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www.thelancet.com/neurology Vol 23 October 2024
973
Articles
Lancet Neurol 2024;
23: 973–1003
See Comment page 952
*Members are listed at the end
of the Article
Correspondence to:
Prof Valery L Feigin, National
Institute for Stroke and Applied
Neurosciences, Auckland
University of Technology,
Auckland 0627, New Zealand
valery.feigin@aut.ac.nz
Global, regional, and national burden of stroke and its risk
factors, 1990–2021: a systematic analysis for the Global
Burden of Disease Study 2021
GBD 2021 Stroke Risk Factor Collaborators*
Summary
Background Up-to-date estimates of stroke burden and attributable risks and their trends at global, regional, and
national levels are essential for evidence-based health care, prevention, and resource allocation planning. We aimed
to provide such estimates for the period 1990–2021.
Methods We estimated incidence, prevalence, death, and disability-adjusted life-year (DALY) counts and age-
standardised rates per 100 000 people per year for overall stroke, ischaemic stroke, intracerebral haemorrhage, and
subarachnoid haemorrhage, for 204 countries and territories from 1990 to 2021. We also calculated burden of stroke
attributable to 23 risk factors and six risk clusters (air pollution, tobacco smoking, behavioural, dietary, environmental,
and metabolic risks) at the global and regional levels (21 GBD regions and Socio-demographic Index [SDI] quintiles),
using the standard GBD methodology. 95% uncertainty intervals (UIs) for each individual future estimate were
derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the
multistage computational pipeline.
Findings In 2021, stroke was the third most common GBD level 3 cause of death (7·3 million [95% UI 6·6–7·8]
deaths; 10·7% [9·8–11·3] of all deaths) after ischaemic heart disease and COVID-19, and the fourth most common
cause of DALYs (160·5 million [147·8–171·6] DALYs; 5·6% [5·0–6·1] of all DALYs). In 2021, there were 93·8 million
(89·0–99·3) prevalent and 11·9 million (10·7–13·2) incident strokes. We found disparities in stroke burden and risk
factors by GBD region, country or territory, and SDI, as well as a stagnation in the reduction of incidence from 2015
onwards, and even some increases in the stroke incidence, death, prevalence, and DALY rates in southeast Asia, east
Asia, and Oceania, countries with lower SDI, and people younger than 70 years. Globally, ischaemic stroke constituted
65·3% (62·4–67·7), intracerebral haemorrhage constituted 28·8% (28·3–28·8), and subarachnoid haemorrhage
constituted 5·8% (5·7–6·0) of incident strokes. There were substantial increases in DALYs attributable to high BMI
(88·2% [53·4–117·7]), high ambient temperature (72·4% [51·1 to 179·5]), high fasting plasma glucose (32·1%
[26·7–38·1]), diet high in sugar-sweetened beverages (23·4% [12·7–35·7]), low physical activity (11·3% [1·8–34·9]),
high systolic blood pressure (6·7% [2·5–11·6]), lead exposure (6·5% [4·5–11·2]), and diet low in omega-6
polyunsaturated fatty acids (5·3% [0·5–10·5]).
Interpretation Stroke burden has increased from 1990 to 2021, and the contribution of several risk factors has also
increased. Eective, accessible, and aordable measures to improve stroke surveillance, prevention (with the emphasis
on blood pressure, lifestyle, and environmental factors), acute care, and rehabilitation need to be urgently implemented
across all countries to reduce stroke burden.
Funding Bill & Melinda Gates Foundation.
Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0
license.
Introduction
Evidence from the Global Burden of Disease, Injuries,
and Risk Factors Study (GBD) suggests that prevalent
cases of total cardiovascular disease (including stroke)
nearly doubled from 271 million (95% uncertainty
interval [UI] 257–285) in 1990 to 523 million (497–550)
in 2019.1 Moreover, despite a consistent decline in age-
standardised cardiovascular disease (including stroke)
mortality rates globally in the second half of the 20th
century,1 there has been a subsequent deceleration in
the decline and an overall flattening of the decline in
the past few years.1 Since 2010, age-standardised cardio-
vascular disease (including stroke) mortality rates have
even increased in many locations (eg, Mexico, the UK,
and the USA),1,2 and the age-standardised incidence
of stroke in individuals younger than 55 years has
increased substantially in high-income countries.3,4 The
previous GBD study on stroke burden and risks covered
the period 1990–2019, and identified stroke as the second
leading cause of death in the world.5 The most recent
GBD stroke burden project6 has estimated an almost
doubling of disability-adjusted life-years (DALYs),
Articles
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deaths, and cost due to stroke from 2020 to 2050.6
Globally, the age-standardised prevalence of cardiovas-
cular disease (including stroke) risk factors (including
hypertension, overweight, and diabetes)1 are also
increasing.7 There has been a rapid increase in
the number of people who died or remained disabled
from stroke over the past 30 years,5 with a trend towards
increasing incidence rates in people younger than
55 years, and increased prevalence of major risk factors
for stroke (elevated blood pressure, overweight, and
diabetes) over the past 10–15 years. These findings
necessitate timely updated data on the most recent
changes in stroke burden and risks across the globe to
inform adequate health-care planning, resource alloca-
tion, and priority setting for stroke and to assess
the success or failure of measures to reduce stroke
burden.
The current GBD 2021 study of stroke burden and risks
covers the period from 1990 to 2021. It includes analysis
of the additional data sources for 2019–21, with corre-
sponding re-calculation of all previous stroke burden and
risks estimates, including stroke incidence, prevalence,
deaths, and DALYs for total stroke and its three main
pathological types (ischaemic stroke, intracerebral haem-
orrhage, and subarachnoid haemorrhage). It also
includes analysis of DALYs due to stroke and stroke
pathological type attributable to 23 risk factors and
six risk factor clusters at global, regional, and national
(204 countries and territories) levels. This manuscript
was produced as part of the GBD Collaborator Network
and in accordance with the GBD Protocol.
Methods
Overview
Details of the GBD 2021 methods for stroke burden and
risk factors estimates remained the same as for the latest
GBD estimates and are described elsewhere8–10 (appendix
pp 61–99). Stroke was defined according to the clinical
WHO criteria11 and categorised into three pathological
types (ischaemic stroke, intracerebral haemorrhage, and
subarachnoid haemorrhage).12 To simplify the stroke
modelling process and to ensure that all major patholog-
ical types were estimated correctly, vital registration and
surveillance data were used to separately produce inde-
pendent acute and chronic stroke models for ischaemic
stroke, intracerebral haemorrhage, and subarachnoid
haemorrhage type (appendix pp 75–76). As in previous
GBD stroke burden estimates, we modelled first-ever-in-
a-lifetime ischaemic stroke, intracerebral haemorrhage,
and subarachnoid haemorrhage from the day of stroke
onset to 28 days, and separately modelled survival
(prevalence) beyond 28 days.5
See Online for appendix
Research in context
Evidence before this study
The Global Burden of Diseases, Injuries, and Risk Factors Study
(GBD) is the only global epidemiological study that produces
comprehensive estimates of global, regional, and country-
specific burden due to stroke. To evaluate the availability of
evidence, we carried out a structured review of the published
scientific literature in MEDLINE, Scopus, Google Scholar, and
PubMed for relevant reports published in any language from
Jan 1, 1990, to March 1, 2024, using search terms that included
“stroke”, “cerebral infarction”, “isch(a)emic stroke”,
“intracerebral h(a)emorrage”, “h(a)emorrhagic stroke”, or
“subarachnoid h(a)emorrage”, AND “incidence”, “prevalence”,
“mortality”, or “epidemiology” or “population attributable
fraction (PAF)”, “risk factor(s)”, “trends”, or “disability-adjusted
life-year(s) (DALYs)”. The most recent GBD report on the burden
of stroke and its risk factors covered the period from 1990 to
2019 and found that the annual number of strokes and deaths
due to stroke increased substantially, despite large reductions in
age-standardised rates, particularly reductions among people
aged 70 years or older. The highest age-standardised stroke-
related mortality and DALY rates were in the World Bank low-
income group, and the fastest growing risk factor for stroke
between 1990 and 2019 was high BMI.
Added value of this study
As part of GBD 2021, this study provides the most up-to-date
estimates of the burden of overall stroke, ischaemic stroke,
intracerebral haemorrhage, and subarachnoid haemorrhage
and its risk factors. We found that stroke burden, in terms of
absolute numbers, has increased substantially from
1990 to 2021. From 1990 to 2021, there was an increase in the
contribution to stroke DALYs from not only high BMI, as in the
previous GBD 2019 study, but also high ambient temperature,
high fasting plasma glucose, diet high in sugar-sweetened
beverages, low physical activity, high systolic blood pressure,
and diet low in omega-6 polyunsaturated fatty acids,
emphasising the increasing role of environmental factors on
the heightened burden from stroke. Stroke burden was highest
in low-income and middle-income countries.
Implications of all the available evidence
The findings from this study can help to guide evidence-based
health-care planning, prevention, and resource allocation for
stroke and its pathological types, including country-specific
prioritisation of these measures. Effective, accessible, and
affordable measures to improve stroke surveillance, prevention
(with the emphasis on elevated blood pressure, lifestyle, and
environmental factors), acute care, and rehabilitation to reduce
stroke burden need to be urgently implemented across all
countries.
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Cause of Death Ensemble modelling (CODEm) was
used to estimate deaths due to overall stroke and stroke
pathological types. For non-fatal disease modelling
(incidence and prevalence of stroke), we used
the DisMod-MR 2.1 tool,13 a Bayesian modelling software
that uses data on various disease parameters and
the epidemiological relationships between these
parameters.5 In the GBD study, the incidence rate
represents new events in a given year, whereas the death
rate represents those that occurred in that year regardless
of when the stroke occurred.
We used data from 3736 vital registration sources, 147 verbal
autopsy sources, 368 incidence sources, 346 prevalence
sources, 229 excess mortality sources, 7753 risk factor expo-
sure sources, and 2733 risk factor relative risk sources.
Further details of the data sources used in this analysis are
available on the GBD 2021 Sources Tool website.
Stroke incidence, mortality, prevalence, and DALY esti-
mates are presented in absolute numbers and as
age-standardised rates per 100 000 population (with
95% UIs) and are stratified by age, sex, 21 GBD regions,
and seven GBD super-regions (appendix pp 202–203).
Countries and territories were also grouped into quintiles
of high, high-middle, middle, low-middle, and low
Socio-demographic Index (SDI; a summary indicator
of geometric mean of normalised values of a location’s
lag-distributed income per capita, the average years
of schooling in the population aged 15 years or older, and
the total fertility rate in females younger than 25 years),14
on the basis of their 2021 values. Expressed on a scale
from 0 to 1, a location with an SDI of 0 would have a
theoretical minimum level of development relevant to
health, whereas a location with an SDI of 1 would have a
theoretical maximum level.
Count data in tables are rounded to the nearest thou-
sand or, when the count is less than 1000, to the nearest
10. Uncertainty was propagated throughout all of these
calculations by creating 500 values for each incidence,
prevalence, death, or DALY estimate and performing
aggregations across causes and locations at the level
of each of the 500 values for all intermediate steps
in the calculation. The lower and upper bounds
of the 95% UI are the 2·5th and 97·5th percentiles.
Attributable burden of stroke due to risk factors
To analyse the attributable burden of stroke and its
three pathological types due to 23 risk factors currently
available for such analysis in GBD 2021, we calculated
population attributable fractions (PAFs) of DALYs
(appendix pp 31–43), using the exposure level for each
risk factor and theoretical minimum risk exposure level
(TMREL) that minimises risk for each individual in
the population as the reference variable.9 We analysed
data on the prevalence of exposure to a risk and derived
relative risks for any risk–outcome pair for which we
found sucient evidence of a causal relationship.15
Adjustments for mediation were applied to account for
relationships involving risk factors that act indirectly on
outcomes via intermediate risks, as described elsewhere.9
Relative risk data were pooled using meta-regression
of cohort, case–control, or intervention studies. From
the prevalence and relative risk results, PAFs were esti-
mated relative to the TMREL. The PAF represents a
proportion of the stroke DALYs that would be decreased
if the exposure to the risk factor in the past had been at
the counterfactual level of the TMREL.
The risks included in the analysis were ambient partic-
ulate matter pollution; household air pollution from
solid fuels; low ambient temperature (daily temperatures
below the TMREL); high ambient temperature (daily
temperatures above the TMREL); lead exposure; diet
high in sodium; diet high in red meat; diet high in
processed meat; diet low in fruits; diet low in vegetables;
diet low in wholegrains; alcohol use (any alcohol dosage
consumption); diet high in sugar-sweetened beverages;
diet low in fibre; diet low in omega-6 polyunsaturated
fatty acids; low physical activity (only for ischaemic stroke
burden); smoking; second-hand smoke; high BMI; high
fasting plasma glucose; high systolic blood pressure;
high LDL cholesterol (only for ischaemic stroke burden);
and kidney dysfunction, as measured by low glomerular
filtration rate (not assessed for subarachnoid haemor-
rhage burden). We set the TMREL to zero for all harmful
dietary risk factors with monotonically increasing risk
functions (eg, processed meat intake), excluding sodium.
For protective risks with monotonically declining risk
functions with exposure (eg, fruit intake), we first deter-
mined the 85th percentile of exposure in the cohorts or
trials used in the meta-regression of each outcome that
was associated with the risk. Then, we determined
the TMREL by weighting each risk–outcome pair by
the relative global magnitude of each outcome.5
As with causes, GBD organises risk factors into
four levels, from the broadest (level 1: environmental
risks, behavioural risks, and metabolic risks) to the most
specific (level 4; 23 individual risk factors). The PAFs
of risk factor groups took into account mediation
between risk factors included in the group, as explained
elsewhere.16 Percentages and number of DALYs are not
mutually exclusive. The crude sum of the PAF of the risk
factors might exceed 100% because the eects of many
of these risk factors are mediated partly or wholly
through another risk factor or risk factors.5 Definitions
of risk factors and risk groups and further details of risk
factors are in the appendix (pp 31–43). Changes in
the modelling of stroke for GBD 2021 are presented in
the appendix (pp 44–47). Analyses were also done by
cluster of risk factors. The air pollution cluster includes
ambient PM2·5 pollution and household air pollution.
The behavioural risks cluster includes smoking
(including second-hand smoking), dietary risks (diet
high in sodium, diet high in processed meat diet, high
in red meat, diet high in sugar-sweetened beverages, diet
low in omega-6 polyunsaturated fatty acids, diet low in
For the GBD 2021 Sources Tool
see https://ghdx.healthdata.org/
gbd-2021/sources
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Incident cases Deaths Prevalent cases DALYs
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
World Bank income level
Global 11 946 000
(10 772 000 to 13 220 000)
–21·8%
(–23·7 to –19·8)
7 253 000
(6 567 000 to 7 808 000)
–39·4%
(–44·0 to –34·6)
93 816 000
(89 030 000 to 99 335 000)
–8·5%
(–9·7 to –7·3)
160 457 000
(147 781 000 to 171 643 000)
–38·7%
(–43·4 to –34·0)
High income 1 994 000
(1 822 000 to 2 173 000)
–41·0%
(–43·0 to –39·0)
930 000
(791 000 to 1 002 000)
–62·2%
(–64·2 to –61·1)
21 889 000
(21 018 000 to 22 893 000)
–15·6%
(–17·3 to –13·8)
16 980 000
(15 364 000 to 18 218 000)
–58·0%
(–59·6 to –56·5)
Upper-middle income 5 680 000
(5 053 000 to 6 432 000)
–17·9%
