(1091·76 to
2446·34)
2011·41
(1232·19 to
2804·53)
13·99
(10·84 to 17·36)*
–14·23
(–16·49 to –11·77)*
38 955·49
(24 572·90 to
53 226·78)
42 449·79
(26 679·81 to
58 466·30)
8·97
(5·77 to 12·22)*
–14·60
(–17·01 to –12·09)*
·· Diabetes mellitus 114·84
(56·65 to
181·83)
143·63
(70·17 to
227·67)
25·08
(22·53 to 27·51)*
–4·23
(–6·13 to –2·42)*
5864·75
(2906·59 to
9518·13)
7043·18
(3475·81 to
11 463·51)
20·09
(17·73 to 22·23)*
–4·46
(–6·22 to –2·86)*
3 Diet low in milk: all
causes
100·32
(35·93 to
172·49)
123·21
(45·00 to
213·85)
22·82
(16·96 to 27·74)*
–7·13
(–11·47 to –3·44)*
2168·24
(770·59 to
3718·76)
2581·50
(930·45 to
4435·44)
19·06
(12·71 to 24·09)*
–7·45
(–12·28 to –3·58)*
·· Colon and rectum
cancer
100·32
(35·93 to
172·49)
123·21
(45·00 to
213·85)
22·82
(16·96 to 27·74)*
–7·13
(–11·47 to –3·44)*
2168·24
(770·59 to
3718·76)
2581·50
(930·45 to
4435·44)
19·06
(12·71 to 24·09)*
–7·45
(–12·28 to –3·58)*
3 Diet high in red meat: all
causes
22·59
(10·56 to
36·85)
31·88
(15·08 to
51·44)
41·16
(33·97 to 50·38)*
7·35
(1·88 to 14·18)*
893·25
(363·00 to
1485·05)
1247·33
(508·19 to
2077·33)
39·64
(33·18 to 47·68)*
10·30
(4·94 to 16·49)*
·· Colon and rectum
cancer
12·29
(2·60 to 22·66)
17·88
(3·78 to 32·44)
45·48
(36·21 to 56·91)*
9·72
(2·77 to 18·65)*
268·60
(57·30 to 493·26)
377·78
(80·75 to 679·93)
40·65
(32·05 to
50·68)*
9·01
(2·19 to 16·95)*
(Table 4 continues on next page)
Global Health Metrics
1392
www.thelancet.com Vol 390 September 16, 2017
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Diabetes mellitus 10·29
(1·45 to 18·98)
14·00
(1·98 to 25·60)
36·00
(29·34 to 43·64)*
4·40
(–0·81 to 10·51)
624·65
(87·34 to 1153·77)
869·55
(120·69 to
1598·30)
39·21
(32·22 to 47·76)*
10·89
(5·38 to 17·71)*
3 Diet high in processed
meat: all causes
146·70
(29·93 to
269·63)
139·62
(29·84 to
271·40)
–4·83
(–15·30 to 5·01)
–28·85
(–36·31 to –21·48)*
3499·30
(1121·42 to
6024·02)
3196·04
(1091·35 to
5836·23)
–8·67
(–19·23 to 2·16)
–28·87
(–36·93 to –20·27)*
·· Colon and rectum
cancer
9·84
(5·09 to 15·48)
10·28
(5·24 to 16·68)
4·45
(–3·10 to 11·58)
–21·45
(–26·99 to
–16·30)*
196·63
(102·18 to 308·02)
194·85
(98·50 to 321·61)
–0·90
(–8·58 to 6·64)
–23·50
(–29·34 to –17·79)*
·· Ischaemic heart
disease
121·78
(5·27 to
240·19)
114·54
(4·50 to
238·06)
–5·94
(–17·54 to 5·48)
–29·84
(–38·03 to –21·64)*
2421·47
(107·64 to
4745·93)
2116·23
(82·94 to 4522·45)
–12·61
(–24·84 to 0·05)
–32·11
(–41·45 to –22·48)*
·· Diabetes mellitus 15·09
(7·05 to 24·10)
14·80
(6·45 to 25·75)
–1·89
(–13·21 to 10·00)
–25·27
(–33·73 to –16·41)*
881·20
(420·66 to
1466·58)
884·96
(395·86 to
1583·28)
0·43
(–9·91 to 10·71)
–20·75
(–28·79 to –12·62)*
3 Diet high in
sugar-sweetened
beverages: all causes
17·80
(11·49 to
29·39)
22·56
(15·33 to
33·36)
26·77
(–20·93 to 56·21)
–4·36
(–40·34 to 19·43)
605·81
(401·43 to
932·96)
779·51
(523·90 to
1145·18)
28·67
(–13·65 to 50·53)
1·96
(–32·16 to 19·85)
·· Oesophageal cancer 0·29
(0·09 to 0·55)
0·37
(0·11 to 0·70)
28·96
(–25·95 to 56·82)
–1·48
(–44·00 to 19·43)
7·08
(2·10 to 13·48)
8·90
(2·55 to 17·04)
25·73
(–26·29 to 51·70)
–2·25
(–41·60 to 17·56)
·· Colon and rectum
cancer
0·36
(0·21 to 0·69)
0·43
(0·27 to 0·65)
20·23
(–39·42 to 72·88)
–9·20
(–53·49 to 29·91)
8·10
(4·86 to 15·13)
9·67
(6·13 to 14·54)
19·38
(–40·62 to 73·22)
–7·22
(–53·29 to 33·26)
·· Liver cancer due to
hepatitis B
0·11
(0·04 to 0·31)
0·16
(0·07 to 0·29)
46·97
(–48·94 to
107·46)
15·78
(–61·11 to 65·31)
3·43
(1·25 to 9·32)
4·91
(2·09 to 9·01)
43·11
(–47·86 to
105·26)
15·74
(–58·01 to 66·01)
·· Liver cancer due to
hepatitis C
0·09
(0·04 to 0·17)
0·13
(0·06 to 0·22)
35·33
(–26·26 to 72·90)
2·46
(–42·04 to 30·86)
2·08
(0·95 to 3·96)
2·77
(1·23 to 4·80)
32·68
(–26·61 to
69·83)
2·19
(–45·21 to 30·56)
·· Liver cancer due to
alcohol use
0·07
(0·03 to 0·14)
0·09
(0·04 to 0·15)
38·16
(–35·40 to 78·76)
5·72
(–51·04 to 33·84)
1·56
(0·67 to 3·18)
2·15
(0·98 to 3·68)
37·92
(–31·88 to 72·80)
7·05
(–47·30 to 34·90)
·· Liver cancer due to
other causes
0·07
(0·03 to 0·22)
0·11
(0·05 to 0·19)
47·23
(–49·13 to 98·67)
14·78
(–59·69 to 53·95)
2·05
(0·74 to 5·72)
2·93
(1·22 to 5·21)
42·72
(–40·63 to
97·68)
14·45
(–56·86 to 57·63)
·· Gallbladder and biliary
tract cancer
0·10
(0·06 to 0·17)
0·12
(0·07 to 0·20)
21·19
(–26·26 to 53·17)
–8·78
(–44·49 to 16·53)
2·17
(1·19 to 3·59)
2·56
(1·49 to 4·06)
18·44
(–25·82 to 54·57)
–8·47
(–43·46 to 17·51)
·· Pancreatic cancer 0·12
(0·04 to 0·25)
0·15
(0·05 to 0·27)
20·30
(–51·55 to 98·90)
–9·06
(–63·71 to 47·86)
2·64
(0·91 to 6·08)
3·12
(1·12 to 5·98)
18·12
(–53·72 to 96·56)
–8·66
(–65·03 to 47·86)
·· Breast cancer 0·15
(0·06 to 0·38)
0·17
(0·08 to 0·32)
14·33
(–56·82 to
109·36)
–14·46
(–67·38 to 58·17)
3·61
(1·40 to 9·75)
4·14
(1·92 to 7·86)
14·87
(–62·30 to
129·42)
–12·26
(–69·56 to 74·33)
·· Uterine cancer 0·10
(0·07 to 0·16)
0·13
(0·09 to 0·20)
31·24
(–14·19 to 56·34)
–1·19
(–31·40 to 16·65)
2·50
(1·60 to 3·91)
3·32
(2·20 to 4·84)
32·82
(–12·54 to 58·57)
2·48
(–32·80 to 21·96)
·· Ovarian cancer 0·03
(0·00 to 0·11)
0·03
(0·00 to 0·07)
–5·42
(–97·18 to
231·08)
–27·30
(–97·63 to 169·46)
0·81
(0·06 to 2·86)
0·75
(0·00 to 1·90)
–7·10
(–97·19 to
231·03)
–26·75
(–97·31 to 156·14)
·· Kidney cancer 0·13
(0·08 to 0·20)
0·17
(0·11 to 0·26)
34·51
(–0·86 to 60·98)
2·17
(–24·86 to 21·78)
3·08
(1·96 to 4·80)
4·06
(2·61 to 6·04)
31·80
(–2·65 to 56·85)
2·54
(–25·05 to 22·42)
·· Thyroid cancer 0·02
(0·01 to 0·03)
0·02
(0·01 to 0·04)
23·74
(–45·68 to 71·02)
–5·05
(–56·07 to 32·19)
0·48
(0·23 to 0·95)
0·58
(0·31 to 0·95)
22·78
(–42·37 to 72·84)
–2·72
(–53·99 to 35·78)
·· Non-Hodgkin
lymphoma
0·07
(0·03 to 0·14)
0·08
(0·04 to 0·14)
14·80
(–48·12 to 89·49)
–11·36
(–59·15 to 44·86)
1·93
(0·88 to 3·84)
2·17
(1·02 to 3·70)
12·11
(–53·36 to
89·82)
–9·92
(–61·71 to 50·34)
·· Multiple myeloma 0·04
(0·02 to 0·07)
0·04
(0·02 to 0·08)
20·90
(–36·98 to 86·20)
–8·58
(–52·81 to 40·23)
0·80
(0·33 to 1·52)
0·96
(0·43 to 1·68)
19·88
(–41·55 to 79·48)
–7·25
(–53·09 to 39·99)
·· Acute lymphoid
leukaemia
0·01
(0·01 to 0·03)
0·02
(0·01 to 0·03)
21·67
(–42·96 to 84·50)
–0·50
(–52·49 to 49·86)
0·60
(0·33 to 1·16)
0·72
(0·44 to 1·15)
20·31
(–42·71 to 77·73)
2·35
(–50·81 to 53·90)
(Table 4 continues on next page)
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1393
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Chronic lymphoid
leukaemia
0·02
(0·01 to 0·03)
0·02
(0·01 to 0·03)
11·87
(–40·76 to 59·76)
–17·04
(–56·19 to 17·59)
0·31
(0·17 to 0·60)
0·34
(0·20 to 0·57)
9·98
(–47·66 to
65·06)
–15·46
(–59·33 to 28·08)
·· Acute myeloid
leukaemia
0·04
(0·02 to 0·06)
0·04
(0·03 to 0·07)
20·64
(–33·37 to 72·72)
–5·40
(–47·60 to 32·17)
1·08
(0·62 to 1·88)
1·27
(0·79 to 2·04)
17·65
(–34·43 to
64·82)
–3·94
(–45·68 to 33·05)
·· Chronic myeloid
leukaemia
0·01
(0·01 to 0·02)
0·01
(0·01 to 0·02)
–11·77
(–55·23 to 31·96)
–31·15
(–64·52 to 1·81)
0·31
(0·17 to 0·64)
0·26
(0·16 to 0·42)
–15·95
(–62·20 to 24·58)
–31·30
(–68·51 to 2·27)
·· Other leukaemia 0·04
(0·02 to 0·10)
0·04
(0·02 to 0·07)
7·12
(–62·16 to 97·24)
–16·02
(–65·44 to 48·79)
1·08
(0·54 to 3·48)
1·09
(0·64 to 1·86)
1·35
(–70·91 to 82·39)
–16·40
(–73·68 to 53·84)
·· Ischaemic heart
disease
5·98
(3·78 to 9·98)
7·03
(4·58 to 10·61)
17·56
(–29·12 to 52·55)
–11·34
(–46·57 to 15·33)
150·69
(97·37 to 229·60)
176·63
(116·37 to 263·07)
17·21
(–23·17 to 43·80)
–7·75
(–40·83 to 13·00)
·· Ischaemic stroke 1·09
(0·61 to 2·32)
1·17
(0·73 to 1·80)
7·45
(–54·16 to 58·69)
–19·02
(–66·03 to 20·18)
31·26
(19·20 to 57·90)
36·59
(24·19 to 54·00)
17·06
(–40·45 to 50·57)
–9·27
(–55·99 to 17·68)
·· Haemorrhagic stroke 2·14
(1·36 to 3·88)
2·47
(1·65 to 3·69)
15·27
(–38·70 to 39·47)
–9·12
(–52·40 to 11·93)
72·43
(47·51 to 116·88)
84·66
(57·32 to 122·05)
16·90
(–28·03 to 35·60)
–4·88
(–42·13 to 11·16)
·· Hypertensive heart
disease
0·94
(0·53 to 1·57)
1·33
(0·73 to 2·21)
41·34
(1·13 to 61·93)*
5·33
(–25·08 to 21·50)
22·35
(14·24 to 34·30)
30·09
(18·45 to 46·26)
34·67
(1·67 to 50·99)*
5·72
(–20·06 to 18·87)
·· Atrial fibrillation and
flutter
0·18
(0·10 to 0·30)
0·25
(0·15 to 0·39)
44·03
(–1·62 to 70·54)
–0·79
(–32·88 to 17·51)
5·37
(3·13 to 8·88)
7·33
(4·22 to 11·54)
36·35
(0·50 to 59·28)*
2·11
(–24·17 to 18·00)
·· Asthma 0·15
(0·08 to 0·36)
0·14
(0·09 to 0·23)
–3·48
(–64·53 to 42·11)
–25·38
(–72·46 to 10·94)
15·00
(8·45 to 25·64)
17·32
(10·00 to 28·61)
15·47
(–28·42 to
40·68)
–3·65
(–40·85 to 18·12)
·· Gallbladder and biliary
diseases
0·13
(0·08 to 0·21)
0·19
(0·12 to 0·28)
45·16
(5·28 to 65·76)*
7·18
(–20·39 to 22·76)
3·01
(1·95 to 4·61)
4·24
(2·75 to 6·19)
40·81
(3·82 to 57·89)*
10·74
(–18·83 to 24·22)
.. Alzheimer’s disease
and other dementias
1·09
(0·44 to 2·12)
1·57
(0·66 to 2·80)
43·79
(–31·20 to 81·61)
–1·32
(–48·85 to 25·19)
13·62
(5·90 to 27·29)
18·90
(7·82 to 34·07)
38·78
(–28·85 to
85·48)
0·35
(–48·69 to 32·06)
.. Diabetes mellitus 2·68
(1·80 to 3·83)
3·72
(2·50 to 5·30)
38·65
(13·79 to 51·31)*
6·23
(–14·28 to 15·85)
160·49
(104·01 to 235·77)
228·01
(146·36 to 338·55)
42·07
(24·20 to 52·10)*
14·03
(–0·94 to 22·21)
·· Chronic kidney
disease due to
diabetes mellitus
0·69
(0·34 to 1·18)
1·05
(0·53 to 1·79)
51·91
(15·47 to 70·56)*
14·19
(–12·15 to 27·71)
21·91
(9·80 to 37·27)
32·96
(15·37 to 55·88)
50·42
(16·36 to
69·24)*
15·99
(–9·49 to 29·45)
·· Chronic kidney disease
due to hypertension
0·28
(0·12 to 0·51)
0·43
(0·18 to 0·78)
52·70
(–0·59 to 74·23)
10·13
(–27·47 to 28·35)
6·42
(3·12 to 10·98)
9·82
(4·86 to 16·38)
52·91
(2·60 to 73·37)*
15·09
(–21·34 to 31·55)
·· Chronic kidney
disease due to
glomerulonephritis
0·29
(0·14 to 0·50)
0·43
(0·20 to 0·73)
46·29
(22·10 to 61·05)*
11·21
(–7·71 to 21·11)
9·59
(3·88 to 17·35)
13·85
(5·67 to 24·73)
44·51
(18·51 to 60·36)*
13·38
(–7·28 to 24·40)
·· Chronic kidney disease
due to other causes
0·30
(0·14 to 0·52)
0·45
(0·21 to 0·78)
51·23
(14·95 to 70·98)*
13·60
(–11·31 to 26·17)
9·12
(3·96 to 16·64)
13·55
(5·90 to 24·85)
48·59
(10·10 to 67·56)*
15·55
(–13·97 to 28·71)
·· Osteoarthritis ·· ·· ·· ·· 12·25
(6·32 to 21·91)
17·50
(9·15 to 29·79)
42·81
(–10·75 to 77·51)
12·04
(–30·98 to 38·72)
·· Low back pain ·· ·· ·· ·· 23·67
(12·74 to 49·51)
27·62
(16·04 to 44·45)
16·69
(–41·75 to 83·77)
–2·98
(–51·67 to 51·38)
·· Gout ·· ·· ·· ·· 1·82
(0·94 to 3·25)
2·48
(1·29 to 4·40)
36·03
(8·43 to 49·14)*
9·54
(–13·22 to 20·02)
·· Cataract ·· ·· ·· ·· 1·12
(0·43 to 3·45)
1·27
(0·64 to 2·43)
13·37
(–69·99 to
140·46)
–13·50
(–77·20 to 81·95)
3 Diet low in fibre: all
causes
769·74
(446·50 to
1159·77)
877·85
(502·37 to
1337·53)
14·05
(10·69 to 17·13)*
–13·93
(–16·34 to –11·62)*
18 522·14
(10 865·99 to
27 596·25)
20 119·47
(11 653·46 to
30 430·15)
8·62
(5·17 to 11·61)*
–14·06
(–16·63 to –11·74)*
·· Colon and rectum
cancer
77·72
(39·53 to
121·45)
92·53
(46·61 to
146·52)
19·05
(13·44 to 23·86)*
–10·11
(–14·25 to –6·60)*
1658·67
(852·86 to
2584·40)
1905·91
(965·68 to
3008·24)
14·91
(8·93 to 19·90)*
–10·61
(–15·08 to –6·81)*
(Table 4 continues on next page)
Global Health Metrics
1394
www.thelancet.com Vol 390 September 16, 2017
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Ischaemic heart
disease
692·02
(395·74 to
1063·40)
785·32
(440·85 to
1225·40)
13·48
(9·90 to 16·76)*
–14·36
(–16·96 to –11·96)*
16 863·47
(9820·85 to
25 673·28)
18 213·56
(10 409·47 to
28 118·37)
8·01
(4·25 to 11·19)*
–14·42
(–17·20 to –11·94)*
3 Diet low in calcium: all
causes
135·49
(86·00 to
194·76)
159·88
(101·07 to
232·62)
18·00
(11·94 to 22·51)*
–10·61
(–15·09 to –7·34)*
2935·65
(1882·89 to
4176·04)
3353·07
(2127·08 to
4832·47)
14·22
(7·84 to 18·84)*
–11·07
(–15·93 to –7·61)*
·· Colon and rectum
cancer
135·49
(86·00 to
194·76)
159·88
(101·07 to
232·62)
18·00
(11·94 to 22·51)*
–10·61
(–15·09 to –7·34)*
2935·65
(1882·89 to
4176·04)
3353·07
(2127·08 to
4832·47)
14·22
(7·84 to 18·84)*
–11·07
(–15·93 to –7·61)*
3 Diet low in seafood
omega 3 fatty acids: all
causes
1347·53
(575·06 to
2186·21)
1538·76
(641·93 to
2518·12)
14·19
(11·23 to 17·24)*
–13·43
(–15·57 to –11·15)*
30 245·39
(13 187·67 to
48 313·74)
33 347·84
(14 222·64 to
53 678·05)
10·26
(7·23 to 13·38)*
–13·56
(–15·86 to –11·20)*
·· Ischaemic heart
disease
1347·53
(575·06 to
2186·21)
1538·76
(641·93 to
2518·12)
14·19
(11·23 to 17·24)*
–13·43
(–15·57 to –11·15)*
30 245·39
(13 187·67 to
48 313·74)
33 347·84
(14 222·64 to
53 678·05)
10·26
(7·23 to 13·38)*
–13·56
(–15·86 to –11·20)*
3 Diet low in
polyunsaturated fatty
acids: all causes
373·71
(152·88 to
579·39)
404·13
(167·80 to
628·84)
8·14
(1·10 to 15·92)*
–18·99
(–24·21 to –13·07)*
8077·08
(3337·50 to
12 512·69)
8351·81
(3443·29 to
12 916·37)
3·40
(–2·82 to 10·23)
–18·99
(–23·72 to –13·61)*
·· Ischaemic heart
disease
373·71
(152·88 to
579·39)
404·13
(167·80 to
628·84)
8·14
(1·10 to 15·92)*
–18·99
(–24·21 to –13·07)*
8077·08
(3337·50 to
12 512·69)
8351·81
(3443·29 to
12 916·37)
3·40
(–2·82 to 10·23)
–18·99
(–23·72 to –13·61)*
3 Diet high in trans fatty
acids: all causes
236·27
(80·11 to
490·84)
223·64
(62·82 to
513·16)
–5·34
(–25·31 to 5·65)
–29·61
(–44·81 to –21·00)*
5426·02
(1751·02 to
11 428·66)
5111·02
(1348·61 to
11 683·02)
–5·81
(–24·90 to 4·01)
–26·47
(–41·85 to –18·49)*
·· Ischaemic heart
disease
236·27
(80·11 to
490·84)
223·64
(62·82 to
513·16)
–5·34
(–25·31 to 5·65)
–29·61
(–44·81 to –21·00)*
5426·02
(1751·02 to
11 428·66)
5111·02
(1348·61 to
11 683·02)
–5·81
(–24·90 to 4·01)
–26·47
(–41·85 to –18·49)*
3 Diet high in sodium: all
causes
2093·86
(641·82 to
4027·16)
2310·47
(654·70 to
4498·83)
10·35
(1·14 to 14·18)*
–17·24
(–23·87 to –14·54)*
44 080·70
(14 013·37 to
84 853·20)
47 567·08
(14 436·69 to
92 411·61)
7·91
(0·83 to 11·33)*
–16·81
(–22·41 to –14·20)*
·· Stomach cancer 87·78
(29·91 to
169·46)
82·00
(25·89 to
164·38)
–6·58
(–19·18 to 0·49)
–28·70
(–37·72 to –24·32)*
1858·76
(665·42 to
3570·13)
1677·96
(551·88 to
3313·52)
–9·73
(–21·11 to
–2·91)*
–30·26
(–38·59 to –25·91)*
·· Rheumatic heart
disease
18·85
(5·42 to 40·97)
16·56
(4·21 to 36·93)
–12·11
(–24·41 to
–3·58)*
–32·10
(–41·39 to –26·32)*
508·22
(141·81 to
1117·27)
433·72
(110·18 to 999·89)
–14·66
(–26·19 to
–7·04)*
–31·92
(–41·31 to –26·21)*
·· Ischaemic heart
disease
933·02
(228·66 to
1899·86)
1097·91
(271·71 to
2220·51)
17·67
(12·33 to 22·92)*
–12·43
(–16·13 to –8·54)*
18 024·31
(4595·42 to
37 082·96)
20 494·46
(5230·42 to
41 448·18)
13·70
(9·53 to 19·27)*
–12·58
(–15·65 to –8·42)*
·· Ischaemic stroke 298·64
(87·80 to
607·24)
312·00
(88·62 to
643·83)
4·48
(–3·00 to 9·19)
–21·85
(–27·22 to –18·72)*
6331·08
(2048·13 to
12 479·99)
6939·12
(2243·57 to
13 630·21)
9·60
(4·34 to 14·91)*
–16·53
(–20·56 to –12·41)*
·· Haemorrhagic stroke 462·49
(172·54 to
842·63)
432·53
(147·88 to
808·61)
–6·48
(–16·08 to
–1·80)*
–29·05
(–36·48 to –25·70)*
10 650·45
(3995·11 to
19 583·09)
9962·38
(3520·25 to
18 774·49)
–6·46
(–14·68 to
–2·50)*
–27·53
(–34·08 to –24·41)*
·· Hypertensive heart
disease
142·05
(27·30 to
351·99)
181·96
(33·21 to
464·61)
28·10
(2·03 to 45·37)*
–5·27
(–24·95 to 6·83)
2731·43
(670·95 to
6388·79)
3298·57
(730·16 to
7748·68)
20·76
(1·85 to 35·50)*
–7·33
(–22·43 to 3·79)
·· Other
cardiomyopathy
10·66
(2·22 to 23·96)
12·52
(2·32 to 28·86)
17·39
(–3·70 to 28·91)
–11·88
(–28·39 to –2·67)*
242·84
(51·24 to 538·45)
270·46
(53·89 to 606·34)
11·38
(–5·12 to 20·11)
–12·68
(–26·01 to –5·61)*
·· Atrial fibrillation and
flutter
11·26
(2·54 to 25·22)
15·56
(3·33 to 35·44)
38·19
(25·76 to 43·79)*
–1·98
(–9·57 to 1·66)
382·36
(95·58 to 800·54)
501·90
(124·45 to
1052·73)
31·26
(25·19 to 34·93)*
–0·58
(–4·89 to 2·57)
·· Aortic aneurysm 9·57
(2·18 to 20·52)
11·02
(2·31 to 24·08)
15·18
(2·55 to 21·25)*
–13·17
(–22·38 to –9·02)*
184·54
(43·83 to 391·55)
204·79
(44·97 to 436·91)
10·97
(–0·89 to 17·09)
–14·32
(–23·42 to –9·77)*
·· Peripheral vascular
disease
1·88
(0·25 to 4·56)
2·51
(0·34 to 6·25)
33·19
(16·18 to 51·41)*
–3·46
(–14·44 to 10·20)
50·71
(9·14 to 121·19)
62·48
(10·88 to 148·77)
23·20
(11·72 to 32·25)*
–7·07
(–15·48 to –0·73)*
(Table 4 continues on next page)
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1395
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Endocarditis 4·12
(0·77 to 9·81)
5·16
(0·90 to 12·34)
25·41
(14·57 to 30·73)*
–5·45
(–13·78 to –1·43)*
92·56
(16·37 to 222·32)
111·10
(18·80 to 270·10)
20·04
(11·39 to 25·25)*
–5·37
(–13·40 to –1·59)*
·· Other cardiovascular
and circulatory
diseases
29·97
(6·67 to 64·98)
34·19
(7·06 to 77·30)
14·10
(–1·52 to 21·18)
–14·37
(–25·64 to –9·26)*
866·90
(215·87 to
1874·08)
970·65
(226·05 to
2134·15)
11·97
(0·73 to 17·31)*
–12·89
(–21·61 to –8·68)*
·· Chronic kidney disease
due to diabetes mellitus
38·25
(9·51 to 82·81)
47·89
(10·61 to
105·42)
25·21
(10·95 to 29·75)*
–5·22
(–15·38 to –2·14)*
1047·19
(279·10 to
2297·95)
1269·79
(302·82 to
2829·29)
21·26
(8·49 to 25·52)*
–5·96
(–15·38 to –2·85)*
·· Chronic kidney disease
due to hypertension
23·28
(5·93 to 50·23)
30·37
(7·11 to 66·16)
30·44
(17·26 to 35·06)*
–4·36
(–12·78 to –1·54)*
497·97
(135·51 to
1054·49)
626·30
(162·35 to
1355·19)
25·77
(15·42 to 29·65)*
–4·23
(–11·72 to –1·41)*
·· Chronic kidney disease
due to
glomerulonephritis
8·03
(1·35 to 19·34)
9·84
(1·48 to 24·04)
22·57
(8·58 to 26·59)*
–6·75
(–16·15 to –4·18)*
241·64
(42·80 to 581·91)
282·61
(47·47 to 682·09)
16·95
(5·24 to 20·87)*
–7·52
(–15·54 to –5·01)*
·· Chronic kidney disease
due to other causes
14·03
(2·72 to 33·20)
18·43
(3·17 to 44·06)
31·38
(15·39 to 36·18)*
–1·84
(–13·73 to 1·01)
369·73
(73·40 to 902·50)
460·78
(84·25 to 1145·85)
24·62
(11·02 to 28·56)*
–2·97
(–13·68 to –0·33)*
2 Sexual abuse and
violence: all causes
149·42
(94·83 to
204·16)
73·83
(53·79 to
94·09)
–50·59
(–54·93 to
–41·98)*
–57·82
(–61·57 to
–50·49)*
11 095·59
(8127·52 to
13 985·73)
8201·58
(6354·86 to
10 332·83)
–26·08
(–34·79 to
–15·42)*
–36·15
(–43·53 to –27·22)*
3 Childhood sexual abuse:
all causes
8·98
(6·58 to 11·81)
8·74
(6·40 to 11·74)
–2·66
(–13·60 to 10·23)
–20·18
(–28·78 to –10·11)*
2495·64
(1766·89 to
3377·91)
2748·30
(1920·53 to
3735·79)
10·12
(7·71 to 12·41)*
–6·09
(–8·31 to –4·17)*
·· Alcohol use disorders 8·98
(6·58 to 11·81)
8·74
(6·40 to 11·74)
–2·66
