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Global, regional, and national progress towards Sustainable Development Goal 3.2 for neonatal and child health: all-cause and cause-specific mortality findings from the Global Burden of Disease Study 2019

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Background Sustainable Development Goal 3.2 has targeted elimination of preventable child mortality, reduction of neonatal death to less than 12 per 1000 livebirths, and reduction of death of children younger than 5 years to less than 25 per 1000 livebirths, for each country by 2030. To understand current rates, recent trends, and potential trajectories of child mortality for the next decade, we present the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 findings for all-cause mortality and cause-specific mortality in children younger than 5 years of age, with multiple scenarios for child mortality in 2030 that include the consideration of potential effects of COVID-19, and a novel framework for quantifying optimal child survival. Methods We completed all-cause mortality and cause-specific mortality analyses from 204 countries and territories for detailed age groups separately, with aggregated mortality probabilities per 1000 livebirths computed for neonatal mortality rate (NMR) and under-5 mortality rate (U5MR). Scenarios for 2030 represent different potential trajectories, notably including potential effects of the COVID-19 pandemic and the potential impact of improvements preferentially targeting neonatal survival. Optimal child survival metrics were developed by age, sex, and cause of death across all GBD location-years. The first metric is a global optimum and is based on the lowest observed mortality, and the second is a survival potential frontier that is based on stochastic frontier analysis of observed mortality and Healthcare Access and Quality Index. Findings Global U5MR decreased from 71·2 deaths per 1000 livebirths (95% uncertainty interval [UI] 68·3–74·0) in 2000 to 37·1 (33·2–41·7) in 2019 while global NMR correspondingly declined more slowly from 28·0 deaths per 1000 live births (26·8–29·5) in 2000 to 17·9 (16·3–19·8) in 2019. In 2019, 136 (67%) of 204 countries had a U5MR at or below the SDG 3.2 threshold and 133 (65%) had an NMR at or below the SDG 3.2 threshold, and the reference scenario suggests that by 2030, 154 (75%) of all countries could meet the U5MR targets, and 139 (68%) could meet the NMR targets. Deaths of children younger than 5 years totalled 9·65 million (95% UI 9·05–10·30) in 2000 and 5·05 million (4·27–6·02) in 2019, with the neonatal fraction of these deaths increasing from 39% (3·76 million [95% UI 3·53–4·02]) in 2000 to 48% (2·42 million; 2·06–2·86) in 2019. NMR and U5MR were generally higher in males than in females, although there was no statistically significant difference at the global level. Neonatal disorders remained the leading cause of death in children younger than 5 years in 2019, followed by lower respiratory infections, diarrhoeal diseases, congenital birth defects, and malaria. The global optimum analysis suggests NMR could be reduced to as low as 0·80 (95% UI 0·71–0·86) deaths per 1000 livebirths and U5MR to 1·44 (95% UI 1·27–1·58) deaths per 1000 livebirths, and in 2019, there were as many as 1·87 million (95% UI 1·35–2·58; 37% [95% UI 32–43]) of 5·05 million more deaths of children younger than 5 years than the survival potential frontier. Interpretation Global child mortality declined by almost half between 2000 and 2019, but progress remains slower in neonates and 65 (32%) of 204 countries, mostly in sub-Saharan Africa and south Asia, are not on track to meet either SDG 3.2 target by 2030. Focused improvements in perinatal and newborn care, continued and expanded delivery of essential interventions such as vaccination and infection prevention, an enhanced focus on equity, continued focus on poverty reduction and education, and investment in strengthening health systems across the development spectrum have the potential to substantially improve U5MR. Given the widespread effects of COVID-19, considerable effort will be required to maintain and accelerate progress. Funding Bill & Melinda Gates Foundation.
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Articles
870
www.thelancet.com Vol 398 September 4, 2021
Global, regional, and national progress towards Sustainable
Development Goal 3.2 for neonatal and child health:
all-cause and cause-specific mortality findings from the
Global Burden of Disease Study 2019
GBD 2019 Under-5 Mortality Collaborators*
Summary
Background Sustainable Development Goal 3.2 has targeted elimination of preventable child mortality, reduction
of neonatal death to less than 12 per 1000 livebirths, and reduction of death of children younger than 5 years to less
than 25 per 1000 livebirths, for each country by 2030. To understand current rates, recent trends, and potential
trajectories of child mortality for the next decade, we present the Global Burden of Diseases, Injuries, and Risk
Factors Study (GBD) 2019 findings for all-cause mortality and cause-specific mortality in children younger than
5 years of age, with multiple scenarios for child mortality in 2030 that include the consideration of potential eects
of COVID-19, and a novel framework for quantifying optimal child survival.
Methods We completed all-cause mortality and cause-specific mortality analyses from 204 countries and territories for
detailed age groups separately, with aggregated mortality probabilities per 1000 livebirths computed for neonatal
mortality rate (NMR) and under-5 mortality rate (U5MR). Scenarios for 2030 represent dierent potential trajectories,
notably including potential eects of the COVID-19 pandemic and the potential impact of improvements preferentially
targeting neonatal survival. Optimal child survival metrics were developed by age, sex, and cause of death across all
GBD location-years. The first metric is a global optimum and is based on the lowest observed mortality, and the second
is a survival potential frontier that is based on stochastic frontier analysis of observed mortality and Healthcare Access
and Quality Index.
Findings Global U5MR decreased from 71·2 deaths per 1000 livebirths (95% uncertainty interval [UI] 68·3–74·0) in
2000 to 37·1 (33·2–41·7) in 2019 while global NMR correspondingly declined more slowly from 28·0 deaths per
1000 live births (26·8–29·5) in 2000 to 17·9 (16·3–19·8) in 2019. In 2019, 136 (67%) of 204 countries had a U5MR
at or below the SDG 3.2 threshold and 133 (65%) had an NMR at or below the SDG 3.2 threshold, and the reference
scenario suggests that by 2030, 154 (75%) of all countries could meet the U5MR targets, and 139 (68%) could meet
the NMR targets. Deaths of children younger than 5 years totalled 9·65 million (95% UI 9·05–10·30) in 2000
and 5·05 million (4·27–6·02) in 2019, with the neonatal fraction of these deaths increasing from 39% (3·76 million
[95% UI 3·53–4·02]) in 2000 to 48% (2·42 million; 2·06–2·86) in 2019. NMR and U5MR were generally higher in
males than in females, although there was no statistically significant dierence at the global level. Neonatal
disorders remained the leading cause of death in children younger than 5 years in 2019, followed by lower
respiratory infections, diarrhoeal diseases, congenital birth defects, and malaria. The global optimum analysis
suggests NMR could be reduced to as low as 0·80 (95% UI 0·71–0·86) deaths per 1000 livebirths and U5MR to 1·44
(95% UI 1·27–1·58) deaths per 1000 livebirths, and in 2019, there were as many as 1·87 million (95% UI 1·35–2·58;
37% [95% UI 32–43]) of 5·05 million more deaths of children younger than 5 years than the survival potential
frontier.
Interpretation Global child mortality declined by almost half between 2000 and 2019, but progress remains slower
in neonates and 65 (32%) of 204 countries, mostly in sub-Saharan Africa and south Asia, are not on track to meet
either SDG 3.2 target by 2030. Focused improvements in perinatal and newborn care, continued and expanded
delivery of essential interventions such as vaccination and infection prevention, an enhanced focus on equity,
continued focus on poverty reduction and education, and investment in strengthening health systems across the
development spectrum have the potential to substantially improve U5MR. Given the widespread eects of
COVID-19, considerable eort will be required to maintain and accelerate progress.
Funding Bill & Melinda Gates Foundation.
Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0
license.
Lancet 2021; 398: 870–905
Published Online
August 17, 2021
https://doi.org/10.1016/
S0140-6736(21)01207-1
See Comment page 821
*Collaborators listed at the end
of the paper
Correspondence to:
Dr Nicholas J Kassebaum,
Department of Health Metrics
Sciences, University of
Washington, 3980 15th Avenue,
Northeast Seattle, WA 98105,
USA
nickjk@uw.edu
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Introduction
Under-5 mortality rate (U5MR) and neonatal mortality
rate (NMR) are important indicators reflecting multiple
aspects of societal wellbeing such as access to nutrition
and food; basic infrastructure such as housing, water,
and sanitation; education; agency; financial security;
access to preventive and treatment health services; and
future human capital. The UN Millennium Development
Goals (MDGs) are credited with mobilising global action
on child health, and manifested as an unprecedented,
accelerated reduction in child mortality and resulted
in 58 countries achieving the MDG 4 target of reducing
U5MR by two-thirds.1,2 Sustainable Development Goal
(SDG) 3.2 specifically calls to, “By 2030, end preventable
deaths of newborn babies and children under 5 years of
age, with all countries aiming to reduce neonatal
mortality to at least as low as 12 per 1000 live births and
under-5 mortality to at least as low as 25 per 1000 live
births.”3 The SDG focus on equity was codified here in a
shifting from relative global targets, that were mainstays
in the MDG agenda, to absolute targets for each country.
