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Population and fertility by age and sex for 195 countries and territories, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017

April 2019

Authors:

Manaye Yihune

Arba Minch University

Santosh Varughese

Christian Medical College & Hospital

Background Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. Methods We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10–54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10–14 years and 50–54 years was estimated from data on fertility in women aged 15–19 years and 45–49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-component method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories. Findings From 1950 to 2017, TFRs decreased by 49·4% (95% uncertainty interval [UI] 46·4–52·0). The TFR decreased from 4·7 livebirths (4·5–4·9) to 2·4 livebirths (2·2–2·5), and the ASFR of mothers aged 10–19 years decreased from 37 livebirths (34–40) to 22 livebirths (19–24) per 1000 women. Despite reductions in the TFR, the global population has been increasing by an average of 83·8 million people per year since 1985. The global population increased by 197·2% (193·3–200·8) since 1950, from 2·6 billion (2·5–2·6) to 7·6 billion (7·4–7·9) people in 2017; much of this increase was in the proportion of the global population in south Asia and sub-Saharan Africa. The global annual rate of population growth increased between 1950 and 1964, when it peaked at 2·0%; this rate then remained nearly constant until 1970 and then decreased to 1·1% in 2017. Population growth rates in the southeast Asia, east Asia, and Oceania GBD super-region decreased from 2·5% in 1963 to 0·7% in 2017, whereas in sub-Saharan Africa, population growth rates were almost at the highest reported levels ever in 2017, when they were at 2·7%. The global average age increased from 26·6 years in 1950 to 32·1 years in 2017, and the proportion of the population that is of working age (age 15–64 years) increased from 59·9% to 65·3%. At the national level, the TFR decreased in all countries and territories between 1950 and 2017; in 2017, TFRs ranged from a low of 1·0 livebirths (95% UI 0·9–1·2) in Cyprus to a high of 7·1 livebirths (6·8–7·4) in Niger. The TFR under age 25 years (TFU25; number of livebirths expected by age 25 years for a hypothetical woman who survived the age group and was exposed to current ASFRs) in 2017 ranged from 0·08 livebirths (0·07–0·09) in South Korea to 2·4 livebirths (2·2–2·6) in Niger, and the TFR over age 30 years (TFO30; number of livebirths expected for a hypothetical woman ageing from 30 to 54 years who survived the age group and was exposed to current ASFRs) ranged from a low of 0·3 livebirths (0·3–0·4) in Puerto Rico to a high of 3·1 livebirths (3·0–3·2) in Niger. TFO30 was higher than TFU25 in 145 countries and territories in 2017. 33 countries had a negative population growth rate from 2010 to 2017, most of which were located in central, eastern, and western Europe, whereas population growth rates of more than 2·0% were seen in 33 of 46 countries in sub-Saharan Africa. In 2017, less than 65% of the national population was of working age in 12 of 34 high-income countries, and less than 50% of the national population was of working age in Mali, Chad, and Niger.

Proportion of the population that is of working age, globally and for GBD super-regions, 1950-2017 Working age is defined as 15-64 years. Data are for both sexes combined from 1950 to 2017. GBD= Global Burden of Diseases, Injuries, and Risk Factors Study.

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Global Health Metrics

www.thelancet.com Vol 392 November 10, 2018

1995

Population and fertility by age and sex for 195 countries and

territories, 1950–2017: a systematic analysis for the Global

Burden of Disease Study 2017

GBD 2017 Population and Fertility Collaborators*

Summary

Background Population estimates underpin demographic and epidemiological research and are used to track progress

on numerous international indicators of health and development. To date, internationally available estimates of

population and fertility, although useful, have not been produced with transparent and replicable methods and do not

use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility

and population by sex with standardised and replicable methods.

Methods We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017

with standardised and replicable methods. We based the estimates on the demographic balancing equation, with

inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital

registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary

birth histories. We estimated age-speciﬁc fertility rates (ASFRs; the annual number of livebirths to women of a

speciﬁed age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used

the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived

through the end of the reproductive age span [age 10–54 years] and experienced at each age a particular set of ASFRs

observed in the year of interest). Because of sparse data, fertility at ages 10–14 years and 50–54 years was estimated

from data on fertility in women aged 15–19 years and 45–49 years, through use of linear regression. Age-speciﬁc

mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data

on population came from 1257 censuses and 761 population registry location-years and were adjusted for

underenumeration and age misreporting with standard demographic methods. Migration was estimated with the

GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model

prior. Final population estimates used the cohort-component method of population projection, with inputs of fertility,

mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing.

With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in

195 countries and territories.

Findings From 1950 to 2017, TFRs decreased by 49·4% (95% uncertainty interval [UI] 46·4–52·0). The TFR decreased

from 4·7 livebirths (4·5–4·9) to 2·4 livebirths (2·2–2·5), and the ASFR of mothers aged 10–19 years decreased from

37 livebirths (34–40) to 22 livebirths (19–24) per 1000 women. Despite reductions in the TFR, the global population

has been increasing by an average of 83·8 million people per year since 1985. The global population increased by

197·2% (193·3–200·8) since 1950, from 2·6 billion (2·5–2·6) to 7·6 billion (7·4–7·9) people in 2017; much of this

increase was in the proportion of the global population in south Asia and sub-Saharan Africa. The global annual rate

of population growth increased between 1950 and 1964, when it peaked at 2·0%; this rate then remained nearly

constant until 1970 and then decreased to 1·1% in 2017. Population growth rates in the southeast Asia, east Asia, and

Oceania GBD super-region decreased from 2·5% in 1963 to 0·7% in 2017, whereas in sub-Saharan Africa, population

growth rates were almost at the highest reported levels ever in 2017, when they were at 2·7%. The global average age

increased from 26·6 years in 1950 to 32·1 years in 2017, and the proportion of the population that is of working age

(age 15–64 years) increased from 59·9% to 65·3%. At the national level, the TFR decreased in all countries and

territories between 1950 and 2017; in 2017, TFRs ranged from a low of 1·0 livebirths (95% UI 0·9–1·2) in Cyprus to a

high of 7·1 livebirths (6·8–7·4) in Niger. The TFR under age 25 years (TFU25; number of livebirths expected by age

25 years for a hypothetical woman who survived the age group and was exposed to current ASFRs) in 2017 ranged

from 0·08 livebirths (0·07–0·09) in South Korea to 2·4 livebirths (2·2–2·6) in Niger, and the TFR over age 30 years

(TFO30; number of livebirths expected for a hypothetical woman ageing from 30 to 54 years who survived the age

group and was exposed to current ASFRs) ranged from a low of 0·3 livebirths (0·3–0·4) in Puerto Rico to a high of

3·1 livebirths (3·0–3·2) in Niger. TFO30 was higher than TFU25 in 145 countries and territories in 2017. 33 countries

had a negative population growth rate from 2010 to 2017, most of which were located in central, eastern, and western

Europe, whereas population growth rates of more than 2·0% were seen in 33 of 46 countries in sub-Saharan Africa.

In 2017, less than 65% of the national population was of working age in 12 of 34 high-income countries, and less than

50% of the national population was of working age in Mali, Chad, and Niger.

Lancet 2018; 392: 1995–2051

*Collaborators listed at the end

of the paper

Correspondence to:

Prof Christopher J L Murray,

Institute for Health Metrics and

Evaluation, Seattle, WA 98121,

USA

cjlm@uw.edu

Global Health Metrics

1996

www.thelancet.com Vol 392 November 10, 2018

Introduction

Age-sex-speciﬁc estimates of population are a bedrock of

epidemiological and economic analyses, and they are

integral to planning across several sectors of society. As

the denominator for most indicators, such estimates

permeate every aspect of our understanding of health

and development. Errors in population estimates aect

national and international target tracking and time-series

and cross-country analyses of development outcomes.

The impor tance of accurate population estimates for

government planning cannot be overstated: population

size, age, and composition dictate the national need for

infrastructure, housing, education, employment, health

care, care of older people, electoral representation,

provision of public health and services, food supply, and

security.1 Similarly, fertility rates, both by maternal age

and overall, are key drivers of population growth and

important social outcomes in their own right.

Many governments typically produce national popu-

lation estimates by age and sex for planning purposes.

Most international studies and comparative indicators,

including the Millennium Development Goals and the

Sustainable Development Goals, rely on the estimates

generated by the UN Population Division at the

Department of Economics and Social Aairs (UNPOP)

for population denomi nators,2,3 although it is not well

documented how often these estimates are used by

national governments. The UNPOP has produced

population estimates since 1951, and it uses a de-

centralised approach to estimation.4 For example, the

Latin American and Caribbean Demographic Centre

produces estimates for Latin America, whereas estimates

for all other groups of countries are developed by analysts

in New York. Although the UNPOP describes a general

approach of examining data on fertility, mortality,

migration, and population and searching for consistency,5

replicable statistical methods are not used. Decisions on

how to deal with inconsistency between the compo-

nents of fertility, mortality, and migration within

population counts are left to individual analysts, leading

to considerable hetero geneity in approaches across

countries. Accordingly, discrepancies between UNPOP

and nationally produced estimates—for instance, in

2015, the population estimates for Mexico by UNPOP

were 4·6 million more than those of Mexico’s National

Population Council (125·9 million by UNPOP vs

Interpretation Population trends create demographic dividends and headwinds (ie, economic beneﬁts and detriments)

that aect national economies and determine national planning needs. Although TFRs are decreasing, the global

population continues to grow as mortality declines, with diverse patterns at the national level and across age groups.

To our knowledge, this is the ﬁrst study to provide transparent and replicable estimates of population and fertility,

which can be used to inform decision making and to monitor progress.

Funding Bill & Melinda Gates Foundation.

Research in context

Evidence before this study

Population estimates by age and sex are extensively used in all

forms of epidemiological and demographic analysis. National

estimates of population and fertility for age and sex groups

have been produced by the UN Population Division since 1951.

The US Census Bureau produces revised demographic estimates

for 15 to 30 countries each year. Several national authorities

produce their own population estimates, particularly those in

high and middle Socio-demographic Index countries. These

eﬀorts are all based on the cohort-component method of

population projection, namely that population in an age group

at a given time t must equal the population in that cohort at

the start of the time period (t–1) plus new entrants and minus

people exiting the population because of migration and death.

Although these estimates are based on the demographic

balancing equation, estimates are not based on standardised,

transparent, or replicable statistical methods.

Added value of this study

To our knowledge, this study presents the ﬁrst estimates of

population by location from 1950 to 2017 that are based on

transparent data and replicable analytical code, applying a

standardised approach to the estimation of population for each

single year of age for each calendar year from 1950 to 2017 for

195 countries and territories and for the globe. This study

provides improved population estimates that are internally

consistent with the Global Burden of Diseases, Injuries, and Risk

Factors Study’s assessment of fertility and mortality, which are

important inputs to other epidemiological research and

government planning.

Implications of all the available evidence

Population counts by age and sex that are produced with a

transparent and empirical approach will be useful for

epidemiological and demographic analyses. The production of

annual estimates will also facilitate timely tracking of progress

on global indicators, including the Sustainable Development

Goals. In the future, the methods applied here can be used to

enhance population estimation at the subnational level.

Global Health Metrics

www.thelancet.com Vol 392 November 10, 2018

1997

121·3 million by National Population Council)—cannot

currently be resolved.4,6

The US Census Bureau’s International Division

periodically releases detailed population analyses for

selected countries, with new revisions produced for

15 to 30 countries per year.7 Other organisations, such as

the Population Reference Bureau,8 the World Bank,9

the Wittgenstein Centre,10 and Gapminder Foundation11

also release population estimates, but these are largely

combinations of national estimates with selected UNPOP

or US Census Bureau analyses. Many of the organi-

sations who estimate or report on population also

provide fertility estimates, which, in addition to aecting

population trends, are used to monitor reproductive

health service delivery in many locations. To our

knowledge, global estimates of annual population by age

and sex with underlying primary data and replicable

computer code and statistical modelling details are not

available from any source.

The Global Burden of Diseases, Injuries, and Risk

Factors Study (GBD) is committed to the Guidelines on

Accurate and Transparent Health Estimates Reporting

(GATHER).12 Continued use of the UNPOP population

estimates in GBD is not compatible with GATHER

because the methods used for UNPOP estimation are

not transparent and uncertainty intervals are not

estimated for populations.4 Moreover, UNPOP population

esti mates, especially in years between or after a census,

are inconsistent with GBD estimates because there is a

marked dierence between UNPOP and GBD estimates

of age-speciﬁc mortality in many instances.13,14 For this

GBD 2017 paper, we sought to produce population

estimates and associated fertility estimates for

195 countries and territories from 1950 to 2017 that were

based on the available census or population registry data

and survey and census data on age-speciﬁc fertility rates

(ASFR; ie, the annual number of livebirths to women of a

speciﬁed age group per 1000 women in that age group)

by use of replicable methods, leveraging the previous

GBD work that estimated age-sex-speciﬁc mortality

rates.15 To achieve this goal, we aimed to conduct

systematic analyses of available sources that could

inform ASFR estimation and to systematically identify

and extract census and population registry data.

Methods

Overview

As with all population estimation, the underlying

equation used for GBD is based on the demographic

balancing equation16

where N (T) is the population at a given time, N (0) is the

population at the start of the interval, B (0,T) is livebirths

during the interval, D (0,T) is deaths during the interval,

and G (0,T) is net migration during the interval.

The cohort-component method of population pro jection

extends this demographic balancing equation to estimate

internally consistent age-sex-speciﬁc popu lations. The

method requires estimates of ASFRs, sex ratio at birth,

age-sex-speciﬁc net migration, and age-sex-speciﬁc mor-

tality rates that are consistent with observed population

counts that have been corrected for underenumeration or

overenumeration. GBD provides a consistent set of age-

sex-speciﬁc mortality rates with standardised methods;15 in

this analysis, we estimated the sex ratio at birth, ASFR, and

age-sex-speciﬁc migration rates consistent with the

available population data to create a full time series of

population estimates by age and sex.

These estimates comply with GATHER (appendix 1

section 5). Analyses were done with R version 3.3.2, Python

version 2.7.14, or Stata version 13.1. Data and statistical

code for all analyses are publicly available online.

Geographical units and time periods

We produced single calendar-year and single year-of-

age population estimates for 195 countries and territories

that were grouped into 21 regions and seven super-

regions. The seven super-regions are central Europe,

eastern Europe, and central Asia; high income;

Latin America and the Caribbean; north Africa and the

Middle East; south Asia; southeast Asia, east Asia, and

Oceania; and sub-Saharan Africa. Each year, GBD includes

sub national analyses for a few new countries and

continues to provide subnational estimates for countries

that were added in previous cycles. Subnational estimation

in GBD 2017 includes ﬁve new countries (Ethiopia, Iran,

New Zealand, Norway, Russia) and countries previously

estimated at subnational levels (GBD 2013: China, Mexico,

and the UK [regional level]; GBD 2015: Brazil, India,

Japan, Kenya, South Africa, Sweden, and the USA; GBD

2016: Indonesia and the UK [local government authority

level]). All analyses are at the ﬁrst level of administrative

organisation within each country except for New Zealand

(by Māori ethnicity), Sweden (by Stockholm and non-

Stockholm), and the UK (by local government authorities).

All subnational estimates for these countries were

incorporated into model development and evaluation as

part of GBD 2017. To meet data use requirements, in this

publication we present all subnational estimates excluding

those pending publication (Brazil, India, Japan, Kenya,

Mexico, Sweden, the UK, and the USA); given space

constraints, these results are presented in appendix 2

instead of the main text. Subnational estimates for

countries with populations of more than 200 million

people (assessed by use of our most recent year of

published estimates) that have not yet been published

elsewhere are presented wherever estimates are illus-

trated with maps but are not included in tables. Estimates

were produced for the years 1950–2017. 1950 was selected

as the start year for the analysis because we were unable to

locate sucient data on ASFR, mortality, and population

before 1950.

N(T)=N(0) + B(0,T) – D(0,T) + G(0,T)

See Online for appendix 1

For the statistical code see

http://ghdx.healthdata.org/gbd-

2017

See Online for appendix 2

Global Health Metrics

1998

www.thelancet.com Vol 392 November 10, 2018

Fertility

Fertility data are obtained from vital registration systems,

complete birth histories, or summary birth histories.

Complete birth histories include the date of birth and, if

applicable, the dates of death of all children ever born

alive to each woman that is interviewed, whereas

summary birth histories include the total number of

children ever born alive to each mother and the total

number of those children born alive to each mother that

have died. In countries with complete birth registration,

vital registration systems typically provide tabulations of

births by age of the mother. From 1890,17 some censuses

asked about the number of children ever born to a

woman, and this question has been widely asked in

censuses and many household surveys in the past

70 years. From the 1970s, fertility information has also

been collected through complete birth histories,

beginning with the World Fertility Survey, then the

Demographic and Health Surveys, and, in some

countries, the Multiple Indicator Cluster Surveys,

sponsored by the UN Children’s Fund. We identiﬁed

977 censuses and household surveys that had summary

birth history data, 429 household surveys that had

complete birth history data, and 7817 country-years of

birth registration systems through searches of national

statistical sources and the Demographic Yearbooks

produced by the UN Statistics Division from 1948 to

present.18 The number and type of sources for each

location are provided in appendix 1 (section 5). The

Global Health Data Exchange provides the metadata for

all these sources.

Given the hetergeneous nature of the data (vital

registration, summary birth histories, complete birth

histories), we used a two-stage approach to modelling the

ASFR for the age groups 15–19 years, 20–24 years,

25–29 years, 30–34 years, 35–39 years, 40–44 years, and

45–49 years. The two-stage approach was designed to take

advantage of the greater availability of some summary

birth history data for the period 1950 to 1975 and to help

to compensate for the lower availability of complete birth

history data in some low-income countries. For the

fertility rates in those aged 10–14 years and 50–54 years,

which are much lower than in other age groups and for

which only vital registration data were available, we used

a separate, simpler approach, described later in this

section.