(–13·60 to 10·23)
–20·18
(–28·78 to –10·11)*
814·13
(574·56 to
1131·70)
854·71
(596·52 to
1200·17)
4·98
(–1·17 to 10·82)
–10·40
(–15·87 to –5·29)*
·· Major depressive
disorder
·· ·· ·· ·· 1681·51
(1101·62 to
2354·77)
1893·59
(1235·31 to
2667·57)
12·61
(11·02 to 14·30)*
–4·04
(–5·26 to –2·80)*
3 Intimate partner
violence: all causes
140·45
(86·78 to
194·82)
65·09
(44·85 to
85·84)
–53·65
(–57·43 to
–45·90)*
–60·39
(–63·74 to –53·55)*
8702·76
(6067·70 to
11 437·85)
5575·29
(4224·54 to
7079·58)
–35·94
(–43·21 to
–25·04)*
–44·53
(–50·72 to –35·17)*
·· Drug-susceptible HIV/
AIDS–tuberculosis
26·36
(12·66 to
43·52)
9·23
(4·43 to 14·96)
–64·97
(–67·40 to
–62·15)*
–70·87
(–72·90 to –68·73)*
1146·54
(540·62 to
1916·16)
423·59
(203·21 to 687·99)
–63·05
(–65·77 to
–59·57)*
–68·63
(–70·89 to –65·77)*
·· Multidrug-resistant
HIV/AIDS–tuberculosis
without extensive
drug resistance
2·07
(0·93 to 3·52)
0·71
(0·32 to 1·22)
–65·82
(–72·96 to
–56·64)*
–71·63
(–77·56 to –64·16)*
88·64
(39·75 to 152·39)
31·67
(14·10 to 54·75)
–64·27
(–71·71 to
–54·47)*
–69·71
(–75·98 to –61·51)*
·· Extensively drug-
resistant HIV/AIDS –
tuberculosis
0·02
(0·01 to 0·04)
0·02
(0·01 to 0·04)
13·96
(–2·84 to 32·78)
–4·23
(–18·41 to 11·39)
0·96
(0·42 to 1·70)
1·12
(0·48 to 1·97)
16·38
(–0·88 to 36·31)
–0·47
(–15·26 to 16·38)
·· HIV/AIDS resulting in
other diseases
84·54
(43·40 to
129·36)
31·10
(16·08 to
47·65)
–63·22
(–66·05 to
–59·87)*
–68·71
(–71·09 to –65·93)*
4027·09
(2046·13 to
6160·95)
1584·24
(814·13 to
2425·13)
–60·66
(–63·52 to
–57·44)*
–66·07
(–68·50 to –63·31)*
·· Maternal abortion,
miscarriage, and
ectopic pregnancy
4·11
(2·45 to 6·19)
3·00
(1·75 to 4·79)
–27·02
(–36·19 to
–17·03)*
–34·53
(–42·83 to –25·55)*
233·81
(137·40 to 351·12)
170·68
(97·12 to 270·75)
–27·00
(–35·84 to
–17·85)*
–34·18
(–42·23 to –25·85)*
·· Major depressive
disorder
·· ·· ·· ·· 1582·91
(966·13 to
2381·34)
1870·82
(1146·94 to
2801·23)
18·19
(15·62 to 21·12)*
–1·74
(–3·69 to 0·18)
·· Assault by firearm 4·73
(3·09 to 5·60)
4·77
(3·13 to 6·06)
0·75
(–8·07 to 24·07)
–10·75
(–18·39 to 9·61)
250·39
(163·69 to 295·57)
246·14
(163·72 to 308·71)
–1·70
(–10·63 to 22·32)
–10·98
(–19·00 to 10·46)
·· Assault by sharp
object
6·88
(4·69 to 8·17)
6·00
(4·41 to 7·87)
–12·69
(–23·31 to 21·78)
–23·39
(–32·53 to 6·30)
367·54
(255·41 to 435·61)
314·12
(235·32 to 404·00)
–14·54
(–24·84 to 18·52)
–23·58
(–32·59 to 5·48)
·· Sexual violence ·· ·· ·· ·· 291·29
(191·88 to 418·18)
298·83
(195·75 to 428·19)
2·59
(0·69 to 4·22)*
–6·63
(–7·90 to –5·72)*
(Table 4 continues on next page)
Global Health Metrics
1396
www.thelancet.com Vol 390 September 16, 2017
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Assault by other
means
11·73
(8·71 to 14·49)
10·26
(8·02 to 13·17)
–12·56
(–24·54 to 5·66)
–23·23
(–33·65 to –7·28)*
713·59
(558·79 to
866·06)
634·08
(509·27 to 793·77)
–11·14
(–22·12 to 4·98)
–20·79
(–30·38 to –7·10)*
2 Unsafe sex: all causes 1799·64
(1709·98 to
1892·29)
1100·90
(1048·42 to
1148·40)
–38·83
(–40·96 to
–36·41)*
–47·76
(–49·54 to
–45·78)*
86 860·81
(81 591·84 to
92 234·51)
54 603·03
(51 340·06 to
58 075·62)
–37·14
(–39·35 to
–34·64)*
–45·34
(–47·21 to –43·21)*
·· Drug-susceptible HIV/
AIDS–tuberculosis
363·54
(244·52 to
481·15)
177·41
(121·51 to
236·50)
–51·20
(–54·00 to
–48·03)*
–58·56
(–60·94 to
–56·00)*
17 186·87
(11 569·46 to
22 809·50)
8948·63
(6232·62 to
11 865·25)
–47·93
(–50·95 to
–44·18)*
–54·73
(–57·35 to –51·55)*
·· Multidrug-resistant
HIV/AIDS–tuberculosis
without extensive
drug resistance
30·65
(18·73 to
45·88)
14·52
(8·81 to 21·68)
–52·62
(–61·10 to
–42·60)*
–59·80
(–67·03 to –51·32)*
1423·49
(866·21 to
2131·53)
714·22
(433·23 to
1064·03)
–49·83
(–58·78 to
–39·15)*
–56·42
(–64·22 to –47·18)*
·· Extensively drug-
resistant HIV/AIDS–
tuberculosis
0·52
(0·33 to 0·79)
0·77
(0·47 to 1·20)
48·49
(29·74 to 70·40)*
27·46
(11·27 to 46·30)*
24·64
(15·40 to 37·14)
36·82
(22·60 to 56·99)
49·44
(30·40 to 72·32)*
30·37
(13·77 to 50·30)*
·· HIV/AIDS resulting in
other diseases
1165·31
(1020·87 to
1330·03)
652·04
(578·82 to
729·70)
–44·05
(–46·83 to
–40·95)*
–51·77
(–54·17 to –49·13)*
58 595·06
(51 311·35 to
66 970·36)
34 615·61
(30 661·75 to
38 960·02)
–40·92
(–43·71 to
–37·90)*
–48·30
(–50·71 to –45·70)*
·· Syphilis 3·31
(2·85 to 3·91)
3·02
(2·55 to 3·44)
–8·89
(–18·77 to 9·37)
–24·89
(–33·13 to –9·95)*
277·24
(229·80 to
328·03)
305·18
(247·07 to 367·04)
10·08
(1·88 to 18·96)*
–8·03
(–14·11 to –0·79)*
·· Chlamydial infection 1·24
(0·99 to 1·37)
1·19
(0·98 to 1·33)
–4·51
(–11·73 to 12·18)
–20·70
(–26·52 to –7·57)*
519·31
(341·24 to 781·67)
562·13
(370·06 to 850·69)
8·25
(5·89 to 10·42)*
–3·33
(–5·56 to –1·40)*
·· Gonococcal infection 3·51
(2·81 to 3·85)
3·37
(2·76 to 3·80)
–4·05
(–11·10 to 12·51)
–20·87
(–26·48 to –7·90)*
581·90
(412·15 to 823·83)
674·77
(467·35 to 974·12)
15·96
(10·35 to 21·76)*
2·68
(–2·71 to 7·83)
·· Trichomoniasis ·· ·· ·· ·· 170·83
(65·10 to 361·77)
198·07
(75·83 to 420·49)
15·95
(14·84 to 17·09)*
1·82
(0·94 to 2·72)*
·· Genital herpes ·· ·· ·· ·· 187·73
(60·91 to 427·68)
221·21
(71·15 to 506·61)
17·84
(15·47 to 19·69)*
–0·16
(–1·64 to 1·54)
·· Other sexually
transmitted diseases
1·73
(1·40 to 1·90)
1·63
(1·35 to 1·83)
–5·92
(–12·96 to 11·16)
–21·03
(–26·82 to –7·18)*
858·99
(589·04 to
1221·09)
942·39
(643·29 to
1348·57)
9·71
(7·38 to 12·17)*
–2·62
(–4·83 to –0·42)*
·· Cervical cancer 229·83
(195·46 to
245·84)
246·95
(203·95 to
263·27)
7·45
(1·21 to 15·47)*
–15·99
(–20·69 to –9·78)*
7034·76
(5873·55 to
7509·99)
7384·00
(6014·77 to
7862·78)
4·96
(–1·30 to 13·23)
–15·71
(–20·76 to –9·21)*
2 Low physical activity:
all causes
1159·60
(607·84 to
1790·07)
1373·34
(717·65 to
2084·16)
18·43
(–7·89 to 55·46)
–12·88
(–31·98 to 13·86)
21 078·75
(11 156·78 to
32 368·81)
24 315·86
(12 811·32 to
36 604·69)
15·36
(–12·15 to 55·44)
–11·79
(–32·55 to 18·47)
·· Colon and rectum
cancer
20·87
(1·07 to 50·40)
25·51
(1·28 to 61·93)
22·21
(–46·00 to
212·87)
–8·42
(–59·11 to 136·31)
411·01
(23·10 to 991·31)
488·61
(26·82 to 1183·40)
18·88
(–48·29 to
205·64)
–8·33
(–59·81 to 136·94)
·· Breast cancer 6·71
(0·04 to 14·74)
7·85
(0·03 to 17·15)
16·97
(–19·98 to 88·93)
–10·88
(–38·58 to 44·86)
175·07
(1·05 to 388·06)
200·14
(0·80 to 440·98)
14·33
(–24·10 to 90·78)
–10·01
(–39·82 to 49·21)
·· Ischaemic heart
disease
835·44
(347·97 to
1353·66)
1005·58
(425·31 to
1640·08)
20·37
(–6·38 to 58·30)
–11·49
(–30·93 to 15·93)
14 658·37
(6114·10 to
23 953·87)
16 943·23
(7260·81 to
27 786·10)
15·59
(–11·49 to 54·73)
–11·42
(–31·91 to 18·08)
·· Ischaemic stroke 267·12
(53·52 to
511·96)
295·28
(49·52 to
562·19)
10·54
(–19·06 to 49·40)
–18·82
(–40·33 to 9·53)
4725·12
(1026·32 to
9141·81)
5268·62
(938·08 to
10 006·35)
11·50
(–18·88 to
54·06)
–15·63
(–38·34 to 16·18)
·· Diabetes mellitus 29·46
(7·36 to 52·61)
39·12
(8·65 to 71·86)
32·80
(–11·95 to
104·74)
–0·40
(–33·63 to 52·78)
1109·18
(248·01 to
2082·98)
1415·26
(312·14 to
2604·08)
27·59
(–17·68 to
104·26)
–0·58
(–35·47 to 58·06)
(Table 4 continues on next page)
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1397
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
1 Metabolic risks: all
causes
14 834·47
(13 966·55 to
15 690·95)
17 493·53
(16 427·65 to
18 524·26)
17·92
(15·73 to 20·58)*
–11·86
(–13·47 to –9·94)*
348 438·17
(324 520·78 to
374 936·88)
401 813·92
(372 407·65 to
434 394·06)
15·32
(13·16 to
17·52)*
–10·29
(–11·98 to –8·60)*
2 High fasting plasma
glucose: all causes
4700·40
(3722·99 to
5906·04)
5612·45
(4457·29 to
6987·54)
19·40
(15·54 to 23·19)*
–10·32
(–13·29 to –7·61)*
123 096·04
(102 887·96 to
146 660·95)
144 088·58
(119 872·60 to
171 585·77)
17·05
(13·94 to
19·89)*
–8·83
(–11·35 to –6·55)*
·· Drug-susceptible
tuberculosis
125·08
(78·55 to
175·35)
99·60
(62·39 to
140·59)
–20·36
(–24·38 to
–16·57)*
–37·14
(–40·10 to
–34·46)*
4013·77
(2581·12 to
5546·25)
3126·64
(2002·75 to
4288·46)
–22·10
(–25·60 to
–18·55)*
–37·09
(–39·78 to –34·65)*
·· Multidrug-resistant
tuberculosis without
extensive drug
resistance
12·50
(7·74 to 18·06)
8·80
(5·32 to 12·83)
–29·61
(–36·56 to
–22·33)*
–44·21
(–49·66 to
–38·40)*
394·88
(253·40 to 557·02)
266·97
(168·75 to 375·02)
–32·39
(–38·90 to
–25·48)*
–45·28
(–50·42 to –39·77)*
·· Extensively drug-
resistant tuberculosis
0·54
(0·33 to 0·79)
0·94
(0·58 to 1·39)
73·66
(50·32 to
101·15)*
37·94
(20·01 to 59·36)*
17·18
(10·63 to 24·33)
28·42
(17·68 to 40·36)
65·41
(42·36 to
90·02)*
34·13
(15·91 to 54·00)*
·· Colon and rectum
cancer
46·54
(11·36 to
101·41)
56·18
(13·51 to
122·80)
20·70
(15·48 to 25·34)*
–9·50
(–13·53 to –5·91)*
884·79
(208·91 to
1926·46)
1047·94
(242·88 to
2300·07)
18·44
(13·03 to 23·15)*
–9·42
(–13·57 to –5·78)*
·· Liver cancer due to
other causes
10·01
(2·06 to 23·07)
11·82
(2·44 to
26·96)
18·12
(13·22 to 22·67)*
–8·69
(–12·24 to –5·26)*
247·67
(51·70 to 571·25)
279·35
(58·20 to 649·12)
12·79
(7·38 to 17·65)*
–11·62
(–15·55 to –7·90)*
·· Pancreatic cancer 21·37
(4·71 to 46·72)
27·75
(6·10 to 60·79)
29·86
(26·27 to 33·12)*
–2·11
(–4·96 to 0·48)
406·22
(90·54 to 891·18)
518·70
(115·69 to
1142·16)
27·69
(24·05 to 30·83)*
–2·46
(–5·20 to 0·04)
·· Tracheal, bronchus,
and lung cancer
100·11
(22·74 to
219·56)
117·06
(26·22 to
256·14)
16·93
(13·75 to 19·76)*
–10·83
(–13·19 to –8·64)*
2028·69
(457·34 to
4483·59)
2304·26
(517·29 to
5093·70)
13·58
(10·25 to 16·54)*
–12·74
(–15·22 to –10·53)*
·· Breast cancer 26·09
(4·93 to 59·11)
31·03
(5·96 to 69·25)
18·94
(11·73 to 26·02)*
–10·02
(–15·31 to –4·93)*
637·43
(120·29 to
1460·33)
749·77
(144·08 to
1694·69)
17·62
(9·76 to 25·72)*
–8·95
(–14·91 to –2·97)*
·· Ovarian cancer 8·11
(1·53 to 19·32)
10·01
(1·88 to 23·94)
23·40
(18·12 to 28·40)*
–6·84
(–10·75 to –3·13)*
182·64
(34·17 to 438·11)
226·27
(41·63 to 545·49)
23·89
(18·46 to
29·09)*
–5·00
(–9·13 to –1·10)*
·· Bladder cancer 10·79
(2·22 to 23·92)
13·47
(2·80 to 29·79)
24·87
(20·66 to 28·68)*
–6·86
(–10·10 to –3·96)*
185·65
(37·01 to 413·28)
225·37
(45·96 to 501·26)
21·39
(16·66 to 25·27)*
–7·62
(–11·16 to –4·73)*
·· Ischaemic heart
disease
1576·70
(935·55 to
2479·20)
1883·33
(1104·82 to
2942·53)
19·45
(13·37 to 25·39)*
–11·29
(–15·21 to –7·64)*
29 401·46
(18 681·34 to
45 481·26)
33 937·53
(21 184·55 to
51 236·60)
15·43
(10·21 to 20·56)*
–11·40
(–15·50 to –7·55)*
·· Ischaemic stroke 449·06
(229·48 to
849·74)
472·53
(246·22 to
879·04)
5·23
(–1·90 to 12·75)
–21·44
(–26·20 to –17·05)*
8810·40
(4539·51 to
14 964·85)
9467·73
(5021·42 to
15 876·46)
7·46
(0·60 to 13·96)*
–18·27
(–23·34 to –13·54)*
·· Haemorrhagic stroke 484·34
(304·87 to
745·37)
473·30
(301·08 to
720·04)
–2·28
(–8·77 to 3·28)
–25·71
(–30·75 to –21·17)*
10 790·83
(6746·12 to
15 844·21)
10 638·08
(6692·27 to
15 613·04)
–1·42
(–7·28 to 3·80)
–23·88
(–28·89 to –19·71)*
·· Peripheral vascular
disease
8·67
(6·23 to 12·26)
11·73
(8·71 to 17·62)
35·33
(24·67 to 48·64)*
–2·51
(–9·98 to 6·88)
213·89
(150·72 to 302·55)
271·68
(195·01 to 383·83)
27·02
(21·52 to 34·82)*
–4·49
(–8·52 to 1·11)
·· Alzheimer’s disease
and other dementias
123·02
(26·35 to
274·89)
174·35
(37·30 to
388·04)
41·73
(38·26 to 45·05)*
–1·20
(–3·44 to 1·60)
1584·88
(330·04 to
3563·61)
2138·24
(445·48 to
4857·92)
34·92
(32·10 to 37·60)*
–1·28
(–3·30 to 1·10)
·· Diabetes mellitus 1095·53
(1065·39 to
1121·32)
1436·26
(1401·25 to
1469·57)
31·10
(28·92 to 33·39)*
–0·87
(–2·52 to 0·84)
45 947·41
(38 659·07 to
54 662·94)
57 175·71
(47 919·49 to
68 211·91)
24·44
(22·70 to 26·24)*
–1·66
(–3·03 to –0·22)*
·· Chronic kidney
disease due to
diabetes mellitus
384·78
(349·87 to
418·93)
500·41
(452·11 to
543·57)
30·05
(26·18 to 32·84)*
–0·63
(–3·43 to 1·30)
11 723·50
(10 608·16 to
12 883·32)
14 649·82
(13 196·95 to
16 191·89)
24·96
(21·91 to 27·57)*
–1·37
(–3·51 to 0·51)
(Table 4 continues on next page)
Global Health Metrics
1398
www.thelancet.com Vol 390 September 16, 2017
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Chronic kidney
disease due to
hypertension
103·23
(70·71 to
134·57)
135·31
(92·80 to
176·80)
31·08
(24·95 to 37·33)*
–3·63
(–7·93 to 0·57)
2165·18
(1452·61 to
2857·87)
2724·97
(1827·82 to
3615·75)
25·85
(20·11 to 31·49)*
–4·20
(–8·66 to 0·03)
·· Chronic kidney
disease due to
glomerulonephritis
42·94
(28·57 to
57·82)
54·38
(36·92 to
73·20)
26·63
(21·53 to 31·75)*
–3·73
(–7·53 to 0·00)
1251·76
(815·12 to
1731·15)
1519·82
(1004·85 to
2100·93)
21·42
(17·17 to 25·92)*
–4·39
(–7·34 to –1·01)*
·· Chronic kidney
disease due to other
causes
71·01
(48·20 to
94·44)
94·20
(63·89 to
125·10)
32·66
(26·70 to 38·48)*
–0·15
(–4·43 to 3·81)
1866·69
(1234·98 to
2533·02)
2346·56
(1566·99 to
3183·01)
25·71
(20·33 to
30·88)*
–2·03
(–6·05 to 1·77)
·· Glaucoma ·· ·· ·· ·· 25·15
(5·71 to 58·51)
33·85
(7·75 to 78·65)
34·62
(31·92 to 37·57)*
1·52
(–0·42 to 3·66)
·· Cataract ·· ·· ·· ·· 315·98
(65·93 to 735·41)
410·89
(85·82 to 961·77)
30·04
(27·58 to 32·78)*
–1·16
(–3·06 to 1·06)
2High total cholesterol:
all causes
3802·10
(2971·09 to
4832·93)
4392·51
(3374·22 to
5619·87)
15·53
(11·51 to 19·77)*
–14·14
(–16·62 to –11·41)*
83 976·46
(70 004·69 to
98 804·76)
93 844·03
(78 027·31 to
111 266·48)
11·75
(8·59 to 15·13)*
–13·29
(–15·68 to –10·73)*
·· Ischaemic heart
disease
3343·63
(2597·25 to
4187·22)
3896·10
(2982·29 to
4940·40)
16·52
(12·29 to 20·89)*
–13·22
(–15·68 to –10·56)*
73 403·57
(61 220·12 to
86 047·11)
82 187·03
(68 385·19 to
96 854·44)
11·97
(8·76 to 15·47)*
–12·88
(–15·33 to –10·24)*
·· Ischaemic stroke 458·46
(185·09 to
924·24)
496·40
(196·69 to
990·11)
8·28
(1·92 to 14·44)*
–20·63
(–23·82 to –17·08)*
10 572·88
(6206·10 to
17 681·57)
11 657·00
(6791·38 to
19 428·74)
10·25
(6·20 to 14·39)*
–16·02
(–19·10 to –12·79)*
2 High systolic blood
pressure: all causes
9083·07
(8209·73 to
9963·14)
10 455·86
(9381·88 to
11 507·49)
15·11
(12·53 to 18·15)*
–14·05
(–15·90 to –11·81)*
188 635·23
(171 004·50 to
205 178·38)
212 105·09
(191 466·22 to
230 661·27)
12·44
(10·03 to 15·07)*
–13·27
(–15·09 to –11·25)*
·· Rheumatic heart
disease
85·51
(58·24 to
126·10)
80·86
(55·41 to
124·17)
–5·43
(–12·76 to 3·81)
–26·92
(–32·20 to –20·72)*
2412·32
(1643·79 to
3500·44)
2234·54
(1547·75 to
3250·51)
–7·37
(–13·42 to 0·51)
–25·82
(–30·59 to –19·76)*
·· Ischaemic heart
disease
4476·47
(3732·81 to
5193·76)
5261·72
(4374·30 to
6188·35)
17·54
(14·19 to 21·12)*
–12·69
(–14·94 to –10·11)*
85 975·47
(74 665·22 to
97 205·97)
97 886·68
(84 378·88 to
110 500·79)
13·85
(10·72 to 17·14)*
–12·44
(–14·77 to –9·97)*
·· Ischaemic stroke 1283·00
(989·81 to
1551·76)
1372·51
(1053·44 to
1670·88)
6·98
(3·05 to 11·54)*
–20·42
(–23·09 to –17·59)*
25 564·17
(20 155·09 to
29 832·63)
28 119·95
(21 993·71 to
32 960·56)
10·00
(6·27 to 13·81)*
–16·42
(–19·14 to –13·56)*
·· Haemorrhagic stroke 1636·18
(1343·27 to
1897·92)
1672·64
(1375·89 to
1947·04)
2·23
(–0·33 to 4·87)
–22·45
(–24·32 to –20·60)*
37 920·74
(31 833·70 to
43 655·27)
38 611·64
(32 491·61 to
44 204·86)
1·82
(–0·41 to 4·22)
–20·91
(–22·79 to –19·01)*
·· Hypertensive heart
disease
694·18
(579·81 to
760·86)
893·14
(698·18 to
982·33)
28·66
(14·46 to 42·90)*
–4·39
(–14·79 to 5·66)
13 562·97
(11 596·54 to
15 040·61)
16 323·95
(13 447·14 to
17 832·20)
20·36
(10·19 to 32·88)*
–6·60
(–14·56 to 2·76)
·· Other
cardiomyopathy
60·64
(44·14 to
77·33)
74·93
(54·83 to
95·99)
23·56
(16·20 to 31·99)*
–7·84
(–13·17 to –1·67)*
1352·53
(1021·85 to
1642·20)
1599·77
(1242·11 to
1935·40)
18·28
(11·07 to 26·29)*
–7·37
(–12·73 to –1·09)*
·· Atrial fibrillation and
flutter
61·68
(45·24 to
81·70)
85·31
(62·06 to
113·83)
38·31
(33·98 to 42·56)*
–2·78
(–5·10 to –0·60)*
1865·37
(1396·49 to
2444·60)
2439·54
(1822·85 to
3211·02)
30·78
(28·89 to
32·53)*
–1·52
(–2·73 to –0·44)*
·· Aortic aneurysm 51·01
(40·98 to
60·67)
60·10
(48·02 to
72·42)
17·81
(13·84 to 22·53)*
–11·62
(–14·32 to –8·15)*
961·55
(799·00 to
1113·52)
1100·81
(930·12 to
1274·73)
14·48
(10·15 to 19·96)*
–11·71
(–14·92 to –7·65)*
·· Peripheral vascular
disease
12·49
(8·26 to 18·74)
16·55
(10·89 to
25·60)
32·49
(20·21 to 47·35)*
–4·83
(–12·82 to 5·05)
290·81
(199·85 to 436·13)
360·60
(246·52 to 530·75)
24·00
(17·02 to 32·57)*
–6·80
(–11·75 to –0·54)*
·· Endocarditis 25·33
(19·02 to
32·47)
33·12
(24·85 to
42·80)
30·76
(25·24 to 36·10)*
–1·38
(–5·39 to 2·95)
589·46
(447·95 to 744·37)
745·71
(570·49 to 942·16)
26·51
(21·03 to 31·79)*
0·16
(–4·21 to 4·17)
·· Other cardiovascular
and circulatory
diseases
174·04
(148·57 to
214·23)
208·84
(177·87 to
255·72)
20·00
(15·44 to 25·66)*
–10·49
(–13·75 to –6·47)*
4740·48
(3978·63 to
5703·94)
5577·83
(4690·60 to
6705·53)
17·66
(14·21 to 22·09)*
–8·60
(–11·32 to –5·34)*
(Table 4 continues on next page)
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1399
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Chronic kidney
disease due to
diabetes mellitus
176·23
(128·84 to
227·68)
233·70
(170·75 to
301·63)
32·61
(29·00 to 35·48)*
–0·06
(–2·77 to 1·94)
4755·11
(3375·01 to
6163·36)
6154·78
(4359·36 to
7973·15)
29·44
(26·62 to 32·07)*
–0·01
(–2·10 to 1·78)
·· Chronic kidney
disease due to
hypertension
222·32
(199·99 to
248·86)
299·48
(268·03 to
335·26)
34·71
(30·47 to 38·02)*
–0·96
(–3·95 to 1·00)
5166·00
(4517·97 to
5842·00)
6602·34
(5756·41 to
7488·87)
27·80
(24·67 to
30·62)*
–1·02
(–3·31 to 0·87)
·· Chronic kidney
disease due to
glomerulonephritis
47·82
(34·20 to
62·17)
60·17
(42·74 to
78·14)
25·83
(22·33 to 29·04)*
–4·69
(–6·90 to –2·70)*
1443·70
(1010·81 to
1931·01)
1739·55
(1207·79 to
2320·30)
20·49
(17·59 to 23·37)*
–4·97
(–6·98 to –2·95)*
·· Chronic kidney
disease due to other
causes
76·17
(51·67 to
99·57)
102·79
(69·50 to
135·39)
34·95
(30·99 to 38·74)*
0·84
(–1·83 to 3·10)
2034·56
(1366·91 to
2688·13)
2607·39
(1738·64 to
3467·30)
28·16
(25·16 to 31·01)*
–0·11
(–2·27 to 1·77)
2 High body-mass index:
all causes
3519·12
(2136·48 to
5165·34)
4525·10
(2867·22 to
6434·24)
28·59
(23·43 to 35·93)*
–2·71
(–6·52 to 2·81)
105 257·57
(65 833·95 to
150 547·40)
135 381·33
(88 608·73 to
187 363·70)
28·62
(23·09 to
36·63)*
0·88
(–3·40 to 7·02)
·· Oesophageal cancer 57·66
(18·86 to
112·99)
70·33
(22·52 to
133·63)
21·97
(11·96 to 36·08)*
–6·97
(–14·64 to 3·67)