(–21·2 to –14·4)
3 557 000
(3 113 000 to 4 005 000)
–43·4%
(–50·5 to –35·1)
38 997 000
(36 331 000 to 41 833 000)
–1·6%
(–3·6 to 0·5)
73 780 000
(65 305 000 to 82 892 000)
–45·1%
(–51·7 to –37·4)
Lower-middle income 3 702 000
(3 374 000 to 4 043 000)
–19·7%
(–21·8 to –17·4)
2 410 000
(2 225 000 to 2 592 000)
–26·3%
(–32·1 to –18·6)
28 336 000
(26 853 000 to 30 089 000)
–8·9%
(–10·0 to –7·7)
60 018 000
(55 442 000 to 64 220 000)
–27·7%
(–33·3 to –20·5)
Low income 561 000
(518 000 to 605 000)
–19·6%
(–21·8 to –16·9)
349 000
(302 000 to 397 000)
–28·2%
(–35·8 to –19·9)
4 520 000
(4 365 000 to 4 673 000)
–13·8%
(–15·1 to –12·6)
9 596 000
(8 261 000 to 10 976 000)
–30·6%
(–38·5 to –22·2)
SDI level
High SDI 1 800 000
(1 632 000 to 1 981 000)
–37·3%
(–39·0 to –35·4)
798 000
(683 000 to 860 000)
–59·4%
(–61·6 to –57·7)
20 249 000
(19 375 000 to 21 279 000)
–13·4%
(–15·1 to –11·5)
15 221 000
(13 730 000 to 16 390 000)
–54·5%
(–56·6 to –52·5)
High-middle SDI 3 094 000
(2 748 000 to 3 480 000)
–25·6%
(–27·7 to –23·3)
1 942 000
(1 726 000 to 2 138 000)
–46·9%
(–52·0 to –41·5)
21 406 000
(20 065 000 to 22 783 000)
–8·4%
(–10·4 to –6·3)
38 405 000
(34 662 000 to 42 300 000)
–46·7%
(–51·9 to –41·3)
Middle SDI 4 215 000
(3 795 000 to 4 707 000)
–14·2%
(–17·2 to –10·9)
2 681 000
(2 384 000 to 2 946 000)
–37·2%
(–44·0 to –28·6)
30 207 000
(28 379 000 to 32 296 000)
–2·0%
(–3·6 to –0·3)
59 875 000
(54 006 000 to 65 175 000)
–39·3%
(–45·6 to –31·4)
Low-middle SDI 2 029 000
(1 855 000 to 2 209 000)
–16·8%
(–18·9 to –14·4)
1 349 000
(1 240 000 to 1 454 000)
–23·6%
(–29·9 to –15·2)
15 293 000
(14 510 000 to 16 186 000)
–6·5%
(–7·8 to –5·3)
33 705 000
(30 995 000 to 36 498 000)
–26·4%
(–32·5 to –18·7)
Low SDI 799 000
(737 000 to 866 000)
–21·8%
(–23·8 to –19·6)
476 000
(425 000 to 528 000)
–26·5%
(–33·6 to –18·1)
6 588 000
(6 323 000 to 6 864 000)
–13·6%
(–14·9 to –12·4)
13 105 000
(11 572 000 to 14 675 000)
–29·5%
(–37·0 to –21·1)
GBD super-regions, regions, and countries and territories
Central Europe, eastern
Europe, and central Asia
1 078 000
(968 000 to 1 198 000)
–28·4%
(–30·5 to –26·3)
725 000
(669 000 to 769 000)
–45·7%
(–48·2 to –43·0)
6 643 000
(6 249 000 to 7 041 000)
–13·7%
(–15·6 to –11·7)
13 875 000
(12 992 000 to 14 683 000)
–43·4%
(–46·1 to –40·6)
Central Asia 166 000
(154 000 to 178 000)
–8·8%
(–12·1 to –5·4)
84 000
(76 000 to 92 000)
–22·2%
(–28·2 to –15·3)
1 119 000
(1 082 000 to 1 160 000)
–11·7%
(–13·1 to –10·0)
1 996 000
(1 809 000 to 2 181 000)
–26·8%
(–32·5 to –20·1)
Armenia 5000
(4000 to 5000)
–40·7%
(–44·2 to –36·7)
3000
(2000 to 3000)
–45·0%
(–50·8 to –38·4)
41 000
(39 000 to 42 000)
–16·6%
(–19·3 to –13·6)
56 000
(50 000 to 62 000)
–45·3%
(–50·8 to –39·0)
Azerbaijan 17 000
(16 000 to 19 000)
6·1%
(0·6 to 12·3)
8000
(7000 to 10 000)
–20·4%
(–34·1 to –2·6)
110 000
(106 000 to 115 000)
–6·3%
(–9·3 to –3·4)
187 000
(153 000 to 226 000)
–27·4%
(–40·7 to –10·5)
Georgia 13 000
(12 000 to 14 000)
–16·6%
(–21·5 to –11·1)
10 000
(9000 to 11 000)
–17·4%
(–26·3 to –7·9)
68 000
(65 000 to 71 000)
–5·7%
(–8·3 to –3·0)
184 000
(164 000 to 205 000)
–23·2%
(–32·1 to –12·9)
Kazakhstan 37 000
(34 000 to 41 000)
–18·4%
(–23·9 to –12·9)
23 000
(20 000 to 26 000)
–10·0%
(–21·2 to 3·0)
275 000
(265 000 to 287 000)
–20·1%
(–22·9 to –17·2)
509 000
(441 000 to 576 000)
–19·3%
(–29·4 to –7·5)
Kyrgyzstan 8000
(7000 to 9000)
–38·2%
(–42·1 to –33·6)
4000
(3000 to 5000)
–53·3%
(–60·5 to –45·7)
56 000
(54 000 to 58 000)
–31·8%
(–34·1 to –29·5)
108 000
(92 000 to 126 000)
–49·2%
(–57·4 to –40·9)
Mongolia 6000
(5000 to 6000)
0·5%
(–4·1 to 5·4)
3000
(2000 to 3000)
–37·6%
(–51·4 to –22·5)
36 000
(35 000 to 37 000)
–0·8%
(–3·3 to 1·8)
72 000
(60 000 to 86 000)
–37·4%
(–50·4 to –22·3)
Tajikistan 12 000
(11 000 to 13 000)
11·9%
(6·0 to 18·6)
6000
(4000 to 7000)
–19·0%
(–36·2 to 1·9)
71 000
(68 000 to 74 000)
–8·3%
(–11·0 to –5·4)
141 000
(112 000 to 170 000)
–25·8%
(–41·2 to –7·3)
Turkmenistan 9000
(9000 to 10 000)
5·9%
(–0·5 to 11·6)
6000
(5000 to 7000)
21·6%
(–2·7 to 50·7)
70 000
(68 000 to 73 000)
18·5%
(14·9 to 22·7)
158 000
(126 000 to 193 000)
21·4%
(–3·4 to 49·9)
Uzbekistan 60 000
(55 000 to 65 000)
9·9%
(3·9 to 16·5)
22 000
(19 000 to 26 000)
–20·8%
(–31·3 to –7·8)
392 000
(377 000 to 407 000)
–3·2%
(–6·7 to 1·0)
581 000
(505 000 to 672 000)
–28·0%
(–37·7 to –16·4)
(Table 1 continues on next page)
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Incident cases Deaths Prevalent cases DALYs
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
(Continued from previous page)
Central Europe 302 000
(273 000 to 328 000)
–36·6%
(–38·3 to –35·1)
215 000
(196 000 to 230 000)
–55·3%
(–58·2 to –52·5)
1 891 000
(1 797 000 to 1 996 000)
–22·8%
(–24·5 to –21·3)
3 740 000
(3 455 000 to 3 993 000)
–55·5%
(–58·5 to –52·7)
Albania 6000
(6000 to 7000)
–14·0%
(–17·5 to –10·7)
6000
(5000 to 7000)
–25·1%
(–40·0 to –8·3)
31 000
(30 000 to 33 000)
–15·4%
(–17·6 to –12·9)
97 000
(79 000 to 116 000)
–34·6%
(–47·6 to –19·8)
Bosnia and
Herzegovina
10 000
(9000 to 11 000)
–17·1%
(–23·2 to –10·6)
7000
(6000 to 8000)
–32·8%
(–46·2 to –18·9)
71 000
(68 000 to 74 000)
–5·2%
(–8·6 to –1·4)
127 000
(103 000 to 148 000)
–36·6%
(–48·8 to –24·1)
Bulgaria 31 000
(28 000 to 34 000)
–20·9%
(–25·3 to –15·9)
28 000
(25 000 to 32 000)
–37·1%
(–44·2 to –29·5)
159 000
(146 000 to 173 000)
–11·3%
(–16·6 to –6·4)
484 000
(424 000 to 551 000)
–37·8%
(–45·4 to –29·0)
Croatia 11 000
(10 000 to 12 000)
–43·1%
(–46·0 to –40·6)
7000
(6000 to 8000)
–66·2%
(–69·9 to –62·3)
72 000
(70 000 to 75 000)
–20·6%
(–25·0 to –16·0)
110 000
(98 000 to 123 000)
–66·9%
(–70·3 to –62·9)
Czechia 23 000
(20 000 to 26 000)
–51·8%
(–55·6 to –47·7)
10 000
(8000 to 11 000)
–79·2%
(–81·4 to –76·8)
184 000
(178 000 to 192 000)
–26·1%
(–29·4 to –22·4)
177 000
(156 000 to 197 000)
–76·8%
(–79·3 to –74·0)
Hungary 22 000
(20 000 to 25 000)
–51·7%
(–54·7 to –48·0)
12 000
(10 000 to 13 000)
–70·7%
(–74·5 to –66·9)
162 000
(155 000 to 169 000)
–39·3%
(–41·4 to –36·9)
223 000
(195 000 to 251 000)
–69·0%
(–72·8 to –65·2)
Montenegro 2000
(2000 to 2000)
–6·6%
(–10·4 to –2·5)
2000
(2000 to 2000)
22·6%
(2·8 to 43·3)
8000
(7000 to 8000)
–10·7%
(–13·1 to –7·9)
32 000
(27 000 to 37 000)
1·3%
(–16·1 to 19·9)
North Macedonia 7000
(7000 to 8000)
–8·6%
(–14·5 to –2·2)
7000
(5000 to 8000)
–1·8%
(–18·1 to 15·4)
40 000
(36 000 to 43 000)
–18·8%
(–23·4 to –13·6)
120 000
(98 000 to 142 000)
–20·1%
(–34·3 to –6·3)
Poland 72 000
(62 000 to 83 000)
–35·1%
(–37·8 to –32·0)
45 000
(40 000 to 49 000)
–65·5%
(–68·2 to –62·8)
485 000
(441 000 to 535 000)
–16·8%
(–20·1 to –13·6)
800 000
(726 000 to 876 000)
–63·9%
(–66·6 to –60·9)
Romania 64 000
(58 000 to 70 000)
–33·4%
(–37·5 to –29·0)
53 000
(47 000 to 59 000)
–45·5%
(–51·4 to –39·7)
369 000
(351 000 to 387 000)
–17·8%
(–21·8 to –13·0)
903 000
(810 000 to 999 000)
–44·8%
(–50·6 to –38·6)
Serbia 32 000
(29 000 to 35 000)
–26·9%
(–31·5 to –22·3)
28 000
(24 000 to 33 000)
–49·6%
(–57·8 to –39·8)
152 000
(141 000 to 165 000)
–25·9%
(–30·2 to –21·6)
458 000
(388 000 to 532 000)
–50·2%
(–58·1 to –40·9)
Slovakia 13 000
(11 000 to 14 000)
–37·2%
(–41·9 to –32·1)
6000
(5000 to 7000)
–54·3%
(–62·0 to –44·1)
106 000
(102 000 to 111 000)
–27·0%
(–29·2 to –24·6)
127 000
(108 000 to 147 000)
–54·7%
(–61·8 to –45·1)
Slovenia 3000
(3000 to 4000)
–56·1%
(–59·2 to –52·9)
2000
(2000 to 2000)
–69·5%
(–73·2 to –66·2)
24 000
(23 000 to 25 000)
–30·8%
(–34·7 to –26·5)
29 000
(25 000 to 32 000)
–71·8%
(–74·8 to –68·7)
Eastern Europe 610 000
(536 000 to 693 000)
–27·9%
(–30·8 to –25·0)
426 000
(389 000 to 460 000)
–43·3%
(–46·8 to –39·5)
3 633 000
(3 342 000 to 3 928 000)
–11·1%
(–13·6 to –8·2)
8 139 000
(7 532 000 to 8 761 000)
–39·4%
(–43·3 to –35·4)
Belarus 27 000
(24 000 to 30 000)
–24·2%
(–29·6 to –19·0)
16 000
(13 000 to 19 000)
–26·7%
(–38·1 to –13·8)
177 000
(168 000 to 186 000)
–10·0%
(–16·1 to –2·7)
322 000
(270 000 to 376 000)
–28·4%
(–39·3 to –15·4)
Estonia 2000
(2000 to 3000)
–58·6%
(–62·1 to –54·8)
1000
(1000 to 1000)
–79·0%
(–81·6 to –76·7)
18 000
(17 000 to 18 000)
–30·5%
(–34·9 to –26·2)
21 000
(18 000 to 23 000)
–76·7%
(–79·5 to –74·2)
Latvia 7000
(6000 to 7000)
–38·3%
(–42·3 to –34·2)
5000
(4000 to 6000)
–46·9%
(–52·4 to –41·3)
39 000
(37 000 to 41 000)
–11·6%
(–17·0 to –4·9)
81 000
(72 000 to 90 000)
–48·6%
(–53·9 to –43·6)
Lithuania 10 000
(9000 to 11 000)
–28·2%
(–34·0 to –22·9)
5000
(4000 to 5000)
–30·2%
(–38·3 to –23·3)
50 000
(44 000 to 56 000)
–6·7%
(–16·7 to 3·9)
79 000
(70 000 to 87 000)
–35·9%
(–43·2 to –29·5)
Moldova 9000
(8000 to 10 000)
–31·9%
(–36·5 to –27·0)
5000
(5000 to 6000)
–52·1%
(–56·4 to –47·1)
53 000
(50 000 to 55 000)
–9·1%
(–12·6 to –5·4)
112 000
(102 000 to 124 000)
–46·8%
(–51·7 to –41·3)
Russia 422 000
(368 000 to 481 000)
–27·0%
(–30·1 to –23·8)
311 000
(285 000 to 335 000)
–43·9%
(–47·5 to –40·5)
2 454 000
(2 247 000 to 2 670 000)
–9·5%
(–12·5 to –6·3)
5 892 000
(5 460 000 to 6 339 000)
–40·3%
(–44·0 to –36·4)
Ukraine 134 000
(116 000 to 153 000)
–29·7%
(–33·8 to –25·4)
82 000
(64 000 to 102 000)
–45·0%
(–56·6 to –31·4)
844 000
(763 000 to 925 000)
–13·5%
(–18·9 to –8·3)
1 632 000
(1 276 000 to 2 023 000)
–38·6%
(–51·6 to –24·1)
(Table 1 continues on next page)
Articles
978
www.thelancet.com/neurology Vol 23 October 2024
Incident cases Deaths Prevalent cases DALYs
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
(Continued from previous page)
High income 1 711 000
(1 563 000 to 1 865 000)
–41·6%
(–43·7 to –39·3)
772 000
(643 000 to 838 000)
–62·3%
(–64·4 to –61·1)
19 822 000
(19 019 000 to 20 724 000)
–15·9%
(–17·6 to –14·0)
13 972 000
(12 517 000 to 15 091 000)
–57·7%
(–59·3 to –56·2)
Australasia 36 000
(33 000 to 40 000)
–40·7%
(–43·8 to –38·0)
15 000
(12 000 to 16 000)
–63·9%
(–66·4 to –61·5)
368 000
(357 000 to 380 000)
–21·5%
(–23·2 to –19·8)
250 000
(221 000 to 273 000)
–60·8%
(–63·1 to –58·5)
Australia 31 000
(28 000 to 34 000)
–40·6%
(–44·2 to –37·0)
12 000
(10 000 to 13 000)
–65·3%
(–67·9 to –62·8)
313 000
(305 000 to 322 000)
–21·5%
(–23·2 to –19·9)
205 000
(182 000 to 224 000)
–61·6%
(–63·9 to –59·2)
New Zealand 6000
(5000 to 6000)
–41·3%
(–45·2 to –37·2)
3000
(2000 to 3000)
–55·5%
(–58·9 to –52·4)
55 000
(50 000 to 60 000)
–21·9%
(–26·6 to –17·2)
45 000
(40 000 to 49 000)
–56·6%
(–59·4 to –53·8)
High-income Asia
Pacific
408 000
(372 000 to 446 000)
–46·2%
(–49·0 to –43·3)
185 000
(147 000 to 207 000)
–71·2%
(–73·3 to –69·6)
4 793 000
(4 539 000 to 5 071 000)
–24·1%
(–26·0 to –21·6)
3 386 000
(2 955 000 to 3 726 000)
–65·5%
(–67·5 to –63·6)
Brunei 510
(460 to 560)
–45·4%
(–48·5 to –41·9)
170
(150 to 200)
–51·1%
(–58·5 to –40·6)
5000
(5000 to 5000)
–39·6%
(–41·1 to –37·9)
5000
(4000 to 6000)
–53·0%
(–60·1 to –43·3)
Japan 305 000
(276 000 to 336 000)
–37·7%
(–41·7 to –32·8)
147 000
(115 000 to 165 000)
–67·1%
(–69·0 to –65·6)
3 607 000
(3 367 000 to 3 852 000)
–14·1%
(–16·8 to –10·5)
2 575 000
(2 246 000 to 2 840 000)
–57·6%
(–59·6 to –55·8)
Singapore 7000
(6000 to 7000)
–63·1%
(–66·0 to –60·2)
1000
(1000 to 1000)
–84·3%
(–85·8 to –83·0)
83 000
(80 000 to 86 000)
–48·6%
(–50·1 to –46·8)
36 000
(31 000 to 40 000)
–79·6%
(–81·3 to –78·0)
South Korea 96 000
(86 000 to 106 000)
–67·1%
(–69·0 to –65·2)
37 000
(31 000 to 42 000)
–82·5%
(–84·7 to –79·2)
1 097 000
(1 063 000 to 1 135 000)
–51·4%
(–52·6 to –50·0)
770 000
(676 000 to 862 000)
–81·5%
(–83·2 to –78·9)
High-income North
America
466 000
(410 000 to 528 000)
–33·6%
(–36·7 to –30·4)
209 000
(177 000 to 225 000)
–34·1%
(–37·0 to –32·0)
7 005 000
(6 589 000 to 7 467 000)
–2·6%
(–6·1 to 0·9)
4 254 000
(3 847 000 to 4 597 000)
–30·1%
(–32·6 to –28·2)
Canada 54 000
(51 000 to 58 000)
–36·8%
(–42·1 to –31·1)
17 000
(14 000 to 19 000)
–57·0%
(–60·0 to –54·0)
705 000
(691 000 to 720 000)
–10·7%
(–13·0 to –8·3)
341 000
(304 000 to 374 000)
–49·3%
(–52·4 to –46·3)
Greenland 80
(80 to 90)
–48·2%
(–51·2 to –44·9)
40
(30 to 40)
–61·1%
(–67·1 to –53·3)
840
(810 to 870)
–39·1%
(–41·7 to –36·9)
880
(760 to 1010)
–60·7%
(–66·4 to –53·7)
USA 412 000
(358 000 to 470 000)
–33·3%
(–36·4 to –30·0)
192 000
(163 000 to 207 000)
–31·3%
(–34·3 to –29·1)
6 299 000
(5 889 000 to 6 761 000)
–1·7%
(–5·5 to 2·2)
3 912 000
(3 534 000 to 4 228 000)
–27·9%
(–30·3 to –25·9)
Southern Latin America 82 000
(76 000 to 89 000)
–42·1%
(–44·8 to –39·7)
36 000
(33 000 to 39 000)
–63·1%
(–65·0 to –61·1)
799 000
(773 000 to 825 000)
–28·6%
(–30·2 to –26·9)
775 000
(726 000 to 825 000)
–62·6%
(–64·5 to –60·7)
Argentina 54 000
(49 000 to 58 000)
–42·5%
(–45·6 to –39·4)
23 000
(21 000 to 25 000)
–64·3%
(–66·4 to –62·3)
518 000
(500 000 to 537 000)
–29·4%
(–31·4 to –27·4)
510 000
(479 000 to 543 000)
–63·3%
(–65·4 to –61·2)
Chile 23 000
(21 000 to 25 000)
–39·0%
(–42·6 to –35·2)
10 000
(9000 to 11 000)
–60·8%
(–63·6 to –58·4)
230 000
(222 000 to 238 000)
–21·0%
(–23·3 to –18·8)
202 000
(187 000 to 218 000)
–60·6%
(–62·9 to –58·3)
Uruguay 6000
(5000 to 6000)
–44·0%
(–47·2 to –40·7)
3000
(3000 to 4000)
–54·7%
(–57·5 to –52·1)
51 000
(49 000 to 53 000)
–36·7%
(–38·4 to –34·6)
62 000
(58 000 to 66 000)
–55·8%
(–58·2 to –53·3)
Western Europe 717 000
(664 000 to 771 000)
–43·4%
(–45·7 to –41·0)
326 000
(272 000 to 355 000)
–68·3%
(–70·2 to –67·0)
6 858 000
(6 651 000 to 7 084 000)
–22·4%
(–23·8 to –20·8)
5 307 000
(4 726 000 to 5 734 000)
–65·1%
(–66·6 to –63·5)
Andorra 90
(80 to 110)
–28·2%
(–32·7 to –24·0)
40
(30 to 50)
–49·5%
(–65·2 to –30·4)
930
(890 to 970)
–19·3%
(–21·7 to –17·0)
640
(500 to 800)
–48·8%
(–63·6 to –31·8)
Austria 17 000
(15 000 to 19 000)
–34·2%
(–39·7 to –27·3)
5000
(4000 to 5000)
–75·7%
(–77·6 to –74·2)
196 000
(191 000 to 202 000)
–1·1%
(–3·5 to 1·2)
94 000
(84 000 to 104 000)
–68·6%
(–71·2 to –66·3)
Belgium 17 000
(16 000 to 19 000)
–44·0%
(–48·5 to –38·8)
8000
(6000 to 8000)
–67·7%
(–70·1 to –65·8)
151 000
(147 000 to 155 000)
–18·1%
(–21·8 to –14·3)
127 000
(112 000 to 139 000)
–63·7%
(–65·7 to –61·7)
Cyprus 1000
(1000 to 1000)
–48·3%
(–52·2 to –44·1)
770
(640 to 900)
–73·1%
(–78·1 to –66·9)
10 000
(9000 to 11 000)
–39·2%
(–41·4 to –36·5)
12 000
(10 000 to 14 000)
–72·6%
(–77·7 to –66·9)
(Table 1 continues on next page)
Articles
www.thelancet.com/neurology Vol 23 October 2024
979
Incident cases Deaths Prevalent cases DALYs
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
(Continued from previous page)
Denmark 8000
(7000 to 9000)
–49·1%
(–52·5 to –45·2)
4000
(4000 to 5000)
–58·5%
(–61·3 to –56·0)
79 000
(76 000 to 82 000)
–34·9%
(–36·9 to –33·0)
70 000
(63 000 to 76 000)
–60·2%
(–62·5 to –58·1)
Finland 12 000
(11 000 to 13 000)
–40·3%
(–43·5 to –36·6)
5000
(4000 to 5000)
–62·8%
(–65·7 to –60·5)
127 000
(123 000 to 130 000)
–21·9%
(–23·5 to –20·4)
84 000
(74 000 to 91 000)
–61·2%
(–63·4 to –59·1)
France 94 000
(88 000 to 101 000)
–23·1%
(–26·7 to –18·8)
43 000
(36 000 to 47 000)
–65·7%
(–68·3 to –63·5)
929 000
(907 000 to 954 000)
–3·0%
(–6·0 to –0·1)
686 000
(606 000 to 750 000)
–59·8%
(–62·2 to –57·1)
Germany 185 000
(168 000 to 202 000)
–38·6%
(–42·8 to –34·2)
63 000
(52 000 to 69 000)
–70·1%
(–72·3 to –68·2)
1 961 000
(1 908 000 to 2 014 000)
–18·8%
(–20·5 to –16·9)
1 167 000
(1 042 000 to 1 283 000)
–64·2%
(–66·4 to –61·8)
Greece 27 000
(25 000 to 29 000)
–42·9%
(–46·7 to –39·0)
17 000
(15 000 to 19 000)
–66·8%
(–68·7 to –65·2)
185 000
(178 000 to 193 000)
–23·2%
(–25·8 to –20·4)
247 000
(221 000 to 266 000)
–63·1%
(–64·8 to –61·4)
Iceland 400
(360 to 450)
–49·2%
(–53·4 to –45·2)
150
(120 to 170)
–63·0%
(–66·8 to –59·0)
4000
(4000 to 4000)
–30·2%
(–32·2 to –28·4)
3000
(2000 to 3000)
–62·3%
(–65·4 to –59·1)
Ireland 4000
(4000 to 5000)
–58·6%
(–61·5 to –55·7)
2000
(2000 to 2000)
–73·5%
(–76·2 to –71·4)
43 000
(41 000 to 45 000)
–42·3%
(–44·1 to –40·1)
32 000
(28 000 to 35 000)
–72·7%
(–74·5 to –70·9)
Israel 8000
(7000 to 9000)
–50·8%
(–54·1 to –47·3)
3000
(2000 to 3000)
–67·2%
(–70·1 to –64·7)
92 000
(89 000 to 95 000)
–29·6%
(–31·4 to –27·6)
54 000
(48 000 to 59 000)
–64·5%
(–66·7 to –62·2)
Italy 92 000
(84 000 to 102 000)
–52·5%
(–56·5 to –47·8)
62 000
(50 000 to 69 000)
–65·0%
(–67·4 to –63·3)
727 000
(671 000 to 790 000)
–28·6%
(–30·9 to –25·8)
871 000
(744 000 to 948 000)
–64·7%
(–66·5 to –63·1)
Luxembourg 610
(560 to 650)
–57·2%
(–59·7 to –54·4)
320
(280 to 360)
–79·2%
(–81·2 to –77·2)
5000
(5000 to 6000)
–39·9%
(–44·1 to –35·6)
5000
(5000 to 6000)
–77·9%
(–79·7 to –75·9)
Malta 560
(510 to 620)
–54·9%
(–57·7 to –51·9)
270
(220 to 300)
–72·8%
(–75·4 to –69·7)
5000
(5000 to 5000)
–34·6%
(–37·1 to –32·2)
4000
(4000 to 5000)
–71·1%
(–73·6 to –68·3)
Monaco 80
(70 to 90)
–43·3%
(–47·1 to –39·3)
50
(40 to 70)
–59·3%
(–68·9 to –43·5)
700
(670 to 740)
–26·9%
(–29·0 to –24·6)
800
(650 to 960)
–57·6%
(–67·0 to –44·1)
Netherlands 26 000
(23 000 to 29 000)
–46·5%
(–50·0 to –42·8)
12 000
(10 000 to 14 000)
–54·1%
(–57·2 to –51·3)
251 000
(242 000 to 261 000)
–36·2%
(–38·0 to –34·6)
199 000
(176 000 to 217 000)
–56·5%
(–59·1 to –54·2)
Norway 9000
(8000 to 11 000)
–43·3%
(–47·3 to –39·1)
3000
(2000 to 3000)
–68·6%
(–70·7 to –67·0)
91 000
(84 000 to 99 000)
–28·7%
(–31·7 to –25·4)
50 000
(44 000 to 55 000)
–65·9%
(–68·0 to –64·2)
Portugal 18 000
(17 000 to 20 000)
–68·7%
(–70·3 to –67·0)
14 000
(12 000 to 15 000)