The SDG framework aims to build on the successes of
the MDG era, albeit with a notably broader lens in which
health (SDG 3) is one of several goals related to healthier
lives, wellbeing, and equity.3 Even within SDG 3, the
SDG agenda is broader than the MDG agenda, reflecting a
growing understanding of the intersectional nature of
health outcomes with basic infrastructural considerations
such as health system performance, sustainability, and
environment. This intersectional perspective is illustrated
in the language of initiatives such as the call from the
UN Global Strategy for Women’s, Children’s and
Adolescents’ Health 2016–2030 to integrate survival,
prevention, thriving, and enabling environ ments,4 the
Every Newborn Action Plan, the World Bank’s Global
Financing Facility for Women, Children and Adolescents,
The Lancet Global Health Commission on High Quality
Health Systems, and the Countdown to 2030.5–7 Although
this broader focus has not necessarily led to child and
neonatal health receiving less investment in development
assistance for health (DAH; which, for child and neonatal
health, grew by 2·66% from 2015 to 2019 and remained
the second largest DAH focus area in 2019), the growth in
investment in this period was less than during the period
between 2000 and 2015, when DAH for child and neonatal
health increased by 314%.8
Research in context
Evidence before this study
During the Millennium Development Goal (MDG) era (2000–15),
numerous organisations comprehensively described global
progress in reducing child and neonatal mortality (MDG 4),
but the early Sustainable Development Goal (SDG) period has
seen few comparable efforts to track progress and none to date
have attempted to quantify the preventable portion of child
mortality (SDG 3.2). Past preventable mortality analyses have
focused on health-care delivery, or were limited to high-income
countries and adult populations. The most recent child mortality
report from the UN Inter-agency Group for Child Mortality
Estimation (UNIGME), published in 2017 for the year 2015,
reports on all-cause mortality only. The Global Burden of
Diseases, Injuries, and Risk Factors Study (GBD) is the only
annual assessment of trends in all-cause mortality and cause-
specific mortality by detailed age groups for all locations with a
population greater than 50 000 people from 1990 to the
present that is compliant with the Guidelines for Accurate and
Transparent Health Estimates Reporting.
Added value of this study
This analysis presents levels and trends in all-cause and cause-
specific neonatal and under-5 mortality from 2000 to 2019.
Multiple future health scenarios for child mortality in 2030
were constructed to represent potential trajectories, including
the potential impacts of the COVID-19 pandemic and scenarios
with targeted improvements in neonatal survival. Additionally,
this study presents for the first time all-cause mortality
estimates for granular age groups of 0–6 days, 7–27 days,
1–5 months, 6–11 months, 12–23 months, and 2–4 years.
SDG 3.2 explicitly prioritises ending preventable child deaths.
Therefore, based on all-cause and cause-specific mortality
estimates from GBD 2019, this study introduces a novel,
reproducible, and holistic heuristic for quantifying optimal child
survival. Within this framework are two complementary cause-
specific benchmarks: a global optimum, based on the lowest
observed neonatal and under-5 mortality, and a survival
potential frontier, based on stochastic frontier analysis of
observed mortality and the Healthcare Access and Quality
Index. The latter allows for comparing performance between
similar countries, and specifically helps those countries with
high mortality to establish intermediate goals.
Implications of all the available evidence
The prevention of child deaths accelerated in the MDG era.
In the emerging SDG period, progress to prevent child deaths
remains slowest in neonates. The study findings highlight
regions with potential imbalances in health priorities.
The findings can also identify causes of death with the most
potential for reduction, and those with the greatest need for
resources, expertise, and service delivery, or for basic research
into prevention and treatment. To reach the SDG targets
by 2030, policy makers must focus on balancing priorities
between early newborn care while continuing prenatal and
older child health initiatives. Strengthening quality health
systems and ensuring effective investment in high-burden
countries are imperative in order to scale up interventions.
Equally pressing are the needs to examine within-country
disparities and pursue integrative action on other determinants
of health.
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There has not yet been a comprehensive assessment
of NMR and U5MR in the SDG era. Selected publications
assessed interim progress towards part of SDG 3.2
or provided projections to 2030,9–13 but none have been
comprehensive with respect to cause, age, trends,
geo graphy, and progress towards 2030 targets. The
comprehensive nature of the Global Burden of Diseases,
Injuries, and Risk Factors Study (GBD) 2019 lends itself
to a detailed analysis of levels, trends, and drivers of
change for specific age groups, causes, and locations.
Additionally, there has not been any previous eort, to
our knowledge, to empirically explore the concept of
preventable mortality in children. Although preventable
death has been theoretically defined since the early
2000s, the definitions has usually been through a health-
care delivery lens14,15 rather than a more holistic lens
of preventability that might be interpreted as the
intended wording of SDG 3.2. Furthermore, although
the Organisation for Economic Co-operation and
Develop ment (OECD) and Eurostat convened to provide
a more uniform approach to interpreting avoidable
deaths in 2019, this was with a singular focus on high-
income countries and the adult population.16
In this study, based on GBD 2019, we have three
objectives. First, we aim to present a detailed, com-
prehensive numerical assessment of progress towards
SDG 3.2 targets for all-cause NMR and U5MR at the
global, regional, and national level, including a series
of scenarios that reflect possible trends over the next
decade including the potential eects of the COVID-19
pandemic on young children. Second, we aim to evaluate
comparative progress in cause-specific mortality in
neonates and children from 2000 to 2019 to highlight
successes and potential focus areas for improvement.
Third, we aim to better define a holistic focus of preventable
mortality by exploring two dierent measures of optimal
child survival that can both inform global progress and
provide a benchmark for intermediate progress evaluation
in high-mortality settings. In so doing, this study seeks to
meet the needs of an expansive, integrative SDG agenda,
and to highlight the locations, age groups, and causes
of preventable deaths, to inform policy and public health
priorities aiming to achieve SDG 3.2. This manuscript
was produced as part of the GBD Collaborator Network
and in accordance with the GBD Protocol.
Methods
Overview
This study is compliant with the Guidelines for Accurate
and Transparent Health Estimates Reporting (GATHER;17
appendix p 9). A brief summary of each component of
our study is described below. Extensive methodological
details are provided in the appendix (pp 10–86).
Dimensions of the GBD study
GBD 2019 includes all-cause and cause-specific mortality
by age and sex for 204 countries and territories, 21 of
which were estimated at the subnational level from 1990
to 2019, inclusive. Results in this study are presented
only for countries and territories. All-cause mortality
estimation covers six under-5 age groups: 0–6 days (early
neonatal), 7–27 days (late neonatal), 1–5 months,
6–11 months, 12–23 months, and 2–4 years. Cause-
specific mortality estimates cover four age groups: early
neonatal, late neonatal, 28–364 days, and 1–4 years.
Although we present all six age groups, we mainly focus
on results for the aggregate neonatal age group
(<28 days) and the under-5 age group (0–4 years), to
best align with the SDG under-5 and neonatal targets.
Similarly, we focus on the years 2000, which marks the
establishment of the MDGs, 2015, which marks the
establishment of the SDGs, and 2019, which is the most
recent year of GBD estimates.
Data sources
All-cause mortality data were compiled from 203 of
204 countries and territories ranging from the years 2000
to 2019, for a total of 3097 location-years. Vital registration
covered a total of 14 889 022 global under-5 deaths in this
period (appendix p 119). A total of 8000 unique sources
were used in estimating cause-specific mortality in
GBD 2019. All input data sources for each component of
analysis are available for download from the GBD 2019
Data Input Sources Tool.
All-cause mortality estimation and assessment of
progress towards SDG 3.2
All-cause mortality estimation closely followed the esti-
mation techniques as described for previous iterations
of GBD,2,18,19 detailed in the appendix (p 9). Progress
towards SDG 3.2 was assessed by examining U5MR and
NMR in 2019. NMR is calculated as the probability of
death between birth and 28 days and U5MR is calculated
as the probability of death between birth and 5 years,
and each metric is expressed as the number of deaths
per 1000 livebirths. Aggregate mortality probabilities
were benchmarked against the SDG thresholds of
25 under-5 deaths per 1000 livebirths and 12 neonatal
deaths per 1000 livebirths.
To assess relative progress across age groups, we
compared the proportion of under-5 deaths occurring in
each age group with the ratio of change in age-specific
deaths to change in total under-5 deaths, for the
periods 2000–14 and 2015–19. If progress towards
SDG 3.2 is equal across age groups, the percentage
contribution to progress and the percentage of total
deaths would be equal. If the percentage of deaths is
greater than the percentage of progress for an age group,
then that age group is making slower progress towards
the target.
Cause-specific mortality estimation
GBD 2019 includes 369 causes of disease and injury in a
mutually exclusive and collectively exhaustive hierarchy
See Online for appendix
For more on the GBD 2019 Data
Input Sources Tool see
http://
ghdx.healthdata.org/gbd-2019/
data-input-sources
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873
of four levels (appendix p 87). Some conditions only
result in fatal burden (eg, sudden infant death syn-
drome), whereas others cause only disability (eg, scabies);
most causes have both fatal and non-fatal burden.
Comprehensive methods for cause-specific mortality
estimation for GBD have been previously described20 and
are detailed in the appendix (p 35). We present most
results at level 3 because this level is suciently detailed
to reflect important cause groupings for the age groups
presented in this analysis (eg, neonatal disorders and
congenital birth defects), but not so detailed as to obscure
important groupings of related conditions.
Scenarios for 2030 and beyond
U5MR and NMR were projected for six scenarios, all
computed at the national level, up to 2030 as previously
described.21 The first three scenarios represent the
reference, better-than-reference, and worse-than-refer-
ence scenarios, while a fourth represents the 2030 NMR
and U5MR in the absence of COVID-19. The remaining
two scenarios are intended to assess outcomes for
interventions that focus only on specific age groups, to
evaluate if opportunity is greater in a particular age group
than in others, and to show the limits of achievement
when eorts do not consider distinct needs of dierent
age groups. For the first of these age-specific scenarios,
neonatal mortality is at the better-than-reference level
and remaining under-5 mortality stays at reference level
(neonatal scenario), and for the second, mortality for
children aged 28–364 days is at the better-than-reference
level and neonatal mortality stays at the reference level
(child scenario). Many strategies to address neonatal
mortality are fundamentally dierent from strategies
targeting older infants and children, so these two
scenarios are a broad representation of those dierences.