1357·91
(431·31 to
2647·91)
1622·45
(516·51 to
3060·94)
19·48
(9·83 to 33·83)*
–7·80
(–15·24 to 3·26)
·· Colon and rectum
cancer
49·63
(26·72 to
79·41)
65·11
(35·86 to
102·02)
31·19
(25·23 to 38·71)*
–0·94
(–5·50 to 4·74)
1075·85
(579·66 to
1714·70)
1394·02
(775·82 to
2160·04)
29·57
(22·96 to
37·66)*
–0·04
(–4·94 to 6·05)
·· Liver cancer due to
hepatitis B
26·37
(8·91 to 55·20)
37·72
(13·44 to
74·28)
43·05
(31·18 to 64·94)*
11·96
(2·72 to 28·75)*
782·13
(261·88 to
1631·20)
1078·26
(379·06 to
2124·71)
37·86
(25·99 to
60·04)*
10·01
(0·61 to 27·39)*
·· Liver cancer due to
hepatitis C
15·00
(6·11 to 28·01)
21·21
(8·95 to 38·36)
41·40
(33·58 to 52·25)*
6·97
(1·14 to 15·12)*
328·28
(134·34 to 602·47)
459·82
(197·62 to 813·07)
40·07
(31·65 to 52·24)*
7·44
(1·26 to 16·40)*
·· Liver cancer due to
alcohol use
11·43
(4·52 to 21·77)
16·59
(6·67 to 31·05)
45·18
(35·92 to 58·24)*
10·99
(3·90 to 20·62)*
265·54
(104·88 to
498·00)
383·17
(157·32 to 707·88)
44·30
(34·95 to 57·95)*
11·45
(4·17 to 21·82)*
·· Liver cancer due to
other causes
14·98
(4·98 to 31·65)
21·93
(7·78 to 43·51)
46·36
(35·51 to 64·87)*
13·85
(5·45 to 27·54)*
415·44
(136·60 to
884·65)
586·63
(208·44 to
1183·29)
41·21
(29·77 to 61·17)*
12·10
(3·36 to 27·26)*
·· Gallbladder and biliary
tract cancer
19·19
(10·00 to
31·40)
24·23
(12·96 to
38·93)
26·31
(20·46 to 33·91)*
–5·19
(–9·47 to 0·48)
398·83
(206·71 to 659·38)
501·99
(271·35 to 804·62)
25·87
(19·54 to
33·96)*
–3·50
(–8·43 to 2·52)
·· Pancreatic cancer 17·09
(6·73 to 32·72)
23·80
(9·45 to 45·36)
39·31
(33·12 to 46·65)*
5·04
(0·00 to 10·77)
355·53
(132·75 to 689·18)
488·30
(185·48 to 937·58)
37·34
(31·04 to
44·82)*
5·41
(0·50 to 11·10)*
·· Breast cancer 24·50
(9·01 to 45·25)
34·14
(14·17 to
61·44)
39·33
(26·71 to 66·63)*
1·49
(–7·17 to 17·88)
478·48
(134·90 to 931·31)
696·82
(241·06 to
1278·23)
45·63
(28·58 to
100·78)*
4·33
(–6·57 to 30·51)
·· Uterine cancer 25·33
(16·84 to
34·86)
31·98
(22·02 to
42·77)
26·29
(17·00 to 39·60)*
–4·35
(–11·15 to 5·40)
616·37
(406·52 to 852·11)
777·06
(534·09 to
1037·37)
26·07
(16·24 to 39·81)*
–2·75
(–10·14 to 7·44)
·· Ovarian cancer 3·96
(–0·06 to 8·73)
5·16
(–0·08 to
11·22)
30·36
(21·79 to 40·84)*
–0·87
(–7·38 to 6·92)
100·08
(–1·45 to 221·53)
130·91
(–2·01 to 284·65)
30·81
(22·13 to 41·76)*
1·63
(–5·03 to 10·00)
·· Kidney cancer 18·46
(10·70 to
28·15)
24·80
(14·55 to
37·29)
34·35
(28·83 to 41·33)*
1·72
(–2·45 to 6·95)
414·68
(240·63 to
629·90)
545·45
(324·36 to 818·33)
31·53
(25·96 to
38·50)*
1·48
(–2·72 to 6·69)
·· Thyroid cancer 2·91
(1·43 to 5·04)
3·99
(2·04 to 6·86)
37·08
(28·94 to 47·41)*
4·67
(–1·57 to 12·41)
75·91
(37·37 to 132·41)
104·28
(53·14 to 180·08)
37·39
(29·21 to
48·00)*
7·53
(1·34 to 15·42)*
·· Non-Hodgkin
lymphoma
8·76
(3·45 to 15·95)
12·11
(4·73 to 21·66)
38·22
(32·55 to 44·75)*
5·46
(1·16 to 10·68)*
214·81
(83·04 to 391·23)
295·53
(117·37 to 532·38)
37·58
(31·28 to 43·90)*
8·27
(3·38 to 13·37)*
·· Multiple myeloma 4·86
(2·10 to 8·71)
6·66
(2·89 to 11·78)
37·06
(31·43 to 44·71)*
3·30
(–1·17 to 9·24)
103·69
(45·00 to 186·13)
142·21
(62·40 to 249·59)
37·15
(31·41 to 45·43)*
5·30
(0·94 to 11·53)*
(Table 4 continues on next page)
Global Health Metrics
1400
www.thelancet.com Vol 390 September 16, 2017
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Acute lymphoid
leukaemia
1·59
(0·75 to 2·73)
2·22
(1·11 to 3·78)
39·14
(29·44 to 48·47)*
10·63
(3·24 to 17·88)*
53·85
(25·63 to 93·15)
73·29
(36·49 to 125·61)
36·10
(26·03 to
46·07)*
12·10
(3·94 to 19·97)*
·· Chronic lymphoid
leukaemia
2·36
(1·18 to 3·92)
2·92
(1·50 to 4·80)
23·58
(17·80 to 31·41)*
–8·23
(–13·00 to –2·59)*
45·52
(22·50 to 75·54)
55·53
(28·44 to 89·89)
21·98
(15·87 to 30·42)*
–6·69
(–11·35 to –0·57)*
·· Acute myeloid
leukaemia
4·65
(2·33 to 7·66)
6·22
(3·15 to 10·05)
33·79
(28·67 to 40·45)*
3·44
(–0·54 to 8·61)
119·03
(59·43 to 197·90)
156·14
(79·64 to 253·37)
31·17
(26·04 to
38·42)*
4·62
(0·50 to 10·33)*
·· Chronic myeloid
leukaemia
1·50
(0·74 to 2·54)
1·56
(0·79 to 2·62)
3·86
(–0·97 to 9·84)
–20·41
(–24·02 to –15·73)*
38·75
(18·95 to 66·00)
39·64
(20·22 to 66·66)
2·32
(–2·78 to 8·83)
–18·45
(–22·36 to –13·42)*
·· Other leukaemia 5·65
(2·63 to 9·91)
6·91
(3·43 to 11·92)
22·36
(13·74 to 33·25)*
–5·39
(–11·58 to 2·67)
150·74
(68·70 to 270·44)
175·88
(86·80 to 306·17)
16·67
(6·35 to 30·31)*
–6·03
(–13·63 to 3·83)
·· Ischaemic heart
disease
1288·03
(750·54 to
1915·37)
1592·33
(949·29 to
2325·60)
23·62
(18·28 to 31·01)*
–6·63
(–10·33 to –1·34)*
30 281·04
(18 069·07 to
44 440·56)
36 991·70
(22 899·69 to
52 749·96)
22·16
(17·05 to 29·82)*
–4·63
(–8·58 to 1·24)
·· Ischaemic stroke 283·31
(157·87 to
446·42)
318·39
(179·56 to
494·72)
12·38
(6·06 to 21·03)*
–14·52
(–19·22 to –8·07)*
7636·97
(4439·93 to
11 465·75)
9139·16
(5520·94 to
13 559·93)
19·67
(13·90 to 27·73)*
–7·74
(–12·19 to –1·69)*
·· Haemorrhagic stroke 517·26
(299·18 to
797·22)
592·91
(364·88 to
872·03)
14·63
(7·65 to 24·31)*
–10·40
(–15·68 to –2·82)*
15 913·88
(9447·13 to
23 465·66)
18 284·39
(11 769·74 to
25 665·62)
14·90
(7·94 to 24·58)*
–8·36
(–13·89 to –0·19)*
·· Hypertensive heart
disease
215·62
(115·71 to
340·33)
300·81
(162·11 to
482·35)
39·51
(23·49 to 54·97)*
4·14
(–6·93 to 14·90)
4745·65
(2865·31 to
6909·68)
6328·03
(3954·75 to
8998·62)
33·34
(20·77 to 46·93)*
3·67
(–6·03 to 13·90)
·· Atrial fibrillation and
flutter
30·66
(15·59 to
50·28)
46·15
(23·86 to
74·25)
50·50
(44·68 to 57·96)*
4·01
(0·15 to 9·07)*
847·22
(424·79 to
1434·95)
1206·94
(614·35 to
2008·58)
42·46
(38·94 to
47·40)*
6·27
(3·63 to 10·00)*
·· Asthma 50·50
(26·16 to
85·76)
60·27
(33·27 to
99·29)
19·33
(9·60 to 32·81)*
–7·72
(–15·43 to 2·78)
3096·98
(1685·64 to
5065·91)
3888·00
(2214·88 to
6203·97)
25·54
(19·00 to 34·22)*
4·23
(–2·03 to 11·64)
·· Gallbladder and biliary
diseases
22·65
(14·01 to
33·32)
31·11
(20·15 to
44·30)
37·32
(30·32 to 47·23)*
1·35
(–3·54 to 8·56)
478·24
(295·15 to 708·79)
634·28
(413·77 to 904·27)
32·63
(25·47 to 42·16)*
3·09
(–2·47 to 10·47)
·· Alzheimer’s disease
and other dementias
185·54
(67·16 to
358·86)
286·44
(106·50 to
545·02)
54·38
(48·77 to 62·72)*
6·35
(2·04 to 13·00)*
2357·11
(900·89 to
4607·68)
3493·12
(1387·03 to
6739·85)
48·20
(43·46 to
55·36)*
7·68
(4·04 to 13·61)*
·· Diabetes mellitus 390·47
(263·53 to
530·40)
553·44
(386·74 to
727·93)
41·74
(35·95 to 49·03)*
8·24
(3·84 to 14·15)*
20 585·42
(13 617·44 to
29 152·97)
28 645·74
(19 660·88 to
39 287·38)
39·16
(33·19 to 47·36)*
10·31
(5·57 to 16·73)*
·· Chronic kidney
disease due to
diabetes mellitus
98·46
(43·76 to
164·31)
146·40
(65·81 to
237·24)
48·69
(39·03 to 60·84)*
12·65
(7·32 to 19·88)*
3124·61
(1338·04 to
5247·93)
4566·00
(2050·51 to
7410·10)
46·13
(38·14 to 58·53)*
13·04
(7·74 to 20·08)*
·· Chronic kidney
disease due to
hypertension
47·69
(18·11 to
89·54)
73·45
(26·32 to
135·91)
54·03
(38·50 to 66·89)*
12·85
(7·10 to 23·44)*
1176·40
(503·88 to
2012·81)
1785·47
(805·84 to
2934·09)
51·77
(41·13 to 65·00)*
15·47
(9·33 to 24·63)*
·· Chronic kidney
disease due to
glomerulonephritis
30·93
(13·32 to
52·67)
41·71
(18·41 to
69·06)
34·87
(25·53 to 45·01)*
2·81
(–1·47 to 7·98)
1032·52
(417·03 to
1809·69)
1358·82
(567·29 to
2326·03)
31·60
(25·38 to
40·49)*
3·51
(–0·63 to 8·87)
·· Chronic kidney
disease due to other
causes
42·14
(18·81 to
71·07)
62·11
(26·60 to
104·77)
47·39
(32·26 to 59·95)*
11·26
(5·17 to 18·01)*
1301·30
(581·61 to
2232·05)
1867·87
(883·35 to
3113·60)
43·54
(35·86 to 54·17)*
11·97
(7·12 to 18·59)*
·· Osteoarthritis ·· ·· ·· ·· 2173·98
(1045·13 to
3867·70)
3225·98
(1624·93 to
5586·80)
48·39
(42·88 to
57·06)*
15·18
(11·04 to 21·86)*
·· Low back pain ·· ·· ·· ·· 2684·27
(1366·73 to
4685·98)
3630·51
(1919·27 to
6254·44)
35·25
(30·07 to
42·46)*
9·39
(5·70 to 14·83)*
(Table 4 continues on next page)
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1401
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Gout ·· ·· ·· ·· 229·32
(105·94 to
410·88)
321·88
(154·53 to 568·22)
40·37
(35·33 to 47·42)*
10·69
(6·98 to 16·07)*
·· Cataract ·· ·· ·· ·· 201·26
(82·62 to 397·74)
306·06
(132·21 to 586·28)
52·08
(45·72 to 61·98)*
15·83
(10·93 to 23·62)*
2 Low bone mineral
density: all causes
341·07
(288·70 to
360·77)
441·23
(374·93 to
466·70)
29·37
(24·07 to 34·07)*
–5·78
(–9·63 to –2·34)*
9412·32
(8030·50 to
11 131·37)
11 955·49
(10 090·79 to
14 196·27)
27·02
(23·65 to
29·65)*
–3·07
(–5·68 to –1·03)*
·· Pedestrian road
injuries
40·74
(38·39 to
43·41)
46·93
(44·13 to
49·88)
15·20
(9·02 to 18·80)*
–13·11
(–17·62 to –10·45)*
1094·99
(979·92 to
1226·14)
1293·53
(1143·70 to
1463·75)
18·13
(12·36 to 21·58)*
–8·69
(–12·96 to –6·11)*
·· Cyclist road injuries 5·17
(4·64 to 5·66)
6·03
(5·44 to 6·73)
16·64
(11·21 to 23·63)*
–10·21
(–14·32 to –5·00)*
343·04
(267·55 to
438·99)
447·83
(343·84 to 583·35)
30·55
(26·30 to 34·15)*
1·26
(–1·80 to 3·89)
·· Motorcyclist road
injuries
8·60
(7·58 to 9·40)
10·61
(9·08 to 11·55)
23·37
(16·31 to 29·85)*
–3·30
(–8·74 to 1·71)
516·37
(422·92 to 630·17)
665·90
(537·39 to 824·92)
28·96
(24·64 to 32·55)*
1·63
(–1·65 to 4·24)
·· Motor vehicle road
injuries
29·15
(26·52 to
32·31)
33·42
(30·60 to
37·18)
14·65
(10·86 to 20·30)*
–12·30
(–15·19 to –8·05)*
1053·30
(916·84 to
1212·20)
1227·50
(1055·45 to
1420·62)
16·54
(13·17 to 21·00)*
–9·15
(–11·70 to –5·87)*
·· Other road injuries 1·11
(0·97 to 1·42)
1·34
(1·19 to 1·71)
20·66
(10·56 to 35·98)*
–10·20
(–17·75 to 1·23)
85·10
(62·48 to 115·48)
129·35
(92·12 to 178·79)
51·99
(46·52 to 56·32)*
16·84
(12·28 to 20·37)*
·· Other transport
injuries
7·41
(6·74 to 7·98)
8·91
(8·22 to 10·00)
20·29
(13·83 to 28·79)*
–8·50
(–13·44 to –2·28)*
330·22
(273·06 to 402·51)
394·33
(321·20 to 482·74)
19·41
(15·51 to 24·18)*
–7·29
(–10·26 to –3·74)*
·· Falls 237·28
(186·55 to
254·10)
321·08
(254·50 to
344·23)
35·32
(28·08 to 41·49)*
–3·51
(–8·60 to 0·81)
5306·53
(4397·57 to
6284·03)
6968·79
(5750·97 to
8276·40)
31·32
(26·75 to 34·67)*
–1·34
(–4·93 to 1·31)
·· Other exposure to
mechanical forces
6·42
(5·21 to 6·94)
7·62
(5·85 to 8·28)
18·75
(11·69 to 23·63)*
–11·15
(–16·26 to –7·33)*
401·92
(305·39 to 523·87)
513·05
(380·93 to 681·19)
27·65
(23·66 to
30·49)*
–1·40
(–4·33 to 0·74)
·· Non-venomous
animal contact
0·68
(0·53 to 0·88)
0·76
(0·59 to 1·02)
12·06
(4·71 to 23·04)*
–15·81
(–21·13 to –7·87)*
21·55
(16·66 to 27·50)
23·45
(18·15 to 30·34)
8·80
(3·65 to 14·68)*
–15·77
(–19·71 to –11·18)*
·· Assault by other
means
3·91
(3·13 to 4·64)
4·14
(3·51 to 5·31)
5·95
(–3·67 to 21·09)
–18·19
(–25·57 to –7·12)*
236·94
(182·44 to
304·08)
268·81
(204·14 to 351·16)
13·45
(7·54 to 19·57)*
–11·45
(–15·88 to –6·82)*
·· Forces of nature,
conflict and terrorism,
and state actor
violence
0·60
(0·40 to 0·81)
0·37
(0·21 to 0·57)
–38·05
(–56·98 to
–20·83)*
–51·96
(–66·57 to –38·70)*
22·35
(13·90 to 36·70)
22·95
(10·58 to 47·82)
2·68
(–28·79 to 30·32)
–19·58
(–43·89 to 1·64)
2 Impaired kidney
function: all causes
2108·45
(1943·12 to
2277·00)
2554·21
(2346·59 to
2766·51)
21·14
(18·37 to 23·96)*
–9·08
(–10·89 to –7·16)*
52 009·54
(48 088·99 to
55 861·74)
60 482·18
(55 678·63 to
65 319·35)
16·29
(13·87 to 18·61)*
–8·10
(–9·94 to –6·30)*
·· Ischaemic heart
disease
753·35
(627·96 to
868·81)
906·02
(749·80 to
1056·12)
20·27
(15·84 to 24·87)*
–11·91
(–14·40 to –9·02)*
13 095·90
(11 202·99 to
14 872·74)
15 068·46
(12 896·50 to
17 267·64)
15·06
(11·42 to 18·84)*
–11·75
(–14·21 to –9·06)*
·· Ischaemic stroke 201·59
(153·59 to
247·89)
219·00
(164·95 to
274·84)
8·63
(2·78 to 14·64)*
–19·04
(–22·23 to –15·40)*
4041·29
(3235·99 to
4810·89)
4478·73
(3577·63 to
5417·18)
10·82
(6·20 to 15·37)*
–15·45
(–18·71 to –12·11)*
·· Haemorrhagic stroke 227·29
(185·54 to
269·78)
236·16
(191·40 to
283·30)
3·91
(0·62 to 7·40)*
–20·50
(–22·52 to –18·46)*
5431·74
(4452·60 to
6455·91)
5578·78
(4537·14 to
6686·22)
2·71
(–0·10 to 5·74)
–19·77
(–21·70 to –17·83)*
·· Peripheral vascular
disease
5·64
(3·81 to 8·18)
7·32
(4·82 to 11·42)
29·76
(16·19 to 45·98)*
–4·33
(–12·92 to 6·28)
170·24
(121·08 to 237·00)
210·83
(147·41 to 296·25)
23·84
(16·07 to 32·93)*
–5·65
(–11·04 to 0·58)
·· Chronic kidney
disease due to
diabetes mellitus
384·78
(349·87 to
418·93)
500·41
(452·11 to
543·57)
30·05
(26·18 to 32·84)*
–0·63
(–3·43 to 1·30)
11 723·50
(10 608·16 to
12 883·32)
14 649·82
(13 196·95 to
16 191·89)
24·96
(21·91 to 27·57)*
–1·37
(–3·51 to 0·51)
(Table 4 continues on next page)
Global Health Metrics
1402
www.thelancet.com Vol 390 September 16, 2017
mutually exclusive categories: population growth,
population ageing, trends in exposure to all risk factors
measured in GBD 2016, and all other factors combined.
Globally, trends in exposure to all risk factors combined
would have led to a decrease of deaths by 9·3% (6·9–11·6)
and DALYs by 10·8% (8·3–13·1). Risk factors play a larger
part in CMNN causes, where trends in exposure to risks
would have resulted in a decrease of deaths by 14·9%
(12·4–17·1) and DALYs by 15·0% (12·7–17·6). Overall,
population ageing and population growth are both driving
deaths and DALYs to increase significantly. At the global
level, across all causes, population growth alone would
have resulted in 12·4% (10·1–14·9) more deaths and
12·4% (10·1–14·9) more DALYs, while population ageing
would have contributed 14·9% (12·7–17·5) more deaths
and 12·4% (10·1–14·9) more DALYs. The contribution of
population ageing in NCDs is noteworthy as it is the
largest driver of trends in NCDs, and accounts for 19·5%
(17·3–22·0) more deaths and 14·0% (11·6–16·3) more
DALYs between 2006 and 2016. The residual category,
which includes improvements in treatment along with
other factors, accounts for a decrease of 15·3% (12·9–17·7)
for deaths and 16·5% (14·1–18·8) for DALYs across all
causes and is particularly large for CMNN causes
accounting for a 30·0% (27·5–32·4) decline in deaths and
a 26·8% (24·4–29·2) decline in DALYs since 2006.
Figure 6 shows the contribution of these drivers across
age groups for DALYs. Across age groups, the contributions
of the four drivers dier greatly. Changes in risk exposure
have played a major part in the declines in DALYs younger
than 5 years, accounting for 26·7% (24·3–29·7) of the
trend in DALYs in the post-neonatal period and 27·3%
(24·9–29·7) among ages 1–4 years. Trends in risks account
for a decline of 8·7% (6·3–11·1) of DALYs in older children
(ages 5–9 years) and 9·0% (6·5–11·4) of DALYs in young
adolescents (ages 10–14 years). As expected, population
ageing is a more significant driver among older age
groups, accounting for up to 51·4% (49·1–53·9) of the
change in DALYs since 2006 among the age group
90–94 years. Finally, the proportion of the change in
DALYs that is due to all other factors—ie, not explained by
these three major drivers—also shows large variation
across age groups, ranging from a decrease of 3·5%
(1·1–6·0) in the age group 15–19 years to a decrease of
28·2% (25·8–30·5) in the age group 1–4 years.
Key results for new risks, leading risks, and risks with
significant changes in GBD 2016
In 2016, for Level 3 risks factors, more DALYs were
attributable to increased SBP than any other risk factor.
Increased SBP was the second leading risk factor for men
and leading risk factor for women globally, accounting for
89·9 million (80·9 million to 98·2 million) DALYs among
women and 124·1 million (111·2 million to 138·0 million)
DALYs among men. IHD was the largest source of DALYs
attributable to increased SBP, followed by haemorrhagic
stroke and ischaemic stroke. Since 1990, the SEV for
increased SBP rose for men (22·9 [21·5–24·6] in 1990 to
24·6 [23·0–26·6] in 2016, a 7·5% increase [7·0–8·0]), and
increased for women (24·2 [22·7–25·8] in 1990 to 24·2
[22·7–25·8] in 2016, a 0·7% increase [0·2–1·2]).
In 2016, 7·1 million (6·5 million to 7·8 million) deaths
and 177·3 million (162·3 million to 194·3 million) DALYs
2006 deaths
(in thousands)
2016 deaths
(in thousands)
Percentage
change of deaths
2006–16
Percentage
change of age-
standardised
deaths rate
2006–16
2006 DALYs
(in thousands)
2016 DALYs
(in thousands)
Percentage
change of DALYs
2006–16
Percentage change
of age-
standardised
DALYs rate
2006–16
(Continued from previous page)
·· Chronic kidney
disease due to
hypertension
222·32
(199·99 to
248·86)
299·48
(268·03 to
335·26)
34·71
(30·47 to 38·02)*
–0·96
(–3·95 to 1·00)
5166·00
(4517·97 to
5842·00)
6602·34
(5756·41 to
7488·87)
27·80
(24·67 to
30·62)*
–1·02
(–3·31 to 0·87)
·· Chronic kidney
disease due to
glomerulonephritis
127·88
(114·79 to
143·00)
149·99
(133·07 to
168·74)
17·29
(13·77 to 20·69)*
–6·33
(–8·54 to –4·22)*
5463·57
(4839·64 to
6152·24)
5927·94
(5222·09 to
6740·39)
8·50
(5·53 to 11·81)*
–7·67
(–9·80 to –5·37)*
·· Chronic kidney
disease due to other
causes
185·61
(164·22 to
208·15)
235·84
(206·86 to
266·27)
27·06
(23·30 to 30·93)*
–0·92
(–3·46 to 1·41)
6815·19
(6057·76 to
7656·73)
7827·49
(6911·39 to
8843·22)
14·85
(11·59 to 18·40)*
–3·98
(–6·26 to –1·43)*
·· Gout ·· ·· ·· ·· 102·11
(70·04 to 141·30)
137·78
(94·34 to 190·02)
34·93
(32·98 to
36·86)*
2·87
(1·60 to 4·23)*
Data in parentheses are 95% uncertainty intervals. DALYs=disability-adjusted life-years. *Statistically significant increase or decrease.
Table 4: Global all-age attributable deaths and DALYs, in 2006 and 2016, and percentage change of deaths, age-standardised death rates, DALYs, and age-standardised DALY rates
between 2006 and 2016, for all risk-outcome pairs, both sexes combined
Figure 3: Leading 30 Level 3 risk factors by attributable DALYs at the global
level, 1990, 2006, and 2016, for males (A) and females (B)
Risks are connected by lines between time periods. Behavioural risk factors are
shown in red, environmental risks in blue, and metabolic risks in green. For the
time period of 1990 to 2006 and for 2006–16, three measures of change are
shown: percent change in the number of DALYs, percent change in the all-age
DALY rate, and percent change in the age-standardised DALY rate. Statistically
significant increases or decreases are shown in bold (p<0·05). DALYs=disability-
adjusted life-years.