In the ﬁrst stage of our analysis, we used spatio temporal

Gaussian process regression to analyse vital registration

and complete birth history data.15,19 For spatiotemporal

Gaussian process regression, the prior was estimated

separately for women aged 20–24 years, with average years

of schooling in women aged 20–24 years as the covariate.

For all other age groups, the prior was estimated with a

spline on the estimated ASFR for women aged 20–24 years

and with the average years of schooling for the age group

of interest. The prior for GBD locations in the high-

income super-region did not include average years of

schooling as a covariate. Spline knots were selected by

inspection of the data to identify where there was a

reversal in trend. The purpose of this approach was to

capture an increase in fertility rates in women aged

30 years or older while the ASFR for women aged

20–24 years decreased below a speciﬁc threshold. Given

that the point of inﬂection for the ASFR for women aged

30 years or older relative to the ASFR for women

aged 20–24 years varied by super-region, we ﬁt the models

separately for some GBD super-regions (high income;

sub-Saharan Africa; and central Europe, eastern Europe,

and central Asia) and modelled the rest of the super-

regions together. The ﬁrst step of the model also included

location-and-source-speciﬁc random eects to correct bias

from non-sampling error in dierent source types, such

as incomplete vital registration. Hyperparameters for the

model were selected on the basis of a measure of data

density. Further details on this process are provided in

appendix 1 (section 2).

In the second stage of the analysis, we used the

ASFR estimates from the ﬁrst stage to process and

incorporate several forms of aggregated data. First, we

split cumulative cohort fertility data (ie, children ever

born) from summary birth history into period ASFR data.

For this split, we computed the ratio between reported

children ever born alive from each 5-year cohort of women

represented in a given data source and the total fertility for

each of these cohorts that was implied by the ﬁrst-stage

estimates of ASFR by location and year. This ratio

was applied as a scaling factor to our estimated cohort

ASFR at 5-year intervals (when all members of the cohort

all belong to a single 5-year GBD age group), to distribute

experienced fertility (ie, from age 10 years until the date of

the survey in women interviewed from the cohorts

speciﬁed in the original data) back across age and time.

Additionally, we used the estimated age proportion of

livebirths from the ﬁrst stage to distribute total reported

livebirths by the age of the mother. Lastly, for historical

location aggregates for which we had registry data (eg, the

Soviet Union), we used the estimated proportions of age-

speciﬁc livebirths in constituent locations from the ﬁrst

stage to allocate births back in time to their current

GBD geographies. This new set of methods allowed us to

supplement the model with a substantial amount of

additional information about the overall fertility. We then

re-estimated ASFR as described, with all vital registration,

complete birth history, and split data to produce ﬁnal

fertility estimates for women aged 15–49 years.

In both the ﬁrst and second stage, data were adjusted in

the mixed-eects model on the basis of random

eects values (appendix 1 section 2) by selecting a

reference or benchmark source. In locations with

complete child death registration (see previous GBD

analyses),15,20 vital registration was typically the benchmark

or reference source. In other locations, Demographic and

Health Survey complete birth history data were used as

the reference source. If neither vital registration nor

For the Global Health Data

Exchange see http://ghdx.

healthdata.org/

Global Health Metrics

www.thelancet.com Vol 392 November 10, 2018

1999

Demographic and Health Survey complete birth histories

were available, other complete birth history sources were

used as the reference. If no vital registration or complete

birth history data were used, then the average of all

remaining summary birth history sources were used as

reference. Where sources were inconsistent or

implausible time trends were identiﬁed, some reference

source designations were modiﬁed; the ﬁnal choice of

reference sources for each location are provided in the

appendix 1 (section 5).

Many household surveys on fertility excluded women in

the age groups 10–14 years and 50–54 years, and

these data were limited to 3947 country-years of vital

registration data. To estimate fertility in girls aged

10–14 years, we used a linear regression of the log of the

ratio of the ASFR of girls aged 10–14 years to the ASFR for

girls aged 15–19 years as a function of the ASFR for girls

aged 15–19 years. For women aged 50–54 years, we found

no covariates that predicted variation in the ratio of ASFR

for women aged 50–54 years to the ASFR for those aged

45–49 years. In this case, we assumed the ratio of ASFR

for women aged 50–54 years to ASFR for women aged

45–49 years was constant across locations and over time.

Our analysis generated a full set of ASFRs for each

location and year from 1950 to 2017; we used these ASFRs

to compute the total fertility rate (TFR), which is the

average number of children a woman would bear if she

survived through the end of the reproductive age span

(age 10–54 years) and experienced at each age a particular

set of ASFRs observed in the year of interest. We also

estimated the total fertility rate under age 25 years

(TFU25; number of livebirths expected by age 25 years for

a hypothetical woman who survived the age group and

was exposed to current ASFRs) and the total fertility in

women older than 30 years (TFO30; number of livebirths

expected for a hypothetical woman ageing from 30 to

54 years who survived the age group and was exposed to

current ASFRs). These age ranges were computed

because nearly all locations show decreases in the TFU25

over time, with few or no reversals. In women aged 30

years or older, there is a clear U-shaped curve, with

decreases followed by sustained increases; in women

aged 25–29 years, the pattern is less consistent. The

fertility rate in girls aged 10–19 years is a Sustainable

Development Goal (SDG) indicator for goal 3, target 3.7:

ensure universal access to sexual and reproductive health-

care services, including for family planning, information

and education, and the integration of reproductive health

into national strategies and programmes.21

We estimated the sex ratio at birth with 4690 unique

location-years of registered livebirths by sex, 1756 location-

years of census and population registry counts that

included children younger than 1 year and younger than

5 years by sex, and 2490 location-years of the proportion

of live-born males from complete birth history. These

data informed a spatiotemporal Gaussian process

regression model of the proportion of live-born males,

assuming a time-invariant prior for the mean because, in

the absence of sex-selective abortion, we would not expect

the sex ratio at birth to deviate signiﬁcantly from its

natural equili brium. Hyperparameters for spatiotemporal

smoothing and Gaussian process regression were chosen

on the basis of data-density scores, taking into account

both the quantity and quality of available data. Our

analysis only produced national estimates of sex ratio at

birth—including for Hong Kong and Macau—for all

years from 1950 to 2017; thus, we assume that subnational

sex ratio at birth equals the national sex ratio at birth.

With additional data seeking and extraction, we will

extend the analysis to all GBD locations in the next GBD

study. Further details regarding sex ratio at birth

estimation are shown in appendix 1 (section 2).

Population

To determine national and subnational populations, we

searched the Integrated Public Use Microdata Series

questionnaires, the UN Demographic Yearbook, the UN

census programme census dates, and the International

Population Census Biography to identify all censuses

conducted between 1950 and 2017 and available popu-

lation registers.22–25 We included 1233 censuses and

26 population registers that contained 730 location-years

of census or population registry data. In some cases,

the same census was reported by dierent sources

in dierent years. We resolved these incon sistencies

through a review of available documentation. A list of

all conﬁrmed censuses is shown in the appendix 1

(section 5). We obtained population counts that were age-

sex-speciﬁc from 1171 censuses and only by sex from

62 censuses. We sought to identify whether the counts in

each census were de facto (allocated to the place of

enumeration) or de jure (allocated to the place of regular

or legal residence). Our basis for population estimation

is the de-facto population and, where both counts were

available, we used de-facto counts. Where only de-jure

counts were available—typically in lower Socio-

demographic Index (SDI) countries—we assumed that

de-jure and de-facto populations were similar. The main

dierence between the counts at the national level is the

exclusion of some migrant workers in some de-jure

counts; where migrant workers are known to be an

important fraction of the population and de-facto counts

were not available, we searched directly for data on

documented migration.

In several cases, the UN does not recognise admin-

istrative splits in territories, including Kosovo and

Serbia, Transnistria and Moldova, and the so-called

Turkish Republic of Northern Cyprus and Cyprus.26

In these cases, we obtained census counts for the

components and interpolated to generate census counts

for the full territory. For east and west Germany before

uniﬁcation, as the input to the model, we used census

counts for each component and interpolation to

generate estimates of joint census counts in years

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closest to the censuses in both locations. We were able

to obtain census counts for ﬁve of the six constituent

components that made up Yugoslavia; for Serbia we

split aggregate Yugoslavia census data with previous

population estimates. For Singapore, we estimated the

population for residents and non-resident workers

combined (appendix 1 section 2). Of the 1963 location-

years of census or population registry data,

72 lo cation-years were identiﬁed as outliers that were

inconsistent with adjacent data, model analysis, or

excluded subpopulations.

Census counts are typically undercounts of the actual

population, although there are known cases in which

censuses have overcounted the population.27–29 Post-

enumeration surveys (PESs) aim to identify instances of

overcounts or undercounts by comparing data. Many, if

not most, PESs are not published or are only reported in

government releases, presentations, or online reports.

PESs themselves are subject to considerable error, whether

they use a direct or indirect method of estimating census

completeness. We searched for all available PES results

and supple mented these results with publications or

presentations that provided summaries of other PESs.30–34

We identiﬁed 165 PESs, although it is likely that many

more were done that did not publicly report their results.

We analysed the 165 PESs to generate a general model of

census com pleteness as a function of SDI. Because of

variable quality of PESs, we assumed that, in aggregate,

the 165 PESs provided an unbiased view of the association

between enumeration completeness and SDI, so we

adjusted census counts by the predictions from this model.

We used nationally reported PES results to adjust census

counts in high SDI countries and used the estimated

census completeness to adjust data in other settings.

To account for systematic age variation in census

enumeration, we input age-sex-speciﬁc PES results into

DisMod-MR 2.1, a Bayesian meta-regression tool, to

estimate a global age pattern of enumeration. This age

pattern was then used to adjust the overall predicted

enumeration to vary by age (appendix 1 section 2).

As has been extensively noted in the demographic

literature, census counts have several common problems:

undercounts (particularly of children younger than

5 years), a tendency to exaggerate age at older ages, and

age heaping (reporting ages rounded to the nearest

5 or 10 years).35–38 The population counts from

four dierent censuses, illustrating the dierent types of

age heaping and undercounts, are shown in ﬁgure 1. We

evaluated the age structure and consistency of census

data by calculating sex and age ratios for each census.

These ratios were then used to calculate sex and age ratio

scores, which were combined into a joint score. The joint

score was used to determine whether to apply a correction

to the census counts or not. For census counts available

in 1-year age groups, we used the Feeney correction;

for counts available in 5-year or 10-year age groups, we

used either the Arriaga or Arriaga strong correction.39,40

More details on the age-heaping corrections are shown in

appendix 1 (section 2). For all censuses in low and middle

SDI countries, we did not use the census count of

children younger than 5 years in our model estimation.

In other words, population estimates in these age groups

were driven by fertility and mortality estimates and

consistency with the later census counts for the same

cohort. Systematic over estimation of age, particularly in

some countries in sub-Saharan Africa and Latin America,

was apparent in the data; for example, census counts

could only be explained by large immigration of

populations at older ages, which appears implausible.

We were unable to correct the data for these issues and

used the modelling strategy that is subsequently

described to deal with these challenges.

Our approach requires an estimate of the population

in 1950 in all locations for detailed age and sex groups;

only 54 countries had a census count in 1950. For

most other locations, we used backwards application of

the cohort-component method of population projection by

use of the oldest available census and the reverse

application of estimated mortality rates and an assumption

of zero net migration (appendix 1 section 2). As sub-

sequently noted, in our GBD Bayesian demographic

balancing modelling framework, the base line population

is assumed to be measured with substantial error, and

the model produced posterior estimates that varied

considerably from this initial baseline.

We used the estimates of population by location and

year for each single year of age to generate other

summary measures, including population growth rates

that assumed logarithmic growth and the proportion

of the population that was of working age, which is

deﬁned by the Organisation for Economic Co-operation

and Development and the World Bank as those aged

15–64 years.41,42

Mortality

The GBD mortality process produced annual abridged life

tables that comprised 24 age groups: younger than 1 year,

1–4 years, and then 5-year age groups up to age 110 years

or older.13 To project populations forwards in time with the

cohort-component method of population projection, we

needed annual period life tables with single-year age

groups up to 95 years or older. For ages 15–99 years, we

interpolated abridged lx values (the number of people still

alive at age x for a hypothetical cohort in a period life table)

by use of a monotone cubic spline with Hyman ﬁltering.43,44

For people younger than 15 years and older than 100 years,

we applied regression coe cients to predict single-year

age group probability of death values. The Human

Mortality Database provided 4557 empirical full-period life

tables for 48 locations. We excluded 1280 of the life tables

because they were identiﬁed by the Human Mortality

Database as proble matic or occurred during time periods

with extremely high mortality, such as World War 2 or

the 1918 inﬂuenza pandemic. To predict probability of

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2001

death qx at age x for single-year age groups, we ﬁt the

following separate linear regression by single-year age

group between ages zero and 110:

where 1qxf is the single-year age group qx value from the full-

period life table, β0 is the coecient for the intercept, β1 is

the coecient for the slope, εxf is the error term, and 5qxa is

the correponding abridged life-table age group’s qx value.

These predicted 1qxf values were scaled to the GBD abridged

life-table 5qx values for consis tency.

For those aged 15–99 years, the non-parametric spline

approach did not require rescaling to match the abridged

5qx values and, consequently, produced smooth steps

in mortality across single-year ages and between 5-year

age groups. The regression coecients were applied to

children younger than 15 years because of the unique

patterns of single-year mortality younger than 15 years

and to adults older than 100 years because of instability

caused by low lx values at older ages. To mitigate instability

caused by spikes in mortality due to fatal discontinuities

such as wars and natural disasters, full-period life tables

were ﬁrst generated based on abridged life tables without

fatal discontinuities, and then fatal discontinuities were

added to nmx (the death rate in age group x to x + 1 for a

hypothetical cohort in a period life table) assuming a

constant death rate for fatal discontinuities within each

age group. To produce full life tables with the complete

set of single-year age group 1qx values, we assumed 1ax (the

average number of years lived in age group x to x + 1 by

people who died during the interval for a hypothetical

log(

f) +

Model posterior

Datapoints

Source

PES-corrected data

Un-age-heaped data

Raw data

Data processing step

500

000

500

000

Population count/age group interval length

000

010 20 40 60 8030 50 70 90 100

100

000

200

000

300

000

Population count/age group interval length

Middle of age group (years)

010 20 40 60 8030 50 70 90 100

500

000

500

000

Middle of age group (years)

Figure 1: Census age patterns for females in 1970 in the USA (A), males in 2001 in Bangladesh (B), females in 1979 in Afghanistan (C), and males in 2010 in Russia (D)

Lines show the model posterior and datapoints. Data processing steps are indicated by symbols. The 95% uncertainty interval is shown by light blue shading around the model posterior.

PES=post-enumeration survey.

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cohort in a period life table) was 0·5 in all age groups

except for those younger than 1 year and older than

110 years; these groups were assumed to be identical to

the abridged life-table 1ax values.

Migration

Real data on age-speciﬁc net migration are more dicult

to obtain than data on fertility, population, and mortality.

Net migration includes any change in the de-facto

population that is not accounted for by births or deaths;

this number would include refugees and temporary

workers. For most country-years, documented net

migration data are not reported and undocumented net

migration is not estimated. For some high-SDI countries,

net migration is tracked and reported,45 and the UN High

Commission for Refugees (UNHCR) reports the stock of

refugees (the count of people not born in the country that

they currently live in) in each country by country of origin

at the end of year. In more recent census rounds, census

questions on the number of foreign-born individuals

living in a country have been used, as have assumptions

on dierential survival to estimate when migration

occurred;46 however, these approaches, especially for the

period before 2000, have considerable uncertainty

associated with them and are heavily dependent on

fertility and mortality assum ptions for migrants.

We developed and applied the GBD Bayesian demo-

graphic balancing model to estimate net migration by

single year of age and single calendar year, consistent

with our estimates of age-sex-speciﬁc mortality and

ASFR and the observed population data. Our model was

developed on the basis of the work of Wheldon and

colleagues47–49 but includes important modiﬁcations, such

as correlation of migration rates across ages and over

time and single-year, single-age estimation. Details on

our GBD Bayesian demographic balancing model,

developed in Template Model Builder, an open-source

statistical package for R,50 are shown in the appendix 1

(section 2).

In applying the model, we dealt with known issues of

age misreporting by including larger input data variance

for population counts at the youngest ages and input

variance that steadily increases after age 45 years. The

choice of data variance was based on testing of a range of

variance assumptions; variance assumptions only change

the point estimates of the results in settings where there

is substantial inconsistency between adjacent census

counts or between census counts (or both) and in the

key inputs. To address age misreporting in the oldest

ages, we ran several model versions for each location. For

each model version, we excluded census counts above a

given maximum age from the model ﬁtting process

(appendix 1 section 5). We then selected the best model

version by prioritising versions that used the highest

maximum age, predicted low absolute values of migration

in the age groups older than 55 years, and had good

in-sample ﬁts. In high-income locations, the selection

algorithm often chose the model version that did not

exclude any of the census data for older ages but, in other

regions, the population estimates at older ages were

driven by the census counts for younger ages and the

mortality estimates that aged those people forwards in

time (appendix 1 section 2).

An example of the ﬁt to the available population data for

the eight largest populations in 2017 is shown in ﬁgure 2.

Overall, the in-sample ﬁt of the model for age-sex-speciﬁc

population log space had an R² value of 0·99. These ﬁts

show that the model closely tracks the available corrected

census counts for all ages combined and by age. Code

for the GBD Bayesian demographic balancing model

is available at the Global Health Data Exchange. The

population estimates and census and registry data for all

195 countries and territories are shown in appendix 2.

The cohort-component method of population

projection and uncertainty

We produced ﬁnal population estimates by single year and

by single-year age groups with the cohort-component

method of population projection.16 The population in

each single-year age group in each year was estimated

on the basis of the estimated starting population and

single-year, single-age rates of migration, fertility, and

mortality. Uncertainty in population estimates comes

from two fundamental sources: uncertainty about the

complete ness of a census count in a census year and

uncertainty between censuses due to errors in estimates

of migration, fertility, and mortality. Uncertainty in the

counts was estimated by sampling the variance-covariance

matrix of the model that predicted census completeness.