Assessment of optimal survival potential
Our approach to inform an assessment of preventable
mortality focused on the quantification of two dierent
measures of optimal child survival based on historical
trends. The first measure, what we term the global
optimum, represents a universal level at which all
additional mortality is theoretically avoidable given
current medical knowledge and technology. This is
analogous to the GBD method used for estimating global
standard life expectancy. The second measure, what we
term the survival potential frontier, aims to quantify the
amount of mortality that is avoidable given the country’s
level of Healthcare Access and Quality (HAQ) Index,
thereby accounting for the dierential resources available
for health investment in dierent locations.
First, we calculated the global optimum for NMR and
U5MR based on the aggregate of the lowest observed
age-specific and cause-specific mortality rates in locations
with populations higher than 10 000 children younger
than 5 years (to remove noise associated with small
numbers) between 2000 and 2019, scaling them to match
an all-cause mortality minimum that was calculated
using the same approach. The scaling step was added to
account for potential dierences due to small numbers
in low-mortality settings or geographical dierences in
cause assignment that can occur between, for example,
subcauses of neonatal disorders. This method is
analogous to that used by GBD to calculate a global
standard life expectancy for the purposes of calculating
years of life lost and therefore can be interpreted to
represent the optimum potential for reductions in child
mortality based on current technology and health delivery
systems.
Second, to help with developing intermediate goals and
to evaluate progress in higher-mortality settings, we
calculated a survival potential frontier using stochastic
frontier analysis22 to evaluate the historical relationship
between cause-specific neonatal and under-5 mortality
rates and HAQ Index,23 which is an aggregate metric of
health system per formance across all age groups
combined. The specific formulation of the stochastic
frontier analysis is described in detail in the appendix
(p 70), but briefly, it uses a spline to estimate the expected
lower bound of mortality for a given value of HAQ Index.
Stochastic frontier analysis was chosen to quantify
system ineciency because of its flexibility in shape, its
assumption of performance possibilities given static
system inputs, and the fact that it allows for random
eects in the model rather than assuming uniformity of
inputs across locations.
All components of the analysis are based on 1000 draws
of the posterior distribution of the quantity of interest
by age, sex, location, and year. Point estimates are the
mean of the draws, and 95% uncertainty intervals (UIs)
represent the 2·5th and 97·5th percentiles.
Presentation of results
Results are presented by country, GBD super-region,
and Socio-demographic Index (SDI)24 quintile. SDI is a
composite index of income per capita, educational
attain ment, and inverse fertility, and it is used to
categorise countries into SDI quintiles: low SDI (ie, low
income per capita, low educational attainment, high
fertility), low-middle SDI, middle SDI, high-middle SDI,
and high SDI. Full results for GBD 2019 are available
in an online visualisation at GBD Compare and for
download from the GBD Results Tool.
Role of the funding source
The funders of the study had no role in study design,
data collection, data analysis, data interpretation, or
writing of the report.
Results
All-cause mortality and progress towards SDG 3.2
Over the past two decades, there has been a substantial
decrease in global deaths of children younger than
5 years, from 9·65 million (95% UI 9·05–10·30) in 2000,
For more on the GBD Compare
see
https://vizhub.healthdata.
org/gbd-compare
For more on the GBD Results
Tool see http://ghdx.healthdata.
org/gbd-results-tool
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Neonatal deaths NMR Under-5 deaths U5MR
2000 2015 2019 2019 2030* 2000 2015 2019 2019 2030*
SDI regions
Global 3 760 000
(3 530 000–4 020 000)
2 820 000
(2 480 000–3 200 000)
2 420 000
(2 060 000–2 860 000)
17·9
(16·3–19·8)
15·4 9 650 000
(9 050 000–10 300 000)
6 100 000
(5 350 000–6 910 000)
5 050 000
(4 270 000–6 020 000)
37·1
(33·2–41·7)
29·6
Low SDI 1 260 000
(1 190 000–1 340 000)
1 190 000
(1 030 000–1 370 000)
1 110 000
(918 000–1 340 000)
27·0
(24·0–30·8)
21·4 4 010 000
(3 780 000–4 260 000)
3 040 000
(2 630 000–3 520 000)
2 670 000
(2 220 000–3 240 000)
71·8
(63·3–82·5)
47·0
Low-middle SDI 1 480 000
(1 370 000–1 600 000)
1 020 000
(883 000–1 170 000)
841 000
(716 000–985 000)
21·7
(19·7–24·0)
19·1 3 390 000
(3 140 000–3 630 000)
1 890 000
(1 640 000–2 150 000)
1 490 000
(1 260 000–1 750 000)
42·0
(37·8–46·7)
30·3
Middle SDI 777 000
(724 000–835 000)
479 000
(419 000–546 000)
368 000
(312 000–432 000)
10·1
(9·11–11·2)
16·3 1 730 000
(1 610 000–1 850 000)
912 000
(803 000–1 040 000)
686 000
(583 000–810 000)
18·9
(17·1–21·0)
27·3
High-middle SDI 199 000
(187 000–213 000)
104 000
(94 200–115 000)
78 100
(67 100–90 900)
5·10
(4·71–5·55)
3·30 427 000
(400 000–455 000)
197 000
(180 000–217 000)
150 000
(130 000–172 000)
9·36
(8·66–10·2)
6·12
High SDI 43 500
(41 600–45 300)
30 500
(29 300–31 700)
26 800
(24 300–29 600)
2·60
(2·51–2·70)
2·57 84 400
(81 000–88 300)
55 800
(54 200–57 600)
48 600
(44 500–53 200)
4·70
(4·56–4·86)
5·02
GBD super-regions
Central Europe,
eastern Europe,
and central Asia
57 800
(54 300–61 800)
39 400
(35 500–43 800)
30 800
(26 400–36 000)
5·88
(5·35–6·52)
4·95 127 000
(119 000–135 000)
77 900
(70 200–86 900)
61 100
(52 200–72 100)
11·5
(10·4–12·8)
9·34
Central Asia 31 600
(28 300–35 100)
25 400
(21 900–29 400)
20 500
(17 200–24 600)
10·8
(9·62–12·2)
8·99 75 400
(68 200–82 900)
49 300
(42 200–57 700)
39 700
(33 200–48 400)
20·7
(18·3–23·7)
16·2
Armenia 661
(595–733)
310
(256–378)
230
(181–292)
5·96
(5·04–7·21)
4·46 1290
(1140–1460)
605
(502–732)
452
(357–575)
11·4
(9·55–13·7)
8·90
Azerbaijan 4450