–80·4%
(–81·9 to –79·2)
121 000
(116 000 to 127 000)
–55·3%
(–57·9 to –52·4)
204 000
(181 000 to 220 000)
–79·4%
(–80·6 to –78·3)
San Marino 50
(50 to 60)
–37·8%
(–41·8 to –34·2)
20
(20 to 30)
–68·7%
(–77·9 to –57·7)
500
(470 to 520)
–23·5%
(–25·5 to –21·5)
360
(270 to 460)
–63·0%
(–72·6 to –52·2)
Spain 71 000
(67 000 to 75 000)
–50·0%
(–53·9 to –45·3)
32 000
(26 000 to 35 000)
–75·1%
(–76·7 to –73·5)
694 000
(679 000 to 711 000)
–19·9%
(–24·4 to –14·9)
518 000
(458 000 to 569 000)
–70·3%
(–72·1 to –68·6)
Sweden 18 000
(16 000 to 21 000)
–35·9%
(–39·6 to –31·6)
7000
(6000 to 8000)
–62·3%
(–65·9 to –58·8)
179 000
(165 000 to 194 000)
–16·9%
(–22·3 to –11·5)
113 000
(99 000 to 127 000)
–59·5%
(–62·8 to –56·3)
Switzerland 10 000
(9 000 to 12 000)
–41·7%
(–45·7 to –36·2)
4 000
(3 000 to 5 000)
–70·6%
(–73·3 to –68·1)
103 000
(99 000 to 106 000)
–19·5%
(–22·3 to –17·1)
67 000
(58 000 to 75 000)
–67·5%
(–69·8 to –65·2)
UK 96 000
(87 000 to 106 000)
–43·3%
(–46·3 to –40·1)
41 000
(35 000 to 44 000)
–67·3%
(–69·0 to –66·2)
895 000
(843 000 to 953 000)
–26·0%
(–27·8 to –24·2)
690 000
(630 000 to 740 000)
–64·8%
(–66·1 to –63·5)
Latin America and
Caribbean
554 000
(503 000 to 611 000)
–39·6%
(–41·2 to –37·9)
279 000
(254 000 to 300 000)
–53·6%
(–56·6 to –50·5)
5 184 000
(4 916 000 to 5 466 000)
–26·1%
(–27·3 to –24·7)
6 414 000
(5 981 000 to 6 862 000)
–53·0%
(–56·1 to –49·9)
Andean Latin America 46 000
(42 000 to 50 000)
–33·6%
(–35·7 to –31·3)
22 000
(19 000 to 26 000)
–48·0%
(–56·5 to –37·7)
496 000
(481 000 to 513 000)
–19·8%
(–21·0 to –18·6)
544 000
(460 000 to 644 000)
–49·8%
(–58·1 to –40·3)
(Table 1 continues on next page)
Articles
980
www.thelancet.com/neurology Vol 23 October 2024
Incident cases Deaths Prevalent cases DALYs
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
(Continued from previous page)
Bolivia 8000
(7000 to 9000)
–31·8%
(–34·3 to –28·6)
5000
(4000 to 7000)
–46·1%
(–57·5 to –28·9)
75 000
(72 000 to 78 000)
–25·1%
(–26·8 to –23·4)
134 000
(98 000 to 180 000)
–51·0%
(–61·9 to –34·1)
Ecuador 14 000
(13 000 to 16 000)
–25·9%
(–28·9 to –21·9)
6000
(5000 to 8000)
–44·7%
(–54·4 to –33·6)
153 000
(148 000 to 158 000)
–18·4%
(–20·3 to –16·5)
146 000
(119 000 to 178 000)
–48·8%
(–57·9 to –38·5)
Peru 24 000
(22 000 to 26 000)
–37·8%
(–40·6 to –34·6)
11 000
(8000 to 13 000)
–49·6%
(–62·0 to –34·1)
269 000
(260 000 to 277 000)
–19·5%
(–21·3 to –17·9)
264 000
(212 000 to 327 000)
–49·3%
(–60·4 to –35·2)
Caribbean 59 000
(55 000 to 63 000)
–17·1%
(–19·2 to –14·9)
39 000
(35 000 to 45 000)
–32·6%
(–40·3 to –23·8)
483 000
(467 000 to 500 000)
–9·7%
(–11·1 to –8·3)
893 000
(778 000 to 1 027 000)
–30·5%
(–38·9 to –20·2)
Antigua and Barbuda 110
(100 to 120)
–26·6%
(–30·5 to –22·9)
70
(60 to 70)
–41·2%
(–45·5 to –36·6)
920
(890 to 950)
–16·9%
(–19·1 to –14·8)
1000
(1000 to 2000)
–45·2%
(–49·4 to –40·4)
The Bahamas 400
(370 to 430)
–21·2%
(–25·2 to –17·2)
200
(170 to 240)
–38·0%
(–49·4 to –25·3)
3700
(3600 to 3800)
–12·4%
(–14·8 to –10·1)
5000
(4000 to 6000)
–40·1%
(–51·2 to –26·9)
Barbados 500
(460 to 550)
–25·2%
(–29·1 to –20·7)
370
(300 to 450)
–38·4%
(–50·0 to –26·0)
4200
(4000 to 4400)
–12·8%
(–15·5 to –10·0)
7000
(6000 to 8000)
–38·4%
(–50·4 to –25·3)
Belize 280
(250 to 300)
–15·2%
(–19·7 to –10·4)
140
(130 to 160)
–24·8%
(–33·6 to –15·6)
3000
(2000 to 3000)
–8·6%
(–11·0 to –6·2)
3000
(3000 to 4000)
–29·2%
(–37·7 to –20·7)
Bermuda 90
(80 to 100)
–38·6%
(–42·1 to –35·2)
50
(40 to 60)
–59·8%
(–65·5 to –51·7)
930
(900 to 960)
–21·3%
(–23·1 to –19·3)
820
(710 to 970)
–58·9%
(–64·5 to –51·6)
Cuba 17 000
(16 000 to 19 000)
–22·6%
(–26·7 to –18·2)
11 000
(10 000 to 13 000)
–30·5%
(–38·6 to –22·2)
145 000
(140 000 to 151 000)
–14·3%
(–16·6 to –11·8)
212 000
(187 000 to 238 000)
–33·1%
(–41·1 to –24·7)
Dominica 80
(70 to 80)
–15·5%
(–20·1 to –10·8)
70
(60 to 80)
–26·1%
(–35·8 to –15·0)
640
(620 to 660)
–13·1%
(–15·2 to –10·8)
1000
(1000 to 2000)
–26·6%
(–37·3 to –14·2)
Dominican Republic 14 000
(12 000 to 15 000)
14·3%
(9·8 to 19·5)
7000
(6000 to 9000)
–21·4%
(–39·3 to 4·9)
111 000
(108 000 to 115 000)
7·7%
(5·2 to 10·2)
170 000
(136 000 to 215 000)
–17·8%
(–36·1 to 8·9)
Grenada 140
(120 to 150)
–25·6%
(–29·8 to –21·7)
90
(70 to 90)
–45·0%
(–51·9 to –38·1)
1000
(1000 to 1000)
–19·3%
(–22·5 to –15·5)
2000
(2000 to 2000)
–50·6%
(–57·0 to –43·9)
Guyana 1000
(1000 to 1000)
–37·5%
(–40·2 to –34·8)
760
(600 to 940)
–49·0%
(–60·0 to –37·1)
8000
(7000 to 8000)
–26·9%
(–29·2 to –24·5)
18 000
(14 000 to 23 000)
–52·6%
(–63·6 to –40·4)
Haiti 13 000
(12 000 to 14 000)
–21·2%
(–24·2 to –17·6)
11 000
(8 000 to 14 000)
–30·2%
(–47·0 to –9·9)
89 000
(86 000 to 93 000)
–15·1%
(–17·4 to –12·9)
310 000
(233 000 to 409 000)
–33·5%
(–49·9 to –13·5)
Jamaica 4000
(3000 to 4000)
–17·6%
(–21·8 to –13·2)
3000
(2000 to 4000)
–27·7%
(–42·4 to –9·6)
26 000
(25 000 to 27 000)
–12·5%
(–15·0 to –10·2)
54 000
(43 000 to 67 000)
–31·4%
(–45·3 to –13·1)
Puerto Rico 4000
(4000 to 4000)
–32·6%
(–36·1 to –29·3)
2000
(1000 to 2000)
–62·7%
(–68·6 to –56·8)
44 000
(42 000 to 45 000)
–11·9%
(–14·2 to –9·3)
30 000
(25 000 to 34 000)
–55·2%
(–61·6 to –48·7)
Saint Kitts and Nevis 90
(90 to 100)
–41·2%
(–44·0 to –38·1)
60
(50 to 70)
–50·4%
(–57·1 to –45·0)
730
(710 to 770)
–35·8%
(–38·3 to –32·9)
1000
(1000 to 2000)
–53·2%
(–60·3 to –46·6)
Saint Lucia 260
(240 to 280)
–40·5%
(–44·0 to –37·4)
200
(160 to 230)
–56·1%
(–62·9 to –49·0)
2000
(2000 to 2000)
–25·1%
(–28·0 to –21·9)
4000
(3000 to 4000)
–55·9%
(–62·8 to –48·4)
Saint Vincent and
the Grenadines
160
(140 to 170)
–23·8%
(–28·1 to –19·2)
110
(100 to 130)
–39·8%
(–45·9 to –32·8)
1000
(1000 to 1000)
–20·9%
(–23·6 to –18·2)
2000
(2000 to 3000)
–39·6%
(–46·4 to –32·2)
Suriname 840
(770 to 900)
–13·0%
(–17·0 to –8·9)
550
(430 to 680)
–29·1%
(–45·7 to –10·9)
6000
(6 000 to 6 000)
–12·8%
(–15·0 to –10·4)
13 000
(10 000 to 16 000)
–29·0%
(–44·7 to –11·3)
Trinidad and Tobago 2000
(2000 to 2000)
–37·9%
(–41·1 to –34·6)
1000
(1000 to 2000)
–49·9%
(–59·8 to –38·5)
17 000
(17 000 to 18 000)
–25·7%
(–27·7 to –23·5)
27 000
(21 000 to 33 000)
–47·8%
(–58·2 to –34·7)
(Table 1 continues on next page)
Articles
www.thelancet.com/neurology Vol 23 October 2024
981
Incident cases Deaths Prevalent cases DALYs
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
(Continued from previous page)
Virgin Islands 150
(140 to 170)
–9·3%
(–13·9 to –4·4)
60
(50 to 70)
–57·1%
(–65·8 to –46·3)
1200
(1200 to 1300)
–7·0%
(–9·4 to –4·6)
1200
(1000 to 1500)
–55·3%
(–64·6 to –43·1)
Central Latin America 204 000
(186 000 to 223 000)
–34·4%
(–36·0 to –32·8)
89 000
(79 000 to 98 000)
–45·6%
(–50·6 to –40·1)
2 100 000
(2 006 000 to 2 208 000)
–23·1%
(–24·3 to –21·9)
2 051 000
(1 852 000 to 2 284 000)
–43·0%
(–48·3 to –37·1)
Colombia 42 000
(38 000 to 46 000)
–44·5%
(–47·3 to –41·3)
17 000
(14 000 to 19 000)
–60·5%
(–66·7 to –53·9)
429 000
(416 000 to 444 000)
–31·6%
(–33·2 to –29·7)
369 000
(312 000 to 432 000)
–59·5%
(–65·5 to –53·0)
Costa Rica 4000
(4000 to 5000)
–29·9%
(–33·4 to –26·2)
2000
(1000 to 2000)
–41·6%
(–47·9 to –35·2)
44 000
(43 000 to 46 000)
–16·9%
(–18·9 to –14·8)
32 000
(28 000 to 35 000)
–40·3%
(–46·4 to –34·5)
El Salvador 5000
(4000 to 5000)
–34·7%
(–37·6 to –31·7)
2000
(2000 to 3000)
–46·7%
(–57·4 to –34·6)
45 000
(43 000 to 47 000)
–25·2%
(–26·8 to –23·3)
47 000
(39 000 to 57 000)
–50·1%
(–59·8 to –39·1)
Guatemala 10 000
(9000 to 10 000)
–22·2%
(–26·0 to –18·1)
4000
(4000 to 5000)
–36·6%
(–44·4 to –27·5)
89 000
(86 000 to 92 000)
–19·9%
(–21·6 to –17·6)
101 000
(88 000 to 115 000)
–39·1%
(–47·5 to –30·6)
Honduras 6000
(6000 to 7000)
0·4%
(–4·3 to 5·2)
6000
(5000 to 8000)
23·4%
(0·2 to 54·4)
56 000
(54 000 to 58 000)
–12·6%
(–14·9 to –10·5)
147 000
(120 000 to 182 000)
5·4%
(–15·0 to 31·6)
Mexico 100 000
(89 000 to 111 000)
–34·6%
(–36·7 to –32·1)
38 000
(34 000 to 43 000)
–50·1%
(–55·1 to –44·9)
1 100 000
(1 029 000 to 1 179 000)
–21·7%
(–23·6 to –19·7)
915 000
(821 000 to 1 020 000)
–43·5%
(–49·0 to –37·6)
Nicaragua 5000
(4000 to 5000)
–33·7%
(–37·5 to –30·2)
1000
(1000 to 2000)
–45·9%
(–54·4 to –34·9)
43 000
(42 000 to 45 000)
–21·5%
(–23·4 to –19·5)
36 000
(31 000 to 43 000)
–47·2%
(–55·0 to –36·9)
Panama 4000
(4000 to 4000)
–30·7%
(–34·6 to –27·1)
2000
(2000 to 2000)
–38·6%
(–51·4 to –27·6)
37 000
(36 000 to 39 000)
–17·5%
(–19·4 to –15·5)
40 000
(32 000 to 48 000)
–39·0%
(–51·1 to –28·1)
Venezuela 29 000
(26 000 to 31 000)
–25·3%
(–29·1 to –21·4)
16 000
(12 000 to 20 000)
–24·3%
(–41·0 to –5·9)
257 000
(248 000 to 266 000)
–18·5%
(–20·6 to –16·4)
363 000
(279 000 to 458 000)
–26·1%
(–42·8 to –7·4)
Tropical Latin America 245 000
(218 000 to 275 000)
–47·3%
(–49·5 to –45·2)
129 000
(118 000 to 137 000)
–61·7%
(–63·3 to –60·3)
2 105 000
(1 950 000 to 2 262 000)
–32·8%
(–34·8 to –30·8)
2 926 000
(2 755 000 to 3 053 000)
–61·4%
(–62·9 to –60·1)
Brazil 239 000
(212 000 to 268 000)
–47·7%
(–49·9 to –45·6)
126 000
(115 000 to 133 000)
–62·2%
(–63·8 to –60·8)
2 053 000
(1 898 000 to 2 207 000)
–33·1%
(–35·1 to –31·1)
2 843 000
(2 679 000 to 2 966 000)
–61·8%
(–63·3 to –60·5)
Paraguay 6000
(6000 to 7000)
–28·6%
(–33·0 to –23·9)
4000
(3000 to 5000)
–37·3%
(–51·6 to –21·8)
52 000
(50 000 to 54 000)
–19·0%
(–21·5 to –16·6)
83 000
(65 000 to 103 000)
–38·1%
(–52·4 to –22·0)
North Africa and Middle
East
615 000
(560 000 to 672 000)
–21·2%
(–23·9 to –18·2)
372 000
(327 000 to 417 000)
–40·3%
(–46·6 to –32·4)
5 573 000
(5 372 000 to 5 794 000)
–11·2%
(–12·7 to –9·7)
8 891 000
(7 809 000 to 10 011 000)
–44·4%
(–51·1 to –37·1)
Afghanistan 22 000
(20 000 to 24 000)
–21·6%
(–25·5 to –17·5)
15 000
(11 000 to 19 000)
–28·1%
(–43·7 to –8·7)
171 000
(164 000 to 178 000)
–14·7%
(–17·3 to –12·3)
459 000
(355 000 to 579 000)
–32·0%
(–47·6 to –11·4)
Algeria 52 000
(46 000 to 57 000)
–24·3%
(–30·6 to –18·0)
27 000
(21 000 to 34 000)
–36·1%
(–47·5 to –21·2)
458 000
(443 000 to 476 000)
–11·9%
(–14·5 to –9·2)
572 000
(456 000 to 712 000)
–40·6%
(–51·1 to –27·7)
Bahrain 730
(650 to 810)
–35·5%
(–39·2 to –31·4)
360
(310 to 420)
–49·1%
(–57·3 to –39·0)
10 000
(10 000 to 10 000)
–21·3%
(–23·5 to –19·4)
10 000
(9000 to 12 000)
–52·9%
(–60·3 to –44·0)
Egypt 105 000
(95 000 to 117 000)
3·4%
(–2·4 to 11·3)
73 000
(60 000 to 89 000)
–36·1%
(–47·4 to –23·7)
897 000
(859 000 to 936 000)
11·5%
(6·6 to 15·8)
1 848 000
(1 512 000 to 2 235 000)
–39·8%
(–50·9 to –27·5)
Iran 76 000
(67 000 to 86 000)
–31·8%
(–34·7 to –29·2)
42 000
(38 000 to 46 000)
–48·8%
(–53·3 to –43·1)
787 000
(727 000 to 855 000)
–14·8%
(–17·4 to –11·9)
905 000
(828 000 to 981 000)
–50·2%
(–54·5 to –45·2)
Iraq 45 000
(41 000 to 50 000)
–8·1%
(–14·0 to –1·9)
31 000
(24 000 to 37 000)
–14·4%
(–34·3 to 6·3)
379 000
(366 000 to 393 000)
–7·2%
(–9·8 to –4·0)
764 000
(602 000 to 932 000)
–27·2%
(–44·8 to –7·1)
Jordan 12 000
(11 000 to 13 000)
–24·1%
(–29·4 to –17·8)
4000
(3000 to 4000)
–53·7%
(–63·8 to –40·2)
118 000
(113 000 to 122 000)
–4·6%
(–7·5 to –1·1)
97 000
(81 000 to 114 000)
–55·8%
(–65·0 to –43·8)
(Table 1 continues on next page)
Articles
982
www.thelancet.com/neurology Vol 23 October 2024
Incident cases Deaths Prevalent cases DALYs
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
(Continued from previous page)
Kuwait 3000
(3000 to 4000)
–9·2%
(–15·1 to –3·7)
770
(630 to 920)
–32·7%
(–43·0 to –20·4)
43 000
(41 000 to 44 000)
–8·0%
(–10·6 to –5·2)
23 000
(20 000 to 27 000)
–36·9%
(–45·8 to –27·0)
Lebanon 7000
(6000 to 8000)
–24·1%
(–28·5 to –19·2)
3000
(2000 to 3000)
–68·6%
(–76·3 to –59·3)
65 000
(62 000 to 68 000)
–1·5%
(–4·1 to 1·2)
57 000
(49 000 to 66 000)
–68·9%
(–76·4 to –60·0)
Libya 6000
(6000 to 7000)
1·7%
(–4·4 to 7·9)
3000
(3000 to 5000)
–3·8%
(–27·3 to 26·3)
66 000
(64 000 to 68 000)
0·8%
(–1·9 to 3·5)
95 000
(71 000 to 125 000)
–6·7%
(–28·8 to 20·8)
Morocco 53 000
(48 000 to 58 000)
–5·8%
(–11·2 to 0·7)
37 000
(29 000 to 46 000)
–16·6%
(–33·7 to 0·8)
413 000
(395 000 to 431 000)
–3·5%
(–6·5 to –0·5)
811 000
(633 000 to 1 024 000)
–25·4%
(–41·0 to –9·4)
Oman 3000
(3000 to 4000)
–13·1%
(–19·4 to –6·0)
1000
(1000 to 1000)
–41·3%
(–55·9 to –18·5)
40 000
(38 000 to 41 000)
–4·7%
(–7·8 to –1·6)
30 000
(25 000 to 35 000)
–48·3%
(–61·1 to –28·6)
Palestine 4000
(3000 to 4000)
–13·4%
(–18·3 to –8·2)
2000
(2000 to 2000)
–42·2%
(–53·3 to –28·8)
29 000
(28 000 to 30 000)
–12·4%
(–15·4 to –9·5)
46 000
(41 000 to 52 000)
–45·6%
(–55·9 to –32·5)
Qatar 1000
(1000 to 2000)
–36·7%
(–40·8 to –33·1)
250
(190 to 320)
–65·4%
(–73·3 to –56·1)
22 000
(21 000 to 23 000)
–37·1%
(–38·9 to –35·4)
10 000
(8000 to 12 000)
–65·8%
(–73·5 to –56·8)
Saudi Arabia 28 000
(25 000 to 31 000)
–21·4%
(–26·5 to –16·4)
13 000
(11 000 to 17 000)
–38·5%
(–53·2 to –17·3)
278 000
(268 000 to 289 000)
–1·8%
(–4·9 to 1·2)
439 000
(351 000 to 545 000)
–39·5%
(–53·4 to –19·3)
Sudan 34 000
(31 000 to 37 000)
–17·0%
(–21·5 to –11·4)
20 000
(15 000 to 25 000)
–40·0%
(–53·3 to –20·3)
293 000
(281 000 to 305 000)
–4·5%
(–7·5 to –1·4)
545 000
(403 000 to 704 000)
–46·1%
(–58·7 to –26·8)
Syria 16 000
(15 000 to 18 000)
–25·7%
(–29·8 to –22·0)
11 000
(8000 to 13 000)
–29·2%
(–47·1 to –2·2)
148 000
(143 000 to 154 000)
–24·3%
(–26·2 to –22·5)
260 000
(202 000 to 332 000)
–40·1%
(–55·6 to –17·1)
Tunisia 15 000
(13 000 to 17 000)
–11·4%
(–16·4 to –6·1)
9000
(7000 to 13 000)
–33·8%
(–52·2 to –11·5)
123 000
(118 000 to 128 000)
0·4%
(–2·3 to 3·5)
189 000
(137 000 to 256 000)
–35·9%
(–53·0 to –15·4)
Türkiye 98 000
(87 000 to 108 000)
–37·9%
(–41·8 to –33·3)
59 000
(49 000 to 71 000)
–50·2%
(–59·8 to –37·5)
927 000
(895 000 to 964 000)
–30·1%
(–31·9 to –28·2)
1 185 000
(996 000 to 1 388 000)
–56·9%
(–65·2 to –46·7)
United Arab Emirates 10 000
(9000 to 12 000)
–28·1%
(–32·8 to –23·6)
1000
(1000 to 2000)
–34·8%
(–46·1 to –21·7)
113 000
(109 000 to 118 000)
–15·8%
(–18·8 to –12·9)
52 000
(43 000 to 62 000)
–43·6%
(–53·2 to –32·2)
Yemen 24 000
(22 000 to 26 000)
–15·9%
(–20·7 to –10·8)
18 000
(14 000 to 25 000)
–25·2%
(–44·1 to 0·4)
188 000
(180 000 to 196 000)
–8·7%
(–11·5 to –5·7)
485 000
(364 000 to 635 000)
–31·6%
(–48·8 to –7·1)
South Asia 1 697 000
(1 540 000 to 1 860 000)
–22·2%
(–24·7 to –19·5)
1 067 000
(976 000 to 1 173 000)
–23·1%
(–32·4 to –12·4)
12 593 000
(11 789 000 to 13 537 000)
–8·2%
(–10·0 to –6·5)
26 602 000
(24 487 000 to 29 128 000)
–26·4%
(–35·3 to –17·2)
Bangladesh 221 000
(204 000 to 241 000)
–15·4%
(–19·3 to –10·7)
177 000
(144 000 to 215 000)
–26·7%
(–42·0 to –6·7)
1 449 000
(1 395 000 to 1 511 000)
–10·5%
(–12·9 to –7·9)
3 942 000
(3 209 000 to 4 819 000)
–35·8%
(–49·2 to –18·1)
Bhutan 670
(610 to 730)
–22·0%
(–26·2 to –16·9)
390
(310 to 480)
–32·2%
(–48·5 to –10·2)
5000
(5000 to 5000)
–10·7%
(–13·2 to –7·9)
9000
(7000 to 11 000)
–37·8%
(–53·2 to –18·4)
India 1 251 000
(1 127 000 to 1 378 000)
–24·0%
(–26·7 to –21·0)
773 000
(695 000 to 858 000)
–22·8%
(–33·8 to –10·0)
9 338 000
(8 687 000 to 10 110 000)
–8·0%
(–10·0 to –6·0)
19 436 000
(17 539 000 to 21 385 000)
–26·1%
(–36·5 to –14·8)
Nepal 26 000
(24 000 to 28 000)
–18·0%
(–21·8 to –13·9)
17 000
(14 000 to 22 000)
–32·0%
(–47·8 to –10·6)
181 000
(173 000 to 189 000)
–14·1%
(–16·6 to –11·4)
411 000
(330 000 to 520 000)
–36·9%
(–51·8 to –18·3)
Pakistan 198 000
(180 000 to 219 000)
–15·1%
(–18·7 to –11·3)
100 000
(83 000 to 124 000)
–8·1%
(–25·0 to 14·7)
1 620 000
(1 500 000 to 1 746 000)
–5·6%
(–8·2 to –2·8)
2 804 000
(2 318 000 to 3 459 000)
–8·5%
(–25·2 to 14·2)
Southeast Asia, east Asia,
and Oceania
5 425 000
(4 831 000 to 6 143 000)
–9·5%
(–13·9 to –5·0)
3 554 000
(3 106 000 to 4 001 000)
–37·5%
(–46·1 to –27·1)
36 232 000
(33 712 000 to 38 979 000)
6·7%
(4·3 to 8·9)
77 453 000
(68 193 000 to 86 258 000)
–39·2%
(–47·1 to –29·8)
East Asia 4 220 000
(3 717 000 to 4 838 000)
–10·5%
(–15·7 to –5·2)
2 664 000
(2 248 000 to 3 100 000)
–43·0%
(–52·6 to –31·2)
27 268 000
(25 077 000 to 29 587 000)
10·0%
(7·0 to 12·8)
54 947 000
(46 857 000 to 63 714 000)
–45·0%
(–54·1 to –33·8)
(Table 1 continues on next page)
Articles
www.thelancet.com/neurology Vol 23 October 2024
983
Incident cases Deaths Prevalent cases DALYs
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
(Continued from previous page)
China 4 090 000
(3 594 000 to 4 700 000)
–9·8%
(–15·2 to –4·2)
2 592 000
(2 179 000 to 3 033 000)
–43·0%
(–52·8 to –30·9)
26 335 000
(24 155 000 to 28 626 000)
11·5%
(8·3 to 14·5)
53 191 000
(45 109 000 to 61 958 000)
–45·2%
(–54·4 to –33·7)
North Korea 79 000
(72 000 to 85 000)
–8·2%
(–12·8 to –2·7)
58 000
(48 000 to 71 000)
–12·8%
(–30·9 to 12·2)
472 000
(455 000 to 492 000)
–6·6%
(–9·4 to –3·5)
1 408 000
(1 132 000 to 1 711 000)
–9·4%
(–29·7 to 16·9)
Taiwan (province of
China)
51 000
(46 000 to 56 000)
–44·6%
(–48·6 to –40·8)
14 000
(12 000 to 15 000)
–76·4%
(–78·3 to –74·8)
461 000
(445 000 to 477 000)
–26·1%
(–28·2 to –24·2)
349 000
(312 000 to 378 000)
–70·8%
(–73·0 to –68·8)
Oceania 12 000
(11 000 to 13 000)
–16·3%
(–19·0 to –12·9)
10 000
(8 000 to 12 000)
–21·8%
(–35·6 to –4·8)
99 000
(96 000 to 102 000)
–11·2%
(–12·7 to –9·6)
291 000
(238 000 to 348 000)
–22·4%
(–37·0 to –4·9)
American Samoa 70
(60 to 80)
–21·4%
(–25·8 to –17·5)
50
(40 to 50)
–25·1%
(–37·3 to –9·2)
620
(600 to 640)
–16·0%
(–18·0 to –14·1)
1000
(1000 to 1000)
–24·0%
(–36·3 to –8·7)
Cook Islands 30