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1403
Leading risks 1990 Leading risks 2006 Mean %
change in
number
of DALYs
1990–2006
Mean %
change in
all-age
DALY rate
1990–2006
Mean %
change in age-
standardised
DALY rate
1990–2006
Leading risks 2016 Mean %
change in
number
of DALYs
2006–16
Mean %
change in
all-age
DALY rate
2006–16
Mean %
change in age-
standardised
DALY rate
2006–16
1 Child growth failure 1 Smoking 18·5 –5·3 –20·1 1 Smokin
g2
·1 –9·3 –20·4
2 Low birthweight and short gestation 2 Low birthweight and short gestation –24·4 –39·6 –24·8 2 High blood pressure 16·2 3·2 –10·5
3 Smoking 3 High blood pressure 32·3 5·8 –12·4 3 Low birthweight and short gestation –28·3 –36·3 –27·8
4 High blood pressure 4 Child growth failure –45·9 –56·8 –46·7 4 Alcohol use2·6 –8·8 –15·5
5 Household air pollution 5 Alcohol use 35·4 8·2 –5·4 5 High fasting plasma glucose 19·56·2 –7·2
6 Ambient particulate matter 6 High fasting plasma glucose 59·7 27·6 7·2 6 High body-mass index 31·0 16·4 2·8
7 Unsafe water 7 Ambient particulate matter –2·6 –22·2 –22·5 7 Ambient particulate matter4·2 –7·4 –14·2
8 Alcohol use 8 Household air pollution –24·7 –39·8 –37·8 8 High total cholesterol 13·3 0·6 –11·6
9 Unsafe sanitation9 High body-mass index 63·3 30·5 10·0 9 Child growth failure –42·3 –48·8 –43·8
10 High fasting plasma glucose 10 High total cholesterol 31·1 4·8 –13·4 10 Household air pollution –27·4 –35·5 –38·3
11 No access to handwashing facility 11 Unsafe water –32·5 –46·0 –37·6 11 Low frui
t2
·2 –9·1 –19·8
12 High total cholesterol 12 Unsafe sex 300·9 220·4 198·8 12 Low whole grains 10·3 –2·0 –13·5
13 High body-mass index 13 Low fruit 22·5 –2·1 –17·3 13 Impaired kidney function 18·9 5·6 –6·3
14 Low fruit 14 Unsafe sanitation –35·6 –48·5 –40·5 14 Low nuts and seeds 12·0 –0·5 –12·0
15 Low whole grains 15 Low whole grains 22·8 –1·8 –17·6 15 High sodium 12·8 0·2 –13·4
16 Suboptimal breastfeeding 16 Impaired kidney function 37·8 10·1 –5·0 16 Unsafe water –34·6 –41·8 –39·4
17 High sodium 17 No access to handwashing facility –29·3 –43·5 –34·2 17 Unsafe sex –35·3 –42·5 –43·8
18 Occupational injury 18 Low nuts and seeds 32·4 5·8 –11·9 18 Drug use 9·1 –3·0 –5·7
19 Impaired kidney function 19 High sodium 7·3 –14·2 –28·3 19 Low vegetables 3·0 –8·5 –19·3
20 Low nuts and seeds 20 Low vegetables 14·9 –8·2 –22·7 20 Low omega 3 12·1 –0·4 –12·0
21 Low vegetables 21 Drug use 55·9 24·6 17·6 21 Unsafe sanitation –39·3 –46·1 –43·9
22 Second-hand smoke 22 Occupational injury –14·8 –31·9 –36·0 22 Occupational injury –2·0 –12·9–14·4
23 Low omega 3 23 Low omega 3 35·9 8·6 –8·9 23 No access to handwashing facility –34·0 –41·4 –38·5
24 Vitamin A deficiency 24 Suboptimal breastfeeding –48·4 –58·7 –48·9 24 Occupational carcinogens 18·7 5·5 –8·0
25 Drug use 25 Occupational carcinogens 29·9 3·8 –12·0 25 Low physical activity 18·8 5·6 –9·6
26 Iron deficiency 26 Low physical activity 33·0 6·3–13·1 26 Iron deficiency 4·2 –7·4 –3·4
27 Unsafe sex 27 Iron deficiency 17·3 –6·2 3·4 27 Low fibre 9·5–2·7–12·9
28 Occupational carcinogens 28 Low fibre 34·3 7·3 –10·3 28 Lead 5·7 –6·0 –15·5
29 Low physical activity 29 Second-hand smoke –39·3 –51·5 –42·6 29 Low legumes 7·3–4·7–15·1
30 Low fibre 30 Lead 33·5 6·7 –5·3 30 Second-hand smoke –10·8 –20·7–21·7
31 Low legumes32 Lead 33 Suboptimal breastfeeding
36 Vitamin A deficiency33 Low legumes
Leading risks 1990 Leading risks 2006 Leading risks 2016
1 Child growth failure 1 Low birthweight and short gestation –25·0 –39·8 –24·9 1 High blood pressure 7·7 –4·1 –16·8
2 Low birthweight and short gestation 2 High blood pressure 17·7 –5·6 –19·0 2 High body-mass index 26·1 12·3 –1·0
3 Household air pollution 3 Child growth failure –48·9 –59·0 –49·0 3 High fasting plasma glucose 14·2 1·6 –10·9
4 High blood pressure 4 High fasting plasma glucose 53·3 23·0 7·0 4 Low birthweight and short gestation –28·8 –36·6 –28·7
5 Unsafe water 5 High body-mass index 52·4 22·3 5·9 5 Child growth failure –47·1 –52·9 –48·7
6 Unsafe sanitation 6 Household air pollution –29·1 –43·2 –39·8 6 Ambient particulate matter –5·8 –16·2 –21·3
7 Ambient particulate matter 7 Unsafe sex 340·3 253·2 204·0 7 High total cholesterol 9·6 –2·5 –15·5
8 No access to handwashing facility 8 Ambient particulate matter –14·9 –31·7 –29·0 8 Household air pollution –30·9 –38·5 –41·1
9 High fasting plasma glucose 9 Unsafe water –36·9 –49·4 –41·0 9 Smoking –2·5 –13·2 –23·9
10 High body-mass index 10 Unsafe sanitation –39·7 –51·7 –43·7 10 Unsafe sex –38·7 –45·4 –46·7
11 High total cholesterol 11 High total cholesterol 20·0 –3·8 –18·1 11 Impaired kidney function 13·2 0·7 –10·5
12 Smoking 12 Smoking 11·6 –10·4 –22·4 12 Low whole grains 7·7 –4·1 –15·6
13 Second-hand smoke 13 No access to handwashing facility –34·5 –47·5 –38·4 13 Unsafe water –37·6 –44·5 –43·0
14 Suboptimal breastfeeding 14 Low whole grains 13·2 –9·2 –21·6 14 Iron deficiency 6·9–4·8–2·7
15 Low fruit 15 Low fruit 8·5 –13·0 –24·5 15 Low frui
t–
4·3 –14·8 –24·8
16 Low whole grains 16 Impaired kidney function 26·9 1·8 –10·2 16 Unsafe sanitation –42·5 –48·8 –47·5
17 High sodium 17 Iron deficiency 16·5 –6·6 –3·5 17 Low nuts and seeds 8·0 –3·9 –15·5
18 Iron deficiency 18 High sodium –7·8 –26·0 –36·2 18 High sodium 0·9 –10·1–21·9
19 Impaired kidney function 19 Low nuts and seeds 24·1 –0·5 –14·6 19 Alcohol use 5·5 –6·1 –13·6
20 Alcohol use 20 Alcohol use 15·1 –7·6 –18·4 20 No access to handwashing facility –38·1 –44·9 –42·9
21 Low nuts and seeds 21 Second-hand smoke –28·3 –42·5 –39·6 21 Second-hand smoke –10·3–20·2–25·9
22 Low vegetables 22 Low vegetables 3·7 –16·8 –27·9 22 Low vegetables –2·2 –12·9–23·3
23 Vitamin A deficiency 23 Suboptimal breastfeeding –49·7 –59·7 –49·8 23 Low omega 3 7·2 –4·6 –16·3
24 Unsafe sex 24 Low omega 3 23·6 –0·8 –14·2 24 Low physical activity 10·6 –1·6 –15·1
25 Low omega 3 25 Drug use 45·4 16·6 11·9 25 Drug use 6·4 –5·3 –7·5
26 Low physical activity 26 Low physical activity 20·9 –3·1 –17·9 26 Low fibre 7·2 –4·6 –15·8
27 Drug use 27 Intimate partner violence 156·0 105·4 93·1 27 Occupational ergonomic 20·9 7·6 1·6
28 Low fibre 28 Low fibre 20·7 –3·1 –17·0 28 Suboptimal breastfeeding –46·3 –52·2 –46·8
29 Zinc deficiency 29 Occupational ergonomic 35·7 8·9 –3·0 29 Occupational carcinogens 17·7 4·8 –7·8
30 Occupational ergonomic 30 Low legumes 28·4 3·0 –12·5 30 Low bone mineral density 26·0 12·1 –3·8
31 Occupational carcinogens31 Low legumes 31 Intimate partner violence
32 Vitamin A deficiency32 Occupational carcinogens 32 Low legumes
33 Low bone mineral density33 Intimate partner violence
36 Zinc deficiency
A
B
Behavioural
Environmental
Metabolic
Mean %
change in
number
of DALYs
1990–2006
Mean %
change in
all-age
DALY rate
1990–2006
Mean %
change in age-
standardised
DALY rate
1990–2006
Mean %
change in
number
of DALYs
2006–16
Mean %
change in
all-age
DALY rate
2006–16
Mean %
change in age-
standardised
DALY rate
2006–16
Global Health Metrics
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www.thelancet.com Vol 390 September 16, 2017
were attributable to tobacco, most of which is attributable to
smoking tobacco. Smoking-attributable deaths have
increased by 20·1% (15·3–25·2) since 1990, with most
deaths occurring in China, India, the USA, and Russia.
Smoking is the second-leading risk factor for men for
deaths and leading for DALYs, accounting for 16·3%
(14·6–17·9) of deaths and 9·5% (8·5–10·7) of DALYs, and
the sixth for women for deaths and ninth for DALYs, with
5·8% (5·0–6·7) of deaths and 2·9% (2·5–2·94) of DALYs.
In 2016, there were 177·3 million (162·3 million to
194·3 million) smoking-attributable DALYs globally. Overall,
in 2016 chronic respiratory diseases (30·3% [25·2–36·0]),
neoplasms (19·2% [16·0–22·8]), and cardiovascular
diseases (18·0% [16·0–20·0] were the three leading causes
of smoking-attributable age-standardised DALYs across
both sexes. For women, the leading cause of DALYs was
COPD, whereas the leading cause for men was IHD.
Second-hand smoke exposure is highest in eastern Asia
and Oceania and higher among women and children
compared with men. The distribution of DALYs attributable
100 thousand 1 million 10 million 100 million 1 billion
–3·0%
–2·5%
–2·0%
–1·5%
–1·0%
–0·5%
0%
0·5%
1·0%
1·5%
2·0%
Annualised rate of change in SEVs (1990–2016)
Number of attributable DALYs (log scale)
Sugar-sweetened beverages
Diesel
Red meat
PAH
Arsenic
Nickel
Sulfuric acid
Ozone
Silica Asbestos
Asthmagens
Sexual abuse
Disc breast
PM, gases, and fumes
Occupational SHS
Physical activity
Impaired kidney
FPG
Ambient PM
Alcohol
Short gestation
SBP
Cholesterol
Drugs
Iron
BMD
Legumes
Ergonomics
Noise
Smokeless
BMI
Milk
Calcium
Radon
Processed meat IPV PUFA Fibre
Nuts and seeds
Omega 3
Part breastfeeding
Unsafe water
Handwashing
Whole grains
Fruits Wasting
Sodium
Smoking
SHS
Lead
Vegetables
Vitamin A
Zinc
Stunting
Underweight
Sanitation
Household air
Trans fatty acids
Environmental or occupational
Behavioural
Metabolic
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1405
to second-hand smoke exposure is bimodal, with peaks in
the post-neonatal period and again in older age groups.
Globally 0·9 million (0·7 million to 1·1 million) deaths
were attributable to second-hand smoke exposure, of
which 56 340 (28 951–89 043) occurred among children
younger than age 10 years.
In estimating the burden attributable to smokeless
tobacco, we found that the risk varies by the toxicity of the
type used; there is sucient evidence that chewing tobacco
and other products of similar toxicity cause excess risk of
oral and oesophageal cancer while, at this time, existing
evidence does not support attributing burden to snus or
similar smokeless tobacco products. Globally, smoking
tobacco causes far more burden than smokeless tobacco;
nonetheless, smokeless tobacco is an important risk factor
for oral and oesophageal cancer in India, where more than
half of the 32 141 (24 930–39 243) global deaths attributable
to smokeless tobacco occur.
Low birthweight and short gestation, new risk factors in
GBD 2016, were the third-ranked Level 3 risk factor globally
for all-ages DALYs in 2016, which reflects a 61·6%
(59·3–64·0) decrease in all-ages DALY rates from 5112·8
(4934·2–5389·6) DALYs per 100 000 in 1990 to 1960·8
(1862·0–2060·3) DALYs in 2016. In 1990, this risk factor
was the second-ranked Level 3 risk factor globally for all-age
DALYs; most of the decrease from 1990 to 2016 is due to a
lower mortality burden in the causes attributable to low
birthweight and short gestation rather than changes in
exposure itself. Increasing SDI was associated with
decreasing exposure, but the exposure gradient between
SDI quintiles was not as large as the dierential between
high and low SDI in attributable burden. Exposure was
highest in South Asia, eastern sub-Saharan Africa, and
parts of the western Sahel zone, while attributable burden
was highest in South Asia and parts of the western Sahel
zone. The trend in exposure to low birthweight for gestation
decreased at the global level from 1990 to 2016, reflective of
the overall decrease in DALYs burden during the same time
period. The biggest improvements were seen in Colombia,
Brunei, and Zimbabwe, with broad improvements also
seen across much of eastern sub-Saharan Africa.
In 2016, high FPG was the third-leading risk factor for
deaths and the fourth-leading risk factor for DALYs globally
among Level 3 risk factors, accounting for more than
5·6 million deaths (4·5 million to 7·0 million) and
144·1 million DALYs (119·9 million to 171·6 million). Since
1990, the age-standardised percent of deaths and DALYs
attributable to high FPG has increased globally from 7·8%
(6·0–10·1) to 10·5% (8·3–13·1) and 4·4% (3·7–5·3) to
6·2% (5·3–7·3), respectively. Diabetes was the largest
source of DALYs attributable to increased FPG, followed by
ischaemic heart disease and chronic kidney disease. We re-
evaluated epidemiological evidence supporting the causal
relationship between high FPG and disease endpoints and
found sucient evidence to include ten new outcomes for
high FPG. These new outcomes included glaucoma,
cataracts, dementia, liver cancer, lung cancer, ovarian
cancer, breast cancer, bladder cancer, colorectal cancer, and
pancreatic cancer. The new outcomes together contributed
to 174 352 (37 297–388 039) additional deaths and 2·6 million
(0·6 million to 5·7 million) additional DALYs beyond the
causes that were included in GBD 2015.
In 2016, BMI was the fifth-ranked Level 3 risk factor for
death globally, accounting for more than 4·5 million
(2·9 million to 6·4 million) deaths and 135·4 million
(88·6 million to 187·4 million) DALYs. Among Level 3 risk
factors with more than 10 million attributable DALYs, high
BMI had the fastest annualised rate of increase in SEV
since 1990 (appendix 2 p 1399). Despite this significant
increase in risk exposure, increases in attributable burden
were attenuated by significant decreases in risk-deleted
DALY rates, mainly due to reductions in cardiovascular
disease mortality rates. We find that the burden attributable
to high BMI increases with increasing development, with
the lowest rates of disease attributable to high BMI found
in sub-Saharan Africa, yet development is not the only
predictor. We conducted a systematic search of health
outcomes caused by excess bodyweight and added eight
new causes for GBD 2016, which together contributed to
442 750 (191 407–796 350) additional deaths beyond the
causes that were included in GBD 2015. Additionally, we
included childhood overweight and childhood obesity as
new risk factors, allowing us to better capture the health
eects of excess bodyweight across the life course. Within
Figure 4: Relationship between attributable DALYs in 2016 for Level 3 risk
factors and annualised rate of change in SEV, at the global level, both sexes
combined, 1990–2016
DALYs are represented on a logarithmic scale. Risks shown exhibited a statistically
significant change in SEV between 1990 and 2016. The following six risks, each of
which is responsible for fewer than 100 thousand DALYs, are not shown:
occupational exposure to benzene, beryllium, cadmium, chromium, formaldehyde,
and trichloroethylene. DALYs=disability-adjusted life-years. SEV=summary exposure
value. Ambient PM=ambient particulate matter pollution. Alcohol=alcohol use.
Arsenic=occupational exposure to arsenic. Asbestos=occupational exposure to
asbestos. Asthmagens=occupational asthmagens. BMD=low bone mineral density.
BMI=high body-mass index. Calcium=diet low in calcium. Cholesterol=high total
cholesterol. Diesel=occupational exposure to diesel engine exhaust. Disc
breast=discontinued breastfeeding. Drugs=drug use. Ergonomics=occupational
ergonomic factors. Fibre=diet low in fibre. FPG=high fasting plasma glucose.
Fruits=diet low in fruits. Handwashing=no access to handwashing facility.
Household air=household air pollution from solid fuels. Impaired kidney=impaired
kidney function. IPV=intimate partner violence. Iron=iron deficiency. Lead=lead
exposure. Legumes=diet low in legumes. Milk=diet low in milk. Nickel=occupational
exposure to nickel. Noise=occupational noise. Nuts and seeds=diet low in nuts and
seeds. Occupational SHS=occupational exposure to second-hand smoke.
Omega 3=diet low in seafood omega 3 fatty acids. Ozone=ambient ozone pollution.
PAH=occupational exposure to polycyclic aromatic hydrocarbons. Part
breastfeeding=non-exclusive breastfeeding. Physical activity=low physical activity.
PM, gases, and fumes=occupational particulate matter, gases, and fumes. Processed
meat=diet high in processed meat. PUFA=diet low in polyunsaturated fatty acids.
Radon=residential radon. Red meat=diet high in red meat. Sanitation=unsafe
sanitation. SBP=high systolic blood pressure. Sexual abuse=childhood sexual abuse.
SHS=second-hand smoke. Silica=occupational exposure to silica.
Smokeless=smokeless tobacco. Sodium=diet high in sodium. Stunting=child
stunting. Sugar-sweetened beverages=diet high in sugar-sweetened beverages.
Sulfuric acid=occupational exposure to sulfuric acid. Transfatty acids=diet high in
transfatty acids. Underweight=child underweight. Vegetables=diet low in
vegetables. Vitamin A=vitamin A deficiency. Wasting=child wasting. Water=unsafe
water source. Whole grains=diet low in whole grains. Zinc=zinc deficiency.
Global Health Metrics
1406
www.thelancet.com Vol 390 September 16, 2017
the CRA framework, the only childhood overweight and
obesity outcome eligible for inclusion was asthma. We
found that 10·4% (3·1–21·2) of asthma can be attributed to
childhood excess bodyweight globally, a total of 1128
(311–2354) deaths and 642 532·1 (180 916·3 to 1 456 342·7)
DALYs. While childhood burden is much smaller com-
pared with adult burden, estimating exposure for children
is crucially important in view of the well described eects of
childhood overweight and obesity on adult health outcomes.
Air pollution was ranked sixth in terms of attributable
DALYs in 2016. We found that 7·5% (6·6–8·4) of deaths
globally were attributable to ambient air pollution in 2016
(4·1 million [3·6 million to 4·6 million] deaths, 1·3 million
[1·1 million to 1·5 million] in South Asia). Countries with
notably high levels of attributable deaths include China
(11·1% [9·7–12·7] of all deaths attributable to ambient
particulate matter) and India (10·6% [9·2–11·9] of all
deaths). The diseases with the largest proportion of burden
attributable to air pollution are LRI and COPD; ambient
particulate matter is responsible for 27·5% (21·4–34·4) of
all LRI and 26·8% (16·1–38·6) of COPD deaths and 33·3%
(26·3–40·5) of LRI deaths in children younger than 5 years.
In terms of overall ranking, ambient particulate matter has
increased from seventh in 1990 with 115·2 million
(99·1 million to 132·9 million) DALYs to sixth in 2016 with
105·7 million (94·2 million to 117·8 million) DALYs. For
deaths, it is among the top ten ranked risk factors in
195 countries and territories, including India and China,
where it was in third and fourth place, respectively. Also of
note is that updated satellite data indicate increased
ambient air pollution in 2015–16 in West Africa that is
driven by wind-blown dust from the Sahara. This eect has
profound eect on disease burden in this region, as intense
particulate matter with an aerodynamic diameter smaller
than 2·5 µm (PM2·5) events aect Africa’s densest region.
Globally, alcohol is estimated to be the seventh-leading
risk factor in 2016 in terms of DALYs. In the same year,
alcohol use was estimated to have caused 99·2 million
DALYs (88·3 million to 111·2 million), accounting for 4·2%
(3·7–4·6) of total DALYs. This is a larger share of total
burden than previously reported, driven primarily by
changes made to both the exposure and RR models. This
burden is distributed unequally among the sexes and
regions. When decomposed by sex, alcohol use accounts
for 6·2% (5·6–6·9) of total DALYs among men and 1·7%
(1·4–2·0) of total DALYs among women. When decomposed
by region in 2016, alcohol use accounts for 13·9%
(11·5–16·8) of age-standardised DALYs in eastern Europe,
4·0% (3·4–4·6) of age-standardised DALYs in Southeast
Asia, but only 0·8% (0·6–1·0) of age-standardised DALYs
in the Middle East. Alcohol use attributable DALYs have
also increased by more than 25% over the years 1990–2016,
driven primarily by increased consumption in South Asia,
Southeast Asia, and Central Asia, among both men and
women. Globally, alcohol use exposure has increased by
15·2% (8·7–22·6) over that time frame among men and
decreased by 3·2% (–9·1 to 3·1) among women. However,
the largest increases in exposure have been in countries in
the low-middle quintile of SDI. Globally, alcohol use is the
leading risk factor in DALYS between the ages of 15 years
and 49 years in 2016. However, unlike tobacco or drugs,
governments have been discouraged from eorts to limit
Communicable, maternal, neonatal,
and nutritional diseases
Injuries
Non-communicable diseases
All causes
Change due to population ageing
Change due to population growth
Change due to risk exposure
Change due to risk-deleted deaths or DALYs
Total percentage
ADeaths
Communicable, maternal, neonatal,
and nutritional diseases
–50 –25 0 25
Change (%)
Injuries
Non-communicable diseases
All causes
BDALYs
Figure 5: Percent change in deaths (A) and DALYs (B) at the global level, 2006–16, due to population growth, population ageing, trends in exposure to all risks
included in GBD 2016, and and all other (risk-deleted or residual) factors
Results are shown for all causes combined; communicable, maternal, neonatal, and nutritional diseases; non-communicable diseases; and injuries.
DALYs=disability-adjusted life-years.
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alcohol’s availability by trade agreements and disputes.
Given alcohol’s health burden within these age groups, an
increased focus on alcohol control policies is needed to
eectively address this risk factor.
It is worth noting some key results for dietary risks as
well. In 2016, suboptimal diet was the second-leading risk
factor for deaths and DALYs globally, accounting for 18·8%
(16·0–21·7) of all deaths and 9·6% (8·2–11·1) of all DALYs.
Comparing men and women, suboptimal diet accounts for
the greatest percentage of total deaths in men (19·0%
[16·3–21·8]) and the second largest in women (18·6%
[15·7–21·7]). Meanwhile, suboptimal diet accounts for the
second-largest percent of total DALYs in both men (10·6%
[9·1–12·2]) and women (8·4% [7·0–9·9]). More than 50%
of deaths (51·5% [44·2–59·2]) and DALYs (54·1%
[47·1–61·5]) attributable to suboptimal diet were due to
cardiovascular diseases. Among the individual dietary
risks, a diet low in whole grains accounted for the largest
number of deaths (4·6% [3·0–6·4]), followed by a diet low
in fruits (4·3% [2·7–6·3]) and a diet high in sodium
(4·2% [1·2–8·3]). Leading dietary risks for DALYs were low
intakes of whole grains (2·6% [1·8–3·6]), fruits (2·6%
[1·6–3·7]), and nuts and seeds (2·1% [1·4–2·8]). The
greatest increase in attributable deaths and DALYs between
1990 and 2016 occurred for a diet high in red meat, followed
by a diet high in sugar-sweetened beverages and a diet low
in milk, respectively.
Discussion
General findings
Based on the analysis of 22717 sources, we estimated
disease burden attributable to 84 metabolic, environmental,
occupational, and behavioural risk factors or clusters of
risks from 1990 to 2016 in 195 countries and territories. In
2016, all risks combined contributed to 59·9% (58·4–61·3)
of deaths and 45·2% (43·2–47·3) of DALYs worldwide,
compared with 60·3% (59·0–61·6) of deaths and 49·6%
(47·6–51·7) of DALYs in 1990. The role of changes in risk
factors in explaining changes in deaths and DALYs varies
considerably across causes and ages, with the largest
eects noted in children due to infectious diseases. Since
1990, exposure increased significantly for 30 risks, did not
change significantly for four risks, and decreased
significantly for 31 risks. The risks with the highest
increases in SEVs include high body-mass index,
occupational exposure to diesel engine exhaust, and
occupational exposure to trichloroethylene, while the risks
with the largest decreases in exposure are diet high in
transfatty acids, household air pollution from solid fuels,
and unsafe sanitation.
We found substantial heterogeneity across countries in
the leading risk factors. Some notable patterns are the role
of unsafe sexual practices as a driver of the HIV epidemic
in Eastern and Southern Africa and the role of alcohol
consumption in Eastern Europe and Central Asia. There
are also marked spatial patterns for other risks such as high
BMI in Central America, North Africa and the Middle East,
and Oceania. Interpreting spatial patterns needs to take
into account the fact that some risks have a strong
relationship with socioeconomic development. Several
environmental and behavioural risks, including water,
sanitation, handwashing, household air pollution, and
childhood growth failure decline profoundly with
development. Another cluster of risks tends to increase
with socioeconomic development, including high BMI,
high SBP, red meat consumption, sugar-sweetened
beverages, alcohol, and high FPG.
Cross-cutting themes
Many factors should determine government priorities for
action including the size of the problem, inequalities
related to the problem, likely future trends, the availability
of eective policy options, and the opportunity cost of
tackling a particular problem. In this analysis, we provided
information about the size of the problem, trends in
exposure in the last 27 years, and the range of exposure at
given levels of socioeconomic development. Problems that
Change due to population ageing
Change due to population growth
Change due to risk-deleted
DALY rate
Change due to risk exposure
Total percentage
–60 –30 0 30 60
Age (years)
Change (%)
≥95
90–94
85–89
80–84
75–79
70–74
65–69
60–64
55–59
50–54
45–49
40–44
35–39
30–34
25–29
20–24
15–19
10–14
5–9
1–4
Post neonatal
Late neonatal
Early neonatal
Figure 6: Percent change in all-cause DALYs, by age, at the global level, 2006-2016, due to the following
drivers: population growth, population ageing, trends in exposure to all risks included in GBD 2016, and all
other factors
DALYs=disability-adjusted life-years.
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are large, increasing, and variable across countries at the
same level of development likely warrant particular policy
attention. Our analysis showed that components of diet,
obesity, FPG, and SBP are the most prominent global risks
fulfilling these criteria. Because of the strong inter-
relationships between these risks, the true driver of this
cluster is likely diet, the risk in BMI, or both, with knock-
on consequences for FPG and SBP. The rise of obesity and
the associated increases in FPG and SBP warrant con-
siderable global policy attention. Other major risks that
should continue to receive attention—even intensified
attention in some locations—such as smoking, are never-
theless declining at the global level. The unique
combination of large current eect and increasing
exposure puts obesity in a special category of risks. Obesity
is likely to not only influence future population health in
many locations, but will have considerable financial
implications for health systems, given what we know about
treatment costs for the associated diseases. Since important
drivers of obesity such as physical activity and diet patterns
are adopted in childhood and adolescence, more work is
needed to proactively address the adoption of these risks in
these younger age groups.
For the first time, we assess the contribution of changes
of risk exposures to the overall global trend for deaths and
DALYs; for example, in the past 10 years, changes in all risk
exposures contributed to an 10·8% (8·3–13·1) decline in
DALYs, while other factors contributed to a 16·5%
(14·1–18·8) decrease in DALYs. More detailed assessments
show large declines in CMNN causes and increases in
injuries and non-communicable DALYs. In each case, the
contribution of other factors was substantially larger than
the contribution of risk reduction. Our findings of the
relatively small contribution of risk reduction to the
declines in NCDs are not at odds with published studies
for the UK and the USA,20–22 because we are reporting at
the global level; our results at the national level suggest a
larger role for risk reduction in some high-SDI locations.