We estimated the uncertainty between counts by use

of out-of-sample predictive validity. We held out data

and estimated the error in estimates as a function of

the minimum of the number of years to the next

or previous census. We combined these two sources of

uncertainty and generated 1000 draws of percentage error

in the population for each location-year. The 1000 draws of

percentage error in the population and the population

mean, generated by the GBD Bayesian demographic

balancing model, were then combined to create 1000 draws

of population by age, sex, location, and year. 95% uncer-

tainty intervals (UIs) were calculated with the 2·5th and

97·5th percentiles. Details of this out-of-sample estimation

of uncertainty are shown in appendix 1 (section 2). Out-of-

sample estimates of uncertainty yielded larger uncertainty

than in-sample methods because of the nearly perfect

inverse correlation between migration and death rates,

which was conditional on census counts with low error.

A dot plot comparison of our total population counts by

country for dierent age groups in 2017 with UNPOP

estimates is shown in appendix 2.

SDI

GBD 2015 developed the SDI as a composite measure of

TFR in a population, lag-distributed income per capita,

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2003

1950 1960 1980 2000 2020

100

150

Population (millions)

Year

GMales

1950 1960 1980 2000 2020

Year

Females

1950 1960 1980 2000 2020

120

Population (millions)

Year

HMales

1950 1960 1980 2000 2020

Year

Females

120

Population (millions)

EMales

Females

100

125

Population (millions)

FMales

Females

150

180

Population (millions)

CMales

120

Females

120

180

Population (millions)

DMales

150

Females

400

600

800

Population (millions)

AMales Females

400

600

800

Population (millions)

BMales

200

Females



Zero migration prior

Model posterior

Datapoints

Source

























Figure 2: Fit of the GBD Bayesian demographic balancing model for the total population of males and females, from 1950 to 2017, in mainland China (A), India (B), the USA (C), Indonesia (D),

Pakistan (E), Brazil (F), Nigeria (G), and Bangladesh (H)

The 95% uncertainty interval is shown by light blue shading around the model posterior line. Mainland China excludes Hong Kong and Macao. GBD=Global Burden of Diseases, Injuries, and Risk Factors

Study.

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and average years of education in the population older

than 15 years.15,20 Each component was rescaled to a value

between 0 and 1, and the SDI was derived from their

geometric mean. The TFR was used in this overall

measure of development as a proxy for the status of

women in society; other plausible measures capturing

the status of women are not available for all countries

over a long time period. Our analysis of detailed ASFR

0·5

1·0

1·5

2·0

2·5

3·0

3·5

4·0

4·5

5·0

Total fertility rate (livebirths per woman)

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2017

100

120

140

Livebirths (millions)

Year

Age group (years)

50−54

45−49

40−44

35−39

30−34

25−29

20−24

15−19

10−14

GBD super-region

Central Europe, eastern Europe, and central Asia

High income

Latin America and Caribbean

North Africa and Middle East

South Asia

Southeast Asia, east Asia, and Oceania

Sub-Saharan Africa

Figure 3: Global total fertility rate distributed by maternal age group (A) and number of livebirths by GBD super-region, for both sexes combined (B), 1950–2017

Total fertility rate is the number of births expected per woman in each age group if she were to survive through the reproductive years (10–54 years) under the age-speciﬁc fertility rates at that

timepoint. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study.

Global Health Metrics

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2005

revealed in many countries that, through the process of

development the TFO30 generally decreased and then

increased. For example, in the USA, the TFO30 has

increased steadily from 1975. In exploratory analysis, we

found that the TFU25 did not show this U-shaped pattern

as countries develop. For GBD 2017, we have recalculated

the SDI by use of the TFU25 as a better proxy for the

status of women in society. The TFU25 not only does

not show a U-shaped pattern with development but

also remains highly correlated with under-5 mortality

(Pearson correlation coecient r=0·873) and other

mortality measures. The revised method for computing

SDI compared with the GBD 2016 method is correlated

with the GBD 2017 method (r=0·992). Detailed

comparisons of the GBD 2015 and GBD 2016 methods

compared with the approach we used are shown in

appendix 1 (section 3).

Role of the funding source

The funder of the study had no role in study design, data

collection, data analysis, data interpretation, or writing of

the report. All authors had full access to all the data in the

study and had ﬁnal responsibility for the decision to

submit for publication.

Results

Global

The global TFR by maternal age group from 1950 to 2017

is shown in ﬁgure 3. In 1950, the TFR was 4·7 livebirths

(95% UI 4·5–4·9) and, by 2017, the TFR had decreased by

49·4% (46·4–52·0) to 2·4 livebirths (2·2–2·5). From 1950

to 1995, the TFR within all 5-year maternal age groups

decreased: the greatest decrease in terms of contribution

to TFR was in women aged 20–24 years (who showed a

decrease of 0·42 livebirths), 25–29 years (0·52 livebirths),

and 30–34 years (0·38 livebirths). Since 1995, decreases in

the contribution to TFR from women aged 30–34 years,

35–39 years, and 40–44 years eectively plateaued at the

global level, whereas decreases in women at younger ages

continued. This slowing trend in reductions in the

number of livebirths per woman in these age groups

masks marked heterogeneity across countries, as we

subsequently discuss. Of the total livebirths globally in

2017, 9·4% occurred in teenage mothers, which is a

reduction from 9·9% of livebirths to teenage mothers in

1950. The age-speciﬁc fertility rate per 1000 women aged

10–19 years decreased from 37 livebirths (34–40) per

1000 women in 1950 to 22 livebirths (19–24) per 1000

women in 2017. The number of livebirths globally

increased from 92·6 million livebirths (88·9–96·4

million) in 1950 to a peak of 141·7 million livebirths

(135·8–147·3 million) in 2012. Over the past 35 years, the

number of livebirths annually has varied within a

relatively narrow range of 133·2 million (130·1–136·2)

livebirths to 141·7 million (135·8–147·3) livebirths.

The trend in world population from 1950 to 2017 by

GBD super-region is shown in ﬁgure 4. From 1950 to

1980, the global population increased exponentially at an

annualised rate of 1·9% (95% UI 1·88–1·92). From

1981 to 2017, however, the pace of the global popu-

lation increase has been largely linear, increasing by

83·6 million (79·8–87·5) people per year. Over the past

10 years (2007–17), the average annual increase in

population has been by 87·2 million (80·8–93·2) people,

compared with 81·5 million (79·0–84·5) people per year

in the previous 10 years (1997–2007). The global

population increased by 197·2% (95% UI 193·3–200·8),

from 2·6 billion (2·5–2·6) people in 1950 to 7·6 billion

(7·4–7·9) people in 2017. Over this period, the

composition of the world’s population changed

substantially. In 1950, the high-income, central Europe,

eastern Europe, and central Asia GBD super-regions

accounted for 35·2% of the global population but, in

2017, the populations of these countries accounted for

19·5% of the global population. Large increases occurred

in the proportion of the world’s population living in

south Asia, sub-Saharan Africa, Latin America and

the Caribbean, and north Africa and the Middle East.

The annual population growth rate between 1950 and

2017, globally and for the GBD super-regions, is shown

in ﬁgure 4. Growth of the global population increased

in the 1950s and reached 2·0% per year in 1964, then

slowly decreased to 1·1% in 2017. The slow shift in the

global population growth rate is determined by

markedly dierent trends by super-region. Growth of

the popu lation in north Africa and the Middle East

increased until the 1970s, and it has remained quite

high, at 1·7% in 2017. Population growth rates in sub-

Saharan Africa increased from 1950 to 1985, decreased

during 1985–1993, increased again until 1997, and then

plateaued; at 2·7% in 2017, population growth rates

were almost the highest rates ever recorded in this

region. The most substantial changes to population

growth rates were in the southeast Asia, east Asia, and

Oceania super-region, where the population growth

rate decreased from 2·5% in 1963 to 0·7% in 2017. The

large reduction in the population growth rate for this

super-region around 1960 was due to the Great Leap

Forward in China. In central Europe, eastern Europe,

and central Asia, the population growth rate dropped

rapidly after 1987 and was negative from 1993 to 2008.

Growth rates in the high-income super-region have

changed the least, starting at 1·2% in 1950 and reaching

0·4% in 2017.

Global population pyramids in 1950, 1975, 2000, and

2017 are shown in ﬁgure 5. As the world’s population

has grown, not only has the distribution of the global

population shifted toward sub-Saharan Africa and

south Asia, but the age structure of the global population

has also changed considerably. In 1950, the global mean

age of a person was 26·6 years, decreasing to 26·0 years,

in 1975, then increasing to 29·0 years in 2000 and

32·1 years in 2017. Demographic change has economic

consequences, and the proportion of the population that

Global Health Metrics

2006

www.thelancet.com Vol 392 November 10, 2018

is of working age (15–64 years) decreased from 59·9% in

1950 to 57·1% in 1975, then increased to 62·9% in 2000

and 65·3% in 2017. Another dimension of the global

population is the proportion of the population that is

female, which decreased from 50·1% to 49·8% over the

67-year period.

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2017

Population growth rate (%)

Year

0·1

0·2

0·3

0·4

0·5

0·6

0·7

0·8

0·9

1·0

Proportion of the global population (%)

GBD super-region

Central Europe, eastern Europe, and central Asia

High income

Latin America and Caribbean

North Africa and Middle East

South Asia

Southeast Asia, east Asia, and Oceania

Sub-Saharan Africa

GBD super-region

Central Europe, eastern Europe, and central Asia

High income

Latin America and Caribbean

North Africa and Middle East

South Asia

Southeast Asia, east Asia, and Oceania

Sub-Saharan Africa

Global

Figure 4: Proportion of the global population accounted for by the GBD super-regions (A) and the annual population growth rates, globally and for the super-regions (B)

Data are shown for both sexes combined, from 1950 to 2017. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study.

Global Health Metrics

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2007

National

Fertility rates vary substantially across countries and

over time (table 1; appendix 2). In 1950, TFR ranged

from a low of 1·7 livebirths (95% UI 1·4–2·0) in Andorra

to a high of 8·9 livebirths (8·7–9·0) in Jordan. The TFR

decreased in all 195 countries and territories between

1950 and 2017, and 102 countries and territories showed

a decrease of more than 50%. By 2017, the TFR ranged

from a low of 1·0 livebirths (0·9–1·2) in Cyprus to a

high of 7·1 livebirths (6·8–7·4) in Niger. Although a

useful summary, the TFR masks variation in trends in

fertility at dierent ages in many countries. The global

decrease in median ASFRs from 1950 to 2017 was

43·4% in women aged 15–19 years and 49·4% in women

aged 20–24 years, which contrasts with the observed

decreases in the median ASFR in older age groups of

mothers of 59·4% in women aged 40–44 years, 65·6% in

women aged 45–49 years, and 68·7% in women aged

50–54 years.

In 2017, the TFU25 ranged from 0·08 livebirths

(95% UI 0·07–0·09) in South Korea to 2·4 livebirths

(2·2–2·6) in Niger (ﬁgure 6), which is 31 times higher.

Countries and territories where the TFU25 was

less than 0·25 livebirths included many in western

Europe, Japan, South Korea, and Taiwan (province of

China). TFU25 exceeded 1·5 livebirths in many parts

of western, eastern, and central sub-Saharan Africa and

in Afghanistan. Trends in TFO30 are more complex;

decreases in fertility rate are observed at earlier stages of

development, and there are sustained increases in

fertility rate at higher levels of development due to

women delaying childbearing. TFO30 ranged from a

low of 0·3 livebirths (0·3–0·4) in Puerto Rico to a high

of 3·1 livebirths (3·0–3·2) in Niger. In 2017, 145 countries

showed higher fertility in women older than 30 years

than in women younger than 25 years. The geographical

pattern shows low fertility in women older than 30 years

in disparate settings: central and eastern Europe, China,

India, many parts of Latin America, and in some parts

of the Middle East. North America, western Europe,

central Europe, eastern Europe, Australasia, and high-

income Asia Paciﬁc had a higher TFO30 in 2017 than

in 1975, with a mean of 60·2% higher TFO30 in

these regions.

Figure 7 shows the areas where the TFO30 has

been increasing since 1975; increases of more than

Females

Males

Female mean

Female median

Male mean

Male median

Age

Sex

400 300 200 100 0 100 200 300 400

100

Age (years)

Population (millions)

400 300 200 100 0 100 200 300 400

Population (millions)

2000 2017

100

Age (years)

1950 1975

Figure 5: Global population pyramids for females and males by age, in 1950, 1975, 2000, and 2017

Global Health Metrics

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50% have been observed in most of western Europe,

high-income North America, Australasia, and high-

income Asia Paciﬁc. The correlation of the ASFR over

maternal age groups is shown in appendix 2. In 2017,

169 countries had a sex ratio of less than 1·07 males

per female at birth. Countries with higher sex ratios at

birth varied geographically (ﬁgure 7). For example,

Greenland, Tunisia, and Afghanistan had sex ratios

between 1·07 and 1·10 males per female at birth, and

India had a sex ratio at birth of 1·10 males per female.

Three countries had higher sex ratios at birth: Armenia

(1·14 males per female), Azerbaijan (1·15 males per

female), and China (1·17 males per female). High sex

ratios at birth lower the eective net reproductive rate

(the number of female livebirths expected per woman,

given observed age-speciﬁc death and fertility rates)

even more than the TFR. Estimates of the net

reproductive rate are shown in table 1. Net reproductive

rate in 2017 ranged from 0·48 female livebirths

(0·42–0·56) expected per woman in Cyprus to

3·00 female livebirths (2·90–3·10) expected per woman

in Niger. 95 countries had a net reproductive rate of less

than 1 meaning that, without changes in fertility, death

rates, or net immigration, populations in those

countries will eventually decrease.

The population growth rate from 2010 to 2017 is shown

in ﬁgure 8. 33 countries had a negative population

growth rate, most of which were located in central,

eastern, and western Europe and the Caribbean.

Outside Europe, negative growth rates were observed

in 14 countries, and the largest negative growth rates

were observed in Syria, the Northern Mariana Islands,

Georgia, Puerto Rico, and the Virgin Islands. Cyprus

(which has a growth rate of 1·7%), Israel (1·9%), and

Luxembourg (2·3%) are notable in the GBD western

Europe region because they are the only countries with a

growth rate greater than 1·2%. Population growth rates

in North America, Latin America, and the Caribbean

ranged from –0·5% in Puerto Rico to 2·6% in Belize.

Population growth rates of more than 2·0% were

seen in 33 of 46 countries in sub-Saharan Africa.

The Persian Gulf states, with the exception of the

United Arab Emirates, all had growth rates of more than

2·2%, mostly due to the migration of workers, not

fertility rates. Australia is of note among the GBD

high-income super-region in the southern hemisphere,

with a high population growth rate of 1·5%.

Even when countries have a TFR of less than the

replacement value (the TFR at which a population

replaces itself from generation to generation, assuming

no migration; generally estimated to be 2·05),51 popu-

lations can continue to grow because of population

momentum: the phenomenon by which the past growth

of birth cohorts leads to more women of childbearing

age and increased births relative to deaths, even though

the TFR for a time period is less than the replacement

value.52 Populations can also grow due to immigration,

as observed in many Persian Gulf nations. A comparison

of the 2017 population growth rate versus the TFR is

shown in ﬁgure 9, which highlights countries in which

the TFR is less than the replacement value but where

the population is still growing. The countries where the

population is declining are also shown. Countries fall

into four quadrants, deﬁned as a TFR of more than

or less than the replacement value and a population

growth rate of more than or less than zero. Divergence

between these two measures, as noted, is a function of

lags between period TFR and growth rate (population

momentum) or net migration.

Population estimates by country since 1950 are shown

in table 2. Age-sex-speciﬁc detail for these same years is

provided in appendix 2. Single-year, single-age population

estimates for the entire period of 1950–2017 are available

from the Global Health Data Exchange.

The proportion of the population that was of working

age from 1950 to 2017 by GBD super-region is shown in

ﬁgure 10. Studies of economic growth have identiﬁed

the potential for a demographic dividend when the

proportion of the population that is of working age

reaches more than 65%.53 In high-income countries, the

proportion of the population that is of working age

increased from the 1960s, crossed the 65% threshold in

the late 1970s, and was relatively constant during

the 1980s and 1990s. In 2005, this proportion began to

decrease and was only just more than the 65% threshold

in 2017. 12 of 34 high-income countries now have a

proportion of the population of working age that is less

than 65%, and Japan has a working-age proportion of

less than 60%. Other than sub-Saharan Africa and high-

income countries, the GBD super-regions have had a

substantially increasing proportion of the population of

working age from the mid-1960s to the present day; in

2017, Latin American and the Caribbean, north Africa

and the Middle East, south Asia, and central Europe,

eastern Europe, and central Asia all had proportions of

the population that are of working age between 64% and

71%. The most pronounced increase in the working-age

population occurred in southeast Asia, east Asia, and

Oceania, which increased from 54·2% of the population

in 1965 to 72·2% in 2011. Sub-Saharan Africa is the clear

outlier among GBD super-regions; the proportion of the

population of working age in this region has remained

at or less than 55% during the entire time period,

although this proportion has more recently increased.

In sub-Saharan Africa, the proportion of the population

that is of working age was less than 50% in Mali

(49·7%), Chad (46·6%), and Niger (46·1%) in 2017.