(3710–5260)
3410
(2860–3990)
2590
(2130–3110)
16·8
(14·8–19·4)
14·0 9530
(8060–11 100)
5750
(4760–6910)
4310
(3430–5370)
27·6
(23·2–33·4)
21·5
Georgia 1060
(892–1250)
376
(308–457)
266
(208–336)
5·79
(4·76–7·10)
4·04 1740
(1480–2040)
669
(553–811)
482
(382–603)
10·2
(8·57–12·5)
7·02
Kazakhstan 3260
(2790–3780)
2520
(2050–3070)
1970
(1530–2600)
5·60
(4·61–6·88)
4·09 8300
(7250–9410)
5420
(4450–6550)
4330
(3410–5540)
12·1
(10·1–14·7)
8·86
Kyrgyzstan 1960
(1760–2170)
2060
(1870–2230)
1560
(1360–1790)
10·8
(9·91–11·8)
8·57 4380
(3910–4890)
3290
(3090–3490)
2520
(2210–2870)
17·4
(15·9–19·0)
13·0
Mongolia 1270
(1120–1430)
990
(836–1170)
773
(633–966)
9·29
(7·86–11·1)
6·89 3400
(3030–3780)
1810
(1550–2120)
1430
(1180–1770)
17·0
(14·5–20·4)
10·2
Tajikistan 4310
(3650–5070)
4100
(3440–4800)
3730
(3180–4380)
14·7
(13·2–16·4)
11·8 12 500
(11 200–13 900)
9220
(7780–11 000)
8100
(6540–10 000)
32·1
(27·5–37·3)
21·8
Turkmenistan 2260
(1900–2640)
1870
(1590–2170)
1510
(1290–1770)
13·4
(11·7–15·1)
10·3 5990
(5240–6870)
3620
(3000–4260)
2870
(2380–3480)
25·2
(21·4–29·6)
19·4
Uzbekistan 12 300
(10 600–14 400)
9760
(7900–11 900)
7900
(6360–10 000)
11·1
(9·25–13·4)
10·0 28 300
(24 700–32 800)
18 900
(15 400–22 900)
15 200
(12 400–19 200)
21·2
(17·8–25·7)
18·2
Central Europe 8250
(7940–8590)
3720
(3560–3890)
2930
(2340–3670)
2·72
(2·44–3·03)
1·99 16 700
(16 200–17 200)
6990
(6690–7290)
5550
(4520–6800)
5·06
(4·52–5·63)
3·67
Albania 845
(739–980)
266
(202–345)
217
(150–317)
5·77
(4·39–7·68)
4·81 1760
(1530–2030)
550
(450–676)
451
(344–595)
11·9
(9·94–14·4)
9·15
Bosnia and
Herzegovina
320
(294–344)
138
(125–153)
103
(85·3–126)
3·95
(3·35–4·67)
3·22 438
(406–471)
191
(173–211)
143
(120–173)
5·41
(4·59–6·39)
4·38
Bulgaria 534
(487–582)
258
(236–280)
214
(168–271)
3·54
(3·03–4·16)
2·71 1220
(1160–1280)
532
(502–566)
447
(359–556)
7·29
(6·21–8·57)
5·61
Croatia 236
(220–254)
115
(104–126)
90·5
(67·5–120)
2·56
(2·17–3·02)
1·93 361
(339–383)
181
(165–198)
141
(107–184)
3·95
(3·35–4·67)
2·96
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2000 2015 2019 2019 2030* 2000 2015 2019 2019 2030*
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Czech Republic 236
(215–256)
174
(158–190)
159
(123–201)
1·45
(1·26–1·67)
1·08 483
(454–513)
342
(318–365)
293
(232–369)
2·64
(2·28–3·06)
1·87
Hungary 572
(530–615)
252
(229–275)
173
(132–224)
2·09
(1·81–2·42)
1·37 1020
(970–1070)
489
(458–521)
336
(262–428)
4·00
(3·46–4·62)
2·80
Montenegro 77·4
(68·7–86·7)
18·1
(15·4–21·4)
15·1
(12·5–18·3)
2·29
(1·96–2·67)
1·60 116
(104–130)
29·8
(25·5–34·6)
24·9
(20·6–30·0)
3·74
(3·19–4·38)
2·58
North
Macedonia
226
(203–248)
155
(140–170)
123
(99·9–149)
5·52
(4·70–6·49)
4·13 399
(366–434)
239
(215–263)
191
(155–230)
8·51
(7·23–10·0)
6·00
Poland 1920
(1780–2060)
988
(916–1060)
787
(579–1060)
2·15
(1·84–2·52)
1·36 3530
(3380–3680)
1770
(1680–1850)
1420
(1070–1880)
3·85
(3·29–4·51)
2·60
Romania 2090
(1920–2260)
884
(815–953)
690
(556–842)
3·98
(3·50–4·60)
3·17 5130
(5010–5260)
1790
(1710–1870)
1420
(1160–1730)
8·03
(7·02–9·33)
5·89
Serbia 865
(736–1010)
269
(250–291)
196
(154–245)
2·45
(2·08–2·92)
1·47 1590
(1360–1860)
454
(426–486)
334
(263–421)
4·12
(3·51–4·93)
2·42
Slovakia 266
(243–289)
174
(160–187)
141
(106–186)
2·52
(2·15–2·97)
1·85 533
(502–565)
364
(340–389)
301
(232–390)
5·33
(4·53–6·27)
4·45
Slovenia 57·9
(53·4–63·0)
31·4
(28·1–35·3)
23·8
(17·9–31·5)
1·26
(1·09–1·46)
0·930 96·1
(88·8–104)
49·4
(44·1–55·4)
38·1
(29·3–49·5)
1·98
(1·70–2·31)
1·43
Eastern Europe 18 000
(17 400–18 600)
10 300
(9920–10 600)
7340
(6140–8710)
3·27
(3·02–3·55)
2·41 34 500
(33 900–35 200)
21 600
(21 000–22 200)
15 900
(13 300–18 600)
6·87
(6·26–7·54)
5·29
Belarus 746
(628–875)
328
(271–394)
244
(188–310)
2·38
(1·99–2·90)
1·51 1510
(1280–1780)
730
(607–884)
562
(437–729)
5·31
(4·44–6·46)
3·64
Estonia 67·4
(62·2–72·9)
19·7
(17·5–22·1)
15·1
(12·4–18·4)
1·14
(0·980–1·35)
0·710 142
(132–152)
46·0
(40·9–51·5)
35·6
(29·5–43·4)
2·65
(2·27–3·14)
1·64
Latvia 139
(127–152)
55·4
(50·2–60·3)
41·0
(33·9–49·5)
2·14
(1·86–2·50)
1·54 272
(256–288)
108
(98·3–117)
82·8
(69·3–99·3)
4·21
(3·63–4·95)
3·01
Lithuania 156
(145–167)
70·8
(63·4–78·3)
48·6
(41·9–56·2)
1·80
(1·65–1·98)
1·20 369
(350–389)
155
(143–168)
110
(93·8–130)
4·00
(3·52–4·62)
2·83
Moldova 734
(639–847)
374
(304–460)
278
(214–361)
8·64
(7·14–10·7)
6·69 1240
(1090–1430)
536
(438–647)
399
(315–505)
12·2
(10·2–14·7)
8·96
Russia 12 400
(11 900–12 900)
7040
(6790–7270)
4990
(4010–6050)
3·00
(2·65–3·38)
2·19 24 500
(24 200–24 900)
15 200
(15 000–15 500)
11 200
(9190–13 400)
6·53
(5·75–7·41)
5·04
Ukraine 3760
(3460–4080)
2360
(2130–2600)
1720
(1390–2120)
4·45
(3·89–5·12)
3·45 6440
(6110–6770)
4770
(4360–5180)
3500
(2920–4260)
8·76
(7·85–9·73)
7·05
High income 47 600
(46 200–49 000)
35 400
(34 300–36 500)
31 200
(27 400–35 500)
2·78
(2·70–2·88)
2·39 88 900
(88 200–89 700)
63 500
(62 700–64 400)
55 600
(49 700–62 600)
4·95
(4·78–5·12)
4·14
Australasia 1060
(1020–1110)
886
(847–927)
794
(677–931)
2·14
(2·03–2·26)
1·77 1980
(1930–2030)
1530
(1480–1580)
1380
(1200–1590)
3·73
(3·53–3·95)
2·96
Australia 863
(824–902)
710
(680–744)
647
(558–751)
2·08
(1·98–2·18)
1·68 1550
(1510–1590)
1220
(1180–1250)
1110
(973–1260)
3·57
(3·41–3·76)
2·80
New Zealand 199
(187–212)
175
(165–187)
147
(120–180)
2·46
(2·26–2·68)
2·21 431
(411–452)
313
(296–331)
270
(225–325)
4·53
(4·16–4·95)
3·80
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(Continued from previous page)
High-income Asia
Pacific
3830
(3530–4140)
1730
(1590–1870)
1430
(1290–1590)
1·04
(0·990–1·08)
0·810 9500
(9190–9820)
4440
(4240–4650)
3670
(3350–4000)
2·62
(2·52–2·71)
2·03
Brunei 36·4
(32·0–41·1)
34·7
(30·8–39·2)
31·9
(24·4–41·7)
4·85
(4·09–5·73)
4·43 70·8
(63·2–79·0)
66·8
(59·4–75·0)
60·2
(46·7–77·5)
9·19
(7·73–10·9)
8·27
Japan 2100
(1860–2370)
964
(880–1050)
782
(697–880)
0·870
(0·850–0·890)
0·640 5290
(5190–5410)
2740
(2650–2830)
2240
(2060–2450)
2·43
(2·36–2·51)
1·86
Singapore 95·7
(86·6–106)
58·8
(44·2–78·8)
50·5
(35·2–71·0)
0·880
(0·770–1·00)
0·650 198
(183–215)
123
(96·5–157)
105
(79·1–140)
1·82
(1·60–2·09)
1·35
South Korea 1600
(1450–1770)