These observations lead to two directions for further
analysis. First, what is the explanation for the declines
driven by other factors? Some of this eect might be social
policy working through various causal channels, and some
is likely due to improvements in access to high-quality
health care. This is particularly true for conditions such as
selected cancers, ischaemic heart disease, cerebrovascular
disease, chronic kidney diseases, HIV/AIDS, tuberculosis,
and maternal mortality, for which health care is known to
have large eects. Second, in view of the enormous
potential of risk reduction to change health outcomes as
documented in this and many other studies, why has
progress on many risks been comparatively slow? For
example, even though global tobacco consumption is
declining in terms of rates, the pace of decline has been
remarkably slow on average, despite more than 50 years of
good evidence on the harms of tobacco. The relatively poor
track record for global risk reduction might in part reflect
the low rate of investment in risk reduction compared with
curative health care. It might also reflect the continuing
challenge of changing many risky behaviours. Relatively
little funding for research on changing behaviours
compared with new diagnostics and therapeutics might
also be part of the explanation of the prevention paradox.23,24
Changing behavioural risks could also require more than
government action; harnessing the private sector to
facilitate behavioural change might also be crucial.
Important changes in GBD 2016 compared with in GBD
2015 (risks ordered by global rank)
Systolic blood pressure
Increased SBP remains the leading global risk at Level 3 in
the GBD risk hierarchy. Highly eective interventions exist
to manage blood pressure at the primary care level, as do a
range of public health interventions, so it is quite remarkable
that global exposure to increased SBP is increasing. Part of
this increase might be tied to the global rise in high BMI,
but the increase in SBP represents significant missed
opportunity for the world’s health systems. In 54 countries
high SBP is actually declining, while its increase in China is
now well documented in a series of population-based
surveys.25–27 Tackling rising SBP is a global concern, but this
is particularly important in those locations where rates are
increasing. In view of the eect of the risk and the large
array of available, eective interventions, health systems
and the global health community need to mobilise increased
resources and policy attention to tackle this problem. It
might be necessary to design a variety of public policies
including food reformulation to reduce sodium content and
eorts to incentivise primary care providers to give priority
to the management of SBP.28–30
Tobacco
In moving toward developing a comprehensive picture of
tobacco use globally, in GBD 2016, we have for the first
time included smokeless tobacco use as a risk factor. While
the burden of smokeless tobacco is minimal in the
majority of countries, it is of huge importance in south
Asia, where the highest risk-weighted exposure is observed
in Bangladesh (risk-weighted exposure of 0·75 [0·61–0·87]),
Bhutan (0·53 [0·44–0·62]), Myanmar (0·50 [0·42–0·59]),
Nepal (0·50 [0·42–0·58]), and India (0·45 [0·43–0·47]). In
these countries more women use smokeless tobacco
products than smoked tobacco products, and we find that
use of any tobacco products, smoked or smokeless,
continuously increases with age, a regional age pattern
that diers from the global and male regional age pattern.
The combination of high exposure and large population
results in a majority of global deaths attributable to
smokeless tobacco in 2016 occurring in India, where it is
also the leading risk factor for oral cancer.
In GBD 2016, we also improved the estimation of burden
attributable to second-hand smoke. At the global level,
while the burden of second-hand smoke remains sub-
stantial, exposure to second-hand smoke has been
declining significantly at an annualised rate of change of
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1·9% (1·5–2·4). These reductions are likely attributable to
a wide range of public health measures to control tobacco,
which have accelerated in a large number of countries
since the implementation of the Framework Convention
on Tobacco Control (FCTC).31
Progress combatting the tobacco epidemic has resulted
in global declines in prevalence of tobacco use and second-
hand smoke exposure, yet the number of deaths and
DALYs attributable to tobacco has increased since 1990.
Increases in burden were driven by a combination of
population growth and population ageing, along with
persistently high smoking prevalence in some of the most
populous countries of the world. Taken together, we can
expect the burden of tobacco to remain high in years to
come, unless the rate of progress is significantly
accelerated. Many countries with persistently high levels of
daily smoking recorded marginal progress in the past
decade, and smoking remains a leading risk factor in most
countries. The fact that tobacco use patterns diverge by
location, level of development, and sex highlights the need
for more tailored approaches to change smoking
behaviours in the future. Particularly worrisome are the
trends among young men and women. For example, in
Indonesia, a country that has not yet ratified the FCTC,31
more than half of men aged 20–24 years are daily smokers.
Understanding what works—and what does not—for
tobacco control across contexts and within subpopulations
(ie, men and women, younger and older individuals,
various socioeconomic groups) is of growing priority. To
significantly and permanently change the toll of tobacco, a
renewed and sustained focus is needed on comprehensive
tobacco control policies around the world.
Fasting plasma glucose
The global increase in FPG is likely tied to the increase in
BMI. While exposure is increasing, age-standardised
attributable mortaliy rate is not; a related pattern is that the
prevalence of diabetes is increasing, but deaths from
diabetes have been declining, likely because clinical
management of the macrovascular complications of
diabetes has improved in many (but not all) locations.
Prevention trials show that with intensive resources
devoted to weight loss and physical activity, reductions in
FPG can be achieved; however, these interventions have
not been implemented at a national scale and adherence in
the long run is challenging. Systematic eorts to screen for
high FPG implemented in some countries may increase
awareness and action in more patients but can be resource-
intensive. Clinical interventions to reduce FPG can be
eective, although there are more recent debates on the
appropriate targets for treatment in some cases. With FPG
increasing in many settings, it is dicult to determine the
population eect of treatment of blood sugar on population
FPG. FPG remains one of the risk factors that is most
likely influenced at the primary health-care level,
emphasising the role of universal coverage for primary care
in a multipronged response to this increasing problem.
Body-mass index
One of the most alarming risks in the analysis is increased
BMI, because its burden is large and increasing, and it is
prevalent across all levels of SDI.32,33 The potential drivers
of this global epidemic include changes in food industries
and systems, which increase availability, accessibility, and
aordability of energy-dense foods, along with intense
marketing of such foods, as well as reduced opportunities
for physical activity.34 A range of interventions have been
proposed to reduce obesity, including restricting the
advertisement of unhealthy foods to children, improving
school meals, taxation of sugar-sweetened beverages, and
taxation to reduce consumption of other unhealthy foods
and subsidies to increase intake of healthy foods, and
using supply-chain incentives to increase production of
healthy foods.35 However, the evidence base that many of
these interventions can aect trends in obesity at scale is
currently weak.36 What we know without a doubt is that
obesity rates continue to increase in almost all locations.
Low-SDI and middle-SDI countries generally have little
financial resources for nutrition programs and mostly rely
on external donors whose programmes often preferentially
target undernutrition.37 The increase in exposure to high
BMI is greater than the increase in attributable burden
largely because cardiovascular disease death rates continue
to decline because of other changes, particularly improve-
ments in treatment and declines in smoking and high
cholesterol. Proposed policies, even if fully imple mented,
are unlikely to rapidly reduce the prevalence of obesity.
While not a solution to the rise of overweight and obesity,
clinical interventions that control high SBP, cholesterol,
and FPG (the major risk factors for cardiovascular disease)
can be used to mitigate some of the cardiovascular ill-
eects.20 Expanded use of such interventions among obese
people could eectively reduce the disease burden of high
BMI. Sustained progress, however, will require policies
that eectively control weight in childhood and in young
and middle-aged adults.
Diet
In GBD 2016, poor dietary habits were the second leading
risk factor at Level 2 of the hierarchy for mortality globally,
accounting for nearly one in every five deaths. The overall
burden of dietary risks at the global level was 14·8%
(11·7–18·5) lower than in GBD 2015. Additionally,
important dierences were observed in the attributable
burden and the ranking of individual dietary risks. Multiple
factors have contributed to these dierences, including
using more data sources, as well as improving the method
of estimation of the mean and distribution of intake for
each dietary factor. In GBD 2016, for the first time, we used
sales data to inform our estimates of consumption for most
dietary factors. Using sales data, in addition to improving
our overall data coverage, allowed us to capture recent
trends in consumption. This was particularly important for
specific dietary factors, such as sugar-sweetened beverages,
which have been the target of dietary policies in several
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countries.38–43 Additionally, to improve the consistency of
definitions of dietary risk factors across surveys, we made a
systematic eort to obtain and re-extract individual-level
data from nutrition surveys. To make the current level of
intake and optimal level of intake more comparable, we
used the absolute level of intake (rather than the intake
standardised to 2000 kcal per day) as the primary exposure
in GBD 2016. We also corrected our estimated daily intake
of each individual dietary factor for within-person variation
and characterised the usual intake at the population level.
Finally, given the dierences in the health eects and
patterns of intake for legumes and vegetables, we estimated
the burden of disease attributable to low intake of legumes
and low intake of vegetables separately.
The decade of 2016–25 has been declared as the Decade
of Action on Nutrition by the United Nations General
Assembly.44 GBD 2016 provides a comprehensive picture
of various forms of malnutrition (ie, undernutrition,
overweight or obesity, and poor dietary habits) across all
countries at the start of the Decade of Action on Nutrition
and can inform priorities for evidence-based interventions
in each country. GBD also provides an independent avenue
to annually monitor the progress of countries toward
achieving their nutrition-related goals in a comparable and
consistent manner. Our results show that among all forms
of malnutrition, poor dietary habits, particularly low intake
of healthy foods, is the leading risk factor for mortality. This
finding has important implications for national govern-
ments and international organisations aiming at ending
malnutrition over the next decade, highlighting the need
for comprehensive food system interventions to promote
the production, distribution, and consumption of healthy
foods across nations.
Low birthweight and short gestation
Low birthweight and short gestation have been added for
GBD 2016; they are the third-leading global risk at Level 3
in the GBD risk hierarchy. Improvements in burden
attributable to low birthweight and short gestation have
been largely driven by other factors influencing neonatal
death rates, given that exposure to low birthweight and
short gestation have not improved much over the past
27 years. Little progress in exposure suggests suboptimal
coverage of interventions and programmes that can prevent
low birthweight and short gestation. These include women-
centred services for optimising nutrition (including
minimising obesity), infection control, smoking cessation,
and preventive care for pregnant women or those
contemplating pregnancy.45–47 Eorts should also focus on
maximising the quality of antenatal care services to identify
and appropriately manage at-risk and high-risk
pregnancies,48 including avoidance of provider-initiated
preterm delivery. If evidence-based interventions are
employed, it should be possible even in resource-limited
settings to shift the risk curve for those babies who will be
born early, small, or both, despite best eorts. Before birth,
this includes potentially antenatal steroid administration to
promote lung development;49 at birth, this requires
presence of adequately trained and equipped neonatal
resuscitation services;50,51 post-delivery, it should include
physicians with neonatal specialisation and availability of
supportive equipment such as continuous positive airway
pressure.52 Facility-based infection control measures are
crucial to prevent nosocomial transmission, as such events
are highly lethal in low birthweight or short gestation
neonates.53 The inclusion of this risk for a major cause of
DALYs—namely, neonatal mortality—also expands the
share of overall burden that can be attributed to risks in
general. More work remains, however, to understand the
relationship between low birthweight and short gestation
and childhood growth failure after 1 month. Our analysis to
date may actually underestimate the importance of this risk
if the share of childhood growth failure that can be traced to
low birthweight and gestational age is fully established.
Alcohol
Globally, alcohol is estimated to be the seventh-leading risk
factor in 2016 in both DALYs (4·2% [3·7–4·6]) and deaths
(5·2% [4·4–6·0]). Previous studies have noted the
possibility that the preventive eects of alcohol might have
been overstated due to selection bias and choice of the
reference population.2,54–56 Our findings lend further
credence to these hypotheses; with the exception of IHD,
our results show either a minor or non-significant
preventive eect for causes previously estimated to have
large preventive eects. Further, our analysis noted a much
larger risk of neoplasms due to alcohol use than previously
reported. Combined with our new data for alcohol use
exposure, alcohol use is ranked as one of the leading risk
factors, surpassing cholesterol as a share of total DALYs,
comparied with previous iterations of GBD.4–6
Ensemble distributions
In GBD 2016 we have introduced a more accurate method
for developing the distributions of exposure for many risk
factors. Our work on distributions and the shift to ensemble
distributions shows that the assessment of attributable
burden is sensitive to distributional assumptions. Given
that a number of risks, such as BMI, SBP, cholesterol, and
FPG, rise exponentially as a function of exposure, the
estimation of the tail of the distribution has an important
eect on the results. The ensemble modelling approach
can provide more accurate estimation of the full
distribution, including the tails of the distribution. In
general, we believe that the assessment of the dis-
trib ution of the risks deserves more careful attention in
future research.
Comparison of GBD 2016 to other estimates
The GBD study is the most comprehensive eort to
conduct a population-level CRA across countries and risks.
Dierences between GBD 2016 estimates and other global
estimates are generally related to approaches to data
processing, access to data sources, and analysis decisions.
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For several risks, including smoking,57 ambient ozone
pollution, household air pollution from solid fuels, lead
exposure,58 intimate partner violence,59 unsafe water
source,60 and breastfeeding, GBD estimates were lower
than published WHO estimates.57–60 These discrepancies
can be attributed to dierent definitions, methodological
decisions, granularity, and input data. For some findings,
annual estimates might disagree, but regional patterns
were consistent between WHO and GBD. UNICEF61
produces estimates for child stunting that are lower than
GBD estimates with some disagreement where progress
has been made globally. There is more consistency in
estimates between UNICEF and GBD for child wasting
and child underweight.61 GBD estimates for the prevalence
of low birthweight and short gestation are slightly lower
when compared with WHO estimates, but show similar
geographical patterns.62 Scientific literature reveals similar
results to GBD for impaired kidney function63 and low
birthweight and short gestation;64,65 research analysing
ambient air pollution66 diered from GBD estimates due to
older methods and less granularity. Research published on
iron-deficiency anaemia67 diers from GBD in methods
and definitions, resulting in generally higher GBD
estimates. GBD estimates were much lower than published
research on occupational estimates,3,68,69 largely due to
dierent cause-outcome pairs and GBD’s application of the
CRA approach (see appendix 1 p 10).
Future directions
Interpretation of our results and prioritisation at the
national level might also need to take into account the
variable strength of evidence supporting the causal
connection for each risk-outcome pair. In GBD 2016, we
have continued to use the World Cancer Research Fund
criteria of convincing or probable evidence to select risk-
outcome pairs for inclusion. Some aspects of these
definitions are subjective. Not all researchers would agree
on the interpretation of the available evidence as fulfilling
these criteria. For example, there are six studies on non-
exclusive breastfeeding and LRI; there are two studies on
discontinued breastfeeding and diarrhoeal diseases. We
have sought to quantify the number of studies of dierent
kinds that are available to support these judgements in
table 1, but not all studies support causality to the same
extent. Randomised trials, if well conducted, provide the
strongest evidence of causality, because they are likely not
aected by confounding. But even randomised trials can
have biases when there are missing observations, as is
often the case. Randomised trials are also not feasible in
many cases, or if feasible, not representative for many
risks, including environmental risks. Cohort studies can
provide compelling evidence, but many cohorts do not
adequately control for socioeconomic confounders and can
suer from many other issues related to the quality of
exposure measurement or outcome ascertainment. To go
beyond, the quantification of the number of studies of each
type we have provided here will necessitate a deeper
analysis of the potential limitations of all 2579 studies used
across the risk-outcome pairs. In future work, we plan to
evaluate the quality of each of these studies with a
standardised approach and work toward an overall evidence
summary. There is also a more fundamental philosophical
question about the presentation of risk information.
Should decision makers only pay attention to risk factor
quantification for those risks supported by the strongest
causal evidence such as randomised trials? Or do notions
such as the precautionary principle suggest that we should
pay attention to risk quantification even for risk-outcome
pairs where the evidence is less definitive.70–72 Because the
social response to risks, particularly risks that might be
emerging, can take considerable time, ignoring risks for
which the evidence is less definitive might actually lead to
worse outcomes for society. Conversely, in a world of scarce
political and financial resources, devoting attention to risks
that might turn out not to be causal might lead to less
action on more well documented risks.
As part of future iterations of GBD, we plan to quantify
the burden attributable to some distal social risks. We have
embarked on this work, but it proves to have challenges
that are qualitatively dierent than many of the risks
included here. For nearly all risk-outcome pairs, we
assume in the absence of other evidence that the RRs by
age and sex are generalisable across populations (the
exception is for BMI in Asian and non-Asian populations
for breast cancer). In principle, if there is evidence of
statistically significant RRs for dierent population groups,
we would incorporate these into the CRA. For distal social
risks, the pathways to outcomes can be modified in many
ways by other risks or by health-system interventions. We
expect that the RR due to low education for 40-year-old
men would be dierent in Norway than in Kenya. Given
the greater potential for variation in RRs for distal risks,
inclusion in GBD will require more local quantification of
RRs and then a further modelling step to estimate RRs for
these determinants for all locations. Our first planned
target for this quantification is educational attainment.
Given the global policy focus on the potential health
eects of climate change driven by rising levels of
greenhouse gases, and consequently temperature, we will
add temperature and precipitation as risk factors that are
quantified on an annual basis in future iterations of GBD.
Even though most of the potential harm that might come
from rising temperatures or extreme weather events will
occur in the future, in some locations, we might already
find significant attributable burden.73 This analysis will
need to examine the relationship between disease and
mortality risk and temperature for each relevant outcome.
For some outcomes, these relationships are likely to be
U shaped, with an optimal temperature for minimum risk.
These U-shaped relationships could mean that for some
outcomes in some locations, rising temperature might
reduce harm, even if in most locations it will increase
burden. Likewise, a major issue in understanding the
temperature and health outcome relationships is that we
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would expect these to be attenuated in high-SDI settings,
where many individuals can protect themselves from some
of the consequences. In other words, generalising from
studies in high-SDI locations to other locations might
underestimate the risk relationships.
In the GBD CRA approach, the TMREL is the level of
risk exposure that leads to minimum risk for individuals.
In principle, the TMREL could vary by location, age, and
sex. To date, the TMREL in the GBD work has been
selected to be universal. For more detail on TMREL, see
appendix 1 (p 22). The analysis of alcohol, where for IHD
there is a protective eect at mild to moderate consumption
but a harmful eect for neoplasms and injuries, is a good
example of where it would be desirable to vary TMREL by
age. In younger ages, injuries will be more important than
cardiovascular diseases, pushing the TMREL toward zero
consumption of alcohol, whereas at older ages, the TMREL
might be higher. Letting the TMREL vary by age and sex
and even location will add an extra analytical step to GBD;
like all other estimation steps, this can have estimation
error. To date, we have thought the estimation error
associated with a TMREL that varies may not make the
eort worthwhile. As evidence accumulates on some risks
like alcohol, we will carefully evaluate this position.
Limitations
A study of this scope has many limitations. Here we discuss
the limitations that apply to the overall risk factor analytical
framework and limitations in the estimation approach for
new risks and risks that have undertaken significant
revisions from GBD 2015. More details and limitations of
the analytical approach for each risk factor are presented in
appendix 1 (p 43). First, we continue to include risk-outcome
pairs that meet the World Cancer Research Fund criteria of
convincing or probable evidence for causality. While these
criteria have proven a useful bar for inclusion, there is an
important subjective element to their inter pretation. Some
risk-outcome pairs included in this study might not meet
these criteria or alternative criteria that are developed as
new randomised trials, cohort studies, or case-control
studies are published. Second, we used published cohort
studies to evaluate the degree to which dierent risks are
mediated through other risks. Estimates of pathways of
mediation are used to compute the burden attributable to
aggregates of risk factors such as all behavioural risks or all
risks combined. While we have conducted pooled cohort
analyses to strengthen the assessment of mediation, this
work was not yet ready for inclusion in this assessment.
Pooled cohort studies have the advantage of providing a
more standardised framework for assessing mediation
across multiple risks. A related issue is the validation of the
aggregation of risks in GBD. Pooled cohort studies will
allow (in some circumstances) the opportunity to estimate
if the aggregation of GBD RRs is as predictive of outcomes
as suggested by the risk-by-risk analysis with mediation.
Third, we have used the Das Gupta formula applied for
each 5-year interval and for GBD Level 3 causes.
Aggregations at higher levels of causes and for longer
periods of time are based on these more granular analyses
to guarantee consistency. Given the non-linear nature of the
Das Gupta decomposition formula, however, alternative
results are possible using dierent time periods and causes
in the formula. Fourth, we have introduced the use of
ensemble distributions to improve the empirical fitting of
distributions of risk exposure in settings where only mean
and standard deviation are known or where we use models
to predict the mean and standard deviation of exposure.
Ensemble models provide more accurate fits as assessed
out of sample for settings with microdata. The underlying
assumption is that the same ensemble weights are
applicable across all settings. It is possible that the shape of
distributions of risk exposure might vary across locations,
for example because of the eects of access to treatment.
Limitations that apply to new risks in GBD 2016 or risks
with significant estimation updates are presented here. For
low birthweight and short gestation, we have included the
eect of low birthweight and short gestation only on
neonatal outcomes; we have not found the evidence to meet
our inclusion criteria for the link between low birthweight
and short gestation and NCDs in adult age groups. Our
analysis of RRs has used a very large US-linked birth cohort
dataset and much more limited data from middle-SDI and
low-SDI populations. Given the large number of observations
from the USA, our results are heavily influenced by the
pattern of RRs across birthweight and gestational age in that
population. The microdata used to develop the ensemble
dis tributions for birthweight and gestational age are largely
from middle-SDI and high-SDI locations. The esti mation of
alcohol use relies heavily on sales data, which are limited
and whose quality we cannot easily assess. Also, the
estimation of unrecorded con sumption of alcohol is based
on limited data and has significant uncertainty; nevertheless,
we feel it is important to include it and plan to continue to
look for additional sources of information to improve the
estimation of unrecorded consumption in future iterations
of GBD. Lastly, methods for calculating TMREL rely on
observed DALYs for a given time rather than on the expected
share of DALYs estimated from alcohol use alone. Future
iterations of GBD will likely need to test this assumption
further and determine if separate TMREL by age and sex
should be calculated.
Conclusion
Understanding the levels and trends of major risks for
human health is essential to prioritise public health action
and evaluate the success of dierent programmes and
policies. This study provides a comprehensive and
comparable assessment of 84 metabolic, environmental,
occupational, and behavioural risks across locations and
time. Our findings show that risk modification has been an
important contributor to reductions in communicable,
maternal, neonatal, and nutritional causes, but has played a
relatively small part in trends in NCDs. Conflicting trends
in risks for NCDs at the global level, such as the decline in
Global Health Metrics
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1413
smoking prevalence coupled with the rise in obesity, FPG,
and SBP, account for this finding. By contrast with trends
in diseases and injuries at the global level and even at the
national level, there is much greater heterogeneity of global
trends across risks and considerable geographical variation
in leading risks as well. Public health action in each country
and region needs to focus on the major risks in that
community. Our findings reinforce the crucial need for
robust monitoring of the exposure to risks to health and
assessment of the evidence supporting causal eects for
each risk-outcome pair; GBD provides the main global
mechanism for this monitoring function.