Discussion

Main ﬁndings

To our knowledge, this study presents the ﬁrst estimates

of population by location from 1950 to 2017 that are

based on transparent data and replicable analytical

code. Annual population estimates are provided for

Global Health Metrics

www.thelancet.com Vol 392 November 10, 2018

2009

Age-speciﬁc fertility rate (livebirths per 1000 women annually) Total

fertility

rate

Total

fertility rate

under age

25 years

Total fertility

rate from ages

30 to 54 years

Number of

livebirths

Net

reproductive

rate

10–14

years

15–19

years

20–24

years

25–29

years

30–34

years

35–39

years

40–44

years

45–49

years

50–54

years

Global 0·81

(0·35–

1·69)

42·9

(38·6–

48·0)

129·4

(117·8–

142·7)

131·9

(125·9–

138·7)

96·8

(91·4–

102·9)

52·4

(47·7–

57·5)

17·2

(15·4–

19·2)

3·4

(3·1–

3·8)

0·06

(0·06–

0·06)

2·4

(2·2–2·5)

0·87

(0·78–0·96)

0·85

(0·79–0·92)

138 810 622

(129 960 385–

149 058 367)

1·08

(1·02–1·16)

Low SDI 1·4

(0·6–

2·9)

71·9

(65·0–

80·0)

202·6

(181·7–

225·9)

188·4

(177·3–

200·9)

147·0

(136·9–

158·2)

93·4

(83·6–

103·5)

44·6

(39·5–

49·9)

15·1

(13·7–

16·7)

0·29

(0·28–

0·3)

3·8

(3·6–4·1)

1·4

(1·2–1·5)

1·5

(1·4–1·6)

37 891 965

(35 159 071–

41 108 482)

1·68

(1·58–1·81)

Low-middle SDI 0·88

(0·39–

1·85)

51·7

(45·5–

59·3)

156·6

(141·0–

174·3)

152·0

(143·4–

161·4)

112·4

(104·4–

121·8)

63·8

(57·0–

72·0)

24·1

(21·0–

27·7)

6·4

(5·5–

7·5)

0·12

(0·11–

0·12)

2·8

(2·6–3·1)

1·0

(0·9–1·2)

1·0

(0·9–1·1)

40 394 490

(37 088 216–

44 296 344)

1·28

(1·18–1·4)

Middle SDI 0·61

(0·27–

1·27)

33·5

(30·1–

37·6)

112·4

(100·6–

125·9)

120·1

(113·3–

127·9)

79·2

(73·7–

85·3)

39·0

(34·4–

44·2)

11·3

(9·9–

13·1)

1·4

(1·1–

1·6)

0·03

(0·03–

0·03)

2·0

(1·8–2·2)

0·73

(0·66–0·82)

0·65

(0·6–0·72)

26 502 966

(24 536 281–

28 871 941)

0·83

(0·77–0·9)

High-middle SDI 0·42

(0·18–

0·86)

19·8

(18·3–

21·6)

84·2

(79·0–

89·9)

107·0

(103·4–

110·7)

69·4

(65·8–

73·1)

32·1

(29·2–

35·3)

8·0

(7·2–

8·8)

0·63

(0·53–

0·74)

0·01

(0·01–

0·01)

1·6

(1·5–1·7)

0·52

(0·49–0·56)

0·55

(0·51–0·59)

22 028 156

(20 983 021–

23 184 413)

0·86

(0·81–0·9)

High SDI 0·25

(0·11–

0·5)

12·5

(11·3–

14·0)

49·6

(44·4–

55·7)

89·5

(84·3–

95·2)

98·6

(91·2–

106·8)

51·8

(45·2–

59·4)

11·1

(9·4–

13·2)

0·63

(0·55–

0·72)

0·01

(0·01–

0·01)

1·6

(1·4–1·7)

0·31

(0·28–0·35)

0·81

(0·73–0·9)

11 638 396

(10 631 265–

12 780 564)

0·76

(0·69–0·83)

Central

Europe, eastern

Europe, and

central Asia

0·08

(0·03–

0·15)

27·0

(23·5–

31·2)

102·9

(89·3–

118·2)

110·0

(102·9–

117·7)

77·0

(70·1–

84·8)

32·6

(27·8–

38·2)

6·3

(5·3–

7·5)

0·29

(0·24–

0·35)

0·01

(0·01–

0·01)

1·8

(1·6–2·0)

0·65

(0·56–0·75)

0·58

(0·52–0·66)

5 224 690

(4 687 984–

5 805 610)

0·84

(0·76–0·94)

Central Asia 0·05

(0·02–

0·1)

35·8

(30·7–

41·8)

172·3

(151·6–

195·2)

145·6

(137·2–

154·7)

91·2

(82·3–

102·1)

39·3

(33·4–

47·0)

9·6

(7·8–

12·0)

0·53

(0·37–

0·79)

0·01

(0·01–

0·01)

2·5

(2·3–2·7)

1·0

(0·9–1·2)

0·7

(0·62–0·81)

1 910 928

(1 754 242–

2 076 808)

1·15

(1·05–1·25)

Armenia 0·04

(0·02–

0·09)

24·8

(21·3–

28·8)

113·7

(99·3–

129·9)

103·4

(95·0–

113·6)

50·3

(44·0–

58·1)

20·5

(16·9–

24·9)

3·7

(2·9–

4·6)

0·2

(0·13–

0·31)

0·0

(0·0–

0·0)

1·6

(1·4–1·7)

0·69

(0·6–0·79)

0·37

(0·33–0·42)

38 128

(34 976–

41 387)

0·73

(0·67–0·8)

Azerbaijan 0·01

(0·0–

0·02)

44·1

(37·4–

52·0)

148·3

(128·8–

169·9)

118·6

(108·9–

129·2)

55·6

(48·9–

63·2)

21·0

(17·5–

25·2)

4·5

(3·6–

5·5)

0·46

(0·31–

0·72)

0·01

(0·01–

0·01)

2·0

(1·7–2·2)

0·96

(0·83–1·11)

0·41

(0·35–0·47)

173 728

(153 488–

196 430)

0·87

(0·77–0·99)

Georgia 0·26

(0·11–

0·54)

46·3

(39·3–

54·6)

126·8

(109·2–

146·5)

119·8

(109·9–

131·5)

71·1

(62·7–

81·3)

35·8

(29·6–

43·9)

9·0

(7·3–

11·3)

0·89

(0·67–

1·21)

0·02

(0·02–

0·02)

2·0

(1·9–2·2)

0·87

(0·74–1·01)

0·58

(0·5–0·69)

50 298

(45 798–

55 247)

0·97

(0·88–1·07)

Kazakhstan 0·05

(0·02–

0·1)

30·1

(26·5–

34·4)

140·1

(120·0–

162·7)

149·2

(137·1–

162·3)

93·7

(81·5–

108·8)

52·5

(41·6–

66·6)

12·4

(9·3–

16·8)

0·5

(0·31–

0·81)

0·01

(0·01–

0·01)

2·4

(2·2–2·6)

0·85

(0·73–0·99)

0·8

(0·66–0·97)

347 980

(315 168–

381 856)

1·13

(1·03–1·25)

Kyrgyzstan 0·01

(0·01–

0·03)

38·2

(32·6–

44·7)

151·2

(132·1–

172·4)

171·9

(159·8–

186·0)

119·1

(107·2–

131·6)

57·0

(48·0–

66·9)

18·0

(14·6–

22·0)

0·26

(0·16–

0·39)

0·0

(0·0–

0·01)

2·8

(2·6–3·0)

0·95

(0·82–1·09)

0·97

(0·85–1·1)

151 035

(141 013–

161 162)

1·31

(1·22–1·4)

Mongolia 0·21

(0·09–

0·43)

27·2

(23·9–

30·8)

147·3

(130·5–

165·0)

159·7

(148·4–

171·4)

114·5

(103·9–

125·6)

69·6

(60·3–

79·7)

20·1

(16·6–

24·1)

1·5

(1·0–

2·1)

0·03

(0·03–

0·03)

2·7

(2·5–2·9)

0·87

(0·77–0·98)

1·0

(0·9–1·2)

75 835

(70 120–

81 639)

1·27

(1·17–1·37)

Tajikistan 0·06

(0·03–

0·13)

55·7

(47·3–

65·4)

226·8

(199·4–

255·5)

208·3

(193·6–

223·8)

128·9

(112·4–

148·6)

68·3

(53·5–

87·5)

19·2

(14·0–

26·4)

2·1

(1·4–

3·4)

0·04

(0·04–

0·04)

3·5

(3·2–3·9)

1·4

(1·2–1·6)

1·1

(0·9–1·3)

285 161

(259 803–

310 494)

1·62

(1·47–1·77)

Turkmenistan 0·04

(0·02–

0·07)

19·2

(16·4–

22·5)

156·1

(135·1–

181·5)

190·9

(176·5–

207·5)

125·8

(113·3–

140·8)

49·1

(41·1–

59·4)

10·4

(8·3–

13·2)

0·01

(0·01–

0·01)

0·0

(0·0–

0·0)

2·8

(2·5–3·1)

0·88

(0·77–1·0)

0·93

(0·81–1·07)

109 634

(98 243–

123 307)

1·29

(1·15–1·45)

Uzbekistan 0·03

(0·01–

0·06)

32·3

(27·4–

38·0)

194·3

(169·4–

221·0)

127·8

(115·6–

142·5)

85·2

(74·9–

97·8)

25·4

(20·8–

31·5)

5·1

(3·9–

6·7)

0·2

(0·13–

0·31)

0·0

(0·0–

0·0)

2·4

(2·1–2·6)

1·1

(1·0–1·3)

0·58

(0·5–0·68)

679 125

(619 142–

740 880)

1·09

(1·0–1·19)

Central Europe 0·19

(0·08–

0·39)

19·5

(17·5–

21·6)

57·2

(49·7–

66·0)

93·1

(86·7–

99·8)

78·0

(70·9–

86·2)

31·9

(27·0–

37·8)

5·5

(4·8–

6·4)

0·24

(0·2–

0·28)

0·0

(0·0–

0·0)

1·4

(1·3–1·6)

0·38

(0·34–0·44)

0·58

(0·52–0·65)

1 066 904

(960 814–

1 187 258)

0·69

(0·62–0·76)

(Table 1 continues on next page)

Global Health Metrics

2010

www.thelancet.com Vol 392 November 10, 2018

Age-speciﬁc fertility rate (livebirths per 1000 women annually) Total

fertility

rate

Total

fertility rate

under age

25 years

Total fertility

rate from ages

30 to 54 years

Number of

livebirths

Net

reproductive

rate

10–14

years

15–19

years

20–24

years

25–29

years

30–34

years

35–39

years

40–44

years

45–49

years

50–54

years

(Continued from previous page)

Albania 0·07

(0·03–

0·13)

19·0

(15·9–

22·7)

104·0

(86·8–

123·8)

144·9

(132·5–

158·3)

73·8

(63·5–

85·5)

28·4

(21·9–

36·5)

5·9

(4·2–

8·1)

0·37

(0·26–

0·52)

0·01

(0·01–

0·01)

1·9

(1·6–2·2)

0·62

(0·51–0·73)

0·54

(0·45–0·65)

37 047

(32 029–

42 830)

0·88

(0·76–1·02)

Bosnia and

Herzegovina

0·05

(0·02–

0·11)

10·1

(8·6–

11·7)

48·3

(42·7–

54·6)

87·1

(81·7–

92·8)

74·0

(67·7–

80·7)

27·3

(23·2–

31·9)

4·8

(3·9–

5·9)

0·32

(0·23–

0·45)

0·01

(0·01–

0·01)

1·3

(1·2–1·4)

0·29

(0·26–0·33)

0·53

(0·49–0·58)

27 688

(25 627–

29 913)

0·6

(0·56–0·65)

Bulgaria 0·74

(0·33–

1·54)

39·3

(34·2–

45·4)

72·2

(61·9–

84·0)

87·0

(79·8–

94·9)

65·4

(57·6–

74·1)

25·7

(20·8–

31·7)

3·9

(3·0–

4·9)

0·2

(0·14–

0·27)

0·0

(0·0–

0·0)

1·5

(1·3–1·7)

0·56

(0·48–0·65)

0·48

(0·41–0·55)

58 874

(51 873–

66 693)

0·71

(0·62–0·8)

Croatia 0·06

(0·03–

0·12)

9·8

(8·5–

11·6)

45·6

(40·5–

51·3)

87·7

(82·7–

93·0)

85·9

(79·4–

92·6)

38·4

(33·5–

43·8)

6·4

(5·2–

7·7)

0·32

(0·23–

0·44)

0·01

(0·01–

0·01)

1·4

(1·3–1·4)

0·28

(0·25–0·31)

0·66

(0·59–0·72)

36 549

(34 544–

38 688)

0·66

(0·63–0·7)

Czech Republic 0·03

(0·01–

0·06)

12·6

(10·7–

14·8)

51·1

(44·9–

58·2)

99·4

(93·5–

105·9)

103·0

(95·5–

111·2)

43·4

(37·6–

50·0)

6·2

(4·7–

7·9)

0·2

(0·12–

0·32)

0·0

(0·0–

0·0)

1·6

(1·4–1·7)

0·32

(0·28–0·37)

0·76

(0·7–0·84)

104 681

(95 942–

114 456)

0·76

(0·7–0·84)

Hungary 0·28

(0·12–

0·56)

21·7

(18·7–

25·3)

47·0

(40·0–

55·3)

82·4

(75·7–

89·7)

86·4

(77·5–

96·4)

39·9

(33·2–

47·7)

7·2

(5·6–

9·5)

0·22

(0·15–

0·3)

0·0

(0·0–

0·0)

1·4

(1·3–1·6)

0·35

(0·29–0·4)

0·67

(0·59–0·76)

86 143

(76 294–

97 319)

0·69

(0·61–0·78)

Macedonia 0·27

(0·12–

0·54)

15·9

(13·7–

18·5)

64·9

(58·5–

71·7)

104·8

(99·2–

110·7)

81·5

(75·4–

87·9)

29·4

(25·1–

34·1)

4·2

(3·1–

5·6)

0·26

(0·17–

0·38)

0·01

(0·0–

0·01)

1·5

(1·4–1·6)

0·41

(0·37–0·44)

0·58

(0·54–0·62)

23 593

(22 076–

25 167)

0·71

(0·67–0·76)

Montenegro 0·12

(0·05–

0·24)

11·3

(9·6–

13·5)

63·3

(56·4–

71·9)

112·5

(106·2–

119·1)

95·1

(88·3–

102·2)

42·9

(37·2–

49·4)

8·8

(6·9–

11·4)

0·45

(0·29–

0·69)

0·01

(0·01–

0·01)

1·7

(1·6–1·8)

0·37

(0·33–0·43)

0·74

(0·69–0·79)

7069

(6742–7432)

0·79

(0·76–0·84)

Poland 0·05

(0·02–

0·11)

12·7

(10·9–

14·9)

49·8

(42·6–

58·2)

89·9

(83·1–

97·3)

73·5

(65·7–

82·2)

29·7

(24·5–

35·8)

5·7

(4·5–

7·1)

0·23

(0·17–

0·31)

0·0

(0·0–

0·0)

1·3

(1·2–1·5)

0·31

(0·27–0·37)

0·55

(0·47–0·63)

355 970

(315 476–

402 395)

0·63

(0·56–0·71)

Romania 0·38

(0·17–

0·79)

34·9

(30·5–

40·6)

71·9

(61·9–

83·5)

97·4

(90·0–

105·5)

73·7

(65·7–

82·8)

28·9

(23·6–

35·3)

4·7

(3·6–

6·1)

0·22

(0·16–

0·29)

0·0

(0·0–

0·0)

1·6

(1·4–1·7)

0·54

(0·48–0·6)

0·54

(0·47–0·62)

177 010

(158 216–

198 220)

0·75

(0·67–0·84)

Serbia 0·2

(0·09–

0·4)

15·4

(13·3–

18·0)

60·1

(51·0–

70·7)

90·8

(83·4–

99·0)

74·7

(66·3–

84·2)

28·5

(23·4–

34·6)

4·4

(3·4–

5·7)

0·32

(0·22–

0·45)

0·01

(0·01–

0·01)

1·4

(1·2–1·6)

0·38

(0·32–0·44)

0·54

(0·47–0·62)

80 547

(71 021–

91 372)

0·66

(0·58–0·75)

Slovakia 0·13

(0·06–

0·27)

23·1

(20·2–

26·5)

54·5

(46·4–

63·9)

87·0

(79·9–

94·8)

76·7

(68·2–

86·3)

31·8

(26·1–

38·6)

5·3

(4·1–

6·7)

0·21

(0·15–

0·28)

0·0

(0·0–

0·0)

1·4

(1·2–1·6)

0·39

(0·33–0·45)

0·57

(0·5–0·65)

52 596

(46 603–

59 441)

0·67

(0·59–0·76)

Slovenia 0·03

(0·01–

0·07)

5·0

(4·1–

6·0)

41·5

(34·9–

49·4)

108·1

(100·3–

117·2)

103·0

(93·7–

114·0)

39·7

(33·0–

48·5)

5·9

(4·3–

8·0)

0·23

(0·14–

0·36)

0·0

(0·0–

0·0)

1·5

(1·4–1·7)

0·23

(0·2–0·28)

0·74

(0·66–0·84)

19 132

(17 463–

21 101)

0·73

(0·67–0·8)

Eastern Europe 0·03

(0·01–

0·07)

25·3

(21·8–

29·4)

80·1

(68·1–

94·0)

98·6

(90·2–

107·8)

70·6

(61·7–

80·6)

30·4

(24·2–

37·8)

5·6

(4·3–

7·3)

0·23

(0·17–

0·3)

0·0

(0·0–

0·0)

1·6

(1·4–1·8)

0·53

(0·45–0·62)

0·53

(0·45–0·63)

2 246 857

(1 958 844–

2 577 202)

0·74

(0·64–0·85)

Belarus 0·02

(0·01–

0·03)

19·2

(16·5–

22·2)

84·7

(74·5–

96·3)

104·3

(95·9–

113·6)

72·3

(64·1–

81·4)

29·3

(24·3–

35·3)

4·8

(3·8–

6·1)

0·16

(0·11–

0·23)

0·0

(0·0–

0·0)

1·6

(1·4–1·8)

0·52

(0·46–0·59)

0·53

(0·46–0·62)

101 939

(90 523–

114 916)

0·75

(0·67–0·85)

Estonia 0·04

(0·02–

0·07)

14·1

(11·8–

16·9)

52·8

(44·6–

62·5)

98·6

(91·1–

106·9)

90·7

(81·6–

100·8)

46·9

(38·9–

56·2)