673
(589–774)
567
(481–658)
1·37
(1·23–1·51)
1·15 3930
(3660–4200)
1500
(1370–1650)
1260
(1100–1450)
3·03
(2·82–3·26)
2·43
High-income
North America
19 900
(18 700–21 000)
16 800
(15 800–17 700)
15 200
(14 000–16 500)
3·61
(3·55–3·67)
3·29 35 400
(35 200–35 700)
29 500
(29 200–29 800)
26 600
(24 600–28 700)
6·32
(6·18–6·47)
5·57
Canada 1200
(1120–1280)
1220
(1140–1310)
1110
(996–1250)
2·98
(2·86–3·10)
2·66 2040
(2000–2090)
2010
(1960–2060)
1820
(1640–2010)
4·86
(4·67–5·07)
4·23
Greenland 9·98
(8·56–11·5)
4·92
(3·88–6·22)
4·10
(2·66–6·22)
5·14
(3·74–6·97)
3·78 18·8
(15·9–22·0)
9·09
(7·09–11·6)
7·59
(4·97–11·4)
9·47
(6·85–12·9)
6·69
USA† 18 700
(17 500–19 800)
15 500
(14 600–16 400)
14 000
(13 000–15 300)
3·67
(3·62–3·73)
3·36 33 400
(33 100–33 600)
27 500
(27 200–27 800)
24 700
(23 000–26 700)
6·46
(6·33–6·60)
5·71
Southern Latin
America
9300
(9060–9520)
6180
(5950–6420)
5240
(4140–6640)
5·38
(5·08–5·72)
4·30 17 100
(16 900–17 300)
11 100
(10 900–11 300)
9370
(7600–11 600)
9·61
(9·09–10·2)
7·59
Argentina 7380
(7150–7610)
4810
(4590–5020)
4120
(3300–5160)
5·89
(5·70–6·10)
4·67 13 400
(13 200–13 500)
8710
(8580–8840)
7420
(6110–9050)
10·6
(10·3–11·0)
8·37
Chile 1420
(1360–1500)
1140
(1090–1180)
907
(686–1200)
3·98
(3·41–4·65)
3·31 2840
(2750–2930)
1960
(1880–2030)
1560
(1200–2020)
6·84
(5·87–7·99)
5·44
Uruguay 493
(442–545)
240
(207–276)
211
(154–286)
4·54
(3·97–5·19)
3·37 884
(818–955)
439
(395–486)
388
(289–516)
8·29
(7·26–9·48)
6·11
Western Europe 13 500
(13 000–14 000)
9810
(9300–10 300)
8550
(7370–9960)
2·00
(1·91–2·10)
1·61 24 900
(24 700–25 200)
17 000
(16 600–17 300)
14 700
(12 900–16 800)
3·42
(3·29–3·57)
2·69
Andorra 1·11
(0·900–1·35)
0·585
(0·469–0·729)
0·516
(0·384–0·674)
0·820
(0·690–0·980)
0·540 2·59
(2·06–3·15)
1·30
(1·06–1·62)
1·11
(0·843–1·43)
1·77
(1·48–2·10)
1·16
Austria 238
(217–257)
186
(170–201)
166
(141–192)
1·90
(1·69–2·10)
1·52 445
(427–463)
307
(294–322)
282
(252–316)
3·22
(3·03–3·42)
2·50
Belgium 343
(303–387)
258
(222–291)
230
(189–279)
1·89
(1·78–2·01)
1·48 690
(666–715)
480
(457–502)
423
(354–505)
3·48
(3·27–3·71)
2·68
Cyprus 43·4
(38·7–48·6)
28·7
(24·4–33·3)
27·3
(19·9–36·6)
1·80
(1·42–2·24)
1·31 77·3
(69·6–85·7)
49·3
(42·2–57·1)
47·9
(35·4–63·2)
3·17
(2·52–3·94)
2·32
Denmark 216
(187–245)
157
(143–171)
145
(118–179)
2·31
(2·12–2·52)
1·90 371
(348–392)
237
(221–255)
218
(179–264)
3·48
(3·20–3·79)
2·73
Finland 136
(124–149)
65·4
(59·4–71·9)
58·9
(49·3–70·5)
1·18
(1·08–1·29)
0·860 244
(230–258)
125
(117–134)
110
(94·0–130)
2·20
(2·01–2·41)
1·63
France 2150
(1910–2370)
1740
(1590–1900)
1480
(1270–1720)
2·05
(1·95–2·16)
1·72 4160
(4080–4250)
3110
(3040–3190)
2600
(2280–2960)
3·60
(3·42–3·79)
2·87
Germany 2110
(1920–2280)
1610
(1490–1730)
1440
(1320–1580)
1·95
(1·88–2·03)
1·64 4120
(4050–4190)
2730
(2660–2790)
2450
(2250–2670)
3·33
(3·21–3·47)
2·63
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Greece 390
(361–416)
242
(223–262)
188
(152–233)
2·17
(1·99–2·38)
1·54 643
(617–671)
484
(459–508)
339
(279–413)
3·85
(3·53–4·21)
2·87
Iceland 9·77
(8·00–11·8)
6·69
(5·23–8·58)
6·28
(3·84–9·95)
1·45
(1·04–2·00)
1·20 15·9
(13·1–19·0)
10·9
(8·48–13·8)
9·95
(6·21–15·5)
2·31
(1·65–3·20)
1·84
Ireland 221
(201–241)
157
(141–175)
124
(97·7–157)
2·04
(1·88–2·21)
1·60 385
(364–407)
253
(234–274)
200
(160–249)
3·25
(3·00–3·54)
2·48
Israel 483
(425–544)
369
(325–413)
331
(265–408)
1·72
(1·65–1·79)
1·27 920
(896–944)
675
(656–696)
609
(508–724)
3·18
(3·06–3·30)
2·37
Italy 1710
(1510–1890)
982
(906–1050)
770
(680–878)
1·75
(1·69–1·81)
1·20 2980
(2930–3030)
1710
(1670–1750)
1320
(1190–1480)
2·98
(2·88–3·08)
2·14
Luxembourg 14·0
(11·7–16·5)
9·02
(7·20–11·1)
8·56
(5·35–13·3)
1·32
(0·930–1·83)
1·00 27·3
(23·4–31·9)
16·7
(13·5–20·6)
15·5
(9·89–23·7)
2·42
(1·71–3·38)
1·84
Malta 19·7
(17·2–22·4)
18·7
(15·2–22·7)
16·3
(10·8–24·0)
3·80
(2·96–4·83)
3·08 31·1
(27·4–35·3)
27·7
(23·1–33·1)
24·7
(16·8–35·7)
5·74
(4·54–7·23)
4·68
Monaco 0·477
(0·323–0·667)
0·320
(0·229–0·433)
0·287
(0·220–0·367)
1·02
(0·850–1·23)
0·830 1·26
(0·895–1·69)
0·808
(0·606–1·05)
0·727
(0·560–0·925)
2·58
(2·15–3·10)
2·03
Netherlands 777
(723–828)
425
(383–469)
421
(345–513)
2·37
(2·26–2·49)
1·94 1280
(1240–1310)
674
(653–697)
659
(550–790)
3·72
(3·54–3·91)
2·96
Norway 155
(145–164)
92·9
(87·3–99·0)
80·1
(68·0–94·5)
1·41
(1·32–1·51)
1·10 284
(272–297)
166
(157–174)
142
(123–164)
2·50
(2·34–2·67)
1·93
Portugal 405
(363–445)
178
(154–202)
129
(100–166)
1·61
(1·49–1·75)
1·08 841
(802–884)
313
(289–340)
229
(181–286)
2·82
(2·60–3·06)
1·78
San Marino 0·977
(0·724–1·27)
0·655
(0·493–0·861)
0·606
(0·466–0·773)
1·95
(1·62–2·33)
1·57 1·83
(1·35–2·39)
1·23
(0·928–1·61)
1·13
(0·873–1·44)
3·63
(3·03–4·36)
2·85
Spain 1090
(983–1200)
760
(680–833)
603
(493–731)
1·63
(1·39–1·86)
1·20 2130
(2090–2170)
1400
(1360–1430)
1130
(996–1290)
2·98
(2·88–3·10)
2·19
Sweden 193
(182–203)
180
(153–208)
165
(141–191)
1·41
(1·27–1·54)
1·12 357
(341–375)
335
(317–352)
302
(265–345)
2·58
(2·38–2·81)
2·07
Switzerland 269
(244–294)
263
(242–284)
227
(200–259)
2·57
(2·43–2·72)
2·26 457
(440–475)
375
(358–392)
323
(286–365)
3·66
(3·46–3·88)
3·04
UK† 2510
(2420–2610)
2070
(1800–2290)
1920
(1590–2310)
2·45
(2·14–2·76)
2·09 4440
(4350–4530)
3470
(3400–3550)
3210
(2810–3660)
4·10
(3·97–4·25)
3·41
Latin America and
Caribbean
181 000
(164 000–198 000)
112 000
(94 800–131 000)
93 900
(74 900–116 000)
9·56
(8·28–11·1)
7·77 397 000
(369 000–427 000)
226 000
(192 000–263 000)
187 000
(149 000–231 000)
19·0
(16·2–22·3)
14·3
Andean Latin
America
22 700
(20 100–25 400)
14 900
(12 400–17 800)
12 600
(9620–16 300)
9·42
(8·38–10·6)
7·58 56 000
(51 000–61 500)
29 600
(25 000–34 800)
24 900
(19 200–31 900)
18·6
(16·5–21·1)
14·3
Bolivia 6710
(5980–7470)
5560
(4680–6610)
4840
(3880–5990)
14·8
(12·6–17·7)
12·2 18 300
(16 500–20 100)
11 400
(9660–13 400)
9630
(7800–11 800)
29·5
(25·2–35·1)
22·7
Ecuador 5400
(4430–6510)
3050
(2290–3990)
2720
(1870–3830)
7·74
(6·19–9·65)
6·22 11 300
(9570–13 100)
5980
(4730–7430)
5300
(3790–7240)
15·1
(12·7–18·1)
11·5
Peru 10 600
(9120–12 200)
6290
(4750–8220)
5010
(3550–6960)
7·61
(6·37–9·11)
5·92 26 400
(23 100–29 700)
12 200