(30 to 40)
–21·9%
(–25·7 to –17·7)
20
(10 to 20)
–52·0%
(–61·6 to –40·2)
320
(310 to 330)
–4·9%
(–7·2 to –2·6)
380
(310 to 440)
–49·5%
(–59·5 to –37·1)
Federated States of
Micronesia
180
(160 to 190)
–13·8%
(–17·4 to –10·1)
120
(90 to 150)
–25·8%
(–40·6 to –5·7)
1000
(1000 to 1000)
–10·3%
(–12·4 to –8·2)
4000
(3000 to 5000)
–25·9%
(–41·3 to –3·5)
Fiji 1000
(1000 to 1000)
–23·0%
(–26·5 to –18·5)
770
(600 to 960)
–20·7%
(–37·4 to 1·5)
12 000
(11 000 to 12 000)
–14·5%
(–16·5 to –12·4)
21 000
(17 000 to 27 000)
–25·4%
(–40·7 to –4·7)
Guam 260
(240 to 280)
–21·0%
(–25·3 to –16·5)
80
(70 to 90)
–60·9%
(–65·9 to –54·7)
3000
(3000 to 3000)
–1·7%
(–4·3 to 0·9)
3000
(2000 to 3000)
–40·5%
(–47·8 to –32·3)
Kiribati 220
(210 to 240)
–16·4%
(–19·9 to –12·6)
110
(90 to 140)
–9·2%
(–25·7 to 14·4)
2000
(2000 to 2000)
–13·3%
(–15·4 to –11·4)
4000
(3000 to 5000)
–12·5%
(–29·0 to 11·3)
Marshall Islands 90
(80 to 90)
–9·8%
(–13·2 to –6·3)
60
(40 to 70)
–19·4%
(–35·2 to –0·9)
670
(650 to 690)
–5·6%
(–8·0 to –3·4)
2000
(1000 to 2000)
–17·7%
(–34·3 to 3·6)
Nauru 10
(10 to 20)
–26·0%
(–29·3 to –22·4)
10
(10 to 20)
–14·9%
(–31·4 to 9·4)
150
(140 to 150)
–9·6%
(–12·0 to –7·4)
410
(320 to 530)
–13·8%
(–31·0 to 11·9)
Niue 0
(0 to 0)
–22·5%
(–26·1 to –18·1)
0
(0 to 0)
–25·4%
(–38·9 to –8·7)
30
(30 to 30)
–13·0%
(–15·2 to –10·7)
60
(50 to 80)
–22·8%
(–37·8 to –6·0)
Northern Mariana
Islands
70
(60 to 80)
–17·2%
(–21·9 to –12·0)
40
(30 to 40)
–34·4%
(–47·1 to –20·9)
650
(630 to 670)
–14·0%
(–16·2 to –11·9)
1000
(1000 to 1000)
–35·0%
(–47·2 to –21·3)
Palau 40
(40 to 50)
–14·7%
(–18·7 to –10·5)
20
(20 to 30)
–23·7%
(–41·1 to –2·0)
400
(380 to 410)
–3·9%
(–6·1 to –1·5)
720
(600 to 870)
–23·8%
(–40·6 to –2·2)
Papua New Guinea 7000
(7000 to 8000)
–13·1%
(–17·2 to –8·5)
7000
(5000 to 9000)
–20·4%
(–40·5 to 6·0)
59 000
(57 000 to 61 000)
–7·5%
(–9·8 to –5·1)
207 000
(161 000 to 258 000)
–22·4%
(–42·9 to 5·0)
Samoa 280
(260 to 310)
–16·7%
(–21·1 to –12·2)
190
(160 to 230)
–22·8%
(–36·0 to –4·0)
2000
(2000 to 3000)
–4·6%
(–7·1 to –1·9)
5000
(4000 to 6000)
–20·7%
(–34·4 to –0·9)
Solomon Islands 1000
(1000 to 1000)
–3·8%
(–8·1 to 0·9)
630
(510 to 790)
–12·5%
(–30·2 to 12·5)
8000
(8 000 to 8 000)
–3·4%
(–6·0 to –0·8)
19 000
(15 000 to 23 000)
–12·1%
(–32·1 to 18·7)
Tokelau 0
(0 to 0)
–29·3%
(–33·1 to –25·7)
0
(0 to 0)
–39·1%
(–51·3 to –23·7)
20
(20 to 20)
–10·0%
(–12·3 to –7·5)
40
(40 to 50)
–35·4%
(–48·5 to –18·5)
Tongo 110
(100 to 110)
–11·7%
(–16·3 to –7·0)
60
(50 to 70)
–15·3%
(–34·8 to 10·5)
950
(920 to 980)
–7·2%
(–9·3 to –5·0)
1000
(1000 to 2000)
–17·4%
(–36·1 to 7·0)
Tuvalu 20
(20 to 20)
–19·7%
(–23·3 to –15·7)
20
(10 to 20)
–35·8%
(–45·9 to –23·8)
160
(150 to 160)
–8·5%
(–10·9 to –6·5)
430
(370 to 510)
–36·6%
(–47·1 to –23·4)
Vanuatu 450
(420 to 490)
–5·3%
(–9·5 to –1·0)
260
(210 to 320)
–20·2%
(–35·5 to –3·0)
4000
(4000 to 4000)
–1·0%
(–3·6 to 1·4)
8000
(7000 to 10 000)
–18·5%
(–35·3 to 1·8)
(Table 1 continues on next page)
Articles
984
www.thelancet.com/neurology Vol 23 October 2024
Incident cases Deaths Prevalent cases DALYs
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
(Continued from previous page)
Southeast Asia 1 193 000
(1 090 000 to 1 305 000)
–12·4%
(–14·5 to –10·2)
880 000
(791 000 to 959 000)
–20·6%
(–29·9 to –9·5)
8 865 000
(8 402 000 to 9 372 000)
–6·7%
(–8·1 to –5·3)
22 214 000
(19 885 000 to 24 341 000)
–22·8%
(–31·3 to –13·1)
Cambodia 24 000
(22 000 to 25 000)
–11·1%
(–15·5 to –6·6)
18 000
(14 000 to 22 000)
–22·1%
(–39·4 to –3·3)
145 000
(140 000 to 151 000)
–6·5%
(–9·1 to –3·4)
435 000
(339 000 to 535 000)
–29·8%
(–45·5 to –11·9)
Indonesia 543 000
(487 000 to 611 000)
–0·1%
(–3·7 to 3·9)
405 000
(338 000 to 464 000)
5·5%
(–14·2 to 26·3)
3 942 000
(3 639 000 to 4 286 000)
–6·7%
(–9·0 to –4·3)
10 624 000
(8 957 000 to 12 309 000)
–5·8%
(–20·9 to 12·4)
Laos 9000
(9000 to 10 000)
–19·7%
(–23·7 to –15·1)
7000
(6000 to 9000)
–38·2%
(–52·9 to –19·9)
66 000
(63 000 to 68 000)
–10·8%
(–13·5 to –8·2)
193 000
(154 000 to 240 000)
–43·3%
(–57·6 to –26·5)
Malaysia 45 000
(41 000 to 49 000)
–29·5%
(–33·0 to –25·1)
23 000
(21 000 to 26 000)
–32·7%
(–40·1 to –23·6)
401 000
(388 000 to 415 000)
–9·1%
(–11·4 to –6·6)
593 000
(541 000 to 650 000)
–36·1%
(–42·2 to –28·7)
Maldives 460
(430 to 510)
–47·5%
(–50·2 to –44·3)
220
(180 to 260)
–64·7%
(–71·3 to –57·1)
4000
(4000 to 4000)
–40·2%
(–41·8 to –38·4)
6000
(5000 to 6000)
–70·1%
(–75·6 to –63·6)
Mauritius 2000
(2000 to 2000)
–48·2%
(–51·4 to –44·9)
1000
(1000 to 1000)
–62·4%
(–65·0 to –60·3)
18 000
(18 000 to 19 000)
–35·2%
(–37·0 to –33·3)
29 000
(27 000 to 30 000)
–60·7%
(–63·4 to –58·6)
Myanmar 88 000
(82 000 to 96 000)
–25·3%
(–29·1 to –20·6)
77 000
(62 000 to 96 000)
–37·5%
(–52·5 to –16·7)
597 000
(577 000 to 620 000)
–15·4%
(–17·6 to –13·0)
1 961 000
(1 594 000 to 2 424 000)
–42·6%
(–56·5 to –23·5)
Philippines 133 000
(121 000 to 148 000)
27·9%
(22·7 to 33·7)
84 000
(71 000 to 96 000)
–14·1%
(–25·9 to –0·9)
1 052 000
(981 000 to 1 134 000)
19·6%
(16·7 to 22·3)
2 364 000
(2 037 000 to 2 715 000)
–4·2%
(–17·1 to 11·3)
Sri Lanka 31 000
(28 000 to 34 000)
–24·9%
(–28·5 to –20·7)
25 000
(18 000 to 33 000)
–37·4%
(–56·2 to –16·5)
271 000
(260 000 to 281 000)
–15·2%
(–17·3 to –13·0)
498 000
(361 000 to 642 000)
–39·1%
(–57·3 to –18·5)
Seychelles 150
(130 to 160)
–25·7%
(–29·3 to –22·2)
80
(70 to 90)
–39·1%
(–46·2 to –31·3)
1000
(1000 to 1000)
–16·1%
(–18·5 to –13·5)
2000
(2000 to 2000)
–42·3%
(–48·9 to –35·1)
Thailand 118 000
(108 000 to 128 000)
–35·1%
(–38·5 to –31·9)
70 000
(54 000 to 86 000)
–46·1%
(–57·9 to –29·7)
1 070 000
(1 037 000 to 1 109 000)
–18·2%
(–20·3 to –15·9)
1 676 000
(1 347 000 to 2 042 000)
–40·5%
(–52·8 to –24·2)
Timor-Leste 2000
(1000 to 2000)
2·9%
(–2·5 to 8·6)
1000
(1000 to 2000)
–7·6%
(–30·4 to 23·1)
10 000
(10 000 to 10 000)
1·3%
(–1·4 to 4·2)
32 000
(25 000 to 41 000)
–11·9%
(–34·4 to 16·1)
Viet Nam 196 000
(183 000 to 209 000)
–9·3%
(–14·8 to –4·4)
167 000
(140 000 to 193 000)
–14·3%
(–33·4 to 7·4)
1 275 000
(1 238 000 to 1 318 000)
1·7%
(–1·4 to 5·2)
3 769 000
(3 132 000 to 4 444 000)
–17·4%
(–36·5 to 5·5)
Sub-Saharan Africa 867 000
(795 000 to 944 000)
–17·8%
(–19·8 to –15·3)
484 000
(433 000 to 539 000)
–23·0%
(–30·6 to –13·2)
7 769 000
(7 457 000 to 8 094 000)
–12·0%
(–13·3 to –10·8)
13 251 000
(11 716 000 to 14 891 000)
–26·6%
(–33·8 to –17·4)
Central sub-Saharan
Africa
103 000
(94 000 to 113 000)
–18·4%
(–22·0 to –14·6)
57 000
(44 000 to 73 000)
–16·4%
(–33·2 to 3·8)
837 000
(808 000 to 868 000)
–13·6%
(–15·8 to –11·6)
1 587 000
(1 225 000 to 1 988 000)
–21·6%
(–36·9 to –2·4)
Angola 23 000
(21 000 to 25 000)
–23·6%
(–28·2 to –18·5)
12 000
(9000 to 15 000)
–25·2%
(–43·0 to –2·5)
202 000
(195 000 to 209 000)
–12·3%
(–15·3 to –9·7)
340 000
(269 000 to 418 000)
–31·4%
(–48·4 to –11·3)
Central African
Republic
5000
(4000 to 5000)
–14·0%
(–18·7 to –9·1)
3000
(2000 to 4000)
–14·8%
(–31·8 to 4·8)
35 000
(34 000 to 36 000)
–11·3%
(–14·1 to –8·7)
97 000
(70 000 to 132 000)
–18·0%
(–35·5 to 1·7)
Congo (Brazzaville) 5000
(5000 to 6000)
–25·6%
(–29·8 to –21·0)
3000
(2000 to 4000)
–31·4%
(–45·1 to –14·2)
46 000
(44 000 to 48 000)
–18·5%
(–21·0 to –15·8)
82 000
(62 000 to 103 000)
–35·1%
(–49·1 to –18·5)
Democratic Republic
of the Congo
67 000
(62 000 to 74 000)
–16·2%
(–20·5 to –11·7)
38 000
(28 000 to 51 000)
–10·9%
(–32·9 to 16·9)
530 000
(510 000 to 550 000)
–13·9%
(–16·6 to –11·2)
1 032 000
(760 000 to 1 368 000)
–16·2%
(–35·8 to 9·3)
Equatorial Guinea 860
(770 to 950)
–35·7%
(–39·7 to –31·6)
410
(270 to 590)
–48·9%
(–63·9 to –26·5)
8000
(8000 to 9000)
–18·0%
(–20·2 to –15·7)
11 000
(8000 to 16 000)
–53·8%
(–67·3 to –34·5)
Gabon 2000
(2000 to 2000)
–20·8%
(–25·7 to –15·4)
940
(710 to 1210)
–26·6%
(–42·9 to –6·7)
17 000
(16 000 to 17 000)
–15·8%
(–18·1 to –13·1)
24 000
(18 000 to 31 000)
–30·1%
(–45·6 to –11·4)
(Table 1 continues on next page)
Articles
www.thelancet.com/neurology Vol 23 October 2024
985
Incident cases Deaths Prevalent cases DALYs
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
(Continued from previous page)
Eastern sub-Saharan
Africa
311 000
(285 000 to 338 000)
–21·6%
(–23·8 to –19·0)
169 000
(147 000 to 193 000)
–31·3%
(–40·8 to –21·0)
2 612 000
(2 509 000 to 2 720 000)
–13·7%
(–15·0 to –12·3)
4 698 000
(4 075 000 to 5 316 000)
–34·1%
(–44·0 to –24·0)
Burundi 9000
(9000 to 10 000)
–36·9%
(–40·3 to –33·2)
6000
(4000 to 7000)
–45·9%
(–59·3 to –27·1)
76 000
(74 000 to 79 000)
–29·4%
(–31·4 to –27·5)
158 000
(126 000 to 194 000)
–49·4%
(–62·4 to –31·9)
Comoros 830
(750 to 910)
–26·9%
(–31·7 to –22·6)
470
(360 to 580)
–38·3%
(–53·0 to –17·8)
7000
(7000 to 8000)
–19·9%
(–22·1 to –17·5)
12 000
(9000 to 15 000)
–41·6%
(–56·0 to –21·2)
Djibouti 1000
(1000 to 1000)
–14·6%
(–19·3 to –9·4)
570
(420 to 760)
–25·0%
(–43·9 to 2·2)
11 000
(11 000 to 12 000)
–9·1%
(–11·7 to –6·2)
16 000
(12 000 to 22 000)
–27·5%
(–46·2 to –0·8)
Eritrea 6000
(5000 to 6000)
–28·5%
(–32·1 to –24·5)
3000
(3000 to 4000)
–33·1%
(–46·3 to –16·3)
47 000
(45 000 to 49 000)
–12·7%
(–15·2 to –10·1)
99 000
(76 000 to 126 000)
–38·5%
(–50·9 to –21·9)
Ethiopia 59 000
(54 000 to 65 000)
–42·2%
(–45·2 to –38·9)
30 000
(25 000 to 36 000)
–51·6%
(–65·8 to –39·6)
500 000
(467 000 to 539 000)
–29·9%
(–32·5 to –27·2)
807 000
(674 000 to 944 000)
–56·0%
(–69·2 to –44·5)
Kenya 37 000
(34 000 to 42 000)
–9·3%
(–12·2 to –6·0)
18 000
(14 000 to 22 000)
–0·6%
(–18·5 to 24·0)
332 000
(308 000 to 358 000)
–8·8%
(–10·5 to –7·1)
468 000
(374 000 to 572 000)
–4·0%
(–20·0 to 18·4)
Madagascar 30 000
(28 000 to 32 000)
–13·4%
(–17·7 to –9·0)
18 000
(14 000 to 24 000)
–18·9%
(–38·3 to 3·1)
251 000
(243 000 to 259 000)
–9·9%
(–12·2 to –7·5)
572 000
(431 000 to 733 000)
–21·8%
(–40·2 to –0·1)
Malawi 14 000
(12 000 to 15 000)
–15·6%
(–20·3 to –10·7)
9000
(8000 to 11 000)
–6·0%
(–23·0 to 14·0)
117 000
(113 000 to 122 000)
–10·8%
(–13·3 to –8·0)
257 000
(215 000 to 305 000)
–9·4%
(–26·0 to 11·0)
Mozambique 29 000
(27 000 to 32 000)
3·4%
(–1·9 to 9·1)
21 000
(17 000 to 26 000)
7·9%
(–18·2 to 35·1)
221 000
(213 000 to 229 000)
8·8%
(5·1 to 12·7)
599 000
(466 000 to 738 000)
9·7%
(–15·9 to 36·0)
Rwanda 11 000
(10 000 to 12 000)
–43·6%
(–46·8 to –40·2)
6000
(5000 to 8000)
–56·6%
(–68·9 to –42·9)
88 000
(85 000 to 91 000)
–34·4%
(–36·4 to –32·5)
166 000
(123 000 to 212 000)
–61·3%
(–72·5 to –47·8)
Somalia 13 000
(12 000 to 15 000)
–20·5%
(–24·5 to –16·6)
8000
(5000 to 10 000)
–29·3%
(–45·3 to –8·6)
107 000
(103 000 to 111 000)
–14·4%
(–16·6 to –12·1)
238 000
(169 000 to 320 000)
–31·0%
(–47·6 to –9·3)
South Sudan 6000
(6000 to 7000)
–19·8%
(–24·2 to –16·0)
4000
(3000 to 6000)
–25·0%
(–43·0 to –1·5)
55 000
(53 000 to 57 000)
–15·9%
(–18·1 to –13·5)
121 000
(88 000 to 165 000)
–26·2%
(–44·3 to –1·2)
Tanzania 49 000
(45 000 to 54 000)
–3·7%
(–8·5 to 1·8)
23 000
(18 000 to 29 000)
–19·1%
(–35·3 to 5·2)
432 000
(417 000 to 449 000)
6·5%
(3·2 to 9·6)
583 000
(461 000 to 738 000)
–25·2%
(–41·6 to –2·8)
Uganda 28 000
(25 000 to 31 000)
–15·4%
(–20·4 to –9·8)
12 000
(10 000 to 16 000)
–36·0%
(–51·5 to –14·4)
242 000
(233 000 to 251 000)
–12·2%
(–14·5 to –9·4)
348 000
(273 000 to 441 000)
–36·7%
(–52·1 to –16·3)
Zambia 16 000
(14 000 to 17 000)
–1·2%
(–6·8 to 4·9)
9000
(7000 to 12 000)
–11·6%
(–32·6 to 14·3)
122 000
(118 000 to 126 000)
–2·4%
(–5·2 to 0·6)
251 000
(189 000 to 328 000)
–14·8%
(–37·4 to 10·7)
Southern sub-Saharan
Africa
89 000
(78 000 to 100 000)
–8·9%
(–12·1 to –5·4)
54 000
(50 000 to 58 000)
14·8%
(4·0 to 34·6)
789 000
(732 000 to 845 000)
–15·3%
(–18·2 to –12·7)
1 325 000
(1 226 000 to 1 427 000)
4·6%
(–4·2 to 18·2)
Botswana 3000
(3000 to 3000)
–5·2%
(–11·1 to 1·3)
1000
(1000 to 2000)
–43·2%
(–57·1 to –22·1)
25 000
(24 000 to 26 000)
–3·7%
(–7·0 to –0·3)
30 000
(24 000 to 38 000)
–43·8%
(–57·9 to –25·1)
Eswatini 1000
(1000 to 1000)
3·3%
(–3·0 to 10·1)
700
(490 to 980)
–10·0%
(–35·0 to 21·4)
8000
(8000 to 8000)
–0·6%
(–3·6 to 2·7)
19 000
(13 000 to 27 000)
–7·7%
(–33·5 to 27·6)
Lesotho 2000
(2000 to 3000)
36·6%
(28·6 to 45·7)
2000
(1000 to 2000)
43·4%
(3·4 to 104·2)
14 000
(14 000 to 15 000)
19·5%
(15·1 to 23·7)
48 000
(36 000 to 65 000)
50·3%
(8·4 to 115·9)
Namibia 3000
(2000 to 3000)
–16·7%
(–21·7 to –11·1)
2000
(1000 to 2000)
–18·9%
(–36·9 to 1·1)
19 000
(19 000 to 20 000)
–16·0%
(–18·7 to –13·3)
40 000
(31 000 to 51 000)
–21·9%
(–40·1 to –1·3)
South Africa 69 000
(60 000 to 79 000)
–13·8%
(–17·3 to –9·9)
39 000
(35 000 to 43 000)
16·4%
(4·7 to 37·4)
621 000
(569 000 to 674 000)
–20·8%
(–23·9 to –17·9)
944 000
(853 000 to 1 022 000)
0·1%
(–8·3 to 11·4)
(Table 1 continues on next page)
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Incident cases Deaths Prevalent cases DALYs
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
Counts, 2021 Percentage
change in age-
standardised
rates, 1990–2021
(Continued from previous page)
Zimbabwe 11 000
(10 000 to 12 000)
14·6%
(7·9 to 21·7)
9000
(7000 to 11 000)
39·1%
(10·0 to 82·3)
102 000
(97 000 to 106 000)
10·3%
(7·1 to 13·5)
244 000
(195 000 to 304 000)
50·0%
(19·1 to 97·3)
Western sub-Saharan
Africa
365 000
(335 000 to 397 000)
–17·3%
(–19·5 to –14·6)
204 000
(176 000 to 235 000)
–24·6%
(–34·8 to –11·3)
3 531 000
(3 383 000 to 3 685 000)
–9·6%
(–10·7 to –8·4)
5 641 000
(4 774 000 to 6 597 000)
–26·9%
(–36·9 to –13·6)
Benin 10 000
(9000 to 11 000)
–19·9%
(–24·2 to –15·1)
6000
(5000 to 7000)
–22·7%
(–35·7 to –3·8)
93 000
(90 000 to 97 000)
–14·1%
(–16·3 to –11·8)
161 000
(133 000 to 194 000)
–25·8%
(–39·0 to –6·9)
Burkina Faso 14 000
(13 000 to 15 000)
–8·0%
(–12·4 to –3·1)
8 000
(7 000 to 10 000)
–9·9%
(–28·1 to 14·9)
131 000
(127 000 to 136 000)
–9·4%
(–11·8 to –7·3)
230 000
(186 000 to 285 000)
–13·9%
(–30·5 to 9·5)
Cabo Verde 660
(610 to 720)
–3·7%
(–8·6 to 1·9)
440
(360 to 530)
7·1%
(–16·2 to 40·2)
7000
(7000 to 7000)
–6·8%
(–9·0 to –4·6)
10 000
(8000 to 11 000)
–6·7%
(–26·3 to 20·6)
Cameroon 25 000
(23 000 to 27 000)
–6·6%
(–11·0 to –1·7)
15 000
(11 000 to 20 000)
–7·1%
(–28·6 to 24·2)
233 000
(226 000 to 241 000)
–4·2%
(–6·7 to –1·8)
438 000
(325 000 to 575 000)
–9·3%
(–30·7 to 20·6)
Chad 13 000
(12 000 to 14 000)
–5·3%
(–9·8 to –0·6)
8000
(6000 to 10 000)
4·3%
(–19·3 to 34·3)
115 000
(111 000 to 119 000)
–5·8%
(–8·2 to –3·5)
242 000
(188 000 to 302 000)
2·2%
(–20·9 to 30·8)
Côte d’Ivoire 23 000
(21 000 to 24 000)
–17·9%
(–22·0 to –13·6)
13 000
(10 000 to 16 000)
–13·7%
(–31·9 to 12·0)
224 000
(217 000 to 232 000)
–16·2%
(–18·2 to –14·1)
378 000
(288 000 to 485 000)
–16·6%
(–35·7 to 9·2)
The Gambia 2000
(2000 to 2000)
–8·0%
(–12·3 to –2·7)
1000
(1000 to 2000)
3·3%
(–22·1 to 36·6)
19 000
(18 000 to 20 000)
–10·3%
(–13·0 to –7·9)
38 000
(29 000 to 47 000)
–1·9%
(–26·4 to 30·1)
Ghana 42 000
(39 000 to 45 000)
–6·3%
(–11·3 to –1·2)
25 000
(20 000 to 31 000)
–9·6%
(–30·9 to 18·8)
422 000
(409 000 to 436 000)
–2·3%
(–5·0 to 0·4)
694 000
(558 000 to 855 000)
–15·1%
(–34·5 to 11·9)
Guinea 12 000
(12 000 to 13 000)
–4·1%
(–8·9 to 1·2)
8000
(6000 to 10 000)
–2·7%
(–25·8 to 31·4)
108 000
(104 000 to 112 000)
–3·0%
(–5·6 to –0·2)
215 000
(166 000 to 270 000)
–6·7%
(–28·7 to 24·0)
Guinea-Bissau 2000
(2000 to 2000)
–15·2%
(–19·1 to –10·8)
1000
(1000 to 2000)
–13·8%
(–33·8 to 9·0)
16 000
(16 000 to 17 000)
–12·9%
(–15·1 to –10·7)
40 000
(31 000 to 50 000)
–20·1%
(–39·3 to 1·1)
Liberia 4000
(4000 to 4000)
–22·8%
(–26·4 to –18·8)
3000
(2000 to 3000)
–11·3%
(–31·3 to 16·9)
38 000
(37 000 to 39 000)
–18·1%
(–20·0 to –16·1)
74 000
(57 000 to 96 000)
–15·9%
(–35·4 to 11·5)
Mali 14 000
(13 000 to 16 000)
–19·4%
(–22·9 to –15·0)
9000
(7000 to 11 000)
–21·3%
(–37·4 to –1·2)
138 000
(133 000 to 143 000)
–14·7%
(–16·5 to –12·8)
253 000
(202 000 to 312 000)
–24·7%
(–39·8 to –5·5)
Mauritania 4000
(3000 to 4000)
–30·8%
(–34·0 to –27·3)
2000
(2000 to 3000)
–32·4%
(–47·9 to –11·3)
36 000
(35 000 to 38 000)
–24·6%
(–26·4 to –22·4)
59 000
(45 000 to 77 000)
–37·3%
(–51·6 to –19·1)
Niger 16 000
(15 000 to 17 000)
–18·9%
(–22·6 to –15·2)
9000
(7000 to 12 000)
–10·8%
(–29·1 to 14·2)
143 000
(138 000 to 147 000)
–17·2%
(–19·4 to –15·3)
259 000
(196 000 to 334 000)
–17·4%
(–35·5 to 6·5)
Nigeria 153 000
(138 000 to 170 000)
–22·9%
(–25·5 to –20·0)
74 000
(61 000 to 92 000)
–39·7%
(–51·0 to –22·7)
1 518 000
(1 415 000 to 1 633 000)
–10·3%
(–11·9 to –8·6)
2 010 000
(1 637 000 to 2 544 000)
–41·2%
(–53·2 to –23·9)
São Tomé and
Príncipe
250
(230 to 270)
–4·8%
(–9·3 to 0·3)
120
(100 to 150)
–2·0%
(–16·3 to 17·0)
3000
(2000 to 3000)
–3·9%
(–6·4 to –1·6)
3000
(3000 to 4000)
–4·9%
(–19·6 to 14·9)
Senegal 14 000
(13 000 to 15 000)
–17·7%
(–21·7 to –13·5)
10 000
(8000 to 12 000)
–13·2%
(–32·2 to 8·7)
139 000
(135 000 to 144 000)
–14·9%
(–17·0 to –12·8)
247 000
(199 000 to 304 000)
–20·1%
(–37·6 to –0·3)
Sierra Leone 8000
(7000 to 8000)
–12·4%
(–16·6 to –7·4)
5000
(4000 to 7000)
–10·8%
(–28·7 to 15·1)
77 000
(74 000 to 79 000)
–10·3%
(–12·9 to –8·2)
148 000
(113 000 to 194 000)
–14·3%
(–32·0 to 11·6)
Togo 7000
(7000 to 8000)
–14·4%
(–18·5 to –10·2)
5000
(4000 to 6000)
–5·5%
(–26·0 to 21·4)
71 000
(69 000 to 74 000)
–13·7%
(–15·7 to –11·2)
142 000
(107 000 to 178 000)
–9·6%
(–30·2 to 15·9)
Data in parentheses are 95% uncertainty intervals. DALYs=disability-adjusted life-years.