GBD 2016 Risk Factors Collaborators
Emmanuela Gakidou, Ashkan Afshin, Amanuel Alemu Abajobir,
Kalkidan Hassen Abate, Cristiana Abbafati, Kaja M Abbas, Foad Abd-Allah,
Abdishakur M Abdulle, Semaw Ferede Abera, Victor Aboyans,
Laith J Abu-Raddad, Niveen M E Abu-Rmeileh, Gebre Yitayih Abyu,
Isaac Akinkunmi Adedeji, Olatunji Adetokunboh, Mohsen Afarideh,
Anurag Agrawal, Sutapa Agrawal, Aliasghar Ahmad Kiadaliri, Hamid
Ahmadieh, Muktar Beshir Ahmed, Amani Nidhal Aichour, Ibtihel Aichour,
Miloud Taki Eddine Aichour, Rufus Olusola Akinyemi, Nadia Akseer,
Fares Alahdab, Ziyad Al-Aly, Khurshid Alam, Noore Alam, Tahiya Alam,
Deena Alasfoor, Kefyalew Addis Alene, Komal Ali, Reza Alizadeh-Navaei,
Ala’a Alkerwi, François Alla, Peter Allebeck, Rajaa Al-Raddadi,
Ubai Alsharif, Khalid A Altirkawi, Nelson Alvis-Guzman,
Azmeraw T Amare, Erfan Amini, Walid Ammar, Yaw Ampem Amoako,
Hossein Ansari, Josep M Antó, Carl Abelardo T Antonio, Palwasha Anwari,
Nicholas Arian, Johan Ärnlöv, Al Artaman, Krishna Kumar Aryal,
Hamid Asayesh, Solomon Weldegebreal Asgedom, Tesfay Mehari Atey,
Leticia Avila-Burgos, Euripide Frinel G Arthur Avokpaho, Ashish Awasthi,
Peter Azzopardi, Umar Bacha, Alaa Badawi, Kalpana Balakrishnan,
Shoshana H Ballew, Aleksandra Barac, Ryan M Barber,
Suzanne L Barker-Collo, Till Bärnighausen, Simon Barquera,
Lars Barregard, Lope H Barrero, Carolina Batis, Katherine E Battle,
Bernhard T Baune, Justin Beardsley, Neeraj Bedi, Ettore Beghi,
Michelle L Bell, Derrick A Bennett, James R Bennett, Isabela M Bensenor,
Adugnaw Berhane, Derbew Fikadu Berhe, Eduardo Bernabé,
Balem Demtsu Betsu, Mircea Beuran, Addisu Shunu Beyene,
Anil Bhansali, Zulfiqar A Bhutta, Boris Bikbov, Charles Birungi,
Stan Biryukov, Christopher D Blosser, Dube Jara Boneya,
Ibrahim R Bou-Orm, Michael Brauer, Nicholas J K Breitborde,
Hermann Brenner, Traolach S Brugha, Lemma Negesa Bulto Bulto,
Blair R Baumgarner, Zahid A Butt, Lucero Cahuana-Hurtado,
Rosario Cárdenas, Juan Jesus Carrero, Carlos A Castañeda-Orjuela,
Ferrán Catalá-López, Kelly Cercy, Hsing-Yi Chang, Fiona J Charlson,
Odgerel Chimed-Ochir, Vesper Hichilombwe Chisumpa,
Abdulaal A Chitheer, Hanne Christensen,
Devasahayam Jesudas Christopher, Massimo Cirillo, Aaron J Cohen,
Haley Comfort, Cyrus Cooper, Josef Coresh, Leslie Cornaby,
Paolo Angelo Cortesi, Michael H Criqui, John A Crump, Lalit Dandona,
Rakhi Dandona, José das Neves, Gail Davey, Dragos V Davitoiu, Kairat
Davletov, Barbora de Courten, Louisa Degenhardt, Selina Deiparine,
Robert P Dellavalle, Kebede Deribe, Aniruddha Deshpande,
Samath D Dharmaratne, Eric L Ding, Shirin Djalalinia, Huyen Phuc Do,
Klara Dokova, David Teye Doku, E Ray Dorsey, Tim R Driscoll,
Manisha Dubey, Bruce Bartholow Duncan, Sarah Duncan, Natalie Ebert,
Hedyeh Ebrahimi, Ziad Ziad El-Khatib, Ahmadali Enayati,
Aman Yesuf Endries, Sergey Petrovich Ermakov, Holly E Erskine,
Babak Eshrati, Sharareh Eskandarieh, Alireza Esteghamati, Kara Estep,
Emerito Jose Aquino Faraon, Carla Sofia e Sa Farinha,
André Faro, Farshad Farzadfar, Kairsten Fay, Valery L Feigin,
Seyed-Mohammad Fereshtehnejad, João C Fernandes, Alize J Ferrari,
Tesfaye Regassa Feyissa, Irina Filip, Florian Fischer, Christina Fitzmaurice,
Abraham D Flaxman, Nataliya Foigt, Kyle J Foreman, Joseph J Frostad,
Nancy Fullman, Thomas Fürst, Joao M Furtado, Morsaleh Ganji,
Alberto L Garcia-Basteiro, Tsegaye Tewelde Gebrehiwot,
Johanna M Geleijnse, Ayele Geleto, Bikila Lencha Gemechu,
Hailay Abrha Gesesew, Peter W Gething, Alireza Ghajar,
Katherine B Gibney, Paramjit Singh Gill, Richard F Gillum,
Ababi Zergaw Giref, Melkamu Dedefo Gishu, Giorgia Giussani,
William W Godwin, Philimon N Gona, Amador Goodridge,
Sameer Vali Gopalani, Yevgeniy Goryakin, Alessandra Carvalho Goulart,
Nicholas Graetz, Harish Chander Gugnani, Jingwen Guo, Rajeev Gupta,
Tanush Gupta, Vipin Gupta, Reyna A Gutiérrez, Vladimir Hachinski,
Nima Hafezi-Nejad, Gessessew Bugssa Hailu, Randah Ribhi Hamadeh,
Samer Hamidi, Mouhanad Hammami, Alexis J Handal, Graeme J Hankey,
Hilda L Harb, Habtamu Abera Hareri, Mohammad Sadegh Hassanvand,
Rasmus Havmoeller, Caitlin Hawley, Simon I Hay, Mohammad T Hedayati,
Delia Hendrie, Ileana Beatriz Heredia-Pi, Hans W Hoek, Nobuyuki Horita,
H Dean Hosgood, Sorin Hostiuc, Damian G Hoy, Mohamed Hsairi,
Guoqing Hu, Hsiang Huang, John J Huang, Kim Moesgaard Iburg,
Chad Ikeda, Manami Inoue, Caleb Mackay Salpeter Irvine,
Maria Delores Jackson, Kathryn H Jacobsen, Nader Jahanmehr,
Mihajlo (Michael) B Jakovljevic, Alejandra Jauregui, Mehdi Javanbakht,
Panniyammakal Jeemon, Lars R K Johansson, Catherine O Johnson,
Jost B Jonas, Mikk Jürisson, Zubair Kabir, Rajendra Kadel, Amaha Kahsay,
Ritul Kamal, André Karch, Corine Kakizi Karema, Amir Kasaeian,
Nicholas J Kassebaum, Anshul Kastor, Srinivasa Vittal Katikireddi,
Norito Kawakami, Peter Njenga Keiyoro, Sefonias Getachew Kelbore,
Laura Kemmer, Andre Pascal Kengne,
Chandrasekharan Nair Kesavachandran, Yousef Saleh Khader,
Ibrahim A Khalil, Ejaz Ahmad Khan, Young-Ho Khang, Ardeshir Khosravi,
Jagdish Khubchandani, Christian Kieling, Daniel Kim, Jun Y Kim,
Yun Jin Kim, Ruth W Kimokoti, Yohannes Kinfu, Adnan Kisa,
Katarzyna A Kissimova-Skarbek, Mika Kivimaki, Luke D Knibbs,
Ann Kristin Knudsen, Jacek A Kopec, Soewarta Kosen, Parvaiz A Koul,
Ai Koyanagi, Michael Kravchenko, Kristopher J Krohn, Hans Kromhout,
Barthelemy Kuate Defo, Burcu Kucuk Bicer, G Anil Kumar, Michael Kutz,
Hmwe H Kyu, Dharmesh Kumar Lal, Ratilal Lalloo, Tea Lallukka,
Qing Lan, Van C Lansingh, Anders Larsson, Alexander Lee, Paul H Lee,
James Leigh, Janni Leung, Miriam Levi, Yichong Li, Yongmei Li,
Xiaofeng Liang, Misgan Legesse Liben, Shai Linn, Patrick Liu, Rakesh
Lodha, Giancarlo Logroscino, Katherine J Looker, Alan D Lopez,
Stefan Lorkowski, Paulo A Lotufo, Rafael Lozano, Raimundas Lunevicius,
Erlyn Rachelle King Macarayan, Hassan Magdy Abd El Razek, Mohammed
Magdy Abd El Razek, Marek Majdan, Reza Majdzadeh, Azeem Majeed,
Reza Malekzadeh, Rajesh Malhotra, Deborah Carvalho Malta,
Abdullah A Mamun, Helena Manguerra, Lorenzo G Mantovani,
Chabila C Mapoma, Randall V Martin, Jose Martinez-Raga,
Francisco Rogerlândio Martins-Melo, Manu Raj Mathur,
Kunihiro Matsushita, Richard Matzopoulos, Mohsen Mazidi,
Colm McAlinden, John J McGrath, Suresh Mehata,
Man Mohan Mehndiratta, Toni Meier, Yohannes Adama Melaku,
Peter Memiah, Ziad A Memish, Walter Mendoza,
Melkamu Merid Mengesha, George A Mensah, Gert B M Mensink,
Seid Tiku Mereta, Atte Meretoja, Tuomo J Meretoja,
Haftay Berhane Mezgebe, Renata Micha, Anoushka Millear, Ted R Miller,
Shawn Minnig, Mojde Mirarefin, Erkin M Mirrakhimov, Awoke Misganaw,
Shiva Raj Mishra, Karzan Abdulmuhsin Mohammad,
Kedir Endris Mohammed, Shafiu Mohammed, Norlinah Mohamed
Ibrahim, Murali B V Mohan, Ali H Mokdad, Lorenzo Monasta,
Julio Cesar Montañez Hernandez, Marcella Montico, Maziar Moradi-Lakeh,
Paula Moraga, Lidia Morawska, Shane D Morrison,
Cli Mountjoy-Venning, Ulrich O Mueller, Erin C Mullany, Kate Muller,
Gudlavalleti Venkata Satyanarayana Murthy, Kamarul Imran Musa,
Mohsen Naghavi, Aliya Naheed, Vinay Nangia, Gopalakrishnan Natarajan,
Ionut Negoi, Ruxandra Irina Negoi, Cuong Tat Nguyen, Grant Nguyen,
Minh Nguyen, Quyen Le Nguyen, Trang Huyen Nguyen, Emma Nichols,
Dina Nur Anggraini Ningrum, Marika Nomura, Vuong Minh Nong,
Ole F Norheim, Bo Norrving, Jean Jacques N Noubiap, Carla Makhlouf
Obermeyer, Felix Akpojene Ogbo, In-Hwan Oh, Olanrewaju Oladimeji,
Andrew Toyin Olagunju, Tinuke Oluwasefunmi Olagunju,
Pedro R Olivares, Helen E Olsen, Bolajoko Olubukunola Olusanya,
Jacob Olusegun Olusanya, John Nelson Opio, Eyal Oren, Alberto Ortiz,
Erika Ota, Mayowa O Owolabi, Mahesh PA, Rosana E Pacella, Adrian Pana,
Basant Kumar Panda, Songhomitra Panda-Jonas, Jeyaraj D Pandian,
Christina Papachristou, Eun-Kee Park, Charles D Parry, Scott B Patten,
George C Patton, David M Pereira, Norberto Perico, Konrad Pesudovs,
Max Petzold, Michael Robert Phillips, Julian David Pillay,
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Michael A Piradov, Farhad Pishgar, Dietrich Plass, Martin A Pletcher,
Suzanne Polinder, Svetlana Popova, Richie G Poulton, Farshad Pourmalek,
Narayan Prasad, Carrie Purcell, Mostafa Qorbani, Amir Radfar,
Anwar Rafay, Afarin Rahimi-Movaghar, Vafa Rahimi-Movaghar,
Mahfuzar Rahman, Mohammad Hifz Ur Rahman,
Muhammad Aziz Rahman, Rajesh Kumar Rai, Sasa Rajsic, Usha Ram,
Salman Rawaf, Colin D Rehm, Jürgen Rehm, Robert C Reiner,
Marissa B Reitsma, Luz Myriam Reynales-Shigematsu, Giuseppe Remuzzi,
Andre M N Renzaho, Serge Resniko, Satar Rezaei, Antonio L Ribeiro,
Juan A Rivera, Kedir Teji Roba, David Rojas-Rueda, Yesenia Roman,
Robin Room, Gholamreza Roshandel, Gregory A Roth,
Dietrich Rothenbacher, Enrico Rubagotti, Lesley Rushton, Nafis Sadat,
Mahdi Safdarian, Sare Safi, Saeid Safiri, Ramesh Sahathevan,
Joseph Salama, Joshua A Salomon, Abdallah M Samy,
Juan Ramon Sanabria, Maria Dolores Sanchez-Niño,
Tania G Sánchez-Pimienta, Damian Santomauro, Itamar S Santos,
Milena M Santric Milicevic, Benn Sartorius, Maheswar Satpathy,
Monika Sawhney, Sonia Saxena, Elke Schaener, Maria Inês Schmidt,
Ione J C Schneider, Aletta E Schutte, David C Schwebel, Falk Schwendicke,
Soraya Seedat, Sadaf G Sepanlou, Berrin Serdar, Edson E Servan-Mori,
Gavin Shaddick, Amira Shaheen, Saeid Shahraz, Masood Ali Shaikh,
Teresa Shamah Levy, Mansour Shamsipour, Morteza Shamsizadeh,
Sheikh Mohammed Shariful Islam, Jayendra Sharma, Rajesh Sharma,
Jun She, Jiabin Shen, Peilin Shi, Kenji Shibuya, Chloe Shields,
Mekonnen Sisay Shiferaw, Mika Shigematsu, Min-Jeong Shin,
Rahman Shiri, Reza Shirkoohi, Kawkab Shishani, Haitham Shoman,
Mark G Shrime, Inga Dora Sigfusdottir, Diego Augusto Santos Silva,
João Pedro Silva, Dayane Gabriele Alves Silveira, Jasvinder A Singh,
Virendra Singh, Dhirendra Narain Sinha, Eirini Skiadaresi,
Erica Leigh Slepak, David L Smith, Mari Smith, Badr H A Sobaih,
Eugene Sobngwi, Samir Soneji, Reed J D Sorensen, Luciano A Sposato,
Chandrashekhar T Sreeramareddy, Vinay Srinivasan, Nicholas Steel,
Dan J Stein, Caitlyn Steiner, Sabine Steinke, Mark Andrew Stokes,
Bryan Strub, Michelle Subart, Muawiyyah Babale Sufiyan,
Rizwan Abdulkader Suliankatchi, Patrick J Sur, Soumya Swaminathan,
Bryan L Sykes, Cassandra E I Szoeke, Rafael Tabarés-Seisdedos,
Santosh Kumar Tadakamadla, Ken Takahashi, Jukka S Takala,
Nikhil Tandon, Marcel Tanner, Yihunie L Tarekegn, Mohammad Tavakkoli,
Teketo Kassaw Tegegne, Arash Tehrani-Banihashemi,
Abdullah Sulieman Terkawi, Belay Tesssema, JS Thakur,
Ornwipa Thamsuwan, Kavumpurathu Raman Thankappan,
Andrew M Theis, Matthew Lloyd Thomas, Alan J Thomson,
Amanda G Thrift, Taavi Tillmann, Ruoyan Tobe-Gai, Myriam Tobollik,
Mette C Tollanes, Marcello Tonelli, Roman Topor-Madry, Anna Torre,
Miguel Tortajada, Mathilde Touvier, Bach Xuan Tran, Thomas Truelsen,
Kald Beshir Tuem, Emin Murat Tuzcu, Stefanos Tyrovolas,
Kingsley Nnanna Ukwaja, Chigozie Jesse Uneke, Rachel Updike,
Olalekan A Uthman, Job F M van Boven, Aaron van Donkelaar,
Santosh Varughese, Tommi Vasankari, Lennert J Veerman,
Vidhya Venkateswaran, Narayanaswamy Venketasubramanian,
Francesco S Violante, Sergey K Vladimirov, Vasiliy Victorovich Vlassov,
Stein Emil Vollset, Theo Vos, Fiseha Wadilo, Tolassa Wakayo,
Mitchell T Wallin, Yuan-Pang Wang, Scott Weichenthal,
Elisabete Weiderpass, Robert G Weintraub, Daniel J Weiss,
Andrea Werdecker, Ronny Westerman, Harvey A Whiteford,
Charles Shey Wiysonge, Belete Getahun Woldeyes, Charles D A Wolfe,
Rachel Woodbrook, Abdulhalik Workicho, Sarah Wulf Hanson,
Denis Xavier, Gelin Xu, Simon Yadgir, Bereket Yakob, Lijing L Yan,
Mehdi Yaseri, Hassen Hamid Yimam, Paul Yip, Naohiro Yonemoto,
Seok-Jun Yoon, Marcel Yotebieng, Mustafa Z Younis, Zoubida Zaidi,
Maysaa El Sayed Zaki, Luis Zavala-Arciniega, Xueying Zhang,
Stephanie Raman M Zimsen, Ben Zipkin, Sanjay Zodpey, Stephen S Lim,
Christopher J L Murray.
Affiliations
Institute for Health Metrics and Evaluation (Prof E Gakidou PhD,
A Afshin MD, T Alam MPH, K Ali BSc, N Arian BA, R M Barber BS,
J R Bennett BA, S Biryukov BS, Prof M Brauer ScD, B Bumgarner MBA,
K Cercy BS, F J Charlson PhD, A J Cohen DSc, H Comfort BS,
L Cornaby BS, Prof L Dandona MD, Prof R Dandona PhD,
Prof L Degenhardt PhD, S Deiparine, A Deshpande MPH, S Duncan,
H E Erskine PhD, K Estep MPA, K Fay BS, A J Ferrari PhD,
C Fitzmaurice MD, A D Flaxman PhD, K J Foreman PhD, J J Frostad MPH,
N Fullman MPH, W W Godwin BS, N Graetz MPH, J Guo BS,
C Hawley MSPH, Prof S I Hay DSc, C Ikeda BS, C M S Irvine BA,
C O Johnson PhD, N J Kassebaum MD, L Kemmer PhD, I A Khalil MD,
J Y Kim BS, K J Krohn BA, M Kutz BS, H H Kyu PhD, A Lee BA,
J Leung PhD, Prof S S Lim PhD, P Liu MPH, H Manguerra BS,
A Millear BA, S Minnig MS, M Mirarefin MPH, A Misganaw PhD,
Prof A H Mokdad PhD, C Mountjoy-Venning BA, E C Mullany BA,
K Muller MPH, Prof M Naghavi PhD, G Nguyen MPH, M Nguyen BS,
E Nichols BA, H E Olsen MA, M A Pletcher BS, C Purcell BS, R C Reiner
PhD, M B Reitsma BS, Y Roman MLIS, G A Roth MD, N Sadat MA,
J Salama MSc, D Santomauro PhD, C Shields BS, E L Slepak MLIS,
Prof D L Smith PhD, M Smith MPA, R J D Sorensen MPH,
V Srinivasan BA, C Steiner MPH, B Strub BS, M Subart BA, P J Sur BA,
O Thamsuwan PhD, A M Theis BA, A Torre BS, R Updike AB,
V Venkateswaran BDS, Prof S E Vollset DrPH, Prof T Vos PhD,
Prof H A Whiteford PhD, R Woodbrook MLIS, S Wulf Hanson MPH,
S Yadgir BS, S R M Zimsen MA, B Zipkin BS, Prof C J L Murray DPhil),
Division of Hematology, Department of Medicine (C Fitzmaurice MD),
Center for Health Trends and Forecasts, Institute for Health Metrics and
Evaluation (Prof M B Jakovljevic PhD), University of Washington, Seattle,
WA, USA (C D Blosser MD, S D Morrison MD); School of Public Health
(A A Abajobir MPH, F J Charlson PhD, H E Erskine PhD, A J Ferrari PhD,
L D Knibbs PhD, J Leung PhD, D Santomauro PhD, L J Veerman PhD,
Prof H A Whiteford PhD), School of Dentistry (Prof R Lalloo PhD),
University of Queensland, Brisbane, QLD, Australia (S R Mishra MPH);
Department of Epidemiology, College of Health Sciences
(M B Ahmed MPH), Jimma University, Jimma, Ethiopia (K H Abate MS,
Prof T T Gebrehiwot MPH, H A Gesesew MPH, S T Mereta PhD,
T Wakayo MS, A Workicho MPH); La Sapienza University, Rome, Italy
(C Abbafati PhD); Virginia Tech, Blacksburg, VA, USA
(Prof K M Abbas PhD); Department of Neurology, Cairo University, Cairo,
Egypt (Prof F Abd-Allah MD); New York University Abu Dhabi, Abu Dhabi,
United Arab Emirates (A M Abdulle PhD); School of Public Health
(S F Abera MSc, Y A Melaku MPH), College of Health Sciences
(S F Abera MSc, K E Mohammed MPH), School of Pharmacy
(D F Berhe MS), Mekelle University, Mekelle, Ethiopia (Prof G Y Abyu MS,
S W Asgedom MS, T M Atey MS, B D Betsu MS, G B Hailu MSc,
A Kahsay MPH, H B Mezgebe MS, K B Tuem MS); Food Security and
Institute for Biological Chemistry and Nutrition, University of Hohenheim,
Stuttgart, Germany (S F Abera MSc); Dupuytren University Hospital,
Limoges, France (Prof V Aboyans PhD); Infectious Disease Epidemiology
Group, Weill Cornell Medical College in Qatar, Doha, Qatar
(L J Abu-Raddad PhD); Institute of Community and Public Health, Birzeit
University, Ramallah, Palestine (N M Abu-Rmeileh PhD); Olabisi Onabanjo
University, Ago-Iwoye, Nigeria (I A Adedeji MS); Department of Psychiatry
(Prof C D Parry PhD), Stellenbosch University, Cape Town, South Africa
(O Adetokunboh MD, Prof S Seedat PhD, Prof C S Wiysonge PhD);
CSIR - Institute of Genomics and Integrative Biology, Delhi, India
(A Agrawal PhD); Department of Internal Medicine, Baylor College of
Medicine, Houston, TX, USA (A Agrawal PhD); Centre for Control of
Chronic Conditions (P Jeemon PhD), Indian Institute of Public Health
(Prof G V S Murthy MD), Public Health Foundation of India, Gurugram,
India (S Agrawal PhD, Prof L Dandona MD, Prof R Dandona PhD,
G A Kumar PhD, D K Lal MD, M R Mathur PhD, Prof S Zodpey PhD);
Department of Clinical Sciences Lund, Orthopedics, Clinical Epidemiology
Unit (A Ahmad Kiadaliri PhD), Skane University Hospital, Department of
Clinical Sciences Lund, Neurology (Prof B Norrving PhD), Lund University,
Lund, Sweden; Ophthalmic Research Center (H Ahmadieh MD, S Safi MS,
M Yaseri PhD), School of Public Health (N Jahanmehr PhD), Shahid
Beheshti University of Medical Sciences, Tehran, Iran; Department of
Ophthalmology, Labbafinejad Medical Center, Tehran, Iran
(H Ahmadieh MD); University Ferhat Abbas of Setif, Setif, Algeria
(A N Aichour BS); National Institute of Nursing Education, Setif, Algeria
(I Aichour MS); High National School of Veterinary Medicine, Algiers,
Algeria (M T Aichour MD); University of Ibadan, Ibadan, Nigeria
(R O Akinyemi PhD); Newcastle University, Newcastle upon Tyne, UK
(R O Akinyemi PhD); Centre for Global Child Health, The Hospital for Sick
Children, Toronto, ON, Canada (N Akseer MSc, Z A Bhutta PhD); Dalla
Lana School of Public Health (N Akseer MSc, Prof J Rehm PhD),
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Department of Nutritional Sciences, Faculty of Medicine (A Badawi PhD),
Centre for Addiction and Mental Health (S Popova PhD), University of
Toronto, Toronto, ON, Canada; Mayo Clinic Foundation for Medical
Education and Research, Rochester, MN, USA (F Alahdab MD); Syrian
American Medical Society, Washington, DC, USA (F Alahdab MD);
Washington University in St Louis, St Louis, MO, USA (Z Al-Aly MD);
Murdoch Childrens Research Institute (K Alam PhD, P Azzopardi PhD,
R G Weintraub MBBS), Department of Paediatrics (P Azzopardi PhD,
Prof G C Patton MD), Melbourne School of Population and Global Health
(Prof A D Lopez PhD), Department of Medicine (A Meretoja PhD),
Institute of Health and Ageing (Prof C E I Szoeke PhD), The University of
Melbourne, Melbourne, VIC, Australia (K Alam PhD, M A Rahman PhD,
R G Weintraub MBBS); Sydney School of Public Health
(Prof T R Driscoll PhD), The University of Sydney, Sydney, NSW, Australia
(K Alam PhD, J Leigh PhD); Department of Health, Queensland, Brisbane,
QLD, Australia (N Alam MAppEpid); Ministry of Health, Al Khuwair,
Oman (D Alasfoor MSc); Department of Epidemiology and Biostatistics,
Institute of Public Health (K A Alene MPH), University of Gondar, Gondar,
Ethiopia (B Tesssema PhD); Department of Global Health, Research School
of Population Health, Australian National University, Canberra, ACT,
Australia (K A Alene MPH); Gastrointestinal Cancer Research Center
(R Alizadeh-Navaei PhD), Department of Medical Mycology and
Parasitology, School of Medicine (Prof M T Hedayati PhD), Mazandaran
University of Medical Sciences, Sari, Iran; Luxembourg Institute of Health,
Strassen, Luxembourg (A Alkerwi PhD); School of Public Health,
University of Lorraine, Nancy, France (Prof F Alla PhD); Department of
Public Health Sciences (P Allebeck PhD, Z Z El-Khatib PhD), Department
of Neurobiology, Care Sciences and Society, Division of Family Medicine
and Primary Care (Prof J Ärnlöv PhD), Department of Medical
Epidemiology and Biostatistics (Prof J J Carrero PhD, E Weiderpass PhD),
Department of Neurobiology, Care Sciences and Society (NVS)
(S Fereshtehnejad PhD), Karolinska Institutet, Stockholm, Sweden
(R Havmoeller PhD); Joint Program of Family and Community Medicine,
Jeddah, Saudi Arabia (R Al-Raddadi PhD); Charité Universitätsmedizin,
Berlin, Germany (U Alsharif MPH, N Ebert MD, Prof E Schaener MSc);
King Saud University, Riyadh, Saudi Arabia (K A Altirkawi MD,
B H A Sobaih MD); Universidad de Cartagena, Cartagena de Indias,
Colombia (Prof N Alvis-Guzman PhD); School of Medicine
(A T Amare MPH, Prof B T Baune PhD, Y A Melaku MPH), Discipline of
Psychiatry, School of Medicine (A T Olagunju MD), University of Adelaide,
Adelaide, South Australia, Australia; College of Medicine and Health
Sciences, Bahir Dar University, Bahir Dar, Ethiopia (A T Amare MPH);
Uro-Oncology Research Center (E Amini MD, F Pishgar MD), Non-
Communicable Diseases Research Center (H Ebrahimi MD,
F Farzadfar MD, A Khosravi PhD, F Pishgar MD), Endocrinology and
Metabolism Research Center (E Amini MD, Prof A Esteghamati MD,
N Hafezi-Nejad MD, A Kasaeian PhD), Center for Air Pollution Research,
Institute for Environmental Research (M S Hassanvand PhD), Hematology-
Oncology and Stem Cell Transplantation Research Center
(A Kasaeian PhD), Knowledge Utilization Research Center and Community
Based Participatory Research Center (Prof R Majdzadeh PhD), Liver and
Pancreaticobiliary Diseases Research Center (H Ebrahimi MD), Digestive
Diseases Research Institute (Prof R Malekzadeh MD, G Roshandel PhD,
S G Sepanlou PhD), Iranian National Center for Addiction Studies (INCAS)
(A Rahimi-Movaghar MD), Sina Trauma and Surgery Research Center
(Prof V Rahimi-Movaghar MD, M Safdarian MD), Institute for
Environmental Research (M Shamsipour PhD), Cancer Research Center
(Prof R Shirkoohi PhD), Tehran University of Medical Sciences, Terhan,
Iran (M Afarideh MD, M Ganji MD, A Ghajar MD, M Yaseri PhD);
Ministry of Public Health, Beirut, Lebanon (W Ammar PhD,
I R Bou-Orm MD, H L Harb MPH); Department of Medicine, Komfo
Anokye Teaching Hospital, Kumasi, Ghana (Y A Amoako MD); Health
Promotion Research Center, Department of Epidemiology and Biostatistics,
Zahedan University of Medical Sciences, Zahedan, Iran (H Ansari PhD);
ISGlobal Barcelona Institute for Global Health, Barcelona, Spain
(Prof J M Antó MD); Department of Health Policy and Administration,
College of Public Health (C A T Antonio MD, E J A Faraon MD), University
of the Philippines Manila, Manila, Philippines; Self-employed, Kabul,
Afghanistan (P Anwari MS); School of Health and Social Studies, Dalarna
University, Falun, Sweden (Prof J Ärnlöv PhD); University of Manitoba,
Winnipeg, MB, Canada (A Artaman PhD); Nepal Health Research Council,
Kathmandu, Nepal (K K Aryal MPH); University of Oslo, Oslo, Norway
(K K Aryal MPH); Department of Medical Emergency, School of Paramedic,
Qom University of Medical Sciences, Qom, Iran (H Asayesh MS); National
Council for Science and Technology (C Batis PhD), National Institute of
Public Health, Cuernavaca, Mexico (L Avila-Burgos PhD, S Barquera PhD,
L Cahuana-Hurtado PhD, I B Heredia-Pi PhD, A Jauregui MSc,
R Lozano PhD, J C Montañez Hernandez MSc,
LMReynales-ShigematsuPhD, Prof J A Rivera PhD,
T G Sánchez-Pimienta MSc, Prof E E Servan-Mori MSc,
T Shamah Levy PhD, L Zavala-Arciniega MS); Institut de Recherche
Clinique du Bénin (IRCB), Cotonou, Benin (E F G A Avokpaho MPH);
Laboratoire d’Etudes et de Recherche-Action en Santé (LERAS Afrique),
Parakou, Benin (E F G A Avokpaho MPH); Indian Institute of Public
Health, Gandhinagar, India (A Awasthi PhD); Burnet Institute, Melbourne,
VIC, Australia (P Azzopardi PhD); Wardliparingga Aboriginal Research
Unit, South Australian Health and Medical Research Institute, Adelaide,
South Australia, Australia (P Azzopardi PhD); School of Health Sciences,
University of Management and Technology, Lahore, Pakistan
(U Bacha PhD); Public Health Agency of Canada, Toronto, ON, Canada
(A Badawi PhD); Department of Environmental Health Engineering,
Sri Ramachandra University, Chennai, India (K Balakrishnan PhD);
Johns Hopkins Bloomberg School of Public Health (S H Ballew PhD,
J Coresh PhD, K Matsushita PhD), Johns Hopkins University, Baltimore,
MD, USA (B X Tran PhD); Faculty of Medicine (A Barac PhD), Institute of
Social Medicine, Faculty of Medicine (M M Santric Milicevic PhD), Centre
School of Public Health and Health Management, Faculty of Medicine
(M M Santric Milicevic PhD), University of Belgrade, Belgrade, Serbia;
School of Psychology, University of Auckland, Auckland, New Zealand
(S L Barker-Collo PhD); Department of Global Health and Population,
Harvard T H Chan School of Public Health (Prof T Bärnighausen MD,
J A Salomon PhD), Harvard T H Chan School of Public Health
(E L Ding ScD), Ariadne Labs (E R K Macarayan PhD), Harvard University,
Boston, MA, USA; Africa Health Research Institute, Mtubatuba, South
Africa (Prof T Bärnighausen MD); Institute of Public Health, Heidelberg
University, Heidelberg, Germany (Prof T Bärnighausen MD,
S Mohammed PhD); Department of Occupational and Environmental
Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg,
Sweden (Prof L Barregard MD); Department of Industrial Engineering,
School of Engineering, Pontificia Universidad Javeriana, Bogota, Colombia
(L H Barrero ScD); Malaria Atlas Project, Oxford Big Data Institute
(K E Battle DPhil), Li Ka Shing Centre for Health Information and
Discovery (Prof S I Hay DSc), Nueld Department of Population Health
(D A Bennett PhD), Department of Zoology (P W Gething PhD), University
of Oxford, Oxford, UK (D J Weiss PhD); Oxford University, Ho Chi Minh
City, Vietnam (J Beardsley MBChB); College of Public Health and Tropical
Medicine, Jazan, Saudi Arabia (N Bedi MD); IRCCS - Istituto di Ricerche
Farmacologiche Mario Negri, Bergamo, Italy (E Beghi MD, B Bikbov MD,
G Giussani BiolD, N Perico MD, Prof G Remuzzi MD); Yale University,
New Haven, CT, USA (Prof M L Bell PhD, J J Huang MD); Center for
Clinical and Epidemiological Research Center, Hospital Universitario
(A C Goulart PhD), Internal Medicine Department (Prof I S Santos PhD),
University of São Paulo, São Paulo, Brazil (I M Bensenor PhD,
Prof P A Lotufo DrPH); College of Health Sciences, Debre Berhan
University, Debre Berhan, Ethiopia (A Berhane PhD); University Medical
Center Groningen (D F Berhe MS), Department of Psychiatry, University
Medical Center Groningen (Prof H W Hoek MD), University of Groningen,
Groningen, Netherlands (J F M van Boven PhD); Division of Health and
Social Care Research (Prof C D Wolfe MD), King’s College London,
London, UK (E Bernabé PhD); Carol Davila University of Medicine and
Pharmacy, Bucharest, Romania (Prof M Beuran PhD, D V Davitoiu PhD,
S Hostiuc PhD, I Negoi PhD, R I Negoi PhD); Emergency Hospital of
Bucharest, Bucharest, Romania (Prof M Beuran PhD, I Negoi PhD);
College of Health and Medical Sciences (A S Beyene MPH,
M M Mengesha MPH), Haramaya University, Harar, Ethiopia
(L N B Bulto MS, A Geleto MPH, M D Gishu MS, K T Roba PhD,