(9430–15 400)
9980
(7070–13 800)
15·1
(12·6–18·1)
11·4
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(Continued from previous page)
Caribbean 18 100
(16 500–19 800)
17 200
(14 200–20 300)
15 800
(12 300–20 000)
19·3
(15·8–23·3)
16·5 44 900
(40 800–49 200)
36 300
(31 000–42 500)
32 000
(26 000–39 000)
38·8
(33·2–45·3)
28·9
Antigua and
Barbuda
13·4
(10·8–16·4)
6·15
(4·56–8·09)
5·34
(3·80–7·37)
5·35
(4·61–6·19)
4·47 20·9
(16·9–25·4)
12·2
(9·04–16·2)
10·4
(7·22–14·6)
10·3
(8·61–12·3)
8·97
The Bahamas 38·0
(31·0–45·1)
28·1
(20·4–39·2)
24·7
(18·5–32·7)
6·08
(4·94–7·62)
5·32 80·5
(67·4–94·1)
53·8
(41·1–71·1)
48·2
(37·0–62·2)
11·7
(9·94–14·2)
10·3
Barbados 40·6
(33·5–48·9)
27·0
(20·5–34·8)
24·7
(17·0–35·3)
8·64
(7·24–10·3)
7·85 57·4
(47·5–68·7)
38·0
(29·2–48·6)
34·9
(24·1–49·7)
12·2
(10·2–14·5)
10·9
Belize 87·6
(76·5–101)
72·9
(60·3–86·7)
69·1
(56·4–83·9)
9·13
(7·97–10·6)
8·21 174
(150–202)
124
(101–147)
116
(92·3–144)
15·4
(13·0–18·6)
12·4
Bermuda 2·43
(1·91–3·08)
1·66
(1·32–2·05)
1·40
(0·971–1·95)
2·71
(2·24–3·28)
2·31 4·76
(3·94–5·80)
2·96
(2·39–3·63)
2·44
(1·76–3·34)
4·66
(3·92–5·54)
3·72
Cuba 608
(553–664)
330
(294–368)
236
(188–291)
2·26
(1·98–2·59)
1·67 1260
(1210–1310)
703
(666–741)
503
(409–608)
4·74
(4·12–5·43)
3·40
Dominica 15·8
(12·6–19·4)
14·6
(11·2–18·6)
13·5
(9·43–18·9)
16·2
(13·6–19·3)
19·0 25·8
(20·6–31·5)
23·6
(18·3–30·1)
21·7
(15·3–30·2)
26·0
(21·8–31·0)
29·7
Dominican
Republic
5570
(4700–6490)
4450
(3460–5560)
3910
(2800–5370)
16·9
(14·1–20·2)
14·8 10 100
(8860–11 400)
6740
(5430–8290)
5850
(4230–7950)
25·2
(21·1–30·2)
20·4
Grenada 19·8
(15·0–25·6)
14·1
(10·4–18·9)
12·1
(8·43–17·1)
8·62
(7·26–10·2)
7·58 35·4
(27·5–44·3)
23·1
(17·1–30·9)
19·5
(13·6–27·5)
13·8
(11·6–16·5)
11·7
Guyana 418
(354–490)
241
(179–319)
217
(153–303)
15·0
(12·6–17·9)
12·7 692
(606–777)
377
(278–495)
333
(236–462)
23·1
(19·4–27·4)
19·1
Haiti 8840
(7850–9890)
10 300
(8020–12 900)
9810
(7690–12 200)
29·5
(23·0–36·8)
24·4 28 200
(24 900–31 800)
25 400
(21 600–29 900)
22 600
(18 800–27 000)
68·3
(58·7–79·5)
47·8
Jamaica 732
(578–913)
525
(407–670)
454
(320–627)
12·6
(10·6–15·0)
11·7 1010
(807–1240)
656
(513–839)
568
(403–785)
15·7
(13·1–18·7)
13·4
Puerto Rico 432
(399–467)
150
(133–167)
128
(94·5–171)
5·03
(4·27–5·92)
4·17 650
(617–686)
239
(223–258)
197
(148–259)
7·67
(6·52–9·04)
6·49
Saint Kitts and
Nevis
12·2
(10·3–14·5)
8·43
(6·57–10·8)
7·10
(5·55–9·00)
10·2
(8·82–11·9)
8·77 18·9
(16·1–22·2)
12·7
(9·88–16·1)
10·7
(8·32–13·6)
15·3
(13·1–17·9)
11·4
Saint Lucia 38·1
(30·9–46·0)
22·6
(16·5–30·8)
18·9
(12·9–27·1)
10·6
(8·89–12·6)
9·87 55·9
(45·5–67·7)
31·4
(22·9–42·9)
26·3
(18·0–37·4)
14·6
(12·2–17·4)
13·0
Saint Vincent
and the
Grenadines
35·3
(29·0–41·9)
18·1
(13·8–23·7)
14·8
(10·5–20·5)
9·74
(8·26–11·5)
8·49 55·2
(44·3–68·0)
28·4
(21·6–37·3)
23·4
(16·4–32·8)
15·2
(12·8–18·2)
12·5
Suriname 246
(210–287)
182
(146–224)
155
(109–215)
16·8
(14·1–20·0)
13·8 414
(353–481)
281
(228–344)
238
(169–326)
25·7
(21·6–30·6)
20·4
Trinidad and
Tobago
327
(274–393)
185
(140–238)
156
(110–218)
10·1
(8·48–12·1)
8·84 478
(397–575)
281
(219–354)
238
(169–329)
15·2
(12·7–18·2)
13·1
Virgin Islands 14·9
(12·2–17·8)
7·08
(5·50–8·98)
5·90
(4·21–8·11)
4·62
(3·91–5·46)
3·57 22·7
(18·8–27·2)
10·6
(8·16–13·3)
8·76
(6·28–12·0)
6·79
(5·74–8·05)
5·26
Central Latin
America
70 000
(61 400–79 900)
40 600
(34 000–48 200)
33 200
(26 400–41 200)
7·50
(6·65–8·47)
6·02 143 000
(130 000–157 000)
80 100
(67 100–94 100)
65 400
(50 700–83 700)
14·8
(12·4–17·5)
11·2
Colombia 13 400
(11 300–15 700)
6610
5020–8340)
5410
(3660–7660)
6·68
(5·27–8·35)
5·21 24 900
(22 000–28 200)
12 500
(9900–15 400)
10 300
(7410–13 900)
12·6
(10·6–15·0)
9·85
Costa Rica 553
(503–606)
412
(375–452)
338
(242–466)
5·07
(4·33–5·94)
4·32 959
(889–1040)
641
(589–695)
532
(388–717)
7·93
(6·76–9·30)
6·59
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2000 2015 2019 2019 2030* 2000 2015 2019 2019 2030*
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El Salvador 1770
(1400–2150)
792
(606–1020)
593
(412–835)
5·26
(4·11–6·59)
3·86 4290
(3730–4950)
1680
(1340–2100)
1240
(896–1690)
10·9
(9·18–13·1)
7·90
Guatemala 7130
(6170–8200)
3830
(3090–4680)
3440
(2460–4720)
8·35
(7·07–9·86)
6·22 18 100
(16 000–20 300)
10 400
(8520–12 500)
8870
(6410–12 100)
21·7
(18·3–25·8)
14·8
Honduras 3300
(2790–3850)
2500
(1990–3110)
2180
(1710–2720)
9·33
(7·81–11·1)
7·53 6880
(5900–7990)
4570
(3650–5630)
3970
(3140–4940)
17·2
(14·4–20·5)
12·9
Mexico 35 300
(28 000–43 400)
19 200
(15 400–24 200)
15 700
(12 100–20 200)
7·44
(6·52–8·53)
6·12 69 800
(61 700–78 900)
37 400
(30 500–45 200)
30 300
(24 200–37 500)
14·4
(12·2–17·1)
11·2
Nicaragua 1930
(1580–2280)
1070
(823–1360)
860
(653–1100)
6·58
(5·40–8·01)
4·84 4570
(3930–5230)
2350
(1930–2830)
1880
(1530–2300)
14·4
(12·1–17·1)
10·0
Panama 617
(509–741)
539
(447–646)
490
(355–665)
6·42
(5·39–7·64)
4·98 1430
(1200–1700)
1210
(1000–1440)
1100
(818–1460)
14·5
(12·1–17·3)
11·5
Venezuela 5990
(5650–6320)
5640
(4850–6590)
4240
(3000–5870)
8·83
(7·42–10·5)
7·64 11 700
(11 100–12 400)
9370
(8140–10 800)
7220
(5200–9880)
14·8
(12·4–17·6)
11·8
Tropical Latin
America
70 000
(61 000–79 000)
39 600
(32 300–47 300)
32 300
(25 500–39 700)
9·96
(8·41–11·8)
8·10 154 000
(138 000–169 000)
79 800
(66 800–95 400)
64 600
(51 300–79 200)
19·8
(16·7–23·4)
15·1
Brazil† 68 100
(59 200–77 200)
38 700
(31 500–46 500)
31 600
(24 900–38 700)
10·1
(8·55–12·0)
8·26 150 000
(135 000–166 000)
77 700
(65 100–92 700)
62 800
(49 900–77 000)
20·0
(16·9–23·7)
15·3
Paraguay 1900
(1590–2220)
853
(648–1100)
733
(525–1010)
5·80
(4·88–6·90)
4·53 3490
(2950–4040)
2060
(1580–2650)
1770
(1280–2410)
14·0
(11·8–16·6)
12·0
North Africa and
Middle East
298 000
(268 000–328 000)
182 000
(159 000–210 000)
150 000
(129 000–173 000)
12·2
(11·1–13·3)
9·82 682 000
(623 000–742 000)
382 000
(333 000–442 000)
300 000
(255 000–353 000)
24·4
(22·3–26·7)
18·2
Afghanistan 38 900
(32 600–45 400)
34 900
(29 100–41 100)
37 400
(31 300–44 200)
25·0
(21·6–28·4)
19·5 120 000
(108 000–133 000)
83 800
(71 000–99 000)
81 400
(67 900–97 200)
55·3
(47·9–63·5)
37·2
Algeria 15 600
(12 800–18 600)