Table 1: Incident cases, deaths, prevalent cases, and DALYs for stroke in 2021 and percentage change in age-standardised rates for 1990–2021, by location, for both sexes
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987
(Figure 1 continues on next page)
Caribbean and central America Persian Gulf West Africa
Balkan Peninsula
Eastern
Mediterranean
Southeast Asia
Northern Europe
56·7 to 76·3
76·3 to 87·8
87·8 to 97·7
97·7 to 110·4
110·4 to 139·2
139·2 to 154·7
154·7 to 169·8
169·8 to 187·6
187·6 to 212·7
212·7 to 356
Age-standardised rates (per 100
000 people)
of stroke incidence
Caribbean and central America Persian Gulf West Africa
Balkan Peninsula
Eastern
Mediterranean
Southeast Asia
Northern Europe
520·5 to 783·2
783·2 to 870·3
870·3 to 965·6
965·6 to 1047·8
1047·8 to 1100
1100 to 1180·2
1180·2 to 1255·3
1255·3 to 1318·3
1318·3 to 1454·6
1454·6 to 2106·4
Age-standardised rates (per 100
000 people)
of stroke prevalence
A
B
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Caribbean and central America Persian Gulf West Africa
Balkan Peninsula
Eastern
Mediterranean
Southeast Asia
Northern Europe
332·3 to 600·5
600·5 to 725·4
725·4 to 947·5
947·5 to 1346·8
1346·8 to 1771·6
1771·6 to 2071·5
2071·5 to 2516·2
2516·2 to 2897·2
2897·2 to 3499·8
3499·8 to 6892·6
Age-standardised rates (per 100
000 people)
of stroke DALYs
Caribbean and central America Persian Gulf West Africa
Balkan Peninsula
Eastern
Mediterranean
Southeast Asia
Northern Europe
13·2 to 26·7
26·7 to 31·9
31·9 to 44·5
44·5 to 66·0
66·0 to 87·3
87·3 to 101·9
101·9 to 120·5
120·5 to 142·0
142·0 to 173·4
173·4 to 309·4
Age-standardised rates (per 100
000 people)
of stroke deaths
C
D
Figure 1: Global age-standardised rates (per 100 000 people) of stroke incidence (A), prevalence (B), DALYs (C), and deaths (D) for both sexes, 2021
DALYs=disability-adjusted life-years.
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989
fruits, diet low in vegetables, and diet low in wholegrains),
alcohol use, and low physical activity. The dietary risks
cluster includes diet high in sodium, diet high in
processed meat, diet high in red meat, diet high in
sugar-sweetened beverages, diet low in omega-6
polyunsaturated fatty acids, diet low in fruits, diet low in
vegetables, and diet low in wholegrains. The environ-
mental risks cluster includes the air pollution cluster,
low ambient temperature, high ambient temperature,
and lead exposure. The metabolic risks cluster includes
high fasting plasma glucose, high LDL cholesterol, high
systolic blood pressure, high BMI, and kidney dysfunc-
tion. Finally, the tobacco smoke cluster includes smoking
and second-hand smoking.
Role of the funding source
The funder of the study had no role in study design, data
collection, data analysis, data interpretation, or the writing
of the report.
Results
Stroke incidence, prevalence, death, and DALYs by
geographical location
In 2021, there were 93·8 million (95% UI 89·0–99·3)
stroke survivors, 11·9 million (10·7–13·2) new stroke
events, 7·3 million (6·6–7·8) deaths from stroke, and
160·5 million (147·8–171·6) DALYs from stroke, comp-
rising 10·7% (9·8–11·3) of all deaths and 5·6% (5·0–6·1)
of all DALYs from all causes, the third leading cause
of deaths (after ischaemic heart disease and COVID-19)
and the fourth leading cause of DALYs (after COVID-19,
ischaemic heart disease, and neonatal disorders; table 1;
appendix p 204).
In 2021, 83·3% incident, 76·7% prevalent, and 87·2%
fatal strokes, and 89·4% stroke-related DALYs occurred
in all low-income and middle-income countries
(LMICs) combined (appendix pp 48–49). We also
observed geographical dierences in age-standardised
stroke incidence, with the lowest in Luxembourg
(57·7 [95% UI 53·5–62·1] per 100 000) and highest in
the Solomon Islands (355·0 [332·7–378·1] per 100 000);
prevalence, with the highest in Ghana (2045·8
[1977·3–2120·1] per 100 000) and lowest in Cyprus
(521·5 [495·7–553·5] per 100 000); deaths, with
the lowest in Singapore (14·2 [12·3–15·6] per 100 000)
and highest in North Macedonia (277·4 [235·5–321·2]
per 100 000); and in DALY rates, with the lowest in
Switzerland (333·3 [291·0–368·8] per 100 000) and
highest in Nauru (6100·0 [4917·8–7576·1] per 100 000).
Overall, the highest stroke burden (as measured by age-
standardised incidence, prevalence, death, and DALY
rates) in 2021 was observed in east Asia, central Asia,
and sub-Saharan regions and lowest in high-income
North America, Australasia, and Latin America regions,
with the majority of the stroke burden in middle SDI,
high-middle, and low-middle SDI regions (figure 1;
appendix pp 50–85).
Burden by pathological type of stroke
Ischaemic stroke constituted the largest proportion of all
incident strokes (7·8 million [95% UI 6·7–8·9], or 65·3%
[62·4–67·7] of all strokes), followed by intracerebral
haemorrhage (3·4 million [3·1–3·8] incident events, or
28·8% [28·3–28·8] of all strokes). However, the absolute
number of DALYs due to intracerebral haemorrhage
(79·5 million [72·7–85·2], or 49·6% [49·3–49·8] of total
DALYs due to stroke) was greater than the number
of DALYs due to ischaemic stroke (70·4 million
[64·1–76·0], or 43·8% [43·5–44·3]). In 2021, subarachnoid
haemorrhage occurred in 0·7 million (0·6–0·8) people
(5·8% [5·7–6·0] of all strokes), and there were
10·6 million (9·4–12·1) DALYs due to subarachnoid
haemorrhage (6·6% of DALYs from all strokes combined).
Similar to total stroke, dierences were observed for age-
standardised rates for the three pathological types
of stroke and their trends from 1990 to 2021 globally and
by SDI (appendix pp 50–85, 208): rates of incident and
fatal stroke were highest for ischaemic stroke
(92·4 [79·8–105·8] per 100 000 and 44·2 [39·5–47·8] per
100 000, respectively) followed by intracerebral
haemorrhage (40·8 [36·2–45·2] per 100 000 and
39·1 [35·4–42·6] per 100 000, respectively) and
subarachnoid haemorrhage (8·3 [7·3–9·5] per 100 000
and 4·2 [3·7–4·8] per 100 000, respectively).
In 2021, there were large variations in the proportion
of ischaemic stroke and intracerebral haemorrhage
between high-income countries and LMICs (appendix
pp 100–101). Whereas in high-income countries
ischaemic stroke constituted 74·9% (95% UI 72∙3−84∙1)
and intracerebral haemorrhage constituted 17·8%
(17∙3−17∙9) of all incident strokes, in all LMICs combined
these stroke subtypes constituted 63·4% (53∙6−73∙7) and
31·1% (30∙2−31∙3), respectively. Among all LMICs
combined, the proportion of intracerebral haemorrhage
was highest in low-income countries (36·9%
[36∙5−37∙1]). The proportion of subarachnoid haemor-
rhage in high-income countries (7·3% [7∙2−8∙3]) was
higher than that in all LMICs combined (5·5% [5∙4−5∙7]).
Trends in stroke burden by age, sex and geographical
location
Among 11∙9 million new strokes in 2021, 6·3 million
(95% UI 5·6 to 7·0; or 52·6% [52∙4 to 53∙1]) occurred in
males and 5·7 million (5·1 to 6·3; or 47·4% [47∙3 to 47∙6])
in females; the corresponding sex distribution of prevalent
stroke was 51·0% (47·8 million [45·3 to 50·6]) for males
and 49·0% (46·0 million [43·5 to 48·8]) for females; that
for deaths from stroke was 52·1% (3·8 million [3·4 to 4·1])
for males and 47·9% (3·5 million [3·1 to 3·8] for females;
and that for stroke-related DALYs was 55·0% (88·3 million
[80·6 to 97·2]) for males and 45·0% (72·2 million
[65·6 to 78·2) for females (table 1; appendix pp 138–139).
From 1990 to 2021, the age-standardised incidence,
pre valence, death, and DALY rates (table 1) of stroke and
its patho logical types were reduced virtually across all
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990
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Low-income countries Lower-middle-income countries Upper-middle-income countries High-income countries
Absolute number Percentage Absolute number Percentage Absolute number Percentage Absolute number Percentage
Air pollution and environmental risks
Ambient particulate matter pollution 655 000
(449 000 to 922 000)
7·3%
(4·9 to 9·9)
9 103 000
(5 695 000 to 12 188 000)
15·3%
(9·5 to 20·2)
15 557 000
(10 431 000 to 20 052 000)
20·9%
(14·8 to 25·7)
1 448 000
(1 043 000 to 1 897 000)
8·8%
(6·5 to 11·5)
High ambient temperature 169 000
(78 000 to 305 000)
1·7%
(0·8 to 3·1)
1 141 000
(365 000 to 2 202 000)
1·9%
(0·6 to 3·7)
407 000
(–142 000 to 1 340 000)
0·6%
(–0·2 to 1·8)
78 000
(–25 000 to 222 000)
0·6%
(–0·1 to 1·5)
Household air pollution from solid
fuels
3 492 000
(2 721 000 to 4 278 000)
38·3%
(31·7 to 44·6)
11 410 000
(7 024 000 to 17 273 000)
19·1%
(11·7 to 28·3)
3 244 000
(479 000 to 10 588 000)
4·3%
(0·6 to 14·3)
13 000
(0 to 134 000)
0·1%
(0·0 to 0·8)
Lead exposure 822 000
(–106 000 to 1 793 000)
9·3%
(–1·3 to 20·9)
4 965 000
(–672 000 to 10 922 000)
8·4%
(–1·1 to 18·8)
5 572 000
(–727 000 to 12 374 000)
7·4%
(–1·0 to 16·5)
656 000
(–87 000 to 1 485 000)
3·7%
(–0·5 to 8·5)
Low ambient temperature 299 000
(248 000 to 363 000)
3·2%
(2·8 to 3·8)
1 248 000
(801 000 to 1 801 000)
2·1%
(1·4 to 3·0)
4 939 000
(4 153 000 to 5 937 000)
6·7%
(6·0 to 7·5)
1 119 000
(964 000 to 1 295 000)
6·3%
(5·6 to 7·2)
Dietary risks
Alcohol use 302 000
(61 000 to 603 000)
3·2%
(0·7 to 6·3)
2 018 000
(483 000 to 3 902 000)
3·2%
(0·8 to 6·1)
4 710 000
(1 122 000 to 8 961 000)
6·2%
(1·5 to 11·8)
1 399 000
(251 000 to 2 856 000)
8·1%
(1·6 to 16·0)
Diet high in processed meat 8 000
(2 000 to 14 000)
0·1%
(0·0 to 0·2)
58 000
(13 000 to 105 000)
0·1%
(0·0 to 0·2)
181 000
(42 000 to 326 000)
0·2%
(0·1 to 0·4)
189 000
(46 000 to 334 000)
1·1%
(0·3 to 1·9)
Diet high in red meat –162 000
(–607 000 to 250 000)
–1·3%
(–5·1 to 2·1)
–905 000
(–3 475 000 to 1 258 000)
–1·3%
(–5·0 to 1·9)
–3 528 000
(–15 465 000 to 4 994 000)
–4·7%
(–20·0 to 6·8)
–561 000
(–2 380 000 to 834 000)
–4·3%
(–19·0 to 6·4)
Diet high in sodium 528 000
(71 000 to 1 443 000)
6·1%
(0·8 to 16·0)
4 558 000
(574 000 to 11 393 000)
7·5%
(0·9 to 19·0)
11 095 000
(3 669 000 to 22 286 000)
14·3%
(4·8 to 27·5)
1 207 000
(159 000 to 3 109 000)
7·1%
(1·0 to 18·0)
Diet high in sugar-sweetened
beverages
2000
(1000 to 3000)
0·0%
(0·0 to 0·0)
24 000
(12 000 to 39 000)
0·0%
(0·0 to 0·1)
64 000
(31 000 to 101 000)
0·1%
(0·0 to 0·1)
54 000
(26 000 to 85 000)
0·3%
(0·2 to 0·5)
Diet low in fibre 240 000
(–51 000 to 502 000)
2·1%
(–0·5 to 4·4)
2 357 000
(–567 000 to 4 845 000)
3·5%
(–0·8 to 7·2)
1 175 000
(–243 000 to 2 521 000)
1·6%
(–0·3 to 3·4)
298 000
(–59 000 to 643 000)
2·2%
(–0·5 to 4·7)
Diet low in fruits 784 000
(38 000 to 1 393 000)
7·1%
(0·4 to 12·7)
5 318 000
(395 000 to 9 047 000)
7·9%
(0·7 to 13·8)
2 969 000
(183 000 to 5 711 000)
3·9%
(0·2 to 7·2)
550 000
(49 000 to 995 000)
4·1%
(0·3 to 7·2)
Diet low in omega-6 polyunsaturated
fatty acids
1000
(0 to 2000)
0·0%
(0·0 to 0·0)
7000
(2000 to 13 000)
0·0%
(0·0 to 0·0)
8000
(2000 to 16 000)
0·0%
(0·0 to 0·0)
2000
(0 to 3000)
0·0%
(0·0 to 0·0)
Diet low in vegetables 689 000
(97 000 to 1 189 000)
6·3%
(1·1 to 11·0)
1 477 000
(374 000 to 2 504 000)
2·3%
(0·6 to 3·8)
278 000
(106 000 to 461 000)
0·4%
(0·1 to 0·6)
93 000
(18 000 to 167 000)
0·6%
(0·3 to 1·0)
Diet low in wholegrains 187 000
(–202 000 to 497 000)
1·8%
(–1·9 to 5·1)
1 000 000
(–1 041 000 to 2 710 000)
1·5%
(–1·5 to 4·4)
1 595 000
(–1 612 000 to 4 382 000)
2·1%
(–2·1 to 5·8)
340 000
(–335 000 to 1 083 000)
2·3%
(–2·4 to 6·7)
Physical activity
Low physical activity 163 000
(68 000 to 272 000)
1·7%
(0·6 to 3·0)
1 191 000
(427 000 to 2 092 000)
2·0%
(0·5 to 3·7)
1 587 000
(361 000 to 3 094 000)
2·1%
(0·3 to 4·3)
415 000
(–56 000 to 951 000)
2·5%
(0·4 to 5·0)
Tobacco smoking
Second-hand smoke 320 000
(212 000 to 434 000)
3·1%
(2·1 to 4·2)
2 694 000
(1 843 000 to 3 599 000)
4·3%
(3·0 to 5·7)
3 548 000
(2 410 000 to 4 722 000)
4·8%
(3·3 to 6·4)
402 000
(270 000 to 549 000)
2·8%
(1·9 to 3·7)
Smoking 780 000
(627 000 to 941 000)
7·3%
(6·2 to 8·5)
7 248 000
(6 162 000 to 8 491 000)
11·1%
(9·5 to 12·6)
12 606 000
(10 200 000 to 15 551 000)
16·4%
(13·9 to 19·0)
1 870 000
(1 573 000 to 2 225 000)
13·1%
(11·2 to 15·0)
(Table 2 continues on next page)
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Low-income countries Lower-middle-income countries Upper-middle-income countries High-income countries
Absolute number Percentage Absolute number Percentage Absolute number Percentage Absolute number Percentage
(Continued from previous page)
Physiological factors
High BMI 357 000
(31 000 to 752 000)
3·4%
(0·3 to 7·0)
2 276 000
(202 000 to 4 653 000)
3·5%
(0·3 to 7·0)
3 848 000
(294 000 to 8 127 000)
5·1%
(0·4 to 10·5)
1 197 000
(90 000 to 2 420 000)
8·2%
(0·5 to 16·4)
High fasting plasma glucose 672 000
(491 000 to 873 000)
8·4%
(6·4 to 10·5)
5 510 000
(4 189 000 to 6 899 000)
10·1%
(7·9 to 12·4)
7 838 000
(6 087 000 to 9 926 000)
10·6%
(8·3 to 13·2)
2 433 000
(1 909 000 to 2 978 000)
13·0%
(10·5 to 15·5)
High LDL cholesterol 857 000
(304 000 to 1 429 000)
9·7%
(3·3 to 16·2)
6 557 000
(2 425 000 to 10 763 000)
11·4%
(3·9 to 19·2)
10 521 000
(3 652 000 to 17 446 000)
14·1%
(4·9 to 23·0)
3 019 000
(1 003 000 to 5 043 000)
17·3%
(6·1 to 27·8)
High systolic blood pressure 5 004 000
(3 684 000 to 6 327 000)
55·5%
(41·1 to 66·2)
35 018 000
(26 696 000 to 42 407 000)
59·0%
(44·3 to 69·9)
42 461 000
(30 731 000 to 54 201 000)
56·7%
(42·4 to
68·4)
9 286 000
(6 804 000 to 11 360 000)
53·3%
(39·5 to 64·0)
Kidney dysfunction 844 000
(592 000 to 1 103 000)
9·3%
(6·9 to 11·7)
6 603 000
(4 928 000 to 8 309 000)
11·1%
(8·2 to 14·0)
6 119 000
(4 382 000 to 8 066 000)
8·1%
(5·8 to 10·5)
1 430 000
(946 000 to 1 948 000)
7·9%
(5·5 to 10·4)
Cluster of risk factors
Air pollution* 4 147 000
(3 281 000 to 5 058 000)
45·5%
(37·9 to 52·7)
20 516 000
(16 091 000 to 24 931 000)
34·3%
(27·8 to 41·4)
18 805 000
(13 812 000 to 24 803 000)
25·2%
(19·6 to 32·0)
1 461 000
(1 053 000 to 1 920 000)
8·9%
(6·6 to 11·8)
Behavioural risks† 3 009 000
(1 856 000 to 4 044 000)
30·0%
(18·4 to 40·5)
20 601 000
(14 385 000 to 26 447 000)
32·5%
(22·6 to 42·5)
28 745 000
(20 905 000 to 38 190 000)
37·6%
(27·8 to 48·4)
5 093 000
(3 497 000 to 6 929 000)
31·9%
(23·1 to 41·8)
Dietary risks‡ 1 887 000
(585 000 to 2 974 000)
18·7%
(5·9 to 31·2)
10 873 000
(3 590 000 to 18 064 000)
17·0%
(5·6 to 29·0)
12 749 000
(4 384 000 to 23 122 000)
16·5%
(5·5 to 29·2)
1 846 000
(649 000 to 3 690 000)
11·1%
(3·4 to 21·5)
Environmental or occupational risks§4 828 000
(3 842 000 to 5 781 000)
53·0%
(44·2 to 60·8)
24 989 000
(19 266 000 to 29 973 000)
41·9%
(33·0 to 50·3)
26 451 000
(19 722 000 to 33 653 000)
35·5%
(27·7 to 43·4)
3 093 000
(2 287 000 to 3 958 000)
18·1%
(13·6 to 22·7)
Metabolic risks¶ 5 925 000
(4 692 000 to 7 204 000)
65·9%
(54·9 to 74·8)
41 558 000
(34 711 000 to 47 433 000)
70·3%
(59·7 to 78·7)
51 450 000
(41 291 000 to 61 419 000)
68·9%
(57·4 to 77·9)
11 980 000
(9 919 000 to 13 788 000)
68·7%
(57·9 to 77·5)
Tobacco smoke|| 1 067 000
(830 000 to 1 318 000)
10·2%
(8·2 to 12·2)
9 631 000
(7 866 000 to 11 484 000)
14·9%
(12·3 to 17·5)
15 602 000
(12 447 000 to 19 422 000)
20·4%
(16·8 to 24·0)
2 209 000
(1 825 000 to 2 669 000)
15·4%
(12·8 to 17·8)
Combined risk factors**
All risk factors 7 812 000
(6 670 000 to 8 973 000)
85·4%
(79·3 to 89·2)
51 119 000
(46 489 000 to 55 450 000)
85·7%
(79·7 to 89·8)
62 283 000
(53 668 000 to 70 765 000)
83·6%
(76·9 to 88·7)
13 633 000
(11 938 000 to 15 151 000)
79·2%
(71·6 to 85·4)
Data in parentheses are 95% uncertainty intervals. Percentages and number of DALYs are not mutually exclusive. The sum of percentages and number of DALYs in the columns exceeds the totals for all risk factors combined because the effect of many
of these risk factors are mediated partly or wholly through other risk factors. 0% represents very low numbers. DALYs=disability-adjusted life-years. *Air pollution cluster includes ambient PM2·5 pollution and household air pollution. †Behavioural risks
cluster includes smoking (including second-hand smoking), dietary risks (diet high in sodium, diet high in processed meat diet, high in red meat, diet high in sugar-sweetened beverages, diet low in omega-6 polyunsaturated fatty acids, diet low in
fruits, diet low in vegetables, and diet low in wholegrains), alcohol use, and low physical activity. ‡Dietary risks cluster includes diet high in sodium, diet high in processed meat diet, high in red meat, diet high in sugar-sweetened beverages, diet low in
omega-6 polyunsaturated fatty acids, diet low in fruits, diet low in vegetables, and diet low in whole grains. §Environmental risks cluster includes air pollution cluster, low ambient temperature, high ambient temperature, and lead exposure.