M S Shiferaw MS); Postgraduate Institute of Medical Education and
Research, Chandigarh, India (A Bhansali DM); Centre of Excellence in
Women and Child Health, Aga Khan University, Karachi, Pakistan
(Z A Bhutta PhD); Department of Epidemiology and Public Health
(Prof M Kivimaki PhD), Institute of Epidemiology & Health
(T Tillmann MSc), University College London, London, UK (C Birungi MS,
Global Health Metrics
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M R Mathur PhD); Department of Public Health (D J Boneya MPH), Debre
Markos University, Debre Markos, Ethiopia (T K Tegegne MPH); University
of British Columbia, Vancouver, BC, Canada (Prof M Brauer ScD,
J A Kopec PhD, F Pourmalek PhD); College of Medicine (J Shen PhD), The
Ohio State University, Columbus, OH, USA (Prof N J K Breitborde PhD,
M Yotebieng PhD); German Cancer Research Center, Heidelberg, Germany
(Prof H Brenner MD); University of Leicester, Leicester, UK
(Prof T S Brugha MD); Al Shifa Trust Eye Hospital, Rawalpindi, Pakistan
(Z A Butt PhD); Metropolitan Autonomous University, Mexico City, Mexico
(R Cárdenas ScD); Colombian National Health Observatory, Instituto
Nacional de Salud, Bogota, Colombia (C A Castañeda-Orjuela MSc);
Epidemiology and Public Health Evaluation Group, Public Health
Department, Universidad Nacional de Colombia, Bogota, Colombia
(C A Castañeda-Orjuela MSc); Department of Medicine, University of
Valencia, INCLIVA Health Research Institute and CIBERSAM, Valencia,
Spain (F Catalá-López PhD, Prof R Tabarés-Seisdedos PhD); Clinical
Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON,
Canada (F Catalá-López PhD); National Health Research Institutes, Zgunan
Town, Taiwan (H Chang DrPH); National Yang-Ming University, Taipei,
Taiwan (H Chang DrPH); Queensland Centre for Mental Health Research,
Brisbane, QLD, Australia (F J Charlson PhD, H E Erskine PhD,
A J Ferrari PhD, D Santomauro PhD, Prof H A Whiteford PhD);
Department of Environmental Epidemiology, University of Occupational
and Environmental Health, Kitakyushu, Japan (O Chimed-Ochir MPH);
University of Zambia, Lusaka, Zambia (V H Chisumpa MPhil,
C C Mapoma PhD); University of Witwatersrand, Johannesburg,
South Africa (V H Chisumpa MPhil); Ministry of Health, Baghdad, Iraq
(A A Chitheer MD); Bispebjerg University Hospital, Copenhagen,
Denmark (Prof H Christensen DMSCi); Christian Medical College, Vellore,
India (Prof D J Christopher MD, Prof S Varughese DM); University of
Salerno, Baronissi, Italy (Prof M Cirillo MD); Health Eects Institute,
Boston, MA, USA (A J Cohen DSc); MRC Lifecourse Epidemiology United,
University of Southampton, Southampton, UK (Prof C Cooper MD); NIHR
Biomedical Research Centre, University of Southampton and University
Hospital Southampton NHS Foundation Trust, Southampton, UK
(Prof C Cooper MD); Research Centre on Public Health (CESP), University
of Milan-Bicocca, Monza, Italy (P A Cortesi PhD); University of California,
San Diego, La Jolla, CA, USA (M H Criqui MD); Centre for International
Health, Dunedin School of Medicine (Prof J A Crump MD), University of
Otago, Dunedin, New Zealand (Prof R G Poulton PhD); i3S - Instituto de
Investigação e Inovação em Saúde and INEB - Instituto de Engenharia
Biomédica (J das Neves PhD), UCIBIO@REQUIMTE, Toxicology Group,
Faculty of Pharmacy (J P Silva PhD), University of Porto, Porto, Portugal;
Wellcome Trust Brighton & Sussex Centre for Global Health Research,
Brighton, UK (Prof G Davey MD); Republican Institute of Cardiology and
Internal Diseases, Almaty, Kazakhstan (K Davletov PhD); School of Public
Health, Kazakh National Medical University, Almaty, Kazakhstan
(K Davletov PhD); Department of Medicine, School of Clinical Sciences at
Monash Health (Prof A G Thrift PhD), Monash University, Melbourne,
VIC, Australia (Prof B de Courten PhD); National Drug and Alcohol
Research Centre (Prof L Degenhardt PhD), Brien Holden Vision Institute
(Prof S Resniko MD), School of Optometry and Vision Science
(Prof S Resniko MD), University of New South Wales, Sydney, NSW,
Australia; University of Colorado School of Medicine and the Colorado
School of Public Health, Aurora, CO, USA (R P Dellavalle MD); Brighton
and Sussex Medical School, Brighton, UK (K Deribe MPH); School of
Public Health (K Deribe MPH), Addis Ababa University, Addis Ababa,
Ethiopia (A Z Giref PhD, H A Hareri MS, S G Kelbore MPH,
B G Woldeyes MPH); Department of Community Medicine, Faculty of
Medicine, University of Peradeniya, Peradeniya, Sri Lanka
(S D Dharmaratne MD); Undersecretary for Research & Technology,
Ministry of Health & Medical Education, Tehran, Iran (S Djalalinia PhD);
Institute for Global Health Innovations, Duy Tan University, Da Nang,
Vietnam (H P Do MSc, C T Nguyen MSc, Q L Nguyen MD,
T H Nguyen MSc, V M Nong MSc); Department of Social Medicine,
Faculty of Public Health, Medical University - Varna, Varna, Bulgaria
(K Dokova PhD); University of Cape Coast, Cape Coast, Ghana
(D T Doku PhD); University of Tampere, Tampere, Finland
(D T Doku PhD); University of Rochester Medical Center, Rochester, NY,
USA (E R Dorsey MD); International Institute for Population Sciences,
Mumbai, India (M Dubey MPhil, A Kastor MPhil, B K Panda MPhil,
M H U Rahman MPhil, Prof U Ram PhD); Federal University of Rio
Grande do Sul, Porto Alegre, Brazil (B B Duncan PhD, C Kieling MD,
Prof M I Schmidt MD); University of North Carolina, Chapel Hill, NC,
USA (B B Duncan PhD); Department of Global Health and Social
Medicine, Harvard Medical School, Kigali, Rwanda (Z Z El-Khatib PhD);
School of Public Health and Health Sciences Research Center, Sari, Iran
(Prof A Enayati PhD); Arba Minch University, Arba Minch, Ethiopia
(A Y Endries MPH); The Institute of Social and Economic Studies of
Population, Russian Academy of Sciences, Moscow, Russia
(Prof S P Ermakov DSc); Federal Research Institute for Health
Organization and Informatics, Ministry of Health of the Russian
Federation, Moscow, Russia (Prof S P Ermakov DSc); Ministry of Health
and Medical Education, Tehran, Iran (B Eshrati PhD); Arak University of
Medical Sciences, Arak, Iran (B Eshrati PhD); Multiple Sclerosis Research
Center, Tehran, Iran (S Eskandarieh PhD); Department of Health, Manila,
Philippines (E J A Faraon MD); DGS Directorate General of Health, Lisboa,
Portugal (C S E S Farinha MSc); Universidade Aberta, Lisboa, Portugal
(C S E S Farinha MSc); Federal University of Sergipe, Aracaju, Brazil
(Prof A Faro PhD); National Institute for Stroke and Applied
Neurosciences, Auckland University of Technology, Auckland, New Zealand
(V L Feigin PhD); CBQF - Center for Biotechnology and Fine Chemistry -
Associate Laboratory, Faculty of Biotechnology, Catholic University of
Portugal, Porto, Portugal (J C Fernandes PhD); Wollega University,
Nekemte, Ethiopia (T R Feyissa MPH); Kaiser Permanente, Fontana, CA,
USA (I Filip MD); School of Public Health, Bielefeld University, Bielefeld,
Germany (F Fischer PhD); Fred Hutchinson Cancer Research Center,
Seattle, WA, USA (C Fitzmaurice MD); Institute of Gerontology, Academy
of Medical Science, Kyiv, Ukraine (N Foigt PhD); Department of Infectious
Disease Epidemiology (T Fürst PhD), Department of Primary Care &
Public Health (Prof A Majeed MD), Imperial College London, London, UK
(K J Foreman PhD, Prof S Rawaf MD, L Rushton PhD, S Saxena MD,
H Shoman MPH); Department of Epidemiology and Public Health
(T Fürst PhD), Swiss Tropical and Public Health Institute, Basel,
Switzerland (C K Karema MSc); Swiss Tropical and Public Health Institute
(Prof M Tanner PhD), University of Basel, Basel, Switzerland
(T Fürst PhD); Faculdade de Medicina de Ribeirão Preto, Universidade de
São Paulo, Ribeirão Preto, Brazil (J M Furtado MD); Manhiça Health
Research Center, Manhiça, Mozambique (A L Garcia-Basteiro MSc);
Barcelona Institute for Global Health, Barcelona, Spain
(A L Garcia-Basteiro MSc); Division of Human Nutrition, Wageningen
University, Wageningen, Netherlands (J M Geleijnse PhD); University of
Newcastle, Newcastle, NSW, Australia (A Geleto MPH); Madda Walabu
University, Bale Goba, Ethiopia (B L Gemechu MPH); Flinders University,
Adelaide, SA, Australia (H A Gesesew MPH, Prof K Pesudovs PhD); The
Peter Doherty Institute for Infection and Immunity, The University of
Melbourne & The Royal Melbourne Hospital, Melbourne, VIC, Australia
(K B Gibney MBBS); Warwick Medical School, University of Birmingham,
Birmingham, UK (Prof P S Gill DM); Howard University, Washington, DC,
USA (R F Gillum MD); Kersa Health and Demographic Surveillance
System, Harar, Ethiopia (M D Gishu MS); University of Massachusetts
Boston, Boston, MA, USA (Prof P N Gona PhD); Instituto de
Investigaciones Cientificas y Servicios de Alta Tecnologia - INDICASAT-
AIP, Cuidad del Saber, Panama (A Goodridge PhD); Department of Health
and Social Aairs, Government of the Federated States of Micronesia,
Palikir, Federated States of Micronesia (S V Gopalani MPH); Organisation
for Economic Co-operation and Development, Paris, France
(Y Goryakin PhD); Center of Check of Hospital Sirio Libanes, São Paulo,
Brazil (A C Goulart PhD); Departments of Microbiology and Epidemiology
& Biostatistics, Saint James School of Medicine, The Quarter, Anguilla
(Prof H C Gugnani PhD); Eternal Heart Care Centre and Research
Institute, Jaipur, India (R Gupta PhD); Montefiore Medical Center, Bronx,
NY, USA (T Gupta MD, C D Rehm PhD); Albert Einstein College of
Medicine, Bronx, NY, USA (T Gupta MD, Prof H D Hosgood PhD);
Department of Anthropology, University of Delhi, Delhi, India
(V Gupta PhD); National Institute of Psychiatry Ramon de la Fuente,
Mexico City, Mexico (R A Gutiérrez PhD); Department of Clinical
Neurological Sciences (L A Sposato MD), Western University, London, ON,
Canada (Prof V Hachinski DSc, T O Olagunju MD); Kilte Awlaelo Health
and Demographic Surveillance System, Mekelle, Ethiopia (G B Hailu MSc);
Arabian Gulf University, Manama, Bahrain (Prof R R Hamadeh DPhil);
Hamdan Bin Mohammed Smart University, Dubai, United Arab Emirates
Global Health Metrics
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(S Hamidi DrPH); Wayne County Department of Health and Human
Services, Detroit, MI, USA (M Hammami MD); University of New Mexico,
Albuquerque, NM, USA (A J Handal PhD); School of Medicine and
Pharmacology, University of Western Australia, Perth, WA, Australia
(Prof G J Hankey MD); Harry Perkins Institute of Medical Research,
Nedlands, WA, Australia (Prof G J Hankey MD); Western Australian
Neuroscience Research Institute, Nedlands, WA, Australia
(Prof G J Hankey MD); School of Public Health (D Hendrie PhD), Centre
for Population Health (T R Miller PhD), Curtin University, Perth, WA,
Australia; Department of Epidemiology, Mailman School of Public Health,
Columbia University, New York, NY, USA (Prof H W Hoek MD);
Department of Pulmonology, Yokohama City University Graduate School
of Medicine, Yokohama, Japan (N Horita MD); Public Health Division, The
Pacific Community, Noumea, New Caledonia (D G Hoy PhD); Department
of Epidemiology, Salah Azaiz Institute, Tunis, Tunisia (Prof M Hsairi MD);
Department of Epidemiology and Health Statistics, School of Public
Health, Central South University, Changsha, China (G Hu PhD);
Cambridge Health Alliance, Cambridge, MA, USA (H Huang MD);
National Centre for Register-Based Research, Aarhus School of Business
and Social Sciences (Prof J J McGrath PhD), Aarhus University, Aarhus,
Denmark (K M Iburg PhD); Division of Cohort Consortium Research,
Epidemiology and Prevention Group, Center for Public Health Sciences,
National Cancer Center, Tokyo, Japan (M Inoue MD); University of the
West Indies, Kingston, Jamaica (Prof M D Jackson PhD); Department of
Global and Community Health, George Mason University, Fairfax, VA,
USA (K H Jacobsen PhD); Faculty of Medical Sciences, University of
Kragujevac, Kragujevac, Serbia (Prof M B Jakovljevic PhD); University of
Aberdeen, Aberdeen, UK (M Javanbakht PhD); Centre for Chronic Disease
Control, New Delhi, India (P Jeemon PhD); Independent Consultant, Oslo,
Norway (L R K Johansson PhD); Department of Ophthalmology, Medical
Faculty Mannheim, Ruprecht-Karls-University Heidelberg, Mannheim,
Germany (Prof J B Jonas MD); Institute of Family Medicine and Public
Health, University of Tartu, Tartu, Estonia (M Jürisson MD); University
College Cork, Cork, Ireland (Z Kabir PhD); London School of Economics
and Political Science, London, UK (R Kadel MPH); CSIR - Indian Institute
of Toxicology Research, Lucknow, India (R Kamal MSc,
C N Kesavachandran PhD); Epidemiological and Statistical Methods
Research Group, Helmholtz Centre for Infection Research, Braunschweig,
Germany (A Karch MD); Hannover-Braunschweig Site, German Center for
Infection Research, Braunschweig, Germany (A Karch MD); Quality and
Equity Health Care, Kigali, Rwanda (C K Karema MSc); Department of
Anesthesiology & Pain Medicine, Seattle Children’s Hospital, Seattle, WA,
USA (N J Kassebaum MD); MRC/CSO Social & Public Health Sciences
Unit, University of Glasgow, Glasgow, UK (S V Katikireddi PhD); School of
Public Health (Prof N Kawakami MD), University of Tokyo, Tokyo, Japan
(K Shibuya MD); Institute of Tropical and Infectious Diseases, Nairobi,
Kenya (P N Keiyoro PhD); School of Continuing and Distance Education,
Nairobi, Kenya (P N Keiyoro PhD); Alcohol, Tobacco & Other Drug
Research Unit (Prof C D Parry PhD), UKZN Gastrointestinal Cancer
Research Centre (Prof B Sartorius PhD), South African Medical Research
Council, Cape Town, South Africa (A P Kengne PhD, R Matzopoulos PhD);
School of Public Health and Family Medicine (R Matzopoulos PhD),
Department of Psychiatry (Prof D J Stein PhD), University of Cape Town,
Cape Town, South Africa (A P Kengne PhD, J J N Noubiap MD);
Department of Community Medicine, Public Health and Family Medicine,
Jordan University of Science and Technology, Irbid, Jordan
(Prof Y S Khader ScD); Health Services Academy, Islamabad, Pakistan
(E A Khan MD); Department of Health Policy and Management, Seoul
National University College of Medicine, Seoul, South Korea
(Prof Y Khang MD); Institute of Health Policy and Management, Seoul
National University Medical Center, Seoul, South Korea
(Prof Y Khang MD); Iranian Ministry of Health and Medical Education,
Tehran, Iran (A Khosravi PhD); Department of Nutrition and Health
Science, Ball State University, Muncie, IN, USA (J Khubchandani PhD);
Hospital de Clinicas de Porto Alegre, Porto Alegre, Brazil (C Kieling MD);
Department of Health Sciences, Northeastern University, Boston, MA,
USA (Prof D Kim DrPH); School of Medicine, Xiamen University Malaysia
Campus, Sepang, Malaysia (Y J Kim PhD); Simmons College, Boston, MA,
USA (R W Kimokoti MD); Centre for Research and Action in Public
Health, University of Canberra, Canberra, ACT, Australia (Y Kinfu PhD);
Oslo University, Oslo, Norway (Prof A Kisa PhD); Institute of Public
Health, Faculty of Health Sciences (R Topor-Madry PhD), Jagiellonian
University Medical College, Krakow, Poland (K A Kissimova-Skarbek PhD);
Clinicum, Faculty of Medicine (Prof M Kivimaki PhD), Finnish Institute of
Occupational Health, Work Organizations, Work Disability Program,
Department of Public Health, Faculty of Medicine (T Lallukka PhD,
R Shiri PhD), University of Helsinki, Helsinki, Finland (T J Meretoja PhD);
Center for Disease Burden, Norwegian Institute of Public Health, Bergen,
Norway (A K Knudsen PhD, Prof S E Vollset DrPH); Department of
Psychosocial Science (A K Knudsen PhD), Department of Global Public
Health and Primary Care (Prof S E Vollset DrPH), University of Bergen,
Bergen, Norway (Prof O F Norheim PhD, M C Tollanes PhD); Center for
Community Empowerment, Health Policy and Humanities, National
Institute of Health Research & Development, Jakarta, Indonesia
(S Kosen MD); Sher-i-Kashmir Institute of Medical Sciences, Srinagar,
India (Prof P A Koul MD); Research and Development Unit, Parc Sanitari
Sant Joan de Deu (CIBERSAM), Barcelona, Spain (A Koyanagi MD);
Research Center of Neurology, Moscow, Russia (M Kravchenko PhD,
Prof M A Piradov DSc); Institute for Risk Assessment Sciences, Utrecht
University, Utrecht, Netherlands (Prof H Kromhout PhD); Department of
Social and Preventive Medicine, School of Public Health and Department
of Demography and Public Health Research Institute, University of
Montreal, Montreal, QC, Canada (Prof B Kuate Defo PhD); Institute of
Public Health, Hacettepe University, Ankara, Turkey (B Kucuk Bicer PhD);
National Cancer Institute, Rockville, MD, USA (Q Lan PhD); Help Me See,
Inc, New York, NY, USA (V C Lansingh PhD); Instituo Mexicano de
Oftalmologia, Queretaro, Mexico (V C Lansingh PhD); Department of
Medical Sciences, Uppsala University, Uppsala, Sweden
(Prof A Larsson PhD); Hong Kong Polytechnic University, Hong Kong,
China (P H Lee PhD); Tuscany Regional Centre for Occupational Injuries
and Diseases, Florence, Italy (M Levi PhD); Department of Data
Management, Peking University Clinical Research Institute, Beijing, China
(Y Li PhD); National Center for Chronic and Noncommunicable Disease
Control and Prevention (Y Li PhD), Chinese Center for Disease Control and
Prevention, Beijing, China (Prof X Liang MD); San Francisco VA Medical
Center, San Francisco, CA, USA (Y Li PhD); Samara University, Samara,
Ethiopia (M L Liben MPH); University of Haifa, Haifa, Israel
(Prof S Linn MD); All India Institute of Medical Sciences, New Delhi, India
(R Lodha MD, Prof R Malhotra MS, Prof N Tandon PhD); University of
Bari, Bari, Italy (Prof G Logroscino PhD); University of Bristol, Bristol, UK
(K J Looker PhD); Institute of Nutrition, Friedrich Schiller University Jena,
Jena, Germany (Prof S Lorkowski PhD); Aintree University Hospital
National Health Service Foundation Trust, Liverpool, UK
(ProfRLunevicius PhD); School of Medicine, University of Liverpool,
Liverpool, UK (Prof R Lunevicius PhD); Competence Cluster for Nutrition
and Cardiovascular Health (nutriCARD) Halle-Jena-Leipzig, Jena, Germany
(Prof S Lorkowski PhD); Ateneo de Manila University, Manila, Philippines
(E R K Macarayan PhD); Mansoura Faculty of Medicine, Mansoura, Egypt
(H Magdy Abd El Razek MBBCH); Aswan Faculty of Medicine, Aswan
University Hospital, Aswan, Egypt (M Magdy Abd El Razek MBBCH);
Faculty of Health Sciences and Social Work, Department of Public Health,
Trnava University, Trnava, Slovakia (M Majdan PhD); National Institute of
Health Research, Tehran, Iran (Prof R Majdzadeh PhD); Universidade
Federal de Minas Gerais, Belo Horizonte, Brazil (Prof D C Malta PhD);
The University of Queensland, Brisbane, QLD, Australia
(Prof A A Mamun PhD); University of Milano Bicocca, Monza, Italy
(Prof L G Mantovani DSc); Department of Physics and Atmospheric
Science (A van Donkelaar PhD), Dalhousie University, Halifax, NS, Canada
(Prof R V Martin PhD); Hospital Universitario Doctor Peset, Valencia,
Spain (J Martinez-Raga PhD, M Tortajada PhD); CEU Cardinal Herrera
University, Moncada, Spain (J Martinez-Raga PhD); Federal Institute of
Education, Science and Technology of Ceará, Caucaia, Brazil
(F R Martins-Melo PhD); Key State Laboratory of Molecular Developmental
Biology, Institute of Genetics and Developmental Biology, Chinese
Academy of Sciences, Beijing, China (M Mazidi PhD); University Hospitals
Bristol NHS Foundation Trust, Bristol, UK (C McAlinden PhD); Public
Health Wales, Swansea, UK (C McAlinden PhD); Queensland Centre for
Mental Health Research, The Park Centre for Mental Health, Wacol, QLD,
Australia (Prof J J McGrath PhD); Queensland Brain Institute
(Prof J J McGrath PhD), University of Queensland, Brisbane, QLD,
Australia (S R Mishra MPH); Ipas Nepal, Kathmandu, Nepal
(S Mehata PhD); Janakpuri Superspecialty Hospital, New Delhi, India
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(Prof M M Mehndiratta DM); Martin Luther University Halle-Wittenberg,
Halle (Saale), Germany (T Meier PhD); University of West Florida,
Pensacola, FL, USA (P Memiah PhD); Saudi Ministry of Health, Riyadh,
Saudi Arabia (Prof Z A Memish MD); College of Medicine, Alfaisal
University, Riyadh, Saudi Arabia (Prof Z A Memish MD); United Nations
Population Fund, Lima, Peru (W Mendoza MD); Center for Translation
Research and Implementation Science, National Heart, Lung, and Blood
Institute, National Institutes of Health, Bethesda, MD, USA
(G A Mensah MD); Department of Epidemiology and Health Monitoring,
Robert Koch Institute, Berlin, Germany (G B M Mensink PhD);
Department of Neurology (A Meretoja PhD), Comprehensive Cancer
Center, Breast Surgery Unit (T J Meretoja PhD), Helsinki University
Hospital, Helsinki, Finland; Friedman School of Nutrition Science and
Policy (R Micha PhD), Tufts University, Boston, MA, USA (P Shi PhD);
Pacific Institute for Research & Evaluation, Calverton, MD, USA
(T R Miller PhD); Hunger Action Los Angeles, Los Angeles, CA, USA
(M Mirarefin MPH); Kyrgyz State Medical Academy, Bishkek, Kyrgyzstan
(Prof E M Mirrakhimov PhD); National Center of Cardiology and Internal
Disease, Bishkek, Kyrgyzstan (Prof E M Mirrakhimov PhD); Nepal
Development Society, Chitwan, Nepal (S R Mishra MPH); University of
Salahaddin, Erbil, Iraq (K A Mohammad PhD); ISHIK University, Erbil,
Iraq (K A Mohammad PhD); Health Systems and Policy Research Unit
(S Mohammed PhD), Ahmadu Bello University, Zaria, Nigeria
(M B Sufiyan MBA); Narayana Health, Bangalore, India
(Prof M B V Mohan MD); Institute for Maternal and Child Health, IRCCS
Burlo Garofolo, Trieste, Italy (L Monasta DSc, M Montico MSc);
Department of Community Medicine, Preventive Medicine and Public
Health Research Center (M Moradi-Lakeh MD, A Tehrani-Banihashemi
PhD), Gastrointestinal and Liver Disease Research Center (GILDRC)
(M Moradi-Lakeh MD); Iran University of Medical Sciences, Tehran, Iran;
Lancaster Medical School, Lancaster University, Lancaster, UK
(P Moraga PhD); International Laboratory for Air Quality and Health
(L Morawska PhD), Institute of Health and Biomedical Innovation
(R E Pacella PhD), Queensland University of Technology, Brisbane, QLD,
Australia; Competence Center Mortality-Follow-Up of the German National
Cohort (A Werdecker PhD), Federal Institute for Population Research,
Wiesbaden, Germany (Prof U O Mueller PhD, R Westerman PhD); London
School of Hygiene and Tropical Medicine, London, UK
(Prof G V S Murthy MD); School of Medical Sciences, University of Science
Malaysia, Kubang Kerian, Malaysia (K I Musa MD); International Centre
for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh
(A Naheed PhD, S M Shariful Islam PhD); Suraj Eye Institute, Nagpur,
India (V Nangia MD); Madras Medical College, Chennai, India, India
(Prof G Natarajan DM); Department of Public Health, Semarang State
University, Semarang City, Indonesia (D N A Ningrum MPH); Graduate
Institute of Biomedical Informatics, College of Medical Science and
Technology, Taipei Medical University, Taipei City, Taiwan
(D N A Ningrum MPH); National Institute of Public Health, Saitama,
Japan (M Nomura PhD); Medical Diagnostic Centre, Yaounde, Cameroon
(J J N Noubiap MD); Center for Research on Population and Health, Faculty
of Health Sciences, American University of Beirut, Beirut, Lebanon
(Prof C M Obermeyer DSc); Centre for Health Research (F A Ogbo MPH),
Western Sydney University, Penrith, NSW, Australia
(Prof A M N Renzaho PhD); Department of Preventive Medicine, School of
Medicine, Kyung Hee University, Seoul, South Korea (Prof I Oh PhD);
Human Sciences Research Council (HSRC), South Africa and University of
KwaZulu-Natal, Durban, South Africa (O Oladimeji MS); Department of
Psychiatry, College of Medicine, University of Lagos, Lagos, Nigeria
(A T Olagunju MD); Department of Psychiatry, Lagos University Teaching
Hospital, Lagos, Nigeria (A T Olagunju MD); McMaster University,
Hamilton, ON, Canada (T O Olagunju MD); Universidad Autonoma de
Chile, Talca, Chile (Prof P R Olivares PhD); Center for Healthy Start
Initiative, Lagos, Nigeria (B O Olusanya PhD, J O Olusanya MBA); Lira
District Local Government, Lira Municipal Council, Uganda
(J N Opio MPH); University of Arizona, Tucson, AZ, USA
(Prof E Oren PhD); IIS-Fundacion Jimenez Diaz-UAM, Madrid, Spain
(Prof A Ortiz PhD); St Luke’s International University, Tokyo, Japan
(E Ota PhD); Department of Medicine, Ibadan, Nigeria
(M O Owolabi Dr Med); Blossom Specialist Medical Center, Ibadan, Nigeria
(M O Owolabi Dr Med); JSS Medical College, JSS University, Mysore, India
(Prof M PA DNB); Bucharest University of Economic Studies, Bucharest,
Romania (A Pana MPH); Department of Ophthalmology, Medical Faculty
Mannheim, University of Heidelberg, Mannheim, Germany
(S Panda-Jonas MD); Christian Medical College Ludhiana, Ludhiana, India
(J D Pandian DM); Charité University Medicine Berlin, Berlin, Germany
(C Papachristou PhD); Department of Medical Humanities and Social
Medicine, College of Medicine, Kosin University, Busan, South Korea
(E Park PhD); Department of Community Health Sciences
(Prof S B Patten PhD), University of Calgary, Calgary, AB, Canada
(Prof M Tonelli MD); REQUIMTE/LAQV, Laboratório de Farmacognosia,
Departamento de Química, Faculdade de Farmácia, Universidade do Porto,
Porto, Portugal (Prof D M Pereira PhD); Health Metrics Unit, University of
Gothenburg, Gothenburg, Sweden (Prof M Petzold PhD); University of the
Witwatersrand, Johannesburg, South Africa (Prof M Petzold PhD);
Shanghai Jiao Tong University School of Medicine, Shanghai, China
(Prof M R Phillips MD); Emory University, Atlanta, GA, USA
(Prof M R Phillips MD); Durban University of Technology, Durban,
South Africa (J D Pillay PhD); Exposure Assessment and Environmental
Health Indicators (D Plass DrPH), German Environment Agency, Berlin,
Germany (M Tobollik MPH); Department of Public Health, Erasmus MC,
University Medical Center Rotterdam, Rotterdam, Netherlands
(S Polinder PhD); Sanjay Gandhi Post Graduate Institute of Medical
Sciences, Lucknow, India (Prof N Prasad DM); Non-Communicable
Diseases Research Center, Alborz University of Medical Sciences, Karaj,