10·1

(7·5–

13·5)

0·3

(0·18–

0·46)

0·01

(0·01–

0·01)

1·6

(1·4–1·8)

0·33

(0·28–0·4)

0·74

(0·64–0·85)

13 446

(11 863–

15 268)

0·75

(0·66–0·86)

Latvia 0·03

(0·01–

0·06)

18·7

(15·9–

22·0)

62·8

(53·0–

74·2)

100·2

(92·2–

109·0)

84·4

(74·8–

95·1)

41·2

(33·8–

50·0)

8·2

(6·4–

10·3)

0·32

(0·22–

0·43)

0·01

(0·01–

0·01)

1·6

(1·4–1·8)

0·41

(0·34–0·48)

0·67

(0·59–0·77)

19 399

(17 182–

21 920)

0·76

(0·67–0·86)

Lithuania 0·03

(0·02–

0·07)

15·8

(13·6–

18·5)

61·1

(51·8–

72·0)

114·5

(105·7–

124·2)

89·8

(80·0–

100·8)

36·2

(29·7–

43·8)

6·3

(5·0–

7·7)

0·24

(0·17–

0·31)

0·0

(0·0–

0·0)

1·6

(1·4–1·8)

0·39

(0·33–0·45)

0·66

(0·58–0·75)

29 108

(25 844–

32 717)

0·78

(0·69–0·88)

(Table 1 continues on next page)

Global Health Metrics

www.thelancet.com Vol 392 November 10, 2018

2011

Age-speciﬁc fertility rate (livebirths per 1000 women annually) Total

fertility

rate

Total

fertility rate

under age

25 years

Total fertility

rate from ages

30 to 54 years

Number of

livebirths

Net

reproductive

rate

10–14

years

15–19

years

20–24

years

25–29

years

30–34

years

35–39

years

40–44

years

45–49

years

50–54

years

(Continued from previous page)

Moldova 0·05

(0·02–

0·1)

23·5

(20·3–

27·3)

75·7

(65·1–

89·2)

82·2

(74·9–

91·0)

53·7

(47·3–

61·6)

21·6

(17·9–

26·5)

4·1

(3·4–

5·2)

0·13

(0·09–

0·2)

0·0

(0·0–

0·0)

1·3

(1·2–1·5)

0·5

(0·44–0·56)

0·4

(0·34–0·47)

35 612

(31 581–

40 581)

0·62

(0·55–0·71)

Russia 0·03

(0·01–

0·07)

25·6

(22·0–

29·9)

81·7

(68·9–

96·4)

101·1

(92·5–

110·7)

74·1

(64·7–

84·8)

32·3

(25·6–

40·5)

6·0

(4·4–

7·9)

0·23

(0·15–

0·33)

0·0

(0·0–

0·0)

1·6

(1·4–1·9)

0·54

(0·46–0·63)

0·56

(0·48–0·67)

1 622 870

(1 410 393–

1 868 353)

0·77

(0·66–0·88)

Ukraine 0·03

(0·01–

0·06)

26·8

(23·3–

30·9)

77·7

(66·2–

90·9)

89·4

(81·5–

98·1)

57·8

(50·3–

66·5)

23·7

(19·0–

29·5)

4·6

(3·5–

5·8)

0·24

(0·17–

0·34)

0·0

(0·0–

0·0)

1·4

(1·2–1·6)

0·52

(0·45–0·61)

0·43

(0·37–0·51)

424 480

(369 821–

487 645)

0·66

(0·58–0·76)

High income 0·36

(0·16–

0·73)

16·2

(14·8–

17·8)

53·5

(47·9–

60·1)

91·0

(85·6–

96·9)

104·4

(95·9–

113·6)

56·1

(48·6–

64·8)

11·6

(9·6–

14·1)

0·65

(0·57–

0·75)

0·01

(0·01–

0·01)

1·7

(1·5–1·8)

0·35

(0·32–0·39)

0·86

(0·77–0·97)

11 470 352

(10 419 059–

12 658 766)

0·81

(0·73–0·89)

Australasia 0·22

(0·1–

0·45)

14·5

(12·8–

16·5)

51·9

(45·4–

59·2)

101·1

(94·5–

108·1)

125·9

(116·1–

136·3)

69·8

(60·6–

80·0)

14·4

(11·7–

17·8)

0·81

(0·51–

1·31)

0·02

(0·01–

0·02)

1·9

(1·7–2·1)

0·33

(0·29–0·38)

1·1

(0·9–1·2)

373 680

(338 110–

413 048)

0·91

(0·83–1·01)

Australia 0·15

(0·06–

0·3)

13·3

(11·3–

15·6)

49·2

(41·8–

57·8)

98·9

(91·5–

107·1)

125·0

(113·8–

137·1)

69·4

(58·9–

81·4)

14·4

(11·1–

18·4)

0·82

(0·51–

1·33)

0·02

(0·02–

0·02)

1·9

(1·6–2·1)

0·31

(0·27–0·37)

1·0

(0·9–1·2)

313 630

(278 661–

353 002)

0·89

(0·79–1·01)

New Zealand 0·59

(0·25–

1·2)

20·1

(17·2–

23·9)

66·4

(58·4–

76·4)

114·1

(106·9–

122·5)

131·7

(122·3–

142·7)

72·0

(63·0–

83·0)

14·9

(11·7–

18·6)

0·74

(0·46–

1·2)

0·01

(0·01–

0·01)

2·1

(1·9–2·3)

0·44

(0·38–0·5)

1·1

(1·0–1·2)

60 050

(54 692–

66 222)

1·01

(0·92–1·12)

High-income Asia

Paciﬁc

0·01

(0·01–

0·03)

3·4

(2·9–

4·0)

23·8

(19·5–

29·1)

74·1

(67·8–

80·8)

103·3

(93·5–

114·1)

47·4

(39·7–

56·5)

7·5

(5·8–

9·8)

0·22

(0·15–

0·31)

0·0

(0·0–

0·0)

1·3

(1·2–1·5)

0·14

(0·11–0·17)

0·79

(0·69–0·9)

1 427 130

(1 260 959–

1 623 740)

0·63

(0·56–0·71)

Brunei 0·28

(0·12–

0·57)

12·4

(10·3–

14·9)

53·7

(43·8–

65·4)

107·4

(97·8–

117·5)

111·8

(98·6–

125·8)

70·1

(56·8–

87·0)

19·8

(14·6–

26·3)

0·55

(0·34–

0·86)

0·01

(0·01–

0·01)

1·9

(1·7–2·0)

0·33

(0·27–0·4)

1·0

(0·9–1·1)

7093

(6568–7631)

0·89

(0·82–0·96)

Japan 0·01

(0·0–

0·01)

4·0

(3·3–

4·9)

29·6

(23·1–

37·6)

81·9

(72·7–

92·2)

96·7

(82·6–

112·5)

46·3

(35·2–

59·9)

8·0

(5·6–

11·1)

0·21

(0·13–

0·31)

0·0

(0·0–

0·0)

1·3

(1·1–1·6)

0·17

(0·13–0·21)

0·76

(0·62–0·92)

922 225

(767 131–

1 106 046)

0·65

(0·54–0·77)

Singapore 0·08

(0·03–

0·16)

4·4

(3·6–

5·3)

20·4

(15·8–

26·0)

65·0

(57·4–

73·7)

101·4

(86·9–

117·8)

51·7

(39·5–

66·5)

9·6

(6·8–

13·3)

0·37

(0·23–

0·6)

0·01

(0·01–

0·01)

1·3

(1·1–1·5)

0·12

(0·1–0·16)

0·82

(0·67–0·99)

64 836

(54 182–

77 378)

0·61

(0·51–0·73)

South Korea 0·02

(0·01–

0·04)

1·7

(1·4–

2·1)

13·5

(11·7–

15·5)

60·6

(57·3–

64·1)

117·4

(111·6–

123·3)

48·9

(44·2–

53·8)

6·1

(4·9–

7·4)

0·24

(0·15–

0·39)

0·0

(0·0–

0·0)

1·2

(1·2–1·3)

0·08

(0·07–0·09)

0·86

(0·81–0·92)

432 974

(412 109–

453 553)

0·6

(0·57–0·63)

High-income North

America

0·55

(0·24–

1·11)

20·7

(18·8–

22·7)

70·7

(64·2–

77·9)

99·4

(94·4–

104·8)

103·3

(96·4–

110·8)

52·5

(46·5–

59·2)

11·0

(9·3–

13·1)

0·74

(0·55–

0·99)

0·01

(0·01–

0·01)

1·8

(1·7–1·9)

0·46

(0·42–0·51)

0·84

(0·76–0·92)

4 314 373

(3 982 175–

4 683 089)

0·86

(0·8–0·94)

Canada 0·15

(0·07–

0·31)

12·8

(10·7–

15·3)

46·8

(37·8–

57·7)

99·2

(89·7–

109·7)

111·9

(98·2–

127·0)

51·8

(40·8–

64·8)

9·5

(7·0–

12·7)

0·42

(0·28–

0·62)

0·01

(0·01–

0·01)

1·7

(1·4–1·9)

0·3

(0·24–0·37)

0·87

(0·73–1·02)

390 262

(334 379–

455 010)

0·8

(0·69–0·94)

Greenland 0·62

(0·27–

1·28)

42·5

(35·8–

51·1)

104·5

(87·6–

123·9)

119·1

(107·8–

132·7)

87·0

(74·8–

100·8)

42·9

(33·6–

54·2)

6·5

(4·6–

9·2)

0·05

(0·03–

0·08)

0·0

(0·0–

0·0)

2·0

(1·8–2·3)

0·74

(0·65–0·84)

0·68

(0·57–0·81)

817

(728–910)

0·94

(0·84–1·06)

USA 0·58

(0·25–

1·19)

21·4

(19·6–

23·4)

73·1

(66·9–

80·0)

99·4

(94·9–

104·3)

102·3

(96·2–

108·9)

52·5

(47·2–

58·5)

11·2

(9·6–

13·2)

0·78

(0·56–

1·05)

0·01

(0·01–

0·02)

1·8

(1·7–1·9)

0·48

(0·43–0·52)

0·83

(0·77–0·91)

3 923 218

(3 646 761–

4 226 835)

0·87

(0·81–0·94)

Southern Latin

America

1·5

(0·7–

3·2)

53·7

(49·0–

59·3)

92·0

(82·4–

102·6)

96·7

(91·3–

102·8)

90·6

(81·3–

100·9)

61·8

(52·1–

72·9)

15·6

(12·2–

19·8)

0·99

(0·64–

1·49)

0·02

(0·02–

0·02)

2·1

(1·9–2·2)

0·74

(0·68–0·79)

0·84

(0·73–0·98)

1 041 669

(958 720–

1 130 812)

1·0

(0·91–1·08)

Argentina 1·7

(0·7–

3·5)

58·2

(51·9–

66·0)

100·2

(87·8–

113·6)

101·3

(93·9–

110·2)

92·0

(82·7–

102·2)

63·8

(54·1–

75·0)

15·8

(12·4–

19·8)

1·0

(0·7–

1·6)

0·02

(0·02–

0·02)

2·2

(2·0–2·3)

0·8

(0·73–0·88)

0·86

(0·75–0·99)

747 539

(695 353–

801 816)

1·04

(0·97–1·12)

(Table 1 continues on next page)

Global Health Metrics

2012

www.thelancet.com Vol 392 November 10, 2018

Age-speciﬁc fertility rate (livebirths per 1000 women annually) Total

fertility

rate

Total

fertility rate

under age

25 years

Total fertility

rate from ages

30 to 54 years

Number of

livebirths

Net

reproductive

rate

10–14

years

15–19

years

20–24

years

25–29

years

30–34

years

35–39

years

40–44

years

45–49

years

50–54

years

(Continued from previous page)

Chile 1·2

(0·5–

2·4)

40·9

(35·9–

46·6)

72·3

(62·3–

83·9)

86·3

(79·5–

93·9)

87·5

(78·2–

97·8)

57·4

(48·0–

68·3)

15·6

(12·1–

19·9)

0·88

(0·55–

1·36)

0·02

(0·02–

0·02)

1·8

(1·6–2·1)

0·57

(0·5–0·66)

0·81

(0·69–0·94)

245 912

(215 928–

279 946)

0·88

(0·77–1·0)

Uruguay 1·2

(0·5–

2·6)

53·9

(47·1–

61·6)

86·9

(74·5–

101·2)

93·6

(85·6–

102·5)

87·8

(77·7–

99·2)

56·1

(46·0–

67·9)

14·1

(10·5–

18·6)

1·0

(0·7–

1·5)

0·02

(0·02–

0·02)

2·0

(1·7–2·3)

0·71

(0·61–0·82)

0·8

(0·68–0·93)

48 170

(41 869–

55 322)

0·95

(0·82–1·09)

Western Europe 0·05

(0·02–

0·1)

8·7

(7·5–

10·0)

40·6

(35·0–

47·2)

87·8

(81·6–

94·5)

106·6

(97·6–

116·6)

61·2

(52·7–

70·8)

13·3

(11·1–

15·8)

0·73

(0·64–

0·82)

0·01

(0·01–

0·01)

1·6

(1·4–1·8)

0·25

(0·21–0·29)

0·91

(0·81–1·02)

4 313 498

(3 871 044–

4 807 568)

0·77

(0·69–0·86)

Andorra 0·22

(0·09–

0·45)

4·9

(4·2–

5·6)

26·7

(22·6–

31·3)

57·2

(52·2–

62·6)

85·1

(76·5–

94·1)

52·1

(45·5–

59·3)

12·6

(10·4–

15·1)

0·96

(0·59–

1·45)

0·02

(0·02–

0·02)

1·2

(1·1–1·3)

0·16

(0·13–0·19)

0·75

(0·67–0·85)

642

(567–724)

0·58

(0·51–0·65)

Austria 0·05

(0·02–

0·11)

9·1

(7·7–

10·6)

42·8

(37·4–

49·0)

87·4

(81·8–

93·4)

99·3

(91·8–

107·5)

53·5

(46·7–

61·2)

10·1

(8·2–

12·4)

0·46

(0·32–

0·63)

0·01

(0·01–

0·01)

1·5

(1·4–1·7)

0·26

(0·23–0·3)

0·82

(0·75–0·9)

86 756

(79 382–

94 860)

0·73

(0·67–0·8)

Belgium 0·04

(0·02–

0·07)

7·6

(6·4–

9·1)

43·6

(36·9–

51·5)

114·3

(106·5–

122·7)

111·8

(101·9–

122·7)

49·7

(41·7–

59·0)

9·7

(7·8–

12·0)

0·51

(0·36–

0·71)

0·01

(0·01–

0·01)

1·7

(1·5–1·9)

0·26

(0·22–0·3)

0·86

(0·77–0·96)

121 588

(109 546–

134 907)

0·82

(0·74–0·91)

Cyprus 0·03

(0·01–

0·06)

4·0

(3·3–

4·8)

24·5

(19·6–

30·6)

58·8

(52·8–

65·5)

68·3

(59·7–

78·1)

35·9

(28·7–

44·6)

9·3

(6·9–

12·6)

1·1

(0·7–

1·6)

0·02

(0·02–

0·02)

1·0

(0·9–1·2)

0·14

(0·11–0·18)

0·57

(0·49–0·67)

10 788

(9310–12 496)

0·48

(0·42–0·56)

Denmark 0·01

(0·01–

0·03)

4·6

(3·9–

5·6)

35·7

(29·6–

43·0)

111·4

(103·0–

120·6)

128·2

(116·8–

140·5)

58·3

(49·4–

68·6)

11·0

(8·8–

13·6)

0·49

(0·33–

0·7)

0·01

(0·01–

0·01)

1·7

(1·6–1·9)

0·2

(0·17–0·24)

0·99

(0·89–1·1)

60 724

(54 681–

67 563)

0·84

(0·76–0·94)

Finland 0·02

(0·01–

0·03)

7·0

(5·9–

8·3)

45·0

(38·5–

52·6)

95·5

(88·2–

103·5)

109·9

(99·5–

121·3)

57·1

(48·1–

67·3)

12·8

(10·3–

15·8)

0·69

(0·51–

0·93)

0·01

(0·01–

0·01)

1·6

(1·5–1·8)

0·26

(0·22–0·3)

0·9

(0·81–1·01)

55 235

(49 589–

61 618)

0·8

(0·71–0·89)

France 0·03

(0·01–

0·06)

7·6

(6·6–

8·8)

47·4

(41·0–

54·8)

117·2

(109·7–

125·4)

120·6

(110·6–

131·4)

61·5

(52·6–

71·7)

13·8

(11·1–

17·1)

0·77

(0·55–

1·03)

0·01

(0·01–

0·02)

1·8

(1·7–2·0)

0·28

(0·24–0·32)

0·98

(0·88–1·1)

737 405

(664 102–

819 651)

0·89

(0·81–0·99)

Germany 0·04

(0·02–

0·07)

8·0

(6·9–

9·3)

34·3

(29·4–

40·1)

75·2

(69·5–

81·5)

97·3

(88·5–

107·1)

53·6

(45·4–

63·0)

9·9

(7·9–

12·3)

0·4

(0·28–

0·53)

0·01

(0·01–

0·01)

1·4

(1·2–1·6)

0·21

(0·18–0·25)

0·81

(0·71–0·91)

710 634

(633 455–

798 663)

0·67

(0·6–0·76)

Greece 0·14

(0·06–

0·29)

9·1

(7·9–

10·6)

32·1

(27·0–

38·2)

74·5

(68·4–

81·1)

100·8

(91·0–

111·5)

55·6

(46·7–

65·8)

11·2

(8·6–

14·6)

1·3

(0·9–

1·9)

0·03

(0·02–

0·03)

1·4

(1·3–1·6)

0·21

(0·18–0·24)

0·84

(0·75–0·95)

89 713

(79 740–

100 854)

0·69

(0·61–0·77)

Iceland 0·07

(0·03–

0·14)

9·6

(8·1–

11·3)

54·5

(46·5–

63·9)

109·1

(100·9–

118·1)