¶Metabolic risks cluster includes high fasting plasma glucose, high LDL cholesterol, high systolic blood pressure, high BMI, and kidney dysfunction. ||Tobacco smoke includes smoking and second-hand smoking. **Age-standardised total percentage of
DALYs due to all risk factors combined.
Table 2: Stroke-related DALYs associated with risk factors and their clusters by World Bank country income level, for both sexes, 2021
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World Bank country income levels (except for ischaemic
stroke incidence and prevalence in upper-middle-income
countries, where the rates were increased by 1% [–4 to 5]
for ischaemic stroke incidence and 11% [8 to 14] for
ischaemic stroke prevalence). Although there was a trend
towards lower age-standardised stroke burden rates (inci-
dence, prevalence, deaths, and DALYs) across all quintiles
of the SDI, there was a stagnation in the reduction of inci-
dence rates from 2015 onwards, and even some increase
in the prevalence rates in high-middle SDI countries from
2020 to 2021 (appendix p 209). Similar trend patterns were
observed in seven GBD super-regions, with more promi-
nent increases in age-standardised incidence and
prevalence rates after 2015 in southeast Asia, east Asia,
and Oceania (appendix p 206).
Although from 1990 to 2021 there was a decrease in
the age-standardised incidence (–21·8% [95% UI–23·7 to
–19·8]), prevalence (–8·5% [–9·7 to –7·3]), death (–39·4%
[–44·0 to –34·6]), and DALY (–38·7% [–43·4 to –34·0])
stroke rates, increases were seen over that period in
the numbers of people who had a new stroke (70·2%
[65·9 to 74·6]), survived stroke (86·1% [83·0 to 89·4]),
died from stroke (44·1% [32·3 to 56·0]), and who died or
remained disabled from stroke (as measured by DALYs;
32·2% [21·7 to 42·7]; table 1; appendix pp 100–101). The
percentage decline in age-standardised stroke incidence
rates in the 2019–2021 period (–1·8% [–2·8 to –0·6]) was
smaller than that for the overall 2010–21 period (–3·1%
[–4·2 to –2·0]).
Although all-age (not age-standardised) stroke inci-
dence, death, and DALY rates were substantially reduced
in people aged 70 years or older between 1990 and 2021
(–18·2% [95% UI –21·3 to – 14·6] incidence rate, –34·2%
[–39·4 to –29·3] death rate, and –35·6% [–40·2 to –30·8]
DALY rate), and all-age prevalence rate in this age group
did not change over this period (–1·0 [–3·1 to 1·2]),
all-age incidence increased by 4·1% (0·9 to 7·6), preva-
lence increased in people younger than 70 years by 14·8%
(13·1 to 16·8), and death and DALY rates were reduced in
people younger than 70 years by 17·4% (–25·0 to –8·9)
and 19·0% (–26·0 to –11·6), respectively (appendix
p 140). Similar patterns were observed for all-age
58·0%
29·4%
17·6%
16·9%
11·9%
10·1%
9·8%
8·8%
6·2%
4·6%
4·0%
2·8%
1·6%
High systolic blood pressure
Ambient particulate matter pollution
Smoking
High LDL cholesterol
Household air pollution from solid fuels
Diet high in sodium
High fasting plasma glucose
Kidney dysfunction
Diet low in fruits
High alcohol use
High BMI
Second-hand smoke
Low physical activity
Diet low in vegetables
High systolic blood pressure
AB
High LDL cholesterol
High fasting plasma glucose
Ambient particulate matter pollution
Smoking
Diet high in sodium
Kidney dysfunction
Household air pollution from solid fuels
High BMI
Low physical activity
Second-hand smoke
Diet low in fruits
Diet low in vegetables
0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70 80
High systolic blood pressure
Ambient particulate matter pollution
Smoking
Household air pollution from solid fuels
Diet high in sodium
Kidney dysfunction
High alcohol use
High fasting plasma glucose
Second-hand smoke
High systolic blood pressure
CD
Smoking
Ambient particulate matter pollution
Household air pollution from solid fuels
Diet high in sodium
Second-hand smoke
Low ambient temperature
High ambient temperature
0 10 20 30 40 50 60 70 80
%
0 10 20 30 40 50 60 70
%
56·8%
16·6%
13·8%
13·1%
11·2%
10·6%
10·3%
9·3%
5·9%
5·2%
4·7%
4·4%
2·1%
56·4% 51·6%
14·5%
14·2%
10·3%
8·9%
4·8%
4·5%
1·1%
16·6%
15·4%
13·4%
11·4%
10·0%
5·7%
5·1%
4·8%
1·6%
PAF point estimate
Lower bound of 95% UI
Upper bound of 95% UI
All pathological types of stroke combined Ischaemic stroke
Intracerebral haemorrhage Subarachnoid haemorrhage
Figure 2: Most individually significant risk factors for total stroke (A), ischaemic stroke (B), intracerebral haemorrhage (C), and subarachnoid haemorrhage (D),
as measured by the PAF of stroke DALYs attributable to the risk factors, for both sexes
DALYs=disability-adjusted life-years. PAF=population attributable fraction.
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993
incidence, prevalence, death, and DALY rates of ischaemic
stroke for both age groups (<70 years and ≥70 years). Of
the three pathological types of stroke, only all-age
subarachnoid haemorrhage prevalence rates increased in
people younger than 70 years, by 3·4% (1·3 to 5·5),
whereas all-age incidence, prevalence, death, and DALY
rates of intracerebral haemorrhage and incidence, death,
and DALY rates of subarachnoid haemorrhage were
reduced in both age groups (appendix pp 88, 210–211).
Contribution of risk factors to stroke-related DALYs
Globally, the total number of stroke-related DALYs due
to risk factors increased substantially from 1990
(100·1 million [95% UI 92·7 to 107·8]) to 2021
(135·0 million [122·0 to 147·7]), but there was no
substantial change in the age-standardised stroke DALYs
attributable to risk factors (–0·5% [–2·4 to 1·1]). In 2021,
84·1% (77·8 to 88·8) of DALYs from stroke were attrib-
uted to the 23 risk factors analysed (table 2), with
the largest proportions of attributable risks for total
stroke, ischaemic stroke, intracerebral haemorrhage, and
subarachnoid haemorrhage observed in eastern Europe,
Asia, and sub-Saharan Africa (appendix p 212).
At level 1 of the GBD risk factors hierarchy (table 2,
appendix pp 141–253), metabolic risk factors contributed
most to the stroke-related DALYs (range 66–70%) across
all World Bank country income levels, followed by
the environmental risk cluster in low-income, lower-
middle-income, and upper-middle-income countries
(range 35–53%), and behavioural risks (range 30–38%)
across dierent income level countries. Stroke burden
associated with the environmental or occupational risks
was lowest in high-income countries (18·1% [95% UI
13·6 to 22·7]). Similarly, regions with higher SDI
(appendix pp 141–143) had a larger contribution
of metabolic and behavioural risks to stroke-related
DALYs, whereas environmental risks most prominently
contributed to stroke-related DALYs in lower SDI
quintiles. From 1990 to 2021, the age-standardised
proportion of stroke DALYs attributable to risk factors
increased in north Africa and the Middle East (6·8%
[4·1 to 11·5]) and sub-Saharan Africa (3·3% [1·8 to 5·3]),
but did not change in south Asia (0·4% [–1·0 to 1·9])
and southeast Asia, east Asia, and Oceania (–0·9%
[–4·2 to 1·9]), and decreased in central Europe, eastern
Europe, and central Asia (–2·0% [–3·8 to –0·7]) and
Latin America and the Caribbean (–5·0% [–9·1 to –2·3]),
as well as high-income GBD regions (–7·3%
[–10·1 to –5·0]).
Globally, of the 23 risk factors analysed, 14 individually
significant risk factors for stroke were high systolic blood
pressure (56·8% [95% UI 42·5–68·0] attributable
DALYs), ambient particular matter (16·6% [11·5–20·9]),
smoking (13·8% [2·5–26·0]), high LDL cholesterol
(13·1% [4·6–21·3]), household air pollution (11·2%
[6·4–19·3]), diet high in sodium (10·6% [2·8–22·8]),
high fasting plasma glucose (10·3% [8·1–12·6]), kidney
disfunction (9·3% [6·8–11·8]), diet low in fruits (5·9%
[0·4–10·4]), high alcohol use (5·2% [1·3–9·8]), high BMI
(4·7% [0·4–9·8]), second-hand smoking (4·4% [1·0–7·9]),
low physical activity (2·1% [0·5–3·9]), and diet low in
vegetables (1·6% [0·4–2·6]; figure 2).
Figure 3: Trends in the PAF of stroke DALYs due to risk factors, for both sexes, 1990–2021
Data in parentheses are 95% uncertainty intervals. DALYs=disability-adjusted life-years. PAF=population attributable fraction.
High BMI
High ambient temperature
High fasting plasma glucose
Diet high in sugar-sweetened beverages
Low physical activity
Diet high in red meat
High systolic blood pressure
Lead exposure
Diet low in in omega-6 polyunsaturated fatty acids
High LDL cholesterol
Diet low in wholegrains
Kidney dysfunction
Diet high in sodium
Second-hand smoke
Smoking
Diet low in fruits
Ambient particulate matter pollution
Low ambient temperature
Diet low in fibre
Diet low in vegetables
Diet high in processed meat
–45
–40·1% (–45·3 to –33·3)
–30·3% (–36·5 to –21·6)
–25·1% (–31·0 to –19·0)
–20·8% (–24·7 to –16·9)
–20·4% (–27·3 to –12·9)
–13·5% (–16·5 to –9·5)
–12·8% (–19·5 to –6·0)
–12·8% (–16·5 to –8·8)
–4·6% (–25·1 to 2·8)
88·2% (53·4 to 117·7)
72·4% (51·1 to 179·5)
32·1% (26·7 to 38·1)
23·4% (12·7 to 35·7)
11·3% (1·8 to 34·9)
8·8% (–13·4 to 87·2)
6·7% (2·5 to 11·6)
6·5% (3·5 to 11·2)
5·3% (0·5 to 10·5)
3·0% (–1·1 to 7·9)
2·5% (–2·9 to 13·2)
0·5% (–4·1 to 5·9)
–25 –5 15 35 55 75 95
%
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Stroke attributable to metabolic risks constituted 68·8%
(95% UI 57·6 to 77·5) of all strokes, environmental risks
constituted 36·7% (29·0 to 44·2), and behavioural risks
constituted 35·2% (26·9 to 44·7). Although the propor-
tion of stroke DALYs attributable to metabolic risks
increased from 1990 to 2021 by 6·7% (3·8 to 10·0; mainly
because of the increase in the burden attributable to high
BMI, high fasting plasma glucose, and high systolic blood
pressure), proportions of stroke DALYs attributable to
behavioural risks decreased by 8·0% (–13·6 to –3·4) and
those due to environmental risks by 14·8% (–21·6 to
–8·7), mainly because of the decrease in the burden
attributable to diet high in processed meat, diet low in
vegetables, diet low in fibre, low ambient temperature,
particulate matter pollution, diet low in fruits, and
smoking (figure 3). However, from 1990 to 2021, there
was a substantial increase in the stroke DALYs attribut-
able to high ambient temperature, high fasting plasma
glucose, diet high in sugar-sweetened beverages, low
physical activity, diet high in red meat, lead exposure, and
diet low in omega-6 polyunsaturated fatty acids. There
were noticeable geographical and regional variations in
the PAF of the risk factors for ischaemic stroke, intracer-
ebral haemorrhage, subarachnoid haemorrhage, and all
stroke types combined (appendix p 264), as well as in
the ranking of PAFs of age-standardised stroke DALYs
attributable to risk factors by 21 GBD regions (figure 4).
For the PAF of risk factors by pathological type of stroke,
SDI, 21 GBD regions, and 204 countries and territories
were used (appendix pp 89–201). Unlike the PAF of risk
factors for total stroke, high alcohol use was not associ-
ated with ischaemic stroke-related DALYs (appendix
pp 92–95, 126–149, 199), and diet low in fruits and
vegetables and high BMI were not associated with intrac-
erebral haemorrhage-related DALYs (appendix pp 92–95,
150–173, 200). Unlike ischaemic stroke and intracerebral
haemorrhage, non-optimal ambient temperature
appeared to be associated with the subarachnoid haemor-
rhage-related DALYs, with the greater contribution of low
ambient temperature (4·5% [3·8 to 5·3]) than high
ambient temperature (1·1% [0·2 to 2·5]). Other
substantial risk factors for subarachnoid haemorrhage
(appendix pp 151−153) were second-hand smoking (4·7%
[3·2 to 6·2]), diet high in sodium (8·9% [2·0 to 19·8]),
household air pollution from solid fuels (10·3%
[5·5 to 17·4]), ambient particulate matter pollution (14·2%
[9·8 to 18·0]), smoking (14·5% [2·7 to 27·2]), and high
systolic blood pressure (51·6% [38·0 to 62·6]).
Figure 4: Ranking of age-standardised stroke DALYs attributable to risk factors by 21 GBD regions, for both sexes, 2021
DALYs=disability-adjusted life-years. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study.
Both sexes, all ages, 2021,
percent of total DALYs
Higher rank
Ambient particulate matter pollution
Central Europe
Central Asia
Eastern Europe
Australasia
High-income North America
Southern Latin America
Western Europe
Andean Latin America
Caribbean
Central Latin America
Tropical Latin America
North Africa and Middle East
South Asia
East Asia
Oceania
Southeast Asia
Central sub-Saharan Africa
Eastern sub-Saharan Africa
Southern sub-Saharan Africa
Western sub-Saharan Africa
High-income Asia Pacific
1
High LDL
Kidney dysfunction
Smoking
Lower rank
High fasting plasma glucose
Low ambient temperature
High BMI
Diet high in sodium
High alcohol use
Diet low in fruit
Household air pollution from solid fuels
Second-hand smoke
Lead exposure
Diet low in wholegrains
Low physical activity
High ambient temperature
Diet high in processed meat
Diet low in vegetables
Diet high in sugar-sweetened beverages
Diet low in omega-6 polyunsaturated fatty acids
Diet high in red meat
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
26 8 1010 511 4 9 2 4 4 7 2 3 2 11 3811 2 3
3222 2 22 2 3 3 23 9 597 5 6 4 4
34348
77658
9
7
10
67
7
5
10
4
5 5 353 4 3 3 6 7 9 3 6 8 3 3 10 9 9 11
63 4 34 3 5453 4 465 8 410 66
19
1113
1819
9
16
9
15
13
19
10
6
98998
7
8 9 6 6 14 568 4 96 5 517 10 12 710
12
16
8
66
4
10
16
88
13
8
10
8
9
6
13
10
49
10 79 4 8 7 7 5 12 10 12 10 19 15 10 15 13 11 12 12 9
8
85
9
11
7
13
4
15
111012111212101011111311
12 22
15
11 13 11
High blood pressure
13
14
15
16
17
18
19
20
21
22
23
Diet low in fibre
17
12
11
14
16
15
21
18
20
19
22
23
2
55
2
55
2
15
7
16
18
17
14
20
13
21
22
23
5
2
141515
2
4
2
2
23 23 23
21
20
18
17
22
15
16
12
14
13
19
7
15
10
18
17
15
19
20
13
21
23
22
23
22
21
7
20
19
18
16
17
3
14
23
6
22
21
20
19
16
13
17
7
8
14
14
12
9
18
10
16
17
20
15
23
22
21
23
22
21
12
20
19
17
18
16
13
8
14
8
16
14
12
20
17
18
19
21
22
13
12
16
11
15
20
17
18
19
21
22
16
12
13
14
15
20
17
18
23
19
21
20
11
13
15
14
16
21
18
17
19
22
23
22
11
14
16
13
20
17
18
19
21
23
13
16
7
17
15
18
20
21
14
19
22
23
2
15
6
17
14
16
18
20
11
21
22
23
14
7
18
15
17
19
21
16
20
22
23
16
12
9
18
14
13
19
20
17
21
22
23
10
8
11
18
12
14
20
17
21
22
23
11
6
19
12
18
13
20
14
21
22
23
14
12
7
16
17
15
18
19
21
20
22
23
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995
Discussion
In 2021, stroke remained the second most common
cause (after ischaemic heart disease) of death and
the third most common cause of DALYs (after ischaemic
heart disease and neonatal disorders) among non-
communicable disorders (NCDs) globally. However, at
level 3 of the GBD all-cause hierarchy, stroke was
the third most common cause of death (after ischaemic
heart disease and COVID-19) and the fourth most
common cause of DALYs (after COVID-19, ischaemic
heart disease, and neonatal disorders), with the bulk
of the stroke burden in LMICs and countries with lower
SDI. Consistent with previous studies,5,17 this study
showed disparities in stroke burden (including almost
two times greater proportion of intracerebral haemor-
rhage in LMICs than in high-income countries)18 and
risk factors by GBD region, country, country income
level, and SDI quintiles, as well as an overall trend
towards decreasing age-standardised stroke incidence,
prevalence, and DALY rates from 1990 to 2021. Given
the leading role of arterial hypertension in the occur-
rence of intracerebral haemorrhage,19 the greater
prevalence and poorer control of hypertension in LMICs20
than in high-income countries are the most likely causes
of the dierences in the proportion of intracerebral
haemorrhage in those countries. Similar proportions
of subarachnoid haemorrhage in high-income countries
and LMICs are likely to be related to the significant
reduction in tobacco smoking prevalence that we
observed since 2010 in high-income countries, with
almost no change in tobacco smoking prevalence in
LMICs.21 However, the current study documented a
stagnation in the reduction of age-standardised incidence
rates from 2015 onwards, and even some increase in
the age-standardised stroke incidence, death, prevalence,
and DALY rates in southeast Asia, east Asia, and Oceania,
and countries with lower SDI from 2015 onwards.
Globally, there was also an increase in all-age incidence
and prevalence rates in people younger than 70 years,
whereas there was a reduction in all-age stroke incidence,
prevalence, death, and DALY rates in people aged 70 years
or older. A trend towards increasing incidence and preva-
lence rate of cardiovascular diseases (including stroke) in
people aged 15–39 years globally22 and stroke incidence
rates in people younger than 55 years3 versus older
people has also been shown in previous systematic
reviews and is likely to be related to the increase in preva-
lence of arterial hypertension23,24 (including poorly
controlled and uncontrolled hypertension),25 overweight
or obesity,26–28 and type 2 diabetes29 in young adults, espe-
cially in LMICs.24,26–29 This situation is complicated by
the fact that a large proportion of young adults with
vascular risk factors, arterial hypertension,30 and dyslipi-
daemia31 remain under-treated owing at least partly to
the widespread use of absolute cardiovascular disease
risk32 treatment thresholds.3 The observed slowing down
of the percentage of decline of age-standardised stroke
incidence rates in 2019–21 is likely to be related to
the decreased hospital admissions of patients with acute
stroke that was observed over the COVID-19 period in
many countries.33
Apart from population growth and ageing,1,34 other
factors responsible for the increased burden of stroke, in
terms of absolute numbers in the world, are likely to be
related to the insucient eectiveness of the currently
used primary stroke and cardiovascular disease preven-
tion strategies35,36 as well as the disparities and major
gaps in stroke service provision and accessibility, and
workforce of stroke care providers in many countries
(especially LMICs).17,37,38 Although stroke is highly
preventable, globally there were substantial increases in
DALYs attributable to high BMI, high ambient tempera-
ture, high fasting plasma glucose, diet high in
sugar-sweetened beverages, low physical activity, high
systolic blood pressure, and diet low in polyunsaturated
omega-6 fatty acids, suggesting the growing role of these
environmental and behavioural risks in the stroke
burden. However, from 1990 to 2021, we also observed a
reduction of PAF due to diet high in processed meat, diet
low in vegetables, diet low in fibre, low ambient tempera-
ture, ambient particulate matter pollution, diet low in
fruits, and smoking, suggesting eectiveness of the strat-
egies towards reduction of the exposure to these risk
factors. The observed increase in the age-standardised
proportion of stroke DALYs attributable to risk factors in
north Africa and the Middle East and sub-Saharan Africa
regions might reflect a failure in the control of stroke risk
factors. However, in central Europe, eastern Europe,
central Asia, Latin America and the Caribbean, and high-
income GBD regions, this might reflect a success in
the control of stroke risk factors.
This study is, to our knowledge, the first to show
the large contribution of ambient particulate matter
pollution and household air pollution from solid fuels to
subarachnoid haemorrhage DALYs, with a similar PAF
to that of smoking. A close relationship between ambient
air pollution and subarachnoid haemorrhage mortality
was found in some studies.39–41 Air pollution in 2021
appeared to be highly important to other types of stroke
and also caused 11·9% (95% UI 10·0–13·8) of total
deaths from all causes, making it the second largest
cause of deaths from all causes globally (after high
systolic blood pressure) and the second leading cause
of DALYs (8·2% [6·9–9·6]) from all causes (after malnu-
trition).42 These findings are in line with research
showing that rises in ambient temperature (including
heatwaves) and climate change are associated with
increased stroke morbidity and mortality.43,44 Because
ambient air pollution is reciprocally associated with
the ambient temperature and climate change,44 all
of which synergistically influence cardiovascular disease
(including stroke) occurrence44–46 and overall health,47,48
the importance of urgent climate actions and measures
to reduce ambient air pollution cannot be
Articles
996
www.thelancet.com/neurology Vol 23 October 2024
overestimated.47,48 Experts have recommended that
governments increase implementation of a clean-energy
economy, promote unprocessed plant-based food
choices,44 and globally phase out industrialised animal
farming.49
Every member state of the UN has committed to
meeting the Sustainable Development Goals (SDGs),
but currently few countries are on target to achieve
SDG 3.4, which is to reduce by a third premature
mortality from NCDs through prevention and treatment
and promote mental health and wellbeing by 2030. By
implementing and monitoring the World Stroke
Organization–Lancet Neurology Commission’s recom-
mendations,38 the global burden of stroke would be
reduced drastically this decade and beyond. Not only
would this substantial reduction enable SDG 3.4, as well
as other key SDGs, to be met, it would improve brain
health and the overall wellbeing of millions of people
across the globe. One of the most common problems in
implementing stroke prevention and care recommenda-
tions is the scarcity of funding. The World Stroke
Organization–Lancet Neurology Commission on stroke38
recommends introducing legislative regulations and
taxation of unhealthy products by each government in
the world. Such taxation would not only reduce
consumption of these products and, therefore, lead to
the reduction of burden from stroke and other major
NCDs,50–52 but also generate a large revenue50 sucient to
fund prevention programmes and services for stroke
and other major disorders, reduce poverty and inequality
in health service provision, improve wellbeing
of the population, and boost local economies.