Iran (M Qorbani PhD); A T Still University, Kirksville, MO, USA
(A Radfar MD); Contech International Health Consultants, Lahore,
Pakistan (A Rafay MBBS); Contech School of Public Health, Lahore,
Pakistan (A Rafay MBBS); Research and Evaluation Division, BRAC,
Dhaka, Bangladesh (M Rahman PhD); Oce Of Psychology & Public
Health (Prof R Room PhD), La Trobe University, Melbourne, VIC, Australia
(M A Rahman PhD); Society for Health and Demographic Surveillance,
Suri, India (R K Rai MPH); ERAWEB Program, University for Health
Sciences, Medical Informatics and Technology, Hall in Tirol, Austria
(S Rajsic MD); Centre for Addiction and Mental Health, Toronto, ON,
Canada (Prof J Rehm PhD); Azienda Socio-Sanitaria Territoriale, Papa
Giovanni XXIII, Bergamo, Italy (Prof G Remuzzi MD); Department of
Biomedical and Clinical Sciences “L Sacco,” University of Milan, Milan,
Italy (Prof G Remuzzi MD); Research Center for Environmental
Determinants of Health, School of Public Health, Kermanshah University
of Medical Sciences, Kermanshah, Iran (S Rezaei PhD); Hospital das
Clínicas da Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
(Prof A L Ribeiro MD); Campus MAR, Barcelona Biomedical Research
Park (PRBB), ISGlobal Instituto de Salud Global de Barcelona, Barcelona,
Spain (D Rojas-Rueda PhD); Golestan Research Center of Gastroenterology
and Hepatology, Golestan University of Medical Sciences, Gorgan, Iran
(G Roshandel PhD); Institute of Epidemiology and Medical Biometry, Ulm
University, Ulm, Germany (Prof D Rothenbacher MD); Universidad
Tecnica del Norte, Ibarra, Ecuador (E Rubagotti PhD); Managerial
Epidemiology Research Center, Department of Public Health, School of
Nursing and Midwifery, Maragheh University of Medical Sciences,
Maragheh, Iran (S Safiri PhD); Department of Medicine (Prof N Mohamed
Ibrahim MRCP), Universiti Kebangsaan Malaysia Medical Centre, Kuala
Lumpur, Malaysia (R Sahathevan PhD); Ballarat Health Service, Ballarat,
VIC, Australia (R Sahathevan PhD); Faculty of Science, Ain Shams
University, Cairo, Egypt (A M Samy PhD); J Edwards School of Medicine
(J R Sanabria MD), Department of Public Health (M Sawhney PhD),
Marshall University, Huntington, WV, USA; Case Western Reserve
University, Cleveland, OH, USA (J R Sanabria MD); IIS-Fundacion
Jimenez Diaz, Madrid, Spain (M D Sanchez-Niño PhD); Public Health
Medicine, School of Nursing and Public Health (Prof B Sartorius PhD),
Discipline of Public Health Medicine, School of Nursing and Public Health
(B Yakob PhD), University of KwaZulu-Natal, Durban, South Africa; Centre
of Advanced Study in Psychology, Utkal University, Bhubaneswar, India
(M Satpathy PhD); Federal University of Santa Catarina, Florianópolis,
Brazil (I J C Schneider PhD); Hypertension in Africa Research Team
(HART), North-West University, Potchefstroom, South Africa
(Prof A E Schutte PhD); UKZN Gastrointestinal Cancer Research Centre
(Prof B Sartorius PhD), South African Medical Research Council,
Potchefstroom, South Africa (Prof A E Schutte PhD); University of
Alabama at Birmingham, Birmingham, AL, USA (D C Schwebel PhD,
J A Singh MD); Charité Berlin, Berlin, Germany (F Schwendicke PhD);
University of Colorado, Aurora, CO, USA (B Serdar PhD); University of
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Bath, Bath, UK (G Shaddick PhD, M L Thomas MRes); Department of
Public Health, An-Najah University, Nablus, Palestine (A Shaheen PhD);
Tufts Medical Center, Boston, MA, USA (Prof S Shahraz PhD);
Independent Consultant, Karachi, Pakistan (M A Shaikh MD); Department
of Medical Surgical Nursing, School of Nursing and Midwifery, Hamadan
University of Medical Sciences, Hamadan, Iran (M Shamsizadeh MPH);
The George Institute for Global Health, Sydney, NSW, Australia
(S M Shariful Islam PhD); Ministry of Health, Thimphu, Bhutan
(J Sharma MPH); Indian Institute of Technology Ropar, Rupnagar, India
(R Sharma MA); Department of Pulmonary Medicine, Zhongshan
Hospital, Fudan University, Shanghai, China (J She MD); Research
Institute at Nationwide Children’s Hospital, Columbus, OH, USA
(J Shen PhD); National Institute of Infectious Diseases, Tokyo, Japan
(M Shigematsu PhD); Sandia National Laboratories, Albuquerque, NM,
USA (M Shigematsu PhD); Department of Public Health Sciences
(Prof M Shin PhD), Department of Preventive Medicine, College of
Medicine (S Yoon PhD), Korea University, Seoul, South Korea; Washington
State University, Spokane, WA, USA (K Shishani PhD); Harvard Medical
School, Boston, MA, USA (M G Shrime MD); Reykjavik University,
Reykjavik, Iceland (I D Sigfusdottir PhD); Federal University of Santa
Catarina, Florianopolis, Brazil (D A S Silva PhD); Brasília University,
Brasília, Brazil (D G A Silveira MD); Asthma Bhawan, Jaipur, India
(V Singh MD); School of Preventive Oncology, Patna, India
(D N Sinha PhD); WHO FCTC Global Knowledge Hub on Smokeless
Tobacco, National Institute of Cancer Prevention, Noida, India
(D N Sinha PhD); Hywel Dda University Health Board, Carmarthen, UK
(E Skiadaresi MD); Bristol Eye Hospital, Bristol, UK (E Skiadaresi MD);
King Khalid University Hospital, Riyadh, Saudi Arabia (B H A Sobaih MD);
University of Yaoundé, Yaoundé, Cameroon (Prof E Sobngwi PhD);
Yaoundé Central Hospital, Yaoundé, Cameroon (Prof E Sobngwi PhD);
Dartmouth College, Hanover, NH, USA (S Soneji PhD); Department of
Community Medicine, International Medical University, Kuala Lumpur,
Malaysia (C T Sreeramareddy MD); University of East Anglia, Norwich, UK
(Prof N Steel PhD); Public Health England, London, UK
(Prof N Steel PhD); South African Medical Research Council Unit on
Anxiety & Stress Disorders, Cape Town, South Africa (Prof D J Stein PhD);
Department of Dermatology, University Hospital Muenster, Muenster,
Germany (S Steinke DrMed); Deakin University, Burwood, VIC, Australia
(Prof M A Stokes PhD); Ministry of Health, Kingdom of Saudi Arabia,
Riyadh, Saudi Arabia (R A Suliankatchi MD); Indian Council of Medical
Research, New Delhi, India (S Swaminathan MD); Departments of
Criminology, Law & Society, Sociology, and Public Health, University of
California, Irvine, Irvine, CA, USA (Prof B L Sykes PhD); Grith
University, Gold Coast, QLD, Australia (S K Tadakamadla PhD); Asbestos
Diseases Research Institute, Concord Clinical School
(Prof K Takahashi MD), The University of Sydney, Sydney, NSW, Australia
(K Alam PhD, J Leigh PhD); WSH Institute, Ministry of Manpower,
Singapore, Singapore (J S Takala DSc); Tampere University of Technology,
Tampere, Finland (J S Takala DSc); Ethiopian Public Health Association,
Addis Ababa, Ethiopia (Y L Tarekegn MS); New York Medical Center,
Valhalla, NY, USA (M Tavakkoli MD); 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 MD); Outcomes Research Consortium
(A S Terkawi MD), Cleveland Clinic, Cleveland, OH, USA
(Prof E M Tuzcu MD); School of Public Health, Post Graduate Institute of
Medical Education and Research, Chandigarh, India (Prof J Thakur MD);
Sree Chitra Tirunal Institute for Medical Sciences and Technology,
Trivandrum, Thiruvananthapuram, India (Prof K R Thankappan MD);
Adaptive Knowledge Management, Victoria, BC, Canada
(A J Thomson PhD); National Center for Child Health and Development,
Tokyo, Japan (R Tobe-Gai PhD); National Institute of Public health, Bergen,
Norway (M C Tollanes PhD); Faculty of Health Sciences, Wroclaw Medical
University, Wroclaw, Poland (R Topor-Madry PhD); School of Medicine,
University of Valencia, Valencia, Spain (M Tortajada PhD); INSERM
(French National Institute for Health and Medical Research), Paris, France
(M Touvier PhD); Hanoi Medical University, Hanoi, Vietnam
(B X Tran PhD); Department of Neurology, Rigshospitalet, University of
Copenhagen, Copenhagen, Denmark (T Truelsen DMSc); Parc Sanitari
Sant Joan de Déu, Fundació Sant Joan de Déu, Universitat de Barcelona,
CIBERSAM, Barcelona, Spain (S Tyrovolas PhD); Department of Internal
Medicine, Federal Teaching Hospital, Abakaliki, Nigeria (K N Ukwaja MD);
Ebonyi State University, Abakaliki, Nigeria (C J Uneke PhD); Warwick
Medical School, University of Warwick, Coventry, UK (O A Uthman PhD);
UKK Institute for Health Promotion Research, Tampere, Finland
(Prof T Vasankari PhD); Raes Neuroscience Centre, Raes Hospital,
Singapore, Singapore (N Venketasubramanian MBBS); University of
Bologna, Bologna, Italy (Prof F S Violante MD); Federal Research Institute
for Health Organization and Informatics, Moscow, Russia
(S K Vladimirov PhD); National Research University Higher School of
Economics, Moscow, Russia (Prof V V Vlassov MD); Wolaita Sodo
University, Wolaita Sodo, Ethiopia (F Wadilo MS); VA Medical Center,
Washington, DC, USA (M T Wallin MD); Neurology Department,
Georgetown University, Washington, DC, USA (M T Wallin MD);
University of São Paulo Medical School, São Paulo, Brazil (Y Wang PhD);
McGill University, Ottawa, ON, Canada (S Weichenthal PhD); Department
of Research, Cancer Registry of Norway, Institute of Population-Based
Cancer Research, Oslo, Norway (E Weiderpass PhD); Department of
Community Medicine, Faculty of Health Sciences, University of Tromsø,
The Arctic University of Norway, Tromsø, Norway (E Weiderpass PhD);
Genetic Epidemiology Group, Folkhälsan Research Center, Helsinki,
Finland (E Weiderpass PhD); Royal Children’s Hospital, Melbourne, VIC,
Australia (R G Weintraub MBBS); German National Cohort Consortium,
Heidelberg, Germany (R Westerman PhD); South African Medical
Research Council, Cochrane South Africa, Cape Town, South Africa
(Prof C S Wiysonge PhD); National Institute for Health Research
Comprehensive Biomedical Research Centre, Guy’s & St Thomas’ NHS
Foundation Trust and King’s College London, London, UK
(Prof C D Wolfe MD); Ghent University, Ghent, Belgium
(A Workicho MPH); St John’s Medical College and Research Institute,
Bangalore, India (Prof D Xavier MD); Department of Neurology, Jinling
Hospital, Nanjing University School of Medicine, Nanjing, China
(Prof G Xu PhD); Global Health Research Center, Duke Kunshan
University, Kunshan, China (Prof L L Yan PhD); Mizan Tepi University,
Mizan Teferi, Ethiopia (H H Yimam MPH); Social Work and Social
Administration Department (Prof P Yip PhD), The Hong Kong Jockey Club
Centre for Suicide Research and Prevention (Prof P Yip PhD), University of
Hong Kong, Hong Kong, China; Department of Biostatistics, School of
Public Health, Kyoto University, Kyoto, Japan (N Yonemoto MPH); School
of Public Health, University of Kinshasa, Kinshasa, Democratic Republic of
the Congo (M Yotebieng PhD); Jackson State University, Jackson, MS, USA
(Prof M Z Younis DrPH); University Hospital of Setif, Setif, Algeria
(Prof Z Zaidi DSc); Faculty of Medicine, Mansoura University, Mansoura,
Egypt (Prof M E Zaki PhD); University of Texas School of Public Health,
Houston, TX, USA (X Zhang MS); and MD Anderson Cancer Center,
Houston, TX, USA (X Zhang MS).
Contributors
Please see appendix 1 (p i) for more detailed information about
individual authors’ contributions to the research, divided into the
following categories: managing the estimation process; writing the first
draft of the manuscript; providing data or critical feedback on data
sources; developing methods or computational machinery; applying
analytical methods to produce estimates; providing critical feedback on
methods or results; drafting the work or revising it critically for
important intellectual content; extracting, cleaning, or cataloguing data;
designing or coding figures and tables; and managing the overall
research enterprise.
Declaration of interests
Laith J Abu-Raddad acknowledges the support of Qatar National Research
Fund (NPRP 9-040-3-008), who provided the main funding for generating
the data provided to the GBD-IHME eort. Anurag Agrawal received a
Wellcome Trust DBT India Alliance fellowship. Ashish Awasthi received
financial support from Department of Science and Technology,
Government of India through INSPIRE Faculty program Alaa Badawi
acknowledges the Public Health Agency of Canada. Scientific work of
Aleksandra Barac is part of the Project No. III45005 granted by the Ministry
of Education, Science, and Technological Development of the Republic of
Serbia. Till Bärnighausen is funded by the Alexander von Humboldt
Foundation through the Alexander von Humboldt Professorship endowed
by the German Federal Ministry of Education and Research; he is also
supported by the Wellcome Trust, the European Commission, the Clinton
Global Health Metrics
1420
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Health Access Initiative and NICHD of NIH [R01-HD084233], NIAID of
NIH [R01-AI124389 and R01-AI112339] and FIC of NIH [D43-TW009775].
Boris Bikbov has received funding from the European Union’s Horizon
2020 research and innovation programme under Marie Sklodowska-Curie
grant agreement No. 703226. Boris Bikbov acknowledges that work related
to this paper has been done on the behalf of the GBD Genitourinary
Disease Expert Group. Cyrus Cooper reports personal fees from Alliance
for Better Bone Health, Amgen, Eli Lilly, GSK, Medtronic, Merck, Novartis,
Pfizer, Roche, Servier, Takeda, and UCB, outside the submitted work.
José das Neves was supported in his contribution to this work by a
Fellowship from Fundação para a Ciência e a Tecnologia, Portugal
(SFRH/BPD/92934/2013). Barbora de Courten is supported by National
Heart Foundation Future Leader Fellowship (100864). Kebede Deribe is
funded by a Wellcome Trust Intermediate Fellowship in Public Health and
Tropical Medicine [grant number 201900]. Joao Fernandes is supported by
FCT - Fundação para a Ciência e a Tecnologia (Grant number UID/
Multi/50016/2013). Katharine Gibney is supported by an NHMRC early
career fellowship. Amador Goodridge acknowledges the Sistema Nacional
de Investigación (SNI) de Panamá & Secretaría Nacional de Ciencia,
Tecnología e Innovación (SENACYT). Simon I Hay is funded by grants
from the Bill & Melinda Gates Foundation (OPP1106023, OPP1119467,
OPP1093011, and OPP1132415). Manami Inoue was the beneficiary of a
financial contribution from the AXA Research Fund as chair-holder of the
AXA Department of Health and Human Security, Graduate School of
Medicine, The University of Tokyo. The AXA Research Fund had no role in
this work. Shariful Islam received a postdoctoral research fellowship from
the George Institute for Global Health and career transition grants from
High Blood Pressure Research Council of Australia. Ministry of Education
Science and Technological Development of the Republic of Serbia has
co-financed Serbian part of Mihajlo Jakovljevic’s GBD-related contribution
through Grant OI 175 014. Publication of results was not contingent upon
the Ministry’s censorship or approval. Panniyammakal Jeemon reports a
clinical and public health intermediate fellowship from the Wellcome Trust
and Department of Biotechnology, India Alliance. Nicholas Kassebaum
reports personal fees and non-financial support from Vifor
Pharmaceuticals, outside the submitted work. S Vittal Katikireddi was
funded by a NRS Scottish Senior Clinical Fellowship (SCAF/15/02), the UK
Medical Research Council (MC_UU_12017/13 & MC_UU_12017/15) and
the Scottish Government Chief Scientist Oce (SPHSU13 & SPHSU15).
Christian Kieling has received support from Brazilian governmental
research funding agencies Conselho Nacional de Desenvolvimento
Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de
Pessoal de Nível Superior (CAPES), Fundação de Amparo à Pesquisa do
Estado do Rio Grande do Sul (Fapergs), and Hospital de Clínicas de Porto
Alegre (FIPE/HCPA). Ai Koyanagi’s work was supported by the Miguel
Servet contract financed by the CP13/00150 and PI15/00862 projects,
integrated into the National R + D + I and funded by the ISCIII - General
Branch Evaluation and Promotion of Health Research - and the European
Regional Development Fund (ERDF-FEDER). Katharine J Looker thanks
the National Institute for Health Research Health Protection Research Unit
(NIHR HPRU) in Evaluation of Interventions at the University of Bristol,
in partnership with Public Health England (PHE), for research support.
Katharine J Looker received separate funding from WHO and Sexual
Health 24 during the course of this study. These funders had no role in the
writing of the manuscript nor the decision to submit it for publication. The
views expressed are those of the authors and not necessarily those of the
National Health Service, the NIHR, the Department of Health or Public
Health England. Azeem Majeed and Imperial College London are grateful
for support from the NW London NIHR Collaboration for Leadership in
Applied Health Research & Care. Francisco Martins-Melo received a
postdoctoral fellowship from the CAPES (Brazilian Federal Agency for
Support and Evaluation of Graduate Education), outside the submitted
work. Kunihiro Matsushita reports grants from the US National Kidney
Foundation and the US National Institutes of Health during the conduct of
the study; grants and personal fees from Kyowa Hakko Kirin, and Fukuda
Denshi, and personal fees from Daiichi Sankyo, outside the submitted
work. Mohsen Mazidi was supported by the World Academy of Sciences
and Chinese Academy of Sciences. John McGrath received John Cade
Fellowship APP1056929 from the National Health and Medical Research
Council, and Niels Bohr Professorship from the Danish National Research
Foundation. Toni Meier acknowledges additional institutional support
from the Competence Cluster for Nutrition and Cardiovascular Health
(nutriCARD), Jena-Halle-Leipzig. Philip Mitchell’s research is supported by
an Australian NHMRC Program Grant (no. 1037196). Ulrich Mueller
gratefully acknowledges financial support from the German National
Cohort Study (BMBF grant # 01ER1511/D). Olanrewaju Oladimeji is a
Senior Research Specialist at the Human Sciences Research Council
(HSRC) and Doctoral Candidate at the University of KwaZulu-Natal
(UKZN), South Africa; we acknowledge the institutional supports from
HSRC and UKZN for him to participate in this study. Alberto Ortiz was
supported by Spanish Government (Intensificacion ISCIIII FEDER funds
and RETIC REDINREN RD016/0019). Mayowa Owolabi is supported by
U54HG007479 from the NIH. Norberto Perico would like to acknowledge
that the work related to this paper has been done on the behalf of the GBD
Genitourinary Disease Expert Group. Giuseppe Remuzzi acknowledges
that the work related to this paper has been done on behalf of the GBD
Genitourinary Diseases Expert Group supported by the International
Society of Nephrology (ISN). Luz Myriam Reynales-Shigematsu
acknowledges the Global Adult Tobacco Survey, GATS Mexico 2015, with
financial support provided by the CONADIC, Ministry of Health, Mexico
and the Bloomberg Initiative to Reduce Tobacco Use through the CDC
Foundation with a grant from Bloomberg Philanthropies.
Prof Aletta E Schutte received support from the South African Medical
Research Council and the National Research Foundation’s SARChI
Programme. Mark Shrime acknowledges the Damon Runyon Cancer
Research Foundation GE Safe Surgery 2020 Project. Jasvinder Singh
reports consultancy fees from Savient, Takeda, Regeneron, Merz, Iroko,
Bioiberica, Crealta/Horizon, Allergan, UBM LLC, WebMD, and the
American College of Rheumatology and grants from Savient and Takeda.
JS serves as the principal investigator for an investigator-initiated study
funded by Horizon pharmaceuticals through a grant to DINORA Inc,
a 501c3 entity; he is also on the steering committee of OMERACT, an
international organization that develops measures for clinical trials and
receives arms length funding from 36 pharmaceutical companies. Michael
Soljak received funding from Public Health England for modelling of NCD
prevalence. Cassandra Szoeke reports grants from the Australian National
Medical Health Research Council (NHMRC) during the conduct of the
study, and grants from Lundbeck and Alzheimer’s Association, outside the
submitted work; in addition, Cassandra Szoeke has a patent,
PCT/AU2008/001556 issued. Rafael Tabarés-Seisdedos was supported in
part by grant PROMETEOII/2015/021 from Generalitat Valenciana and the
national grands PI14/00894 and PIE14/00031 from ISCIII-FEDER. Marcel
Tanner reports grants from the Swiss Tropical and Public Health Institute
and the Swiss Federal Government during the conduct of the study.
Amanda Thrift was supported by a Fellowship from the National Health &
Medical Research Council (Australia; 1042600). Stefano Tyrovola’s work was
supported by the Foundation for Education and European Culture (IPEP),
the Sara Borrell postdoctoral programme (reference no. CD15/00019 from
the Instituto de Salud Carlos III (ISCIII - Spain) and the Fondos Europeo
de Desarrollo Regional (FEDER). Job van Boven received support from the
department of Clinical Pharmacy and Clinical Pharmacology, University
Medical Center Groningen, University of Groningen, Netherlands.
Lijing Yan is partially supported by the National Natural Sciences
Foundation of China grants (71233001 and 71490732). Marcel Yotebieng is
partially supported by the NIAID U01AI096299 and the NICHD
R01HD087993.
Acknowledgments
The Palestinian Central Bureau of Statistics granted the researchers access
to relevant data in accordance with license no. SLN2014-3-170, after
subjecting data to processing aiming to preserve the confidentiality of
individual data in accordance with the General Statistics Law–2000. The
researchers are solely responsible for the conclusions and inferences drawn
upon available data. We thank the Russia Longitudinal Monitoring Survey,
RLMS-HSE, conducted by the National Research University Higher School
of Economics and ZAO “Demoscope” together with Carolina Population
Center, University of North Carolina at Chapel Hill and the Institute of
Sociology RAS for making these data available. The Panel Study of Income
Dynamics is primarily sponsored by the National Science Foundation, the
National Institute of Aging, and the National Institute of Child Health and
Human Development and is conducted by the University of Michigan. This
research used data from the National Health Survey 2003 and the National
Health Survey 2009–10. The authors are grateful to the Ministry of Health
Global Health Metrics
www.thelancet.com Vol 390 September 16, 2017
1421
Survey copyright owner, allowing them to have the database. All results of
the study are those of the author and in no way committed to the Ministry.
This research uses data from Add Health, a program project designed by
JRichard Udry, Peter S Bearman, and Kathleen Mullan Harris, and funded
by a grant P01-HD31921 from the Eunice Kennedy Shriver National
Institute of Child Health and Human Development, with cooperative
funding from 17 other agencies. Special acknowledgment is due to Ronald
R Rindfuss and Barbara Entwisle for assistance in the original design.
Persons interested in obtaining data files from Add Health should contact
Add Health, Carolina Population Center, 123 W Franklin Street, Chapel
Hill, NC 27516-2524 (addhealth@unc.edu). No direct support was received
from grant P01-HD31921 for this analysis. The HRS (Health and
Retirement Study) is sponsored by the National Institute on Aging (grant
number NIA U01AG009740) and is conducted by the University of
Michigan. This paper uses data from SHARE Waves 1, 2, 3 (SHARELIFE),
4, 5 and 6 (DOIs: 10.6103/SHARE.w1.600, 10.6103/SHARE.w2.600, 10.6103/
SHARE.w3.600, 10.6103/SHARE.w4.600, 10.6103/SHARE.w5.600, 10.6103/
SHARE.w6.600), see Börsch-Supan et al. (2013) for methodological details.
(1) The SHARE data collection has been primarily funded by the European
Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-
CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE:
CIT4-CT-2006-028812) and FP7 (SHARE-PREP: N°211909, SHARE-LEAP:
N°227822, SHARE M4: N°261982). Additional funding from the German
Ministry of Education and Research, the Max Planck Society for the
Advancement of Science, the US National Institute on Aging
(U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815,
R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064,
HHSN271201300071C) and from various national funding sources is
gratefully acknowledged. HBSC is an international study carried out in
collaboration with WHO/EURO. The International Coordinator of the
1997/98, 2001/02, 2005/06 and 2009/10 surveys was Candace Currie and
the Data Bank Manager for the 1997/98 survey was Bente Wold, whereas
for the following survey Prof Oddrun Samdal was the Databank Manager.
A list of principal investigator in each country can be found online.. This
analysis uses data or information from the LASI Pilot micro data and
documentation. The development and release of the LASI Pilot Study was
funded by the National Institute on Ageing/National Institute of Health
(R21AG032572, R03AG043052, and R01 AG030153). The data used in this
paper come from the 2009–10 Ghana Socioeconomic Panel Study Survey
which is a nationally representative survey of over 5000 households in
Ghana. The survey is a joint eort undertaken by the Institute of Statistical,
Social and Economic Research (ISSER) at the University of Ghana, and the
Economic Growth Centre (EGC) at Yale University. It was funded by the
Economic Growth Center. At the same time, ISSER and the EGC are not
responsible for the estimations reported by the analyst(s). The data reported
here have been supplied by the United States Renal Data System (USRDS).
The interpretation and reporting of these data are the responsibility of the
author(s) and in no way should be seen as an ocial policy or
interpretation of the US Government. We thank the Russia Longitudinal
Monitoring Survey, RLMS-HSE, conducted by the National Research
University Higher School of Economics and ZAO “Demoscope” together
with Carolina Population Center, University of North Carolina at Chapel
Hill and the Institute of Sociology RAS for making these data available.
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