112·7

(102·0–

124·6)

64·9

(55·2–

75·5)

15·3

(12·2–

19·2)

0·5

(0·31–

0·81)

0·01

(0·01–

0·01)

1·8

(1·7–2·0)

0·32

(0·28–0·37)

0·97

(0·88–1·05)

4250

(3897–4639)

0·89

(0·82–0·98)

Ireland 0·05

(0·02–

0·09)

10·6

(9·2–

12·6)

40·5

(34·4–

47·6)

77·1

(71·0–

84·0)

123·4

(112·4–

135·4)

93·4

(80·9–

107·2)

22·0

(17·6–

27·2)

1·2

(0·8–

1·7)

0·02

(0·02–

0·02)

1·8

(1·6–2·1)

0·26

(0·22–0·29)

1·2

(1·1–1·4)

64 902

(57 702–

72 962)

0·89

(0·79–1·0)

Israel 0·03

(0·01–

0·06)

11·3

(9·6–

13·5)

101·1

(88·1–

115·8)

171·3

(161·6–

181·8)

169·7

(157·9–

182·1)

100·8

(88·7–

114·0)

24·1

(19·8–

29·1)

1·7

(1·2–

2·3)

0·03

(0·03–

0·03)

2·9

(2·6–3·2)

0·56

(0·49–0·65)

1·5

(1·3–1·6)

177 148

(161 025–

194 812)

1·4

(1·27–1·54)

Italy 0·02

(0·01–

0·03)

5·6

(4·7–

6·6)

28·4

(24·3–

33·2)

66·2

(61·0–

71·9)

91·6

(83·0–

101·1)

58·5

(49·8–

68·5)

14·9

(12·0–

18·5)

0·92

(0·64–

1·27)

0·02

(0·02–

0·02)

1·3

(1·2–1·5)

0·17

(0·15–0·2)

0·83

(0·73–0·94)

464 442

(410 461–

526 027)

0·64

(0·57–0·73)

Luxembourg 0·08

(0·04–

0·17)

6·2

(5·1–

7·4)

34·4

(28·3–

41·8)

75·7

(69·3–

82·8)

103·8

(94·0–

114·6)

62·2

(52·7–

72·6)

13·4

(10·5–

17·0)

0·61

(0·38–

0·92)

0·01

(0·01–

0·01)

1·5

(1·4–1·6)

0·2

(0·17–0·24)

0·9

(0·83–0·97)

6407

(5861–6965)

0·72

(0·65–0·78)

Malta 0·19

(0·08–

0·39)

12·6

(10·9–

14·4)

38·1

(32·2–

45·2)

89·2

(82·0–

97·0)

101·3

(90·6–

113·2)

47·0

(38·6–

56·9)

9·0

(7·0–

11·5)

0·44

(0·3–

0·65)

0·01

(0·01–

0·01)

1·5

(1·3–1·7)

0·25

(0·22–0·29)

0·79

(0·68–0·91)

4311

(3812–4880)

0·71

(0·63–0·81)

(Table 1 continues on next page)

Global Health Metrics

www.thelancet.com Vol 392 November 10, 2018

2013

Age-speciﬁc fertility rate (livebirths per 1000 women annually) Total

fertility

rate

Total

fertility rate

under age

25 years

Total fertility

rate from ages

30 to 54 years

Number of

livebirths

Net

reproductive

rate

10–14

years

15–19

years

20–24

years

25–29

years

30–34

years

35–39

years

40–44

years

45–49

years

50–54

years

(Continued from previous page)

Netherlands 0·02

(0·01–

0·05)

4·1

(3·4–

4·9)

30·0

(25·1–

35·7)

98·2

(91·1–

105·9)

129·2

(118·1–

141·3)

60·5

(51·0–

71·4)

9·7

(7·5–

12·9)

0·43

(0·29–

0·59)

0·01

(0·01–

0·01)

1·7

(1·5–1·8)

0·17

(0·14–0·2)

1·0

(0·9–1·11)

172 472

(155 190–

191 686)

0·8

(0·72–0·89)

Norway 0·01

(0·01–

0·03)

5·7

(4·7–

6·9)

42·2

(36·3–

49·0)

106·9

(100·2–

114·1)

120·4

(111·6–

129·9)

59·9

(51·9–

68·9)

12·0

(9·2–

15·4)

0·64

(0·4–

0·98)

0·01

(0·01–

0·01)

1·7

(1·6–1·9)

0·24

(0·21–0·28)

0·96

(0·88–1·05)

60 329

(55 230–

66 015)

0·84

(0·77–0·92)

Portugal 0·12

(0·05–

0·25)

10·2

(8·7–

12·0)

31·2

(26·0–

37·3)

64·2

(58·8–

70·2)

89·9

(80·9–

99·9)

51·5

(43·2–

61·2)

11·1

(8·8–

14·0)

0·65

(0·45–

0·88)

0·01

(0·01–

0·01)

1·3

(1·1–1·5)

0·21

(0·17–0·25)

0·77

(0·67–0·88)

85 589

(74 860–

97 921)

0·63

(0·55–0·72)

Spain 0·08

(0·04–

0·17)

8·3

(7·0–

9·8)

26·0

(22·7–

29·8)

57·8

(54·1–

61·9)

95·1

(88·7–

102·1)

66·5

(59·8–

73·9)

15·7

(13·2–

18·6)

0·87

(0·6–

1·23)

0·02

(0·02–

0·02)

1·4

(1·2–1·5)

0·17

(0·15–0·2)

0·89

(0·81–0·98)

407 088

(370 674–

447 913)

0·65

(0·59–0·72)

Sweden 0·02

(0·01–

0·04)

5·2

(4·4–

6·3)

43·6

(38·1–

49·9)

108·8

(102·7–

115·5)

125·7

(117·3–

134·7)

68·7

(60·7–

77·6)

14·4

(11·4–

17·8)

0·73

(0·46–

1·09)

0·01

(0·01–

0·01)

1·8

(1·7–2·0)

0·24

(0·21–0·28)

1·0

(1·0–1·1)

118 087

(109 169–

127 819)

0·88

(0·82–0·96)

Switzerland 0·01

(0·01–

0·03)

3·4

(2·8–

4·1)

28·3

(23·3–

34·3)

77·0

(70·4–

84·3)

111·8

(101·6–

123·0)

65·1

(55·4–

76·2)

13·1

(10·6–

16·1)

0·64

(0·45–

0·85)

0·01

(0·01–

0·01)

1·5

(1·3–1·7)

0·16

(0·13–0·19)

0·95

(0·85–1·06)

87 282

(78 003–

97 459)

0·72

(0·64–0·81)

UK 0·11

(0·05–

0·21)

15·3

(13·5–

17·4)

54·1

(46·8–

62·4)

91·0

(84·7–

97·9)

107·1

(97·8–

117·3)

64·7

(55·9–

74·7)

13·5

(11·0–

16·5)

0·8

(0·58–

1·07)

0·02

(0·01–

0·02)

1·7

(1·6–1·9)

0·35

(0·3–0·4)

0·93

(0·83–1·05)

783 225

(703 221–

873 164)

0·84

(0·75–0·93)

England 0·1

(0·05–

0·21)

14·9

(13·1–

17·0)

54·7

(47·5–

63·0)

92·4

(86·2–

99·3)

108·5

(99·2–

118·7)

66·1

(57·2–

76·2)

14·0

(11·2–

17·2)

0·82

(0·61–

1·09)

0·02

(0·02–

0·02)

1·8

(1·6–2·0)

0·35

(0·3–0·4)

0·95

(0·84–1·06)

672 857

(604 801–

749 278)

0·85

(0·76–0·95)

Northern

Ireland

0·13

(0·06–

0·27)

15·2

(13·1–

17·6)

53·4

(44·9–

63·4)

95·7

(87·9–

104·3)

114·6

(103·4–

126·9)

67·3

(56·7–

79·4)

13·0

(9·7–

16·9)

0·59

(0·31–

1·04)

0·01

(0·01–

0·01)

1·8

(1·6–2·0)

0·34

(0·29–0·41)

0·98

(0·85–1·12)

23 589

(20 766–

26 808)

0·87

(0·76–0·99)

Scotland 0·11

(0·05–

0·22)

18·9

(16·9–

21·2)

44·8

(38·8–

51·8)

72·1

(66·9–

77·8)

93·1

(84·6–

102·5)

55·4

(47·8–

64·0)

10·1

(8·1–

12·4)

0·62

(0·31–

1·16)

0·01

(0·01–

0·01)

1·5

(1·3–1·6)

0·32

(0·28–0·37)

0·8

(0·72–0·88)

53 451

(48 281–

59 123)

0·71

(0·64–0·79)

Wales 0·11

(0·05–

0·22)

16·1

(14·2–

18·3)

59·8

(51·0–

70·0)

96·1

(88·7–

104·2)

100·2

(90·1–

111·4)

52·4

(43·9–

62·3)

10·0

(7·8–

12·6)

0·76

(0·47–

1·16)

0·01

(0·01–

0·02)

1·7

(1·5–1·9)

0·38

(0·33–0·44)

0·82

(0·71–0·94)

33 470

(29 639–

37 854)

0·81

(0·72–0·92)

Latin America and

Caribbean

2·0

(0·9–

4·2)

63·5

(57·1–

71·0)

112·9

(100·1–

127·8)

104·2

(96·7–

112·6)

85·7

(79·2–

92·9)

51·5

(45·2–

58·3)

15·5

(13·2–

18·1)

1·3

(1·1–

1·5)

0·02

(0·02–

0·02)

2·2

(2·0–2·4)

0·89

(0·8–1·0)

0·77

(0·69–0·85)

10 393 604

(9 469 048–

11 430 456)

1·04

(0·95–1·14)

Andean Latin

America

1·4

(0·6–

2·9)

71·4

(63·9–

79·5)

138·2

(121·6–

157·2)

132·8

(123·2–

143·6)

114·3

(102·9–

127·4)

76·8

(65·8–

88·4)

27·0

(22·1–

32·5)

2·7

(2·1–

3·4)

0·05

(0·05–

0·05)

2·8

(2·6–3·1)

1·1

(0·9–1·2)

1·1

(1·0–1·2)

1 386 395

(1 260 285–

1 526 533)

1·34

(1·22–1·47)

Bolivia 2·2

(1·0–

4·6)

71·9

(61·8–

83·4)

156·2

(132·9–

184·4)

154·6

(141·1–

170·5)

136·2

(119·8–

155·6)

92·4

(75·8–

111·0)

30·9

(23·9–

39·4)

4·3

(2·9–

6·3)

0·08

(0·08–

0·09)

3·2

(2·9–3·6)

1·2

(1·0–1·3)

1·3

(1·2–1·5)

301 119

(271 239–

334 416)

1·52

(1·37–1·69)

Ecuador 0·76

(0·33–

1·58)

60·5

(51·5–

70·9)

134·3

(112·3–

159·1)

112·9

(101·6–

125·3)

86·2

(73·8–

100·2)

46·3

(36·0–

58·7)

12·5

(9·3–

16·6)

1·2

(0·8–

1·8)

0·02

(0·02–

0·02)

2·3

(1·9–2·7)

0·98

(0·82–1·15)

0·73

(0·6–0·89)

315 984

(268 796–

370 024)

1·08

(0·92–1·26)

Peru 1·5

(0·6–

3·1)

78·0

(67·0–

91·9)

133·5

(113·1–

158·8)

135·0

(123·0–

149·3)

120·9

(106·5–

138·2)

87·0

(72·2–

103·1)

32·9

(25·8–

41·0)

3·0

(2·0–

4·2)

0·06

(0·05–

0·06)

3·0

(2·6–3·3)

1·1

(0·9–1·3)

1·2

(1·1–1·4)

769 292

(687 072–

866 600)

1·42

(1·26–1·59)

Caribbean 1·2

(0·5–

2·5)

58·1

(51·1–

66·0)

119·3

(108·3–

131·3)

112·8

(105·8–

120·1)

86·0

(78·5–

93·7)

52·7

(46·0–

60·0)

15·3

(12·6–

18·1)

1·9

(1·5–

2·3)

0·03

(0·03–

0·03)

2·2

(2·0–2·4)

0·89

(0·81–0·99)

0·78

(0·7–0·86)

815 882

(746 824–

889 894)

1·04

(0·95–1·13)

Antigua and

Barbuda

2·5

(1·1–

5·1)

50·1

(43·2–

58·2)

81·5

(67·2–

98·3)

75·7

(67·8–

84·6)

57·8

(49·2–

67·8)

27·5

(21·1–

35·5)

6·9

(5·1–

9·3)

0·08

(0·05–

0·13)

0·0

(0·0–

0·0)

1·5

(1·3–1·8)

0·67

(0·56–0·79)

0·46

(0·38–0·56)

1071

(905–1261)

0·73

(0·62–0·86)

(Table 1 continues on next page)

Global Health Metrics

2014

www.thelancet.com Vol 392 November 10, 2018

Age-speciﬁc fertility rate (livebirths per 1000 women annually) Total

fertility

rate

Total

fertility rate

under age

25 years

Total fertility

rate from ages

30 to 54 years

Number of

livebirths

Net

reproductive

rate

10–14

years

15–19

years

20–24

years

25–29

years

30–34

years

35–39

years

40–44

years

45–49

years

50–54

years

(Continued from previous page)

The Bahamas 0·76

(0·33–

1·56)

33·5

(28·2–

39·9)

73·4

(59·3–

90·2)

82·4

(73·3–

92·6)

65·8

(55·4–

77·8)

41·5

(31·7–

53·5)

10·8

(7·9–

14·6)

0·37

(0·24–

0·56)

0·01

(0·01–

0·01)

1·5

(1·3–1·9)

0·54

(0·44–0·65)

0·59

(0·48–0·73)

4679

(3895–5611)

0·74

(0·62–0·88)

Barbados 1·2

(0·5–

2·4)

40·2

(34·3–

47·1)

75·6

(61·9–

91·8)

74·7

(66·6–

83·7)

58·2

(49·2–

68·7)

27·9

(21·2–

36·2)

8·6

(6·3–

11·6)

0·17

(0·11–

0·25)

0·0

(0·0–

0·0)

1·4

(1·2–1·7)

0·58

(0·48–0·7)

0·47

(0·38–0·58)

2850

(2393–3388)

0·68

(0·58–0·81)

Belize 1·1

(0·5–

2·3)

59·0

(51·1–

68·2)

132·5

(115·2–

151·8)

112·5

(102·9–

123·1)

81·3

(72·5–

91·2)

46·8

(39·2–

55·6)

11·3

(9·4–

13·5)

1·3

(1·1–

1·5)

0·03

(0·02–

0·03)

2·2

(2·0–2·5)

0·96

(0·83–1·1)

0·7

(0·61–0·81)

7843

(6904–8895)

1·06

(0·94–1·2)

Bermuda 0·42

(0·18–

0·86)

8·5

(7·3–

9·8)

34·9

(30·0–

40·6)

58·6

(53·5–

64·4)

83·0

(74·8–

92·2)

58·1

(49·8–

67·6)

16·1

(13·3–

19·7)

0·96

(0·61–

1·47)

0·02

(0·02–

0·02)

1·3

(1·2–1·5)

0·22

(0·19–0·25)

0·79

(0·71–0·89)

562

(502–631)

0·63

(0·57–0·71)

Cuba 1·6

(0·7–

3·3)

45·5

(41·1–

51·0)

91·2

(82·5–

100·4)

84·1

(78·8–

90·4)

52·7

(47·5–

58·4)

23·3

(19·3–

28·0)

4·1

(3·1–

5·5)

0·15

(0·1–

0·24)

0·0

(0·0–

0·0)

1·5

(1·4–1·6)

0·69

(0·64–0·75)

0·4

(0·35–0·46)

109 664

(103 731–

116 193)

0·72

(0·68–0·77)

Dominica 1·4

(0·6–

3·0)

44·6

(38·0–

53·2)

84·3

(68·6–

104·7)

79·4

(70·7–

90·2)

66·6

(56·4–

79·5)

34·5

(27·7–

43·3)

8·0

(6·0–

10·6)

0·23

(0·15–

0·36)

0·0

(0·0–

0·0)

1·6

(1·3–1·9)

0·65

(0·54–0·8)

0·55

(0·45–0·67)

801

(677–960)

0·75

(0·63–0·89)

Dominican

Republic

0·91

(0·4–

1·92)

90·9

(78·3–

105·1)

153·6

(131·2–

178·5)

123·0

(111·4–

135·8)

69·6

(58·8–

82·1)

29·5

(22·5–

38·3)

6·2

(4·5–

8·4)

0·65

(0·44–

0·92)

0·01

(0·01–

0·01)

2·4

(2·0–2·7)

1·2

(1·1–1·4)

0·53

(0·43–0·65)

216 514

(186 677–

250 296)

1·12

(0·96–1·29)

Grenada 0·92

(0·41–

1·91)

48·2

(41·8–

55·7)

88·0

(72·1–

106·7)

84·3

(75·2–

94·6)

85·3

(73·2–

99·2)

53·3

(41·7–

67·1)

16·1

(12·2–

21·1)

0·69

(0·43–

1·06)

0·01

(0·01–

0·01)

1·9

(1·6–2·2)

0·69

(0·57–0·82)

0·78

(0·64–0·94)

1514

(1279–1786)

0·9

(0·76–1·06)

Guyana 1·9

(0·8–

3·9)

67·3

(58·2–

77·8)

153·7

(131·8–

178·1)

126·2

(114·6–

138·9)

89·6

(77·8–

102·8)

47·9

(38·4–

59·1)

12·4

(9·6–

15·9)

1·2

(0·8–

1·7)

0·02

(0·02–

0·02)

2·5

(2·2–2·9)

1·1

(1·0–1·3)

0·76

(0·63–0·9)

15 719

(13 597–

18 087)

1·18

(1·02–1·35)

Haiti 1·3

(0·6–

2·6)

49·9

(42·6–

58·3)

128·6

(108·7–

153·2)

147·9

(135·1–

163·1)

139·7

(123·9–

158·4)

107·9

(90·8–

128·2)