The main strength of this study is the extended number
of data sources included in the analysis that allowed us to
generate more accurate and up-to-date stroke burden and
risk factor estimates. This allows evidence-based health-
care planning and resource allocation by health policy
makers on the national, regional, and global levels.
However, good-quality stroke epidemiological studies7 are
still scarce in most countries, which prevented us from
including in the analysis many other important risk
factors, such as sickle cell disease and HIV, which are
particularly important for sub-Saharan Africa. Dierences
in health-care systems and completeness and accuracy
of stroke case ascertainment might play a part in
the observed between-country dierences (eg, very high
stroke prevalence in Ghana compared with neighbouring
countries). Although the GBD methods for estimating
attributable burden of stroke due to risk factors accounts
for a cumulative eect of multiple risk factors, it might
not fully account for all potential confounders. Moreover,
some new risk factors, such as high ambient temperature,
might require further validation and examination to
confirm their impact on stroke burden. Furthermore,
more granular data analysis is needed. For example,
stroke burden variation by race and ethnicity within coun-
tries, which can mask disparities in stroke incidence, risk
factors, and outcomes among dierent population
groups, and analysis of attributable eects of dierent
levels of exposure to smoking, alcohol, and so on. We
expect such analysis will be done in future GBD
iterations.
In summary, our study findings continue to point out
that currently used stroke prevention strategies are not
suciently eective to halt, let alone reduce, the fast-
growing stroke burden. Additional, more eective stroke
prevention strategies (with the emphasis on population-
wide measures, task shifting from doctors to nurses or
health volunteers, and the wider use of evidence-based
mobile and telehealth platforms) and pragmatic solu-
tions to address the critical gaps in stroke service delivery,
along with development of context-appropriate workforce
capacity building and epidemiological surveillance
systems,38 need to be urgently implemented across all
countries. Without scaling up these innovative evidence-
based strategies and policies that target local, national,
regional, and global stroke prevention and care dispari-
ties, the burden of stroke will continue to grow, thus
threatening the sustainability of health systems
worldwide.
GBD 2021 Stroke Risk Factor Collaborators
Valery L Feigin, Melsew Dagnee Abate, Yohannes Habtegiorgis Abate,
Samar Abd ElHafeez, Foad Abd-Allah, Ahmed Abdelalim,
Atef Abdelkader, Michael Abdelmasseh, Sherief Abd-Elsalam,
Parsa Abdi, Arash Abdollahi, Meriem Abdoun, Rami Abd-Rabu,
Deldar Morad Abdulah, Auwal Abdullahi, Mesfin Abebe,
Roberto Ariel Abeldaño Zuñiga, E S Abhilash,
Olugbenga Olusola Abiodun, Olumide Abiodun, Rahim Abo Kasem,
Richard Gyan Aboagye, Mohamed Abouzid, Lucas Guimarães Abreu,
Woldu Aberhe Abrha, Dariush Abtahi, Samir Abu Rumeileh,
Ahmed Abualhasan, Hasan Abualruz, Eman Abu-Gharbieh,
Hana J Abukhadijah, Niveen M E Abu-Rmeileh, Salahdein Aburuz,
Ahmed Abu-Zaid, Juan Manuel Acuna, Denberu Eshetie Adane,
Mesafint Molla Adane, Isaac Yeboah Addo, Rufus Adesoji Adedoyin,
Oyelola A Adegboye, Victor Adekanmbi, Kishor Adhikari,
Qorinah Estiningtyas Sakilah Adnani, Saryia Adra, Leticia Akua Adzigbli,
Abdelrahman Yousry Afify, Aanuoluwapo Adeyimika Afolabi,
Fatemeh Afrashteh, Muhammad Sohail Afzal, Saira Afzal,
Shahin Aghamiri, Williams Agyemang-Duah, Bright Opoku Ahinkorah,
Aqeel Ahmad, Muayyad M Ahmad, Sajjad Ahmad, Shahzaib Ahmad,
Tauseef Ahmad, Amir Mahmoud Ahmadzade, Ali Ahmed,
Ayman Ahmed, Haroon Ahmed, Syed Anees Ahmed, Marjan Ajami,
Budi Aji, Essona Matatom Akara, Rufus Olusola Akinyemi,
Mohammed Ahmed Akkaif, Ashley E Akrami, Salah Al Awaidy,
Hanadi Al Hamad, Syed Mahfuz Al Hasan, Mohammad Al Qadire,
Omar Al Ta’ani, Yazan Al-Ajlouni, Samer O Alalalmeh, Tariq A Alalwan,
Ziyad Al-Aly, Rasmieh Mustafa Al-amer, Wafa A Aldhaleei,
Mohammed S Aldossary, Seyedeh Yasaman Alemohammad,
Bassam Al-Fatly, Adel Ali Saeed Al-Gheethi, Fadwa Naji Alhalaiqa,
Maryam Alharrasi, Abid Ali, Mohammed Usman Ali, Rafat Ali,
Syed Shujait Ali, Waad Ali, Akram Al-Ibraheem,
Sheikh Mohammad Alif, Syed Mohamed Aljunid, Wael Almahmeed,
Sabah Al-Marwani, Mahmoud A Alomari, Jordi Alonso,
Jaber S Alqahtani, Rajaa M Mohammad Al-Raddadi,
Ahmad Alrawashdeh, Mohammed A Alsabri, Najim Z Alshahrani,
Zaid Altaany, Awais Altaf, Alaa B Al-Tammemi, Diala Altwalbeh,
Nelson Alvis-Guzman, Hassan Alwafi, Mohammad Al-Wardat,
Yaser Mohammed Al-Worafi, Hany Aly, Safwat Aly,
Mohammad Sharif Ibrahim Alyahya, Karem H Alzoubi,
Walid Adnan Al-Zyoud, Reza Amani, Prince M Amegbor,
Tewodros Getnet Amera, Tarek Tawfik Amin, Alireza Amindarolzarbi,
Sohrab Amiri, Hubert Amu, Dickson A Amugsi, Ganiyu Adeniyi Amusa,
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997
Robert Ancuceanu, Deanna Anderlini, Dhanalakshmi Angappan,
Abhishek Anil, Mohammed Tahir Tahir Ansari,
Alireza Ansari-Moghaddam, Rockson Ansong, Saeid Anvari,
Saleha Anwar, Sumadi Lukman Anwar,
Ekenedilichukwu Emmanuel Anyabolo,
Anayochukwu Edward Anyasodor, Geminn Louis Carace Apostol,
Francis Appiah, Muhammad Aqeel, Jalal Arabloo,
Razman Arabzadeh Bahri, Mosab Arafat, Aleksandr Y Aravkin,
Ali Ardekani, Demelash Areda, Brhane Berhe Aregawi,
Getnet Mesfin Aregu, Olatunde Aremu, Hidayat Arifin, Johan Ärnlöv,
Anton A Artamonov, Judie Arulappan, Umesh Raj Aryal, Zahra Aryan,
Akram M Asbeutah, Mulusew A Asemahagn, Mulu Tiruneh Asemu,
Mohammad Asghari-Jafarabadi, Mubarek Yesse Ashemo, Tahira Ashraf,
Armin Aslani, Haftu Asmerom Asmerom, Thomas Astell-Burt,
Seyyed Shamsadin Athari, Prince Atorkey, Maha Moh’d Wahbi Atout,
Alok Atreya, Avinash Aujayeb, Marcel Ausloos, Abolfazl Avan,
Hamzeh Awad, Adedapo Wasiu Awotidebe, Lemessa Assefa A Ayana,
Setognal Birara Aychiluhm, Amdehiwot A Aynalem,
Zewdu Bishaw Aynalem, Sina Azadnajafabad, Hiva Azami,
Shahkaar Aziz, Ahmed Y Azzam, Abraham Samuel Babu,
Giridhara Rathnaiah Babu, Muhammad Badar, Ashish D Badiye,
Pegah Bahrami Taghanaki, Saeed Bahramian, Ruhai Bai, Atif Amin Baig,
Shankar M Bakkannavar, Abdulaziz T Bako, Ovidiu Constantin Baltatu,
Kiran Bam, Maciej Banach, Morteza Banakar, Soham Bandyopadhyay,
Palash Chandra Banik, Kannu Bansal, Yanping Bao, Miguel A Barboza,
Mainak Bardhan, Noel C Barengo, Suzanne Lyn Barker-Collo,
Till Winfried Bärnighausen, Hiba Jawdat Barqawi, Amadou Barrow,
Lingkan Barua, Azadeh Bashiri, Hameed Akande Bashiru, Afisu Basiru,
Mohammad-Mahdi Bastan, Sanjay Basu, Saurav Basu, Kavita Batra,
Ahmet Begde, Babak Behnam, Amir Hossein Behnoush,
Melesse B Y Belayneh, Michael Belingheri, Umar Muhammad Bello,
Derrick A Bennett, Isabela M Bensenor, Fentaw Tadese Berhe,
Amiel Nazer C Bermudez, Habtamu B B Beyene, Kebede A Beyene,
Devidas S Bhagat, Akshaya Srikanth Bhagavathula, Neeraj Bhala,
Ashish Bhalla, Nikha Bhardwaj, Pankaj Bhardwaj, Sonu Bhaskar,
Ajay Nagesh Bhat, Vivek Bhat, Gurjit Kaur Bhatti, Jasvinder
Singh Singh Bhatti, Mohiuddin Ahmed Bhuiyan, Subarna Bhusal,
Boris Bikbov, Cem Bilgin, Antonio Biondi, Keralem Anteneh Bishaw,
Atanu Biswas, Bijit Biswas, Trupti Bodhare, Eyob Ketema Bogale,
Archith Boloor, Milad Bonakdar Hashemi, Aime Bonny,
Berrak Bora Basara, Hamed Borhany, Samuel Adolf Bosoka,
Souad Bouaoud, Abdelhakim Bouyahya, Edward J Boyko,
Marija M Bozic, Dejana Braithwaite, Susanne Breitner,
Hermann Brenner, Gabrielle Britton, Andre R Brunoni, Dana Bryazka,
Raaele Bugiardini, Lemma N Bulto, Katrin Burkart, Yasser Bustanji,
Zahid A Butt, Florentino Luciano Caetano dos Santos,
Luis Alberto Cámera, Luciana Aparecida Campos,
Ismael R Campos-Nonato, Fan Cao, Angelo Capodici, Rosario Cárdenas,
Sinclair Carr, Giulia Carreras, Andre F Carvalho, Felix Carvalho,
Joao Mauricio Castaldelli-Maia, Carlos A Castañeda-Orjuela,
Giulio Castelpietra, Alberico L Catapano, Maria Sofia Cattaruzza,
Luca Cegolon, Francieli Cembranel, Edina Cenko, Ester Cerin,
Joshua Chadwick, Chiranjib Chakraborty, Sandip Chakraborty,
Jerey Shi Kai Chan, Rama Mohan Chandika, Eeshwar K Chandrasekar,
Gashaw Sisay Chanie, Vijay Kumar Chattu, Anis Ahmad Chaudhary,
Akhilanand Chaurasia, Haowei Chen, Mingling Chen, Simiao Chen,
Gerald Chi, Fatemeh Chichagi, Ritesh Chimoriya, Patrick R Ching,
Abdulaal Chitheer, So Mi Jemma Cho, Dong-Woo Choi, Bryan Chong,
Chean Lin Chong, Hitesh Chopra, Sonali Gajanan Choudhari, Rahul
Choudhary, Dinh-Toi Chu, Isaac Sunday Chukwu, Sheng-Chia Chung,
Zinhle Cindi, Iolanda Cio, Rebecca M Cogen, Alyssa Columbus,
Simona Costanzo, Rosa A S Couto, Michael H Criqui,
Natalia Cruz-Martins, Silvia Magali Cuadra-Hernández,
Alanna Gomes da Silva, Sriharsha Dadana, Omid Dadras, Xiaochen Dai,
Koustuv Dalal, Lachlan L Dalli, Giovanni Damiani, Emanuele D’Amico,
Lalit Dandona, Rakhi Dandona, Amira Hamed Darwish, Saswati Das,
Mohsen Dashti, Mohadese Dashtkoohi, Mohammad Dashtkoohi,
Maedeh Dastmardi, Kairat Davletov, Vanessa De la Cruz-Góngora,
Sean DeAngelo, Aklilu Tamire Debele, Shayom Debopadhaya,
Ivan Delgado-Enciso, Berecha Hundessa Demessa,
Andreas K Demetriades, Edgar Denova-Gutiérrez, Emina Dervišević,
Hardik Dineshbhai Desai, Aragaw Tesfaw Desale, Fikreab Desta,
Vinoth Gnana Chellaiyan Devanbu, Devananda Devegowda,
Syed Masudur Rahman Dewan, Amol S Dhane, Meghnath Dhimal,
Vishal R Dhulipala, Michael J Diaz, Mengistie Diress, Milad Dodangeh,
Phidelia Theresa Doegah, Sushil Dohare, Mohamed Fahmy Doheim,
Klara Georgieva Dokova, Deepa Dongarwar, Mario D’Oria,
Ojas Prakashbhai Doshi, Rajkumar Prakashbhai Doshi, Abdel Douiri,
Robert Kokou Dowou, Ashel Chelsea Dsouza, Haneil Larson Dsouza,
Viola Savy Dsouza, Bruce B Duncan, Andre Rodrigues Duraes,
Arkadiusz Marian Dziedzic, Michael Ekholuenetale,
Ibrahim Farahat El Bayoumy, Maysaa El Sayed Zaki, Iat Elbarazi,
Faris El-Dahiyat, Islam Y Elgendy, Muhammed Elhadi,
Waseem El-Huneidi, Mohamed A Elmonem, Adel B Elmoselhi,
Chadi Eltaha, Theophilus I Emeto, Christopher Imokhuede Esezobor,
Negin Esfandiari, Zahra Esmaeili, Francesco Esposito,
Mohammad Etoom, Natalia Fabin, Ibtihal Fadhil,
Adeniyi Francis Fagbamigbe, Omotayo Francis Fagbule,
Shahriar Faghani, Ayesha Fahim, Ildar Ravisovich Fakhradiyev,
Luca Falzone, Mohammad Fareed, Jawad Fares, Carla Sofia e Sá Farinha,
MoezAlIslam Ezzat Mahmoud Faris, Pawan Sirwan Faris,
Mohsen Farjoud Kouhanjani, Andre Faro, Hossein Farrokhpour,
Abidemi Omolara Fasanmi, Nelsensius Klau Fauk, Patrick Fazeli,
Timur Fazylov, Alireza Feizkhah, Ginenus Fekadu, Xiaoqi Feng,
Seyed-Mohammad Fereshtehnejad, Pietro Ferrara, Nuno Ferreira,
Getahun Fetensa, Bikila Regassa Feyisa, Florian Fischer, Luisa S Flor,
Kristen Marie Foley, Ana Catarina Fonseca, Roham Foroumadi,
Behzad Foroutan, Daniela Fortuna, Matteo Foschi,
Richard Charles Franklin, Ni Kadek Yuni Fridayani, Sridevi G,
Peter Andras Gaal, Abhay Motiramji Gaidhane, Abduzhappar Gaipov,
Yaseen Galali, Silvano Gallus, Aravind P Gandhi, Balasankar Ganesan,
Danijela Gasevic, Prem Gautam, Rupesh K Gautam,
Miglas Welay Gebregergis, Mesfin Gebrehiwot,
Kebre Gebrekirstos Gebrekidan, Lemma Getacher, Genanew K Getahun,
Molla Getie, Delaram J Ghadimi, Fataneh Ghadirian,
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Sherief Ghozy, Stefano Giannoni Luza, Jaleed Ahmed Gilani,
Tiany K Gill, Richard F Gillum, Ebisa Zerihun Gindaba,
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Mahaveer Golechha, Pouya Goleij, Davide Golinelli, Philimon N Gona,
Giuseppe Gorini, Alessandra C Goulart, Barbara Niegia Garcia Goulart,
Mahdi Gouravani, Michal Grivna, Giuseppe Grosso, Ashna Grover,
Shi-Yang Guan, Giovanni Guarducci, Avirup Guha, Stefano Guicciardi,
Snigdha Gulati, Damitha Asanga Gunawardane, Cui Guo, Zhifeng Guo,
Anish Kumar Gupta, Bhawna Gupta, Mohak Gupta, Rahul Gupta,
Rajat Das Gupta, Rajeev Gupta, Sapna Gupta, Farrokh Habibzadeh,
Najah R Hadi, Mohammad Haghani Dogahe, Hamed Haghi-Aminjan,
Dariush Haghmorad, Arvin Haj-Mirzaian, Aram Halimi,
Nadia M Hamdy, Samer Hamidi, Erin B Hamilton, Asif Hanif,
Nasrin Hanifi, Graeme J Hankey, Md Abdul Hannan, Zaim Anan Haq,
Arief Hargono, Netanja I Harlianto, Josep Maria Haro,
Eka Mishbahatul Marah Has, Ahmed I Hasaballah, Ikramul Hasan,
Md Saquib Hasnain, Ikrama Hassan,
Mahgol Sadat Hassan Zadeh Tabatabaei, Johannes Haubold,
Rasmus J Havmoeller, Simon I Hay, Youssef Hbid, Jerey J Hebert,
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Mihaela Hostiuc, Sorin Hostiuc, Ben Hu, Chengxi Hu, Junjie Huang,
Ayesha Humayun, Salman Hussain, Le Duc Huy, Hong-Han Huynh,
Bing-Fang Hwang, Segun Emmanuel Ibitoye, Nayu Ikeda,
Adalia Ikiroma, Olayinka Stephen Ilesanmi, Irena M Ilic, Milena D Ilic,
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Ramaiah Itumalla, Masao Iwagami, Chidozie Declan CD Iwu,
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Sun Ha Jee, Jayakumar Jeganathan, Mihretu Jegnie, Alelign Tasew Jema,
Bijay Mukesh Jeswani, Angeline Jeyakumar, Anil K Jha,
Ravi Prakash Jha, Zixiang Ji, Heng Jiang, Shuai Jin, Yingzhao Jin,
Mohammad Jokar, Jost B Jonas, Tamas Joo, Jobinse Jose, Nitin Joseph,
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Mikk Jürisson, Ali Kabir, Md Awal Kabir, Zubair Kabir, Vidya Kadashetti,
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Haidong Kan, Mona Kanaan, Himal Kandel, Kehinde Kazeem Kanmodi,
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André Karch, Hanie Karimi, Salah Eddin Karimi, Yeganeh Karimi,
Arman Karimi Behnagh, Prabin Karki, Hengameh Kasraei,
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Foad Kazemi, Sina Kazemian, Emmanuelle Kesse-Guyot,
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Himanshu Khajuria, Amirmohammad Khalaji, Nauman Khalid,
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M Nuruzzaman Khan, Maseer Khan, Mohammad Jobair Khan,
Moien AB Khan, Yusra H Khan, Shaghayegh Khanmohammadi,
Khaled Khatab, Haitham Khatatbeh, Moawiah Mohammad Khatatbeh,
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Robina Khan Niazi, Yeshambel T Nigatu, Nasrin Nikravangolsefid,
Dina Nur Anggraini Ningrum, Chukwudi A Nnaji,
Lawrence Achilles Nnyanzi, Shuhei Nomura, Syed Toukir Ahmed Noor,
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Mario Cesare Nurchis, Dieta Nurrika, Chimezie Igwegbe Nzoputam,
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Tosin Abiola Olasehinde, Omotola O Olasupo, Matthew Idowu Olatubi,
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Ahmad Ozair, Mahesh Padukudru P A, Kevin Pacheco-Barrios,
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Raul Felipe Palma-Alvarez, Feng Pan, Songhomitra Panda-Jonas,
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Payam Tabaee Damavandi, Rafael Tabarés-Seisdedos,
Seyyed Mohammad Tabatabaei, Shima Tabatabai, Celine Tabche,
Mohammad Tabish, Jyothi Tadakamadla, Santosh Kumar Tadakamadla,
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Manoj Tanwar, Ingan Ukur Tarigan, Elvis Enowbeyang Tarkang,
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Muhammad Umar, Brigid Unim, Bhaskaran Unnikrishnan,
Daniele Urso, Jibrin Sammani Usman, Marco Vacante,
Seyed Mohammad Vahabi, Sanaz Vahdati,
Asokan Govindaraj Vaithinathan, Omid Vakili, Rohollah Valizadeh,
Jef Van den Eynde, Orsolya Varga, Shoban Babu Varthya,
Tommi Juhani Vasankari, Balachandar Vellingiri,
Narayanaswamy Venketasubramanian, Madhur Verma,
Massimiliano Veroux, Georgios-Ioannis Verras, Dominique Vervoort,
Jorge Hugo Villafañe, Simona Villani, Manish Vinayak,
Maria Viskadourou, Simona Ruxandra Volovat, Victor Volovici,
Hatem A Wafa, Yasir Waheed, Waseem Wahood, Cong Wang,
Fang Wang, Shu Wang, Song Wang, Yanzhong Wang, Yuan-Pang Wang,
Mary Njeri Wanjau, Muhammad Waqas, Emebet Gashaw Wassie,
Gizachew Tadesse Wassie, Zihan Wei, Robert G Weintraub,
Haftom Legese Weldetinsaa, Dakshitha Praneeth Wickramasinghe,
Nuwan Darshana Wickramasinghe, Tissa Wijeratne, Peter Willeit,
Charles D A Wolfe, Yen Jun Wong, Utoomporn Wongsin, Chenkai Wu,
Felicia Wu, YaJuan Wu, Zenghong Wu, Hong Xiao, Suowen Xu,
Xiaoyue Xu, Kazumasa Yamagishi, Danting Yang, Yuichiro Yano,
Amir Yarahmadi, Habib Yaribeygi, Yuichi Yasufuku, Hiroshi Yatsuya,
Fereshteh Yazdanpanah, Mohammad Hosein Yazdanpanah,
Pengpeng Ye, Renjulal Yesodharan, Saber Yezli, Siyan Yi, Xinglin Yi,
Dehui Yin, Dong Keon Yon, Naohiro Yonemoto, Chuanhua Yu,
Elaine A Yu, Ke Yun, Hadiza Yusuf, Siddhesh Zadey, Nima Zafari,
Burhan Abdullah Zaman, Sojib Bin Zaman, Aurora Zanghì, Iman Zare,
Fatemeh Zarimeidani, Armin Zarrintan, Michael Zastrozhin,
Dawit Zemedikun, Youjie Zeng, Beijian Zhang, Haijun Zhang,
Liqun Zhang, Yunquan Zhang, Zhiqiang Zhang, Hanqing Zhao,
Claire Chenwen Zhong, Shang Cheng Zhou, Bin Zhu, Lei Zhu,
Abzal Zhumagaliuly, Makan Ziafati, Magdalena Zielińska,
Yossef Teshome Zikarg, Ghazal Zoghi, Sa’ed H Zyoud, Samer H Zyoud,
Catherine O Johnson*, Gregory A Roth*, Balakrishnan Sukumaran
Nair*, Ilari Rautalin*, Ajali Bhatia*, Catherine Bisignano*, Theo Vos*,
and Christopher J L Murray*.
*Senior authors.
Please see the appendix (pp 10−47) for the aliations of individual
authors.
Contributors
For individual authors’ contributions to the manuscript, please see the
appendix (pp 47–61), divided into the following categories: managing
the overall research enterprise; writing the first draft of the manuscript;
primary responsibility for applying analytical methods to produce
estimates; primary responsibility for seeking, cataloguing, extracting,
or cleaning data; designing or coding figures and tables; providing data
or critical feedback on data sources; developing methods or
computational machinery; providing critical feedback on methods or
results; drafting the manuscript or revising it critically for important
intellectual content; and managing the estimation or publications
process. The corresponding and senior authors had full access to the
data in the study and had final responsibility for the decision to submit
for publication. V L Feigin, C O Johnson, G A Roth, C Bisignano,
T Vos, and C J L Murray had full access to and verified data.