13 600
(11 200–16 100)
10 700
(8220–13 400)
12·0
(9·83–14·4)
10·4 29 400
(25 400–33 700)
22 200
(19 000–25 900)
17 300
(14 500–20 500)
19·5
(17·0–22·4)
16·0
Bahrain 64·5
(57·6–72·6)
42·9
(35·9–51·4)
30·7
(24·6–38·7)
2·36
(2·13–2·59)
1·57 158
(142–175)
126
(107–147)
87·3
(69·8–109)
6·53
(5·79–7·36)
5·02
Egypt 40 500
(33 400–47 600)
15 400
(12 000–20 100)
11 800
(8250–16 200)
5·55
(4·20–7·20)
3·11 84 400
(72 600–96 300)
47 400
(38 200–57 600)
32 600
(24 600–42 600)
15·3
(12·8–18·3)
8·27
Iran 33 700
(27 500–40 300)
18 000
(15 500–20 800)
9140
(7440–11 100)
6·77
(6·09–7·44)
5·19 60 800
(50 200–72 500)
29 100
(25 500–33 200)
15 200
(12 700–18 400)
11·1
(10·2–12·0)
7·83
Iraq 22 100
(19 500–25 000)
12 300
(9300–16 300)
9130
(6610–12 800)
9·49
(7·92–11·5)
7·51 40 700
(35 800–46 000)
23 000
(18 100–29 100)
15 000
(11 000–20 700)
15·7
(13·2–18·9)
11·9
Jordan 2160
(1830–2520)
2070
(1610–2660)
2130
(1570–2930)
8·80
(7·46–10·6)
6·72 3610
(3040–4220)
3540
(2750–4520)
3640
(2680–5010)
15·3
(13·0–18·3)
11·3
Kuwait 262
(234–292)
369
(324–422)
310
(242–402)
5·09
(4·30–5·99)
4·46 500
(463–545)
659
(599–730)
555
(440–708)
9·18
(7·75–10·8)
8·03
Lebanon 1050
(813–1330)
684
(497–938)
521
(373–734)
4·82
(4·02–5·89)
3·65 1920
(1510–2420)
1290
(958–1730)
983
(708–1380)
8·99
(7·51–11·0)
6·99
Libya 1650
(1350–1990)
560
(436–712)
458
(351–583)
5·62
(4·73–6·69)
4·43 3240
(2640–3890)
1470
(1180–1800)
1110
(869–1390)
13·3
(11·4–15·7)
10·4
Morocco 22 200
(17 600–27 300)
9820
(7510–12 500)
6760
(5120–8700)
11·1
(9·74–12·5)
7·92 40 800
(33 500–48 200)
16 200
(11 700–21 900)
11 100
(7860–15 400)
17·9
(15·0–21·4)
11·9
Oman 478
(408–553)
504
(445–564)
418
(357–482)
5·38
(4·86–5·90)
4·25 926
(792–1070)
958
(842–1070)
809
(690–937)
10·4
(9·40–11·4)
8·17
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Neonatal deaths NMR Under-5 deaths U5MR
2000 2015 2019 2019 2030* 2000 2015 2019 2019 2030*
(Continued from previous page)
Palestine 1720
(1450–2000)
1050
(803–1320)
800
(606–1060)
6·36
(5·38–7·63)
5·67 3750
(3350–4160)
2060
(1620–2580)
1560
(1180–2060)
12·4
(10·5–14·8)
10·1
Qatar 107
(85·9–133)
127
(103–155)
113
(90·6–144)
4·22
(3·52–5·13)
3·45 199
(165–239)
239
(195–291)
214
(172–274)
8·02
(6·71–9·74)
6·62
Saudi Arabia 5910
(4690–7240)
1670
(1320–2100)
1200
(950–1520)
2·64
(2·20–3·24)
1·48 13 000
(9880–16 500)
3830
(3060–4750)
2610
(2070–3300)
5·73
(4·77–7·01)
3·39
Sudan 43 600
(36 800–50 700)
30 700
(24 800–37 300)
25 700
(20 600–32 500)
21·3
(18·9–24·1)
17·0 124 000
(111 000–137 000)
65 100
(51 100–81 800)
50 700
(37 300–68 600)
41·9
(35·7–50·0)
30·0
Syria 5300
(4670–5950)
3320
(2850–3830)
1590
(1320–1900)
6·88
(5·72–8·32)
5·92 10 800
(10 000–11 700)
8940
(7980–10 100)
3210
(2560–3990)
13·6
(11·7–15·8)
12·0
Tunisia 3590
(2900–4370)
1610
(1260–2010)
1140
(903–1420)
6·82
(5·73–8·12)
5·33 6140
(5160–7180)
2700
(2170–3290)
1920
(1530–2380)
11·3
(9·49–13·5)
8·45
Turkey 28 200
(23 400–33 500)
10 900
(8610–13 800)
8380
(6710–10 400)
8·54
(7·15–10·2)
6·62 61 200
(51 700–71 300)
19 500
(15 500–24 300)
15 100
(12 100–18 700)
15·4
(12·9–18·4)
11·4
United Arab
Emirates
305
(269–341)
227
(173–298)
145
(109–197)
2·58
(2·18–3·10)
2·40 587
(532–641)
455
(352–599)
295
(220–399)
4·96
(4·19–5·97)
4·37
Yemen 30 100
(25 300–35 200)
23 900
(19 300–29 200)
21 600
(17 700–26 000)
22·8
(18·8–27·4)
18·7 75 800
(67 600–84 400)
49 200
(40 800–58 800)
44 200
(36 700–53 100)
46·7
(40·2–54·4)
35·1
South Asia 1 560 000
(1 410 000–1 730 000)
1 110 000
(958 000–1 280 000)
899 000
(761 000–1 060 000)
26·9
(24·2–30·0)
21·8 3 040 000
(2 780 000–3 330 000)
1 760 000
(1 510 000–2 020 000)
1 360 000
(1 140 000–1 610 000)
40·5
(36·0–46·0)
29·0
Bangladesh 146 000
(130 000–163 000)
70 100
(54 500–87 600)
52 600
(40 400–66 300)
19·8
(16·4–23·7)
14·2 295 000
(267 000–325 000)
108 000
(90 000–128 000)
77 900
(63 000–94 200)
29·2
(24·9–34·2)
18·4
Bhutan 796
(661–929)
379
(279–499)
287
(207–393)
20·6
(17·5–24·2)
15·0 1580
(1320–1830)
602
(443–788)
445
(322–602)
31·5
(26·7–36·9)
22·2
India 1 070 000
(938 000–1 220 000)
720 000
(606 000–844 000)
558 000
(456 000–689 000)
23·8
(20·1–28·8)
18·8 2 130 000
(1 900 000–2 380 000)
1 150 000
(970 000–1 350 000)
841 000
(692 000–1 040 000)
35·8
(30·2–43·0)
24·6
Nepal 32 500
(28 500–36 700)
14 700
(12 000–18 000)
11 700
(9610–14 100)
19·0
(15·6–23·0)
13·5 65 900
(59 300–73 100)
24 200
(20 500–28 600)
18 000
(14 700–21 800)
29·1
(24·8–34·1)
18·9
Pakistan 309 000
(264 000–356 000)
305 000
(246 000–374 000)
277 000
(226 000–333 000)
41·6
(34·2–50·0)
34·5 552 000
(495 000–614 000)
478 000
(395 000–569 000)
420 000
(346 000–507 000)
63·3
(53·9–74·3)
48·0
Southeast Asia,
east Asia, and
Oceania
507 000
(471 000–543 000)
250 000
(215 000–288 000)
194 000
(162 000–230 000)
7·23
(6·29–8·27)
6·17 1 280 000
(1 200 000–1 380 000)
519 000
(456 000–590 000)
405 000
(349 000–467 000)
14·8
(13·2–16·4)
11·7
East Asia 243 000
(222 000–263 000)
86 700
(77 800–96 600)
59 300
(51 500–67 700)
3·83
(3·37–4·37)
2·89 652 000
(594 000–717 000)
185 000
(166 000–206 000)
136 000
(119 000–155 000)
8·56
(7·52–9·76)
5·89
China 229 000
(208 000–249 000)
82 700
(74 000–92 300)
56 400
(49 100–64 400)
3·78
(3·31–4·31)
2·77 610 000
(556 000–666 000)
177 000
(158 000–198 000)
131 000
(114 000–149 000)
8·52
(7·45–9·71)
5·70
North Korea 13 100
(9580–18 000)
3510
(2720–4470)
2420
(1920–3110)
6·91
(5·74–8·45)
5·85 39 200
(25 000–69 700)
6430
(5000–8140)
4460
(3530–5680)
12·6
(10·5–15·4)
9·55
Taiwan (province
of China)
967
(889–1050)
532
(498–570)
405
(330–497)
2·31
(1·97–2·75)
2·30 2300
(2230–2380)
1030
(988–1080)
815
(672–998)
4·51
(3·85–5·38)
3·98
Oceania 5900
(5080–6800)
7360
(5910–9040)
7540
(5970–9450)
18·1
(15·2–21·4)
15·4 17 400
(15 000–20 000)
20 200
(16 300–24 700)
19 900
(15 800–25 000)
48·4
(40·7–57·5)
39·0
American Samoa 12·2
(10·5–14·0)
5·67
(4·58–6·85)
5·14
(3·94–6·47)
4·86
(4·23–5·52)
3·88 24·6
(20·7–28·9)
12·2
(9·61–15·3)
10·8
(7·95–14·4)
10·2
(8·72–12·0)
7·72
Cook Islands 2·21
(1·71–2·81)
0·412
(0·326–0·519)
0·303
(0·243–0·375)
1·10
(0·930–1·31)
0·940 5·00
(3·87–6·33)
0·942
(0·742–1·20)
0·685
(0·547–0·848)
2·46
(2·06–2·94)
1·98