43·5

(34·9–

53·0)

8·3

(6·3–

10·7)

0·16

(0·15–

0·17)

3·1

(2·8–3·5)

0·9

(0·79–1·02)

1·5

(1·3–1·7)

325 281

(290 528–

365 513)

1·41

(1·25–1·59)

Jamaica 1·1

(0·5–

2·2)

41·0

(35·9–

46·9)

81·8

(70·6–

94·5)

75·7

(69·2–

82·9)

62·7

(55·5–

70·9)

39·2

(33·7–

45·5)

12·8

(10·6–

15·5)

0·93

(0·7–

1·22)

0·02

(0·02–

0·02)

1·6

(1·4–1·8)

0·62

(0·54–0·71)

0·58

(0·5–0·67)

38 063

(33 491–

43 222)

0·76

(0·67–0·86)

Puerto Rico 0·74

(0·32–

1·51)

30·9

(26·9–

35·5)

75·5

(66·6–

86·5)

65·2

(59·9–

71·2)

43·5

(38·1–

49·6)

21·0

(16·8–

26·2)

4·7

(3·4–

6·4)

0·15

(0·1–

0·21)

0·0

(0·0–

0·0)

1·2

(1·1–1·3)

0·54

(0·49–0·59)

0·35

(0·29–0·41)

29 896

(27 172–

32 946)

0·57

(0·52–0·63)

Saint Lucia 0·94

(0·41–

1·95)

44·5

(37·8–

52·3)

84·2

(68·5–

102·8)

71·9

(63·8–

81·0)

60·4

(51·0–

71·2)

34·6

(26·7–

44·4)

10·3

(7·5–

14·0)

0·52

(0·35–

0·77)

0·01

(0·01–

0·01)

1·5

(1·3–1·8)

0·65

(0·53–0·78)

0·53

(0·43–0·65)

2102

(1753–2513)

0·74

(0·62–0·88)

Saint Vincent and

the Grenadines

1·5

(0·6–

3·1)

60·1

(51·4–

70·3)

94·6

(77·7–

114·3)

88·9

(79·4–

99·5)

70·6

(59·8–

83·1)

43·4

(33·3–

55·8)

11·2

(8·4–

14·7)

0·81

(0·5–

1·3)

0·02

(0·01–

0·02)

1·9

(1·6–2·2)

0·78

(0·65–0·93)

0·63

(0·51–0·77)

1551

(1302–1838)

0·88

(0·74–1·05)

Suriname 3·4

(1·5–

7·1)

55·6

(48·1–

64·1)

112·7

(95·7–

132·1)

115·0

(104·8–

126·2)

91·2

(80·1–

103·6)

49·7

(40·5–

60·6)

12·3

(9·5–

15·8)

0·84

(0·68–

1·03)

0·02

(0·02–

0·02)

2·2

(1·9–2·5)

0·86

(0·73–1·0)

0·77

(0·65–0·9)

9614

(8337–11 018)

1·04

(0·9–1·18)

Trinidad and

Tobago

0·74

(0·33–

1·54)

39·4

(34·2–

45·3)

96·7

(82·8–

112·5)

92·3

(84·3–

101·1)

68·8

(61·0–

77·5)

34·6

(28·8–

41·4)

7·9

(6·4–

9·6)

0·49

(0·37–

0·65)

0·01

(0·01–

0·01)

1·7

(1·5–1·9)

0·68

(0·59–0·79)

0·56

(0·48–0·64)

17 521

(15 376–

19 960)

0·81

(0·71–0·92)

Virgin Islands 1·6

(0·7–

3·4)

52·8

(44·9–

62·0)

121·5

(100·8–

145·1)

113·0

(101·8–

125·3)

77·3

(66·0–

90·1)

36·3

(27·9–

46·7)

4·8

(3·6–

6·3)

0·07

(0·04–

0·1)

0·0

(0·0–

0·0)

2·0

(1·7–2·4)

0·88

(0·73–1·04)

0·59

(0·49–0·71)

1287

(1097–1507)

0·98

(0·84–1·15)

Central Latin

America

1·8

(0·8–

3·7)

72·5

(65·4–

80·4)

129·6

(115·3–

145·5)

118·0

(110·1–

126·5)

87·4

(78·9–

97·1)

47·9

(40·7–

56·2)

12·3

(10·0–

15·1)

1·4

(1·0–

1·7)

0·03

(0·02–

0·03)

2·4

(2·1–2·6)

1·0

(0·9–1·1)

0·75

(0·65–0·85)

5 004 522

(4 502 598–

5 583 565)

1·12

(1·01–1·25)

(Table 1 continues on next page)

Global Health Metrics

www.thelancet.com Vol 392 November 10, 2018

2015

Age-speciﬁc fertility rate (livebirths per 1000 women annually) Total

fertility

rate

Total

fertility rate

under age

25 years

Total fertility

rate from ages

30 to 54 years

Number of

livebirths

Net

reproductive

rate

10–14

years

15–19

years

20–24

years

25–29

years

30–34

years

35–39

years

40–44

years

45–49

years

50–54

years

(Continued from previous page)

Colombia 1·2

(0·5–

2·5)

64·1

(55·2–

74·4)

114·6

(97·0–

134·7)

104·7

(94·9–

115·7)

80·9

(70·3–

92·9)

45·4

(36·7–

55·9)

10·9

(8·4–

14·0)

1·3

(0·9–

1·8)

0·02

(0·02–

0·03)

2·1

(1·8–2·5)

0·9

(0·77–1·05)

0·69

(0·58–0·82)

851 115

(733 293–

985 161)

1·01

(0·87–1·17)

Costa Rica 1·3

(0·6–

2·6)

53·8

(48·4–

60·5)

92·1

(82·7–

103·5)

86·6

(80·7–

93·7)

69·7

(63·0–

77·8)

37·0

(31·1–

43·9)

9·6

(7·2–

12·6)

0·71

(0·51–

0·99)

0·01

(0·01–

0·01)

1·8

(1·6–1·9)

0·74

(0·66–0·83)

0·58

(0·53–0·64)

69 820

(64 085–

76 591)

0·85

(0·78–0·93)

El Salvador 1·1

(0·5–

2·2)

63·6

(55·9–

72·3)

105·5

(90·6–

122·3)

93·7

(85·4–

103·0)

72·4

(64·1–

81·7)

40·1

(33·2–

48·2)

11·7

(9·5–

14·4)

1·0

(0·72–

1·36)

0·02

(0·02–

0·02)

1·9

(1·7–2·2)

0·85

(0·74–0·98)

0·63

(0·54–0·73)

107 660

(94 215–

122 854)

0·93

(0·81–1·06)

Guatemala 1·5

(0·7–

3·2)

75·9

(65·7–

87·5)

138·9

(118·3–

161·9)

134·4

(122·4–

147·5)

105·7

(92·1–

120·9)

72·4

(58·5–

88·4)

26·4

(20·7–

33·3)

4·7

(3·3–

6·6)

0·09

(0·09–

0·09)

2·8

(2·4–3·2)

1·1

(0·9–1·3)

1·0

(0·9–1·2)

430 775

(372 362–

496 213)

1·33

(1·15–1·53)

Honduras 2·8

(1·2–

6·0)

86·7

(75·5–

100·7)

148·5

(127·8–

173·7)

130·7

(119·4–

144·2)

109·0

(95·7–

125·1)

72·8

(59·4–

89·5)

25·1

(19·6–

32·3)

3·3

(2·3–

4·7)

0·06

(0·06–

0·07)

2·9

(2·5–3·4)

1·2

(1·0–1·4)

1·1

(0·9–1·3)

244 568

(212 956–

283 074)

1·37

(1·19–1·59)

Mexico 1·7

(0·8–

3·6)

70·0

(59·9–

82·8)

137·1

(115·2–

164·1)

125·9

(113·7–

140·5)

90·0

(77·1–

105·9)

46·9

(36·5–

60·7)

11·0

(7·9–

15·4)

1·1

(0·8–

1·5)

0·02

(0·02–

0·02)

2·4

(2·1–2·9)

1·0

(0·9–1·2)

0·74

(0·61–0·91)

2 518 031

(2 153 263–

2 972 894)

1·16

(0·99–1·37)

Nicaragua 1·5

(0·6–

3·0)

82·8

(71·5–

95·6)

129·6

(108·9–

153·1)

113·9

(102·7–

126·3)

96·6

(83·1–

111·8)

53·5

(41·8–

67·5)

13·1

(9·7–

17·5)

1·5

(1·0–

2·2)

0·03

(0·03–

0·03)

2·5

(2·1–2·9)

1·1

(0·9–1·2)

0·82

(0·68–1·0)

137 802

(117 837–

160 442)

1·18

(1·01–1·37)

Panama 2·4

(1·1–

5·1)

77·1

(66·1–

89·7)

126·4

(105·5–

150·2)

113·5

(102·8–

125·4)

85·3

(73·8–

98·2)

44·7

(35·5–

55·7)

11·3

(8·6–

14·7)

0·78

(0·54–

1·11)

0·01

(0·01–

0·02)

2·3

(2·0–2·7)

1·0

(0·9–1·2)

0·71

(0·59–0·85)

69 684

(59 588–

81 021)

1·1

(0·94–1·27)

Venezuela 2·7

(1·2–

5·8)

92·1

(83·1–

102·1)

122·2

(107·4–

138·7)

105·2

(97·4–

113·7)

75·2

(66·8–

84·5)

38·8

(31·9–

47·1)

10·7

(8·2–

13·9)

1·1

(0·8–

1·7)

0·02

(0·02–

0·02)

2·2

(2·0–2·5)

1·1

(1·0–1·2)

0·63

(0·54–0·74)

575 062

(511 153–

645 003)

1·06

(0·95–1·19)

Tropical Latin

America

2·8

(1·2–

5·8)

50·4

(43·8–

58·9)

82·8

(70·9–

98·1)

78·0

(70·8–

86·7)

76·7

(67·2–

87·2)

48·9

(39·5–

59·9)

16·2

(12·7–

20·3)

0·73

(0·54–

0·96)

0·01

(0·01–

0·01)

1·8

(1·6–2·0)

0·68

(0·59–0·8)

0·71

(0·6–0·84)

3 186 804

(2 886 839–

3 554 625)

0·85

(0·77–0·95)

Brazil 2·9

(1·3–

6·0)

49·9

(43·0–

58·6)

81·6

(69·2–

97·4)

76·4

(69·0–

85·4)

75·7

(66·0–

86·2)

48·3

(38·7–

59·4)

16·1

(12·5–

20·2)

0·72

(0·53–

0·97)

0·01

(0·01–

0·01)

1·8

(1·6–2·0)

0·67

(0·57–0·79)

0·7

(0·59–0·83)

3 040 969

(2 742 060–

3 409 928)

0·84

(0·75–0·94)

Paraguay 0·7

(0·31–

1·45)

64·1

(54·8–

74·8)

115·4

(96·9–

136·6)

124·0

(112·3–

137·0)

110·9

(96·6–

126·8)

70·8

(57·1–

86·8)

22·2

(16·9–

28·8)

0·99

(0·65–

1·47)

0·02

(0·02–

0·02)

2·5

(2·2–3·0)

0·9

(0·76–1·06)

1·0

(0·9–1·2)

145 834

(125 161–

169 315)

1·2

(1·03–1·4)

North Africa and

Middle East

0·28

(0·13–

0·59)

47·3

(42·3–

53·0)

131·6

(121·0–

143·2)

138·9

(131·4–

147·7)

117·7

(108·3–

128·6)

72·7

(63·1–

83·1)

28·2

(25·1–

31·8)

5·1

(4·4–

5·9)

0·09

(0·09–

0·09)

2·7

(2·5–2·9)

0·9

(0·82–0·98)

1·1

(1·0–1·2)

13 008 474

(12 060 286–

14 103 277)

1·26

(1·17–1·37)

Afghanistan 0·38

(0·16–

0·8)

97·5

(84·5–

113·6)

279·2

(255·7–

302·0)

314·6

(304·2–

324·5)

248·6

(234·8–

261·8)

166·7

(150·2–

182·4)

70·9

(61·0–

80·8)

24·0

(20·1–

27·9)

0·46

(0·44–

0·48)

6·0

(5·7–6·3)

1·9

(1·7–2·0)

2·6

(2·4–2·7)

1 376 280

(1 303 953–

1 448 446)

2·64

(2·52–2·76)

Algeria 0·05

(0·02–

0·09)

9·8

(8·2–

11·6)

70·9

(59·1–

84·3)

121·3

(109·5–

133·9)

162·4

(145·4–

179·9)

132·8

(115·8–

149·9)

58·4

(49·7–

67·4)

5·3

(4·3–

6·5)

0·1

(0·1–

0·11)

2·8

(2·5–3·1)

0·4

(0·34–0·48)

1·8

(1·6–2·0)

963 291

(855 732–

1 073 343)

1·32

(1·17–1·47)

Bahrain 0·2

(0·09–

0·4)

15·1

(12·7–

18·4)

95·6

(83·7–

108·6)

127·7

(118·9–

136·8)

87·0

(78·0–

96·6)

59·9

(50·6–

70·2)

20·6

(16·2–

25·7)

3·2

(2·3–

4·4)

0·06

(0·06–

0·06)

2·0

(1·9–2·2)

0·55

(0·49–0·62)

0·85

(0·76–0·95)

19 881

(18 264–

21 611)

0·99

(0·9–1·07)

Egypt 0·3

(0·13–

0·62)

61·8

(52·7–

72·3)

171·9

(147·3–

198·8)

136·6

(124·0–

151·6)

102·0

(88·2–

118·9)

46·7

(37·0–

57·8)

11·0

(8·3–

14·8)

2·0

(1·4–

2·8)

0·04

(0·04–

0·04)

2·7

(2·4–2·9)

1·2

(1·0–1·4)

0·81

(0·71–0·93)

2 127 960

(1 940 392–

2 330 506)

1·26

(1·15–1·37)

Iran 0·52

(0·22–

1·06)

26·4

(22·1–

32·0)

77·0

(62·4–

93·6)

100·2

(89·8–

111·3)

79·9

(68·1–

92·8)

46·2

(36·0–

58·1)

14·3

(10·7–

18·7)

1·2

(0·8–

1·7)

0·02

(0·02–

0·02)

1·7

(1·5–2·0)

0·52

(0·44–0·61)

0·71

(0·58–0·86)

1 274 094

(1 085 203–

1 494 227)

0·82

(0·7–0·96)

(Table 1 continues on next page)

Global Health Metrics

2016

www.thelancet.com Vol 392 November 10, 2018

Age-speciﬁc fertility rate (livebirths per 1000 women annually) Total

fertility

rate

Total

fertility rate

under age

25 years

Total fertility

rate from ages

30 to 54 years

Number of

livebirths

Net

reproductive

rate

10–14

years

15–19

years

20–24

years

25–29

years

30–34

years

35–39

years

40–44

years

45–49

years

50–54

years

(Continued from previous page)

Iraq 0·29

(0·13–

0·61)

59·7

(50·8–

71·0)

173·8

(149·3–

203·1)

186·2

(172·4–

202·2)

175·3

(159·5–

193·5)

113·3

(97·7–

129·5)

37·4

(30·6–

44·9)

5·5

(4·0–

7·2)

0·1

(0·1–

0·11)

3·8

(3·4–4·1)

1·2

(1·0–1·4)

1·7

(1·5–1·8)

1 255 056

(1 135 149–

1 393 958)

1·75

(1·59–1·93)

Jordan 0·09

(0·04–

0·18)

26·1

(21·9–

31·2)

126·5

(106·5–

151·4)

180·3

(165·9–

197·0)

157·5

(140·7–

174·8)

96·7

(81·0–

113·4)

22·1

(16·7–

28·6)

1·5

(1·0–

2·1)

0·03

(0·03–

0·03)

3·1

(2·8–3·4)

0·76

(0·66–0·89)

1·4

(1·2–1·6)

243 217

(223 920–

266 880)

1·46

(1·34–1·6)

Kuwait 0·03

(0·01–

0·06)

8·3

(6·9–

10·2)

62·3

(52·6–

73·2)

78·4

(71·8–

85·5)

68·9

(60·6–

78·0)

44·8

(36·4–

54·3)

18·8

(14·3–

24·0)

2·8

(1·8–

4·1)

0·05

(0·05–

0·06)

1·4

(1·3–1·6)

0·35

(0·3–0·41)

0·68

(0·59–0·77)

60 885

(55 064–

67 021)

0·68

(0·62–0·75)

Lebanon 0·29

(0·13–

0·6)

57·6

(48·9–

68·8)

117·0

(96·9–

142·3)

138·8

(125·9–

154·1)

106·1

(91·9–

123·3)

51·1

(40·5–

64·9)

7·2

(5·6–

9·2)

1·3

(0·9–

1·7)

0·02

(0·02–

0·03)

2·4

(2·1–2·8)

0·87

(0·73–1·06)

0·83

(0·7–0·98)

186 159

(160 797–

217 399)

1·15

(0·99–1·34)

Libya 0·13

(0·06–

0·27)

13·3

(11·0–

16·3)

50·8

(40·2–

65·0)

114·5

(102·8–

128·6)

122·3

(106·1–

141·8)

77·7

(61·4–

98·4)

36·8

(28·1–

48·1)

8·0

(5·4–

11·7)

0·15

(0·15–

0·16)

2·1

(1·8–2·6)

0·32

(0·26–0·41)

1·2

(1·0–1·5)

122 256

(102 820–

146 859)

0·99

(0·84–1·19)

Morocco 0·2

(0·09–

0·42)

20·0

(16·6–

24·0)

73·3

(58·9–

90·4)

97·3

(86·9–

108·8)

106·8

(91·8–

125·1)

80·8

(64·1–

101·8)

43·3

(33·6–

55·7)

6·2

(4·1–

9·5)

0·12

(0·12–

0·12)

2·1

(1·9–2·4)

0·47

(0·38–0·57)

1·2

(1·0–1·5)

601 214

(528 391–

683 943)

1·01

(0·89–1·15)