Declaration of interests
A Abdelalim reports a leadership or fiduciary role in the Middle East and
North Africa Stroke Organization, unpaid, as Vice President, outside the
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submitted work. S Afzal reports support for the present manuscript
from King Edward Medical University through the provision of study
material, research articles, valid data sources and authentic real time
information for this manuscript; payment or honoraria for educational
events and webinars with King Edward Medical University and
collaborative partners including University of Johns Hopkins, University
of California, University of Massachusetts, KEMCAANA, and KEMCA_
UK; participation on a Data Safety Monitoring Board or Advisory Board
with the National Bioethics Committee Pakistan, the King Edward
Medical University Ethical Review Board, the Ethical Review Board of
Fatima Jinnah Medical University and Sir Ganga Ram Hospital, and
being a member of the Technical Working Group on Infectious
Diseases; other financial and non-financial interests in King Edward
Medical University, Annals of King Edward Medical University, Quality
Enhancement Cell King Edward Medical University, Faculty of Public
Health United Kingdom, Scientific Session, KEMCA-UK, International
Scientific Conference, KEMCAANA, Research and Publications Higher
Education Commission Pakistan, Research and Journals Committee
Pakistan, Medical and Dental Council Pakistan, National Bioethics
Committee Pakistan, Corona Experts Advisory Group, Technical
Working Group on Infectious Diseases, Dengue Experts Advisory
Group, Punjab Residency Program Research Committee, all outside the
submitted work. R Akinyemi reports grants U19AG074865, U19
AG076581and R01AG072547 from the US National Institutes of Health/
National Institute of Aging, GBHI ALZ UK-21- 24204 from the
Alzheimer’s Association and the Global Brain Health Institute, UK
Royal Society/African Academy of Sciences FLAIR Grants FLR/
R1/191813 and FCG/R1/201034, and GCRF Networking Grant from the
UK Academy of Medical Sciences, all outside the submitted work.
A Al-Ibraheem reports grants or contracts and support for attending
meetings and/or travel from King Hussein Cancer Center, International
Atomic Energy Agency; consulting fees from University of Jordan;
leadership or fiduciary role, paid or unpaid, with Arab Society of Nuclear
Medicine, and Asia Oceania Federation of Nuclear medicine and biology;
all outside the submitted work. R Ancuceanu reports payment or
honoraria for lectures, presentations, speakers bureaus, manuscript
writing or educational events from AbbVie, Laropharm, and Reckitt,
outside the submitted work. J Ärnlöv reports payment or honoraria for
lectures from AstraZeneca and Novartis; participation on an Advisory
Board with AstraZeneca and Astella; all outside the submitted work.
M Ausloos reports grants or contracts from the project “A better
understanding of socio-economic systems using quantitative methods
from Physics” funded by European Union – NextgenerationEU and
Romanian Government, under National Recovery and Resilience Plan
for Romania, contract no.760034/ 23.05.2023, cod PNRR-C9-I8-CF 255/
29.11.2022, through the Romanian Ministry of Research, Innovation and
Digitalization, within Component 9, Investment I8, outside the
submitted work. O C Baltatu reports grants or contracts from National
Council for Scientific and Technological Development (CNPq,
304224/2022-7) and the Anima Institute through an AI research
professor fellowship; leadership or fiduciary role, paid or unpaid, with
Health and Biotechnology Advisory Board at Technology Park São José
dos Campos – Center for Innovation in Health Technologies (CITS),
outside the submitted work. T W Bärnighausen reports grants or
contracts from National Institutes of Health, Alexander von Humboldt
Foundation, German National Research Foundation (DFG), European
Union, German Ministry of Education and Research, German Ministry
of the Environment, Wellcome, and KfW; payment or honoraria for
serving as Editor-in-Chief of PLOS Medicine; participation on a Data
Safety Monitoring Board or Advisory Board, unpaid, with NIH-funded
research projects in Africa on Climate Change and Health; stocks in
CHEERS, an SME focusing on approaches to measure climate change
and health-related variables in population cohorts; all outside the
submitted work. S Bhaskar reports grants or contracts from Japan
Society for the Promotion of Science (JSPS), Japanese Ministry of
Education, Culture, Sports, Science and Technology (MEXT) through
Grant-in-Aid for Scientific Research (KAKENHI), and from JSPS and the
Australian Academy of Science through the JSPS International
Fellowship; leadership or fiduciary role, paid or unpaid, with National
Cerebral and Cardiovascular Center, Suita, Osaka, Japan, NSW Brain
Clot Bank, Sydney, Australia, Rotary District 9675, Sydney, NSW,
Australia, Global Health & Migration Hub Community, Global Health
Hub Germany, Berlin, Germany, PLOS One, BMC Neurology, Frontiers
in Neurology, Frontiers in Stroke, Frontiers in Aging, Frontiers in Public
Health & BMC Medical Research Methodology, College of Reviewers,
Canadian Institutes of Health Research (CIHR), Government of Canada,
Cardi University Biobank, Cardi, UK, Cariplo Foundation, Milan,
Italy, Pandemic Health System REsilience PROGRAM (REPROGRAM)
Consortium; all outside the submitted work. B Bikbov reports grants or
contracts with European Commission, Politecnico di Milano, and
University of Rome; support for attending meetings and/or travel from
European Renal Association; leadership or fiduciary role, unpaid, with
Advocacy Group, International Society of Nephrology and Western
Europe Regional Board, International Society of Nephrology; other
non-financial in Scientific-Tools.org; all outside the submitted work.
A Biswas reports consulting fees from Lupin Pharmaceuticals India,
Intas Pharmaceuticals India, Alkem Laboratories India, and Eisai
Pharmaceuticals India, all outside the submitted work. E J Boyko reports
Payment or honoraria for lectures, presentations, speakers bureaus,
manuscript writing or educational events from Korean Diabetes
Association, Diabetes Association of the R.O.C. (Taiwan), American
Diabetes Association, and the International Society for the Diabetic Foot;
support for attending meetings and/or travel from Korean Diabetes
Association, Diabetes Association of the R.O.C. (Taiwan), and
International Society for the Diabetic Foot; all outside the submitted
work. A L Catapano reports grants or contracts from Amryt Pharma,
Menarini, and Ultragenyx; payment or honoraria for lectures,
presentations, speakers bureaus, manuscript writing or educational
events from Amarin, Amgen, Amryt Pharma, AstraZeneca, Daiichi
Sankyo Esperion Ionis Pharmaceutical Medscaper, Menarini, Merck,
Novartis, NovoNordisk, Peervoice Pfizer Recordati Regeneron, Sandoz,
Sanofi The Corpus, Ultragenyx, and Viatris; all outside the submitted
work. S Das reports a leadership or fiduciary role, unpaid, with
association of Diagnostic & Laboratory Medicine India Chapter and
Women in Global Health India, outside the submitted work.
A Demetriades reports Leadership or fiduciary role, paid or unpaid as
the Immediate Past President, European Association of Neurosurgical
Societies (EANS) and Vice-President, Global Neuro Foundation, outside
the submitted work. A Faro reports support for the present manuscript
from National Council for Scientific and Technological Development
(CNPq) through a scholarship. M Foschi reports consulting fees from
Novartis and Roche; support for attending meetings and/or travel from
Roche, Novartis, Biogen, Bristol-Meyer, Merck, and Sanofi; leadership or
fiduciary role with MSBase Foundation as a member of the scientific
leadership group; all outside the submitted work. R Franklin reports
support for attending meetings and/or travel from ACTM – Tropical
Medicine and Travel Medicine Conference 2022, 2023 and ISTM – Travel
Medicine Conference, Basel 2023; leadership or fiduciary role, paid or
unpaid, as Director of Kidsafe, Director of Auschem, Governance
Committee of ISASH, Director of Farmsafe, SIG Convenor of PHAA
Injury Prevention, and Vice President of ACTM; all outside the
submitted work. A Guha reports grants or contracts from American
Heart Association and Department of Defense; consulting fees from
Pfizer and Novartis; leadership or fiduciary role, paid or unpaid, with
ZERO Prostate Cancer – health equity task force; all outside the
submitted work. G J Hankey reports payment or honoraria for lectures,
presentations, speakers bureaus, manuscript writing or educational
events from American Heart Association (for serving as Associate Editor
of Circulation) Janssen (Johnson & Johnson) (for serving as Co-chair of
Executive Committee, Librexia Stroke Trial, and lectures at sponsored
scientific symposia), outside the submitted work. N E Ismail reports
leadership or fiduciary role, unpaid, with Malaysian Academy of
Pharmacy, Malaysia, and Malaysian Pharmacists Society Education
Chapter, Malaysia, all outside the submitted work. T Joo reports support
for the present manuscript from National Research, Development and
Innovation Oce in Hungary (RRF-2.3.1-21-2022-00006, Data-Driven
Health Division of National Laboratory for Health Security) for funding
of participation in the research project. J J Jozwiak reports Payment or
honoraria for lectures, presentations, speakers bureaus, manuscript
writing or educational events from Novartis, ADAMED, and Amgen;
outside the submitted work. K Krishan reports non-financial support
from the UGC Centre of Advanced Study, CAS II, awarded to the
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Department of Anthropology, Panjab University, Chandigarh, India,
outside the submitted work. B Lacey reports support for the present
manuscript from UK Biobank, funded largely by the UK Medical
Research Council and Wellcome. P M Lavados reports grants from
Boehringer Ingelheim; consulting fees from Boehringer Ingelheim for
LATAM Stroke Projects Mentorship; payment or honoraria for lectures
from Boehringer Ingelheim, Ferrer, Pfizer, and Novartis; support for
attending Southamerican Angels meetings from Boehringer Ingelheim;
participation on a Data Safety Monitoring Board or Advisory Board with
Janssen, BMS, and Pfizer; leadership or fiduciary role, paid or unpaid,
with Chilean Stroke Association (ACEVE) and Iberoamerican Stroke
Society (SIECV-IASO); all outside the submitted work. M Li reports
grants or contracts from the National Science and Technology Council,
Taiwan (NSTC 112-2410-H-003-031; leadership or fiduciary role, paid or
unpaid, as Technical Editor, Journal of the American Heart Association;
all outside the submitted work. D Lindholm reports previous stock
options and other financial or non-financial interests in AstraZeneca as a
former employee, outside the submitted work. W Lo report stock or
stock options in Abbott Lab, Amgen, Becton Dickson, Bristol Myers
Squibb, Cardinal Health, GE Healthcare, Illumina, McKesson, Merck,
Moderna, Pfizer, and Walgreens Boots, outside the submitted work.
S Lorkowski reports grants or contracts from dsm-firmenich (formerly
DSM Nutritional Products) as payments to their institution; consulting
fees from Danone, Novartis Pharma, and Swedish Orphan Biovitrum
(SOBI); payment or honoraria for lectures, presentations, speakers
bureaus, manuscript writing or educational events from AMARIN
Germany, Amedes Holding, Amgen, Berlin-Chemie, Boehringer
Ingelheim Pharma, Daiichi Sankyo Deutschland, Danone, Hubert Burda
Media Holding, Janssen-Cilag, Lilly Deutschland, Novartis Pharma,
Novo Nordisk Pharma, Roche Pharma, Sanofi-Aventis, Swedish Orphan
Biovitrum (SOBI), SYNLAB Holding Deutschland; support for attending
meetings and/or travel from AMGEN; participation on a Data Safety
Monitoring Board or Advisory Board from AMGEN, Daiichi Sankyo
Deutschland, Novartis Pharma, Sanofi-Aventis; all outside the submitted
work. L G Manotvani reports support for the present manuscript from
the Italian Ministry of Health, Ministero della Salute, Ricerca Corrente,
IRCCS Istituto Auxologico Italiano.H R Marateb reports support for
their participation in present manuscript from Beatriu de Pinós post-
doctoral programme from the Oce of the Secretary of Universities and
Research from the Ministry of Business and Knowledge of the
Government of Catalonia programme: (2020 BP 00261). S Meo reports
grants or contracts from Researchers Supporting Project, King Saud
University, Riyadh, Saudi Arabia (RSP-2024 R47), outside the submitted
work. L Monasta reports support for the present manuscript from the
Italian Ministry of Health (Ricerca Corrente 34/2017), payments made to
the Institute for Maternal and Child Health IRCCS Burlo Garofolo.
R Moreira reports grants or contracts from CNPq (National Council for
Scientific and Technological Development, scholarship registration
number 316607/2021-5) outside the submitted work. S Muthu reports
receiving the Luiz Vialle Award 2024 for travel support to attend Global
Spine Congress 2024 from AO Spine International; leadership or
fiduciary role with SICOT Awards Committee, AO Spine Associate
Knowledge Forum, ICRS Next-Gen Committee; all outside the
submitted work. S Nomura reports grants from Ministry of Education,
Culture, Sports, Science and Technology of Japan (21H03203), and
Precursory Research for Embryonic Science and Technology from the
Japan Science and Technology Agency (JPMJPR22R8); outside the
submitted work. B Norrving reports participation on a data safety or
monitoring board or advisory board with Simbec-Orion (HOVID Trial);
outside the submitted work. A Oneil reports grants or contracts from the
National Health and Medical Research Council via her institution
(#2009295); payment or honoraria for lectures, presentations, speakers
bureaus, manuscript writing or educational events from the Exercise &
Sports Science Australia conference for an invited presentation; outside
the submitted work. A P Okekunle reports support for the present
manuscript from National Research Foundation of Korea funded by the
Ministry of Science and ICT (2020H1D3A1A04081265); support for
attending meetings and/or travel from National Research Foundation of
Korea funded by the Ministry of Science and ICT
(2020H1D3A1A04081265); all outside the submitted work. R Ornello
reports institutional grants from Novartis, consulting fees from Teva;
payment or honoraria for lectures, presentations, speakers bureaus,
manuscript writing or educational events from Eli Lilly, Novartis, Pfizer,
Teva, AbbVie, and Lundbeck; support for attending meetings or travel
from Teva; participation on a Data Safety Monitoring Board or Advisory
Board with Eli Lilly; leadership or fiduciary role with The Journal of
Headache and Pain Editorial Board, and Frontiers in Neurology
Headache and Neurogenic Pain Section; receipt of payment of
publication fees from Novartis and AbbVie; all outside the submitted
work. A Ortiz reports grants to their institute from Sanofi and Catedra
Mundipharma-UAM of diabetic kidney disease and the Catedra
AstraZeneca-UAM of chronic kidney disease and electrolytes;
consultancy or speaker fees Advicciene , Astellas, AstraZeneca, Amicus,
Amgen, Boehringer-Ingelheim, Fresenius Medical Care, GSK, Bayer,
Sanofi-Genzyme, Menarini, Kyowa Kirin, Alexion, Idorsia, Chiesi,
Otsuka, Novo-Nordisk and Vifor Fresenius Medical Care Renal Pharma;
travel support from Advicciene , Astellas, AstraZeneca, Fresenius
Medical Care, Boehringer-Ingelheim Bayer, Sanofi-Genzyme, Menarini,
Chiesi, Otsuka, Sysmex; leadership or fiduciary role, unpaid, with
Council ERA. SOMANE; all outside the submitted work. E Ortiz-Prado
reports grants or contracts from Universidad de las Americas, Quito-
Ecuador outside the submitted work. R Palma-Alvarez reports payment
or honoraria for lectures, presentations, speakers bureaus, manuscript
writing or educational events from Angelini, Casen Recordarti,
Lundbeck, Rubió, Takeda, Servier, and Neuraxpharm; support for
attending meetings or travel from Angelini, Casen Recordati, Takeda,
Italfarmaco, Lundbeck, and Janssen; all outside the submitted work.
R Passera reports participation on Data Safety Monitoring Board dello
studio “Consolidation with ADCT-402 (loncastuximab tesirine) after
immunochemotherapy: a phase II study in BTKi-treated/ineligible
Relapse/Refractory Mantle Cell Lymphoma (MCL) patients” - FIL,
Fondazione Italiana Linfomi, Alessandria; leadership or fiduciary role,
unpaid, as a Member of the EBMT Statistical Committee, European
Society for Blood and Marrow Transplantation, Paris (F), and Past
member 2020-2023 (biostatistician) of the IRB/IEC Comitato Etico AO
SS. Antonio e Biagio Alessandria-ASL AL-VC; all outside the submitted
work. A E Peden reports support for the present manuscript from
[Australian] National Health and Medical Research Council (Grant
Number: APP2009306). A Rane reports being a full-time employee of
Agios Pharmaceuticals and owning stock and stock options, outside the
submitted work. S Sacco reports grants or contracts from Novartis and
Uriach; consulting fees from Novartis, Allergan-AbbVie, Teva, Lilly,
Lundbeck, Pfizer, NovoNordisk, Abbott, and AstraZeneca; payment or
honoraria for lectures, presentations, speakers bureaus, manuscript
writing or educational events from Novartis, Allergan-Abbvie, Teva, Lilly,
Lundbeck, Pfizer, NovoNordisk, Abbott, AstraZeneca; support for
attending meetings and/or travel Lilly, Novartis, Teva, Lundbeck, and
Pfizer; leadership or fiduciary role in other board, society, committee or
advocacy group, paid or unpaid, with President European Stroke
Organisation, and as Editor-in-Chief Cephalalgia; receipt of equipment,
materials, drugs, medical writing, gifts or other services from Allergan-
AbbVie, NovoNordisk; all outside the submitted work. Y L Samodra
reports grants or contracts from FK Unpar, Indonesia; leadership or
fiduciary role in other board, society, committee or advocacy group, paid
or unpaid, with Benang Merah Research Center, Indonesia; all outside
the submitted work. J Sanabria reports support for attending meetings
and/or travel from Continuous Medical Education (CME) funds from
Marshall University School of Medicine; participation on a Data Safety
Monitoring Board or Advisory Board with Quality ocer for the
Department of Surgery; leadership or fiduciary role in other board,
society, committee or advocacy group, paid or unpaid, with SSAT, ACS,
IHPBA, American Board of Surgery; all outside the submitted work.
N Scarmeas reports grants or contracts from Novo Nordisk through
funding to their institution; participation on a Data Safety Monitoring
Board or Advisory Board with the Multicultural Healthy Diet to Reduce
Cognitive Decline & AD Risk NIH Funded Study at Albert Einstein
College of Medicine, and with Primus AD through a Public Private
funded Phase II study in Germany; all outside the submitted work.
B M Schaarschmidt reports grants from Else Kröner-Fresenius
Foundation, Deutsche Forschungsgemeinschaft, and PharmaCept;
payment or honoraria for lectures from AstraZeneca; support for travel
from Bayer AG; all outside the submitted work. A E Schutte reports
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grants or contracts from National Health and Medical Research Council
of Australia, and the Medical Research Future Fund, Australia;
consulting fees from Skylabs, Abbott, and Servier; payment or honoraria
for lectures, presentations, speakers bureaus, manuscript writing or
educational events from Abbott, Servier, Sanofi, Omron, Medtronic, and
Aktiia; support for attending meetings or travel from Medtronic and
Servier; leadership or fiduciary role, paid or unpaid, as Co-Chair National
Hypertension Taskforce of Australia; all outside the submitted work.
A Sharifan reports Leadership or fiduciary role, unpaid, with Cochrane
Early Career Professionals Network; receipt of equipment, materials,
drugs, medical writing, gifts or other services from Elsevier and
Cochrane; all outside the submitted work. V Sharma reports other
financial or non-financial interests in DFSS (MHA)’s research project
(DFSS28(1)2019/EMR/6) at Institute of Forensic Science & Criminology,
Panjab University, Chandigarh, India, outside the submitted work.
S Shrestha reports other financial or non-financial interests in the
School of Pharmacy, Monash University Malaysia, and the Graduate
Research Merit Scholarship, outside the submitted work. J Silva reports
support for the present manuscript from Portuguese Foundation for
Science and Technology through salary payments (contract with
reference 2021.01789.CEECIND/CP1662/CT0014). L R Silva reports
grants or contracts from project code CENTRO-04-3559-FSE-000162,
Fundo Social Europeu (FSE), outside the submitted work. J A Singh
reports consulting fees from ROMTech, Atheneum, Clearview healthcare
partners, American College of Rheumatology, Yale, Hulio, Horizon
Pharmaceuticals, DINORA, Frictionless Solutions, Schipher, Crealta/
Horizon, Medisys, Fidia, PK Med, Two labs, Adept Field Solutions,
Clinical Care options, Putnam associates, Focus forward, Navigant
consulting, Spherix, MedIQ, Jupiter Life Science, UBM, Trio Health,
Medscape, WebMD, and Practice Point communications; and the
National Institutes of Health; Payment or honoraria for lectures,
presentations, speakers bureaus, manuscript writing or educational
events on the speakers bureau of Simply Speaking; Support for
attending meetings and/or travel from OMERACT as a steering
committee member; Participation on a Data Safety Monitoring Board or
Advisory Board with the FDA Arthritis Advisory Committee; Leadership
or fiduciary role in other board, society, committee or advocacy group,
paid as a past steering committee member of the OMERACT, an
international organisation that develops measures for clinical trials and
receives arm’s length funding from 12 pharmaceutical companies,
unpaid as Chair of the Veterans Aairs Rheumatology Field Advisory
Committee, and unpaid as the Editor and Director of the UAB Cochrane
Musculoskeletal Group Satellite Center on Network Meta-analysis; stock
or stock options in Atai life sciences, Kintara therapeutics, Intelligent
Biosolutions, Acumen pharmaceutical, TPT Global Tech, Vaxart
pharmaceuticals, Atyu biopharma, Adaptimmune Therapeutics, GeoVax
Labs, Pieris Pharmaceuticals, Enzolytics, Seres Therapeutics, Tonix
Pharmaceuticals Holding Corp., Aebona Pharmaceuticals, and
Charlotte’s Web Holdings, Inc. and previously owned stock options in
Amarin, Viking, and Moderna Pharmaceuticals; all outside the
submitted work. J Sipila reports grants or contracts from Siun Sote
Foundation and Eemil Aaltonen Foundation; payment or honoraria for
lectures, presentations, speakers bureaus, manuscript writing or
educational events from Novartis; support for attending meetings and/or
travel from Lundbeck; participation on a Data Safety Monitoring Board
or Advisory Board with Boehringer-Ingelheim and Sandoz; stock or
stock options in Orion Corp; all outside the submitted work.
J Sundstrom reports direct or indirect stock ownership in companies
(Anagram kommunikation AB, Sence Research AB, Symptoms Europe
AB, MinForskning AB) providing services to companies and authorities
in the health sector including Amgen, AstraZeneca, Bayer, Boehringer,
Eli Lilly, Gilead, GSK, Göteborg University, Itrim, Ipsen, Janssen,
Karolinska Institutet, LIF, Linköping University, Novo Nordisk, Parexel,
Pfizer, Region Stockholm, Region Uppsala, Sanofi, STRAMA, Takeda,
TLV, Uppsala University, Vifor Pharma, WeMind, all outside the
submitted work. A G Thrift reports grants or contracts from National
Health & Medical Research Council (Australia), Heart Foundation
(Australia), and Stroke Foundation (Australia); outside the submitted
work. J H V Ticolau reports leadership or fiduciary role, paid or unpaid,
with Benang Merah Research Center, Indonesia, outside the submitted
work. S J Tromans reports grants or contracts from NHS Digital, via the
Department of Health and Social Care, as payments to their institution;
leadership or fiduciary role, unpaid, with the Academic Secretary for the
Neurodevelopmental Psychiatry Special Interest Group at the Royal
College of Psychiatrists, Editorial Board for BMC Psychiatry, Advances
in Autism, Advances in Mental Health and Intellectual Disability, and
Progress in Neurology and Psychiatry; all outside the submitted work.
P Willeit reports consulting fees from Novartis Pharmaceuticals, outside
the submitted work. Y Yasufuku reports grants or contracts from
Shionogi & Co, through funding to their institution, outside the
submitted work. M Zielinska reports other financial or non-financial
interests in AstraZeneca as an employee, outside the submitted work.
Data sharing
To download GBD data used in these analyses, please visit the GBD 2021
Sources Tool website. To download forecasted estimates used in these
analyses, please visit the GBD visualisation tools.
Acknowledgments
This study was funded by the Bill & Melinda Gates Foundation.
Editorial note: The Lancet Group takes a neutral position with respect to
territorial claims in published maps and institutional aliations.
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