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881
Neonatal deaths NMR Under-5 deaths U5MR
2000 2015 2019 2019 2030* 2000 2015 2019 2019 2030*
(Continued from previous page)
Federated States
of Micronesia
42·2
(34·0–51·4)
16·4
(12·7–20·5)
13·4
(10·4–16·9)
6·85
(5·72–8·22)
5·05 105
(87·3–125)
35·6
(27·8–44·4)
28·4
(22·1–35·9)
14·5
(12·1–17·4)
10·5
Fiji 217
(172–272)
198
(152–255)
177
(128–241)
10·0
(8·41–12·0)
8·70 471
(376–590)
432
(338–554)
387
(281–521)
21·7
(18·2–25·9)
18·8
Guam 20·2
(18·1–22·5)
24·6
(21·6–28·1)
23·6
(18·8–29·6)
7·18
(6·16–8·40)
7·31 41·3
(36·8–46·1)
46·0
(41·5–51·3)
43·9
(35·6–53·7)
13·3
(11·6–15·3)
12·6
Kiribati 54·1
(44·8–65·3)
48·5
(37·0–63·4)
44·5
(32·3–60·2)
14·6
(12·3–17·5)
12·0 157
(130–190)
116
(88·7–151)
101
(74·0–137)
33·6
(28·1–40·0)
25·4
Marshall Islands 24·5
(19·7–29·6)
13·9
(11·1–17·6)
11·7
(9·32–15·1)
9·38
(7·84–11·4)
7·74 57·9
(49·9–66·6)
29·2
(23·5–36·5)
24·3
(19·4–31·0)
19·3
(16·2–23·4)
16·9
Nauru 5·85
(4·75–7·19)
3·71
(2·96–4·61)
3·04
(2·45–3·89)
10·1
(8·44–12·2)
8·58 17·7
(15·2–20·3)
8·94
(7·27–11·0)
7·03
(5·65–8·95)
23·2
(19·5–28·1)
18·7
Niue 0·415
(0·313–0·540)
0·224
(0·173–0·284)
0·205
(0·160–0·259)
8·12
(6·81–9·68)
6·92 1·01
(0·775–1·28)
0·520
(0·399–0·656)
0·472
(0·367–0·595)
18·6
(15·5–22·2)
15·0
Northern
Mariana Islands
9·67
(7·70–11·8)
2·73
(2·25–3·28)
2·22
(1·82–2·69)
4·40
(3·60–5·29)
3·89 17·5
(14·0–21·3)
6·00
(4·77–7·39)
4·69
(3·68–5·83)
9·11
(7·73–10·7)
8·07
Palau 3·22
(2·44–4·16)
1·52
(1·15–1·97)
1·15
(0·876–1·53)
6·04
(5·05–7·38)
5·15 7·78
(5·78–10·3)
3·72
(2·82–4·76)
2·77
(2·11–3·66)
14·0
(11·7–17·0)
11·3
Papua New
Guinea
4690
(4010–5470)
6280
(5020–7740)
6530
(5180–8160)
19·6
(16·5–23·3)
16·8 14 400
(12 300–16 700)
17 600
(14 100–21 600)
17 600
(14 000–22 000)
53·8
(45·3–63·9)
43·4
Samoa 30·9
(21·8–42·2)
24·0
(17·5–32·9)
22·3
(15·8–31·0)
6·28
(5·24–7·52)
5·35 65·1
(47·2–86·9)
49·9
(36·6–67·4)
46·4
(32·8–64·1)
13·2
(11·1–15·9)
11·4
Solomon Islands 325
(261–398)
272
(216–333)
245
(197–301)
11·5
(9·65–13·8)
9·36 767
(620–935)
593
(474–728)
519
(421–638)
24·6
(20·6–29·4)
19·2
Tokelau 0·406
(0·322–0·506)
0·139
(0·110–0·176)
0·119
(0·0935–0·149)
3·19
(2·54–3·97)
2·71 0·961
(0·767–1·19)
0·338
(0·265–0·420)
0·286
(0·229–0·351)
7·73
(6·45–9·28)
6·24
Tonga 26·9
(22·0–32·6)
17·5
(13·5–22·8)
14·5
(10·5–19·7)
6·36
(5·31–7·61)
5·37 56·4
(46·7–67·1)
38·0
(29·4–48·7)
31·4
(22·9–42·3)
13·6
(11·4–16·3)
13·7
Tuvalu 4·62
(4·00–5·32)
1·70
(1·26–2·22)
1·42
(1·06–1·93)
6·63
(5·51–8·10)
5·65 10·6
(9·00–12·5)
3·50
(2·66–4·52)
2·92
(2·19–3·90)
13·7
(11·4–16·7)
11·0
Vanuatu 99·6
(78·1–125)
93·4
(75·2–115)
88·3
(70·4–110)
11·6
(9·68–13·8)
9·73 223
(175–277)
201
(163–246)
185
(148–228)
24·4
(20·5–29·2)
20·0
Southeast Asia 259 000
(233 000–283 000)
156 000
(129 000–187 000)
127 000
(102 000–156 000)
11·6
(9·92–13·6)
9·17 615 000
(563 000–665 000)
314 000
(269 000–367 000)
250 000
(209 000–297 000)
22·6
(20·1–25·6)
16·5
Cambodia 12 200
(11 100–13 300)
7330
(5800–9240)
6280
(4810–8280)
16·9
(14·4–20·3)
13·1 35 200
(31 600–38 900)
14 200
(11 200–17 800)
11 600
(8890–15 300)
31·3
(26·5–37·5)
21·6
Indonesia 115 000
(102 000–129 000)
66 500
(50 800–83 100)
52 400
(40 900–65 500)
13·7
(10·9–17·0)
10·5 260 000
(236 000–283 000)
129 000
(106 000–153 000)
98 900
(80 600–121 000)
25·5
(21·6–30·2)
17·8
Laos 7790
(6960–8770)
3960
(3270–4670)
3470
(2760–4280)
19·9
(16·9–23·2)
12·8 21 600
(19 600–23 900)
8680
(7220–10 300)
7100
(5650–8900)
40·9
(35·0–47·5)
18·8
Malaysia 2460
(2240–2680)
2340
(2060–2590)
1910
(1540–2330)
3·55
(3·01–4·20)
2·82 4840
(4720–4980)
4270
(4160–4380)
3410
(2790–4150)
6·42
(5·43–7·59)
4·91
Maldives 134
(112–159)
99·9
(76·4–131)
81·9
(60·0–113)
9·58
(8·05–11·5)
8·08 241
(205–283)
169
(131–217)
140
(103–192)
16·2
(13·7–19·4)
13·6
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Neonatal deaths NMR Under-5 deaths U5MR
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(Continued from previous page)
Mauritius 228
(208–249)
120
(109–132)
104
(77·5–136)
8·07
(6·90–9·39)
6·79 349
(326–372)
192
(177–206)
163
(124–211)
12·6
(10·8–14·7)
10·7
Myanmar 48 300
(40 000–57 000)
26 600
(21 200–32 400)
22 200
(16 300–28 700)
21·0
(17·3–24·8)
16·2 135 000
(117 000–156 000)
55 000
(43 800–67 900)
42 800
(32 100–56 300)
40·3
(34·3–47·1)
28·6
Philippines 37 800
(32 500–44 100)
31 200
(21 800–41 700)
27 400
(19 700–36 400)
10·2
(7·91–13·0)
8·59 87 100
(77 600–97 500)
69 000
(55 700–84 400)
60 000
(47 800–73 200)
22·6
(19·0–26·7)
17·5
Seychelles 13·6
(11·7–15·7)
12·7
(10·5–15·0)
10·7
(8·09–14·0)
7·15
(6·18–8·26)
6·12 20·6
(17·7–23·7)
20·3
(16·8–24·2)
17·3
(13·1–22·6)
11·5
(9·89–13·4)
9·79
Sri Lanka 3300
(3050–3560)
1750
(1430–2130)
1300
(882–1860)
4·37
(3·46–5·45)
3·16 5880
(5510–6240)
3060
(2560–3630)
2290
(1640–3150)
7·61
(6·38–9·08)
5·38
Thailand 7780
(6400–9410)
2790
(2380–3230)
2120
(1660–2670)
3·63
(2·99–4·26)
2·39 16 100
(13 800–19 000)
6030
(5510–6560)
4570
(3760–5480)
7·63
(6·84–8·48)
4·97
Timor-Leste 966
(869–1080)
622
(512–748)
602
(486–741)
15·5
(13·1–18·6)
12·4 3150
(2790–3550)
1340
(1110–1610)
1230
(1000–1520)
32·1
(27·2–38·5)
21·0
Vietnam 22 200
(19 400–25 200)
12 300
(9830–15 400)
9200
(7010–12 300)
6·83
(5·76–8·27)
5·21 44 300
(39 400–49 800)
22 600
(18 100–28 400)
17 100
(13 000–22 700)
12·4
(10·5–15·0)
9·42
Sub-Saharan
Africa
1 120 000
(1 050 000–1 190 000)
1 090 000
(938 000–1 270 000)
1 020 000
(847 000–1 250 000)
27·9
(24·7–31·6)
23·6 4 020 000
(3 790 000–4 270 000)
3 070 000
(2 640 000–3 550 000)
2 680 000
(2 220 000–3 250 000)
74·1
(65·3–85·2)
54·4
Central sub-
Saharan Africa
124 000
(111 000–137 000)
114 000
(98 400–132 000)
100 000
(85 800–120 000)
22·5
(19·8–25·8)
17·7 509 000
(468 000–553 000)
333 000
(289 000–387 000)
260 000
(222 000–310 000)
58·8
(51·7–67·5)
36·5
Angola 28 700
(25 200–32 100)
26 200
(21 900–31 100)
24 000
(20 100–28 400)
21·7
(19·1–24·5)
16·9 116 000
(105 000–127 000)
73 000
(60 900–85 700)
58 800
(48 100–70 800)
54·2
(46·4–62·9)
33·7
Central African
Republic
7730
(6570–8970)
8060
(6320–10 200)
7770
(6150–9930)
39·3
(33·2–47·4)
35·4 28 300
(24 500–32 100)
26 900
(21 900–32 600)
24 000
(19 200–30 000)