Oman 0·12

(0·05–

0·25)

12·4

(10·7–

14·3)

83·3

(69·6–

98·7)

142·8

(131·2–

154·8)

133·3

(118·8–

148·4)

92·6

(78·5–

107·8)

39·1

(31·7–

47·2)

6·1

(4·8–

7·5)

0·12

(0·11–

0·12)

2·5

(2·3–2·8)

0·48

(0·41–0·56)

1·4

(1·2–1·5)

80 314

(72 628–

88 378)

1·23

(1·11–1·35)

Palestine 0·05

(0·02–

0·11)

77·9

(67·5–

91·0)

201·0

(176·8–

226·0)

184·9

(171·1–

199·0)

131·4

(116·6–

146·9)

75·8

(63·4–

89·4)

25·6

(21·2–

30·6)

2·0

(1·5–

2·5)

0·04

(0·04–

0·04)

3·5

(3·2–3·9)

1·4

(1·3–1·5)

1·2

(1·0–1·3)

138 165

(125 084–

152 033)

1·66

(1·51–1·83)

Qatar 0·18

(0·08–

0·37)

11·2

(9·5–

13·1)

75·5

(64·1–

88·1)

118·4

(109·1–

128·1)

108·6

(97·6–

120·1)

66·8

(57·6–

76·8)

24·9

(20·3–

30·0)

2·4

(1·8–

3·2)

0·05

(0·04–

0·05)

2·0

(1·9–2·2)

0·43

(0·38–0·5)

1·0

(0·9–1·1)

30 253

(27 787–

32 803)

0·99

(0·9–1·07)

Saudi Arabia 0·11

(0·05–

0·23)

9·7

(8·1–

11·4)

59·6

(48·8–

72·1)

82·9

(75·5–

90·8)

85·4

(74·4–

97·2)

63·0

(50·5–

77·0)

29·6

(23·3–

36·9)

3·5

(2·4–

4·9)

0·07

(0·06–

0·07)

1·7

(1·5–1·9)

0·35

(0·29–0·42)

0·91

(0·78–1·05)

502 343

(444 354–

565 465)

0·8

(0·7–0·9)

Sudan 0·35

(0·15–

0·74)

85·5

(74·2–

99·7)

185·2

(162·3–

212·3)

204·6

(190·7–

220·6)

186·7

(170·1–

205·5)

116·6

(99·2–

134·6)

52·1

(42·9–

62·0)

11·7

(8·6–

15·4)

0·23

(0·22–

0·23)

4·2

(3·9–4·6)

1·4

(1·2–1·6)

1·8

(1·7–2·0)

1 336 735

(1 216 996–

1 472 638)

1·93

(1·78–2·1)

Syria 0·22

(0·1–

0·46)

35·2

(29·9–

42·0)

98·7

(82·1–

117·2)

113·6

(103·1–

124·7)

103·5

(90·2–

117·7)

60·3

(47·9–

74·5)

19·1

(14·3–

24·9)

3·8

(2·5–

5·9)

0·07

(0·07–

0·08)

2·2

(1·9–2·5)

0·67

(0·58–0·77)

0·93

(0·78–1·11)

276 298

(239 261–

317 552)

0·99

(0·85–1·14)

Tunisia 0·44

(0·19–

0·91)

5·9

(4·9–

7·3)

47·2

(37·8–

59·8)

97·8

(87·8–

109·8)

106·5

(93·5–

122·3)

72·0

(59·7–

87·5)

22·8

(18·2–

28·7)

1·6

(1·2–

2·3)

0·03

(0·03–

0·03)

1·8

(1·5–2·1)

0·27

(0·22–0·34)

1·0

(0·9–1·2)

167 745

(144 002–

197 624)

0·84

(0·72–0·99)

Turkey 0·19

(0·08–

0·38)

25·9

(21·9–

30·6)

90·4

(76·2–

108·5)

106·3

(96·4–

118·2)

81·1

(71·3–

93·1)

42·0

(35·1–

49·7)

11·0

(8·9–

13·5)

1·1

(0·8–

1·4)

0·02

(0·02–

0·02)

1·8

(1·6–2·0)

0·58

(0·51–0·67)

0·68

(0·61–0·75)

1 116 714

(1 004 994–

1 245 930)

0·85

(0·77–0·95)

United Arab

Emirates

0·44

(0·19–

0·89)

12·1

(10·2–

14·7)

58·5

(46·9–

73·9)

70·2

(62·1–

80·2)

66·5

(56·6–

77·3)

36·8

(29·6–

45·1)

16·3

(12·9–

20·3)

1·6

(1·1–

2·1)

0·03

(0·03–

0·03)

1·3

(1·2–1·5)

0·36

(0·29–0·45)

0·61

(0·52–0·7)

71 039

(62 614–

80 613)

0·63

(0·56–0·71)

Yemen 0·35

(0·15–

0·73)

82·1

(71·0–

96·0)

205·3

(180·0–

234·7)

221·3

(206·8–

237·8)

177·9

(160·4–

197·8)

124·5

(105·8–

143·5)

64·9

(54·4–

75·7)

29·0

(24·1–

33·7)

0·56

(0·54–

0·58)

4·5

(4·2–5·0)

1·4

(1·3–1·7)

2·0

(1·8–2·2)

1 046 417

(953 260–

1 151 663)

2·05

(1·89–2·23)

South Asia 0·43

(0·19–

0·88)

32·6

(28·4–

37·8)

159·3

(137·3–

185·9)

138·0

(126·3–

151·8)

78·0

(69·5–

87·4)

32·9

(27·6–

39·4)

9·8

(7·4–

12·8)

3·4

(2·5–

4·6)

0·06

(0·06–

0·07)

2·3

(2·0–2·5)

0·96

(0·83–1·12)

0·62

(0·54–0·72)

33 968 926

(30 525 169–

38 074 532)

1·01

(0·92–1·13)

Bangladesh 0·97

(0·42–

2·02)

70·9

(60·8–

82·0)

133·1

(115·0–

155·3)

99·5

(90·3–

110·5)

56·2

(48·6–

64·6)

28·2

(22·2–

35·3)

6·9

(5·2–

9·0)

3·4

(2·3–

4·7)

0·06

(0·06–

0·07)

2·0

(1·8–2·2)

1·0

(0·9–1·1)

0·47

(0·39–0·57)

2 858 475

(2 598 018–

3 180 667)

0·93

(0·84–1·04)

(Table 1 continues on next page)

Global Health Metrics

www.thelancet.com Vol 392 November 10, 2018

2017

Age-speciﬁc fertility rate (livebirths per 1000 women annually) Total

fertility

rate

Total

fertility rate

under age

25 years

Total fertility

rate from ages

30 to 54 years

Number of

livebirths

Net

reproductive

rate

10–14

years

15–19

years

20–24

years

25–29

years

30–34

years

35–39

years

40–44

years

45–49

years

50–54

years

(Continued from previous page)

Bhutan 0·44

(0·2–

0·92)

35·4

(29·6–

42·9)

125·3

(103·6–

152·4)

113·5

(101·8–

127·6)

71·1

(59·9–

83·5)

38·4

(29·1–

49·6)

9·6

(6·8–

13·1)

3·1

(2·0–

4·6)

0·06

(0·06–

0·06)

2·0

(1·8–2·3)

0·81

(0·67–0·98)

0·61

(0·49–0·75)

17 338

(15 394–

19 757)

0·93

(0·82–1·06)

India 0·37

(0·16–

0·76)

25·4

(21·2–

30·8)

162·3

(140·1–

189·1)

133·7

(122·1–

147·4)

70·0

(60·5–

80·3)

26·3

(20·7–

32·8)

8·0

(5·9–

10·4)

2·6

(1·9–

3·6)

0·05

(0·05–

0·05)

2·1

(1·9–2·4)

0·94

(0·81–1·1)

0·53

(0·45–0·63)

24 568 864

(22 072 577–

27 481 958)

0·96

(0·87–1·06)

Nepal 0·58

(0·25–

1·21)

59·0

(50·7–

68·5)

156·9

(134·6–

183·9)

114·0

(102·9–

127·4)

67·7

(57·7–

80·3)

30·8

(24·1–

39·9)

9·9

(7·5–

12·8)

3·3

(2·3–

4·6)

0·06

(0·06–

0·07)

2·2

(2·0–2·5)

1·1

(1·0–1·2)

0·56

(0·48–0·67)

632 646

(560 875–

717 437)

1·03

(0·91–1·17)

Pakistan 0·34

(0·15–

0·71)

43·6

(36·8–

52·4)

161·5

(137·5–

190·5)

200·4

(185·8–

217·2)

152·9

(135·6–

173·1)

85·5

(69·1–

105·8)

26·2

(19·7–

33·8)

9·7

(6·7–

14·0)

0·19

(0·18–

0·19)

3·4

(3·0–3·9)

1·0

(0·9–1·2)

1·4

(1·2–1·6)

5 891 600

(5 173 076–

6 733 145)

1·48

(1·3–1·69)

Southeast Asia,

east Asia, and

Oceania

0·15

(0·07–

0·31)

18·9

(17·3–

20·8)

93·6

(86·4–

101·6)

117·7

(113·6–

121·9)

71·9

(68·4–

75·9)

32·1

(29·5–

35·3)

9·0

(8·1–

10·1)

1·1

(0·9–

1·3)

0·02

(0·02–

0·02)

1·7

(1·6–1·8)

0·56

(0·52–0·61)

0·57

(0·54–0·61)

28 562 870

(27 037 176–

30 297 827)

0·79

(0·75–0·84)

East Asia 0·1

(0·04–

0·21)

8·7

(8·0–

9·4)

89·4

(82·8–

96·2)

116·8

(112·3–

121·4)

61·8

(57·9–

65·7)

20·5

(18·4–

22·7)

5·5

(4·8–

6·2)

0·8

(0·61–

1·07)

0·02

(0·01–

0·02)

1·5

(1·4–1·6)

0·49

(0·45–0·53)

0·44

(0·41–0·48)

17 180 872

(16 167 753–

18 210 317)

0·69

(0·65–0·73)

China 0·1

(0·05–

0·21)

8·9

(8·2–

9·7)

91·5

(84·5–

98·6)

117·6

(112·9–

122·2)

61·4

(57·4–

65·4)

20·2

(18·0–

22·5)

5·5

(4·8–

6·3)

0·82

(0·62–

1·1)

0·02

(0·02–

0·02)

1·5

(1·4–1·6)

0·5

(0·46–0·54)

0·44

(0·4–0·48)

16 469 641

(15 475 992–

17 488 048)

0·69

(0·65–0·74)

North Korea 0·05

(0·02–

0·09)

1·8

(1·5–

2·3)

49·2

(39·0–

61·3)

112·8

(101·2–

126·9)

71·8

(60·5–

84·4)

23·3

(17·2–

30·7)

5·4

(3·8–

7·8)

0·43

(0·26–

0·7)

0·01

(0·01–

0·01)

1·3

(1·2–1·5)

0·26

(0·2–0·32)

0·5

(0·42–0·61)

258 789

(228 429–

295 066)

0·62

(0·55–0·71)

Taiwan (province

of China)

0·08

(0·03–

0·17)

4·0

(3·4–

4·9)

23·5

(19·4–

28·4)

63·5

(58·0–

69·7)

78·8

(70·3–

88·2)

34·1

(28·0–

41·5)

4·4

(3·2–

6·0)

0·03

(0·02–

0·04)

0·0

(0·0–

0·0)

1·0

(0·9–1·2)

0·14

(0·11–0·17)

0·59

(0·51–0·67)

175 666

(154 235–

200 219)

0·5

(0·44–0·57)

Oceania 0·54

(0·24–

1·13)

56·3

(48·0–

65·8)

194·1

(170·4–

219·3)

194·9

(182·0–

208·4)

171·6

(155·8–

189·1)

123·9

(106·5–

143·3)

49·6

(40·7–

60·2)

12·4

(9·1–

16·7)

0·23

(0·22–

0·24)

4·0

(3·7–4·4)

1·3

(1·1–1·4)

1·8

(1·6–2·0)

398 611

(364 718–

433 764)

1·77

(1·62–1·93)

American Samoa 0·72

(0·32–

1·49)

35·3

(29·7–

41·9)

142·8

(122·6–

165·4)

171·4

(157·9–

185·8)

136·3

(120·1–

153·8)

80·4

(65·8–

97·0)

16·1

(12·1–

21·0)

0·91

(0·57–

1·4)

0·02

(0·02–

0·02)

2·9

(2·5–3·3)

0·89

(0·76–1·04)

1·2

(1·0–1·4)

1134

(987–1298)

1·37

(1·2–1·57)

Federated States

of Micronesia

1·8

(0·8–

3·6)

36·0

(30·5–

42·6)

127·9

(106·5–

154·5)

146·1

(133·0–

161·7)

127·2

(110·9–

146·9)

82·6

(66·6–

100·1)

18·0

(13·1–

24·9)

4·3

(2·7–

6·7)

0·08

(0·08–

0·09)

2·7

(2·4–3·1)

0·83

(0·72–0·96)

1·2

(1·0–1·3)

2118

(1891–2392)

1·26

(1·12–1·43)

Fiji 0·07

(0·03–

0·14)

34·4

(29·2–

40·4)

149·8

(129·9–

172·0)

156·6

(144·6–

169·6)

110·6

(98·2–

124·4)

57·2

(47·9–

67·9)

12·5

(9·8–

15·9)

1·2

(0·9–

1·6)

0·02

(0·02–

0·02)

2·6

(2·3–3·0)

0·92

(0·8–1·06)

0·91

(0·78–1·05)

18 373

(16 204–

20 801)

1·21

(1·07–1·37)

Guam 0·65

(0·29–

1·35)

44·8

(38·1–

52·7)

146·7

(128·8–

166·5)

165·2

(153·0–

179·3)

141·2

(127·1–

156·5)

74·3

(61·0–

91·1)

15·5

(11·5–

20·3)

0·56

(0·36–

0·85)

0·01

(0·01–

0·01)

2·9

(2·7–3·2)

0·96

(0·84–1·1)

1·2

(1·1–1·3)

3350

(3080–3638)

1·38

(1·28–1·5)

Kiribati 0·33

(0·15–

0·68)

38·8

(32·8–

45·7)

188·0

(162·6–

215·5)

184·8

(170·7–

199·9)

175·3

(158·1–

193·3)

117·1

(98·6–

136·9)

29·9

(23·3–

37·8)

7·6

(5·5–

10·4)

0·15

(0·14–

0·15)

3·7

(3·3–4·2)

1·1

(1·0–1·3)

1·7

(1·4–1·9)

3544

(3124–4001)

1·66

(1·46–1·86)

Marshall Islands 0·82

(0·36–

1·71)

66·1

(57·8–

76·5)

177·1

(154·4–

201·0)

148·8

(136·4–

161·7)

109·1

(96·0–

123·1)

55·0

(44·7–

66·6)

14·9

(11·5–

18·8)

1·1

(0·7–

1·6)

0·02

(0·02–

0·02)

2·9

(2·5–3·2)

1·2

(1·1–1·4)

0·9

(0·76–1·05)

1298

(1159–1448)

1·32

(1·17–1·47)

Northern Mariana

Islands

0·91

(0·4–

1·89)

38·5

(33·6–

44·0)

102·3

(85·3–

121·1)

107·1

(96·4–

118·4)

104·8

(91·1–

119·6)

49·5

(40·3–

60·0)

9·2

(6·7–

12·3)

0·11

(0·07–

0·17)

0·0

(0·0–

0·0)

2·1

(1·8–2·3)

0·71

(0·61–0·81)

0·82

(0·72–0·92)

547

(483–612)

0·98

(0·86–1·09)

Papua New

Guinea

0·57

(0·25–

1·2)

59·4

(50·4–

70·0)

200·1

(173·1–

229·1)

198·5

(183·9–

213·9)

178·4

(160·3–

199·0)

133·2

(113·1–

156·0)

56·1

(45·0–

69·5)

14·9

(10·6–

20·5)

0·29

(0·28–

0·3)

4·2

(3·8–4·6)

1·3

(1·1–1·5)

1·9

(1·6–2·2)

309 184

(282 124–

336 719)

1·83

(1·67–2·0)

(Table 1 continues on next page)

Global Health Metrics

2018

www.thelancet.com Vol 392 November 10, 2018

Age-speciﬁc fertility rate (livebirths per 1000 women annually) Total

fertility

rate

Total

fertility rate

under age

25 years

Total fertility

rate from ages

30 to 54 years

Number of

livebirths

Net

reproductive

rate

10–14

years

15–19

years

20–24

years

25–29

years

30–34

years

35–39

years

40–44

years

45–49

years

50–54

years

(Continued from previous page)

Samoa 0·17

(0·07–

0·35)

42·9

(36·5–

51·1)

207·9

(182·2–

237·7)

244·8

(230·5–

260·6)

222·1

(205·2–

240·2)

153·5

(134·7–

173·9)

56·0

(46·0–

68·0)

11·0

(8·1–

14·4)

0·21

(0·2–

0·22)

4·7

(4·2–5·2)

1·3

(1·1–1·4)

2·2

(2·0–2·5)

6070

(5464–6756)

2·18

(1·96–2·42)

Solomon Islands 0·59

(0·26–

1·22)

62·2

(52·9–

72·9)

207·0

(180·4–

237·8)

211·7

(196·9–

228·6)

176·0

(158·0–

196·6)

124·1

(104·4–

146·7)

46·9

(37·0–

57·7)

11·1

(7·7–

15·4)

0·21

(0·21–

0·22)

4·2

(3·8–4·6)

1·3

(1·2–1·5)

1·8

(1·6–2·0)

20 410

(18 459–

22 554)

1·91

(1·72–2·12)

Tonga 0·4

(0·17–

0·8)

17·1

(14·3–

20·5)

108·3

(89·8–

129·6)

176·1

(162·2–

191·1)

170·7

(153·8–

188·6)

121·8

(103·5–

141·3)

36·6

(28·7–

45·9)

2·8

(2·0–

3·8)

0·05

(0·05–

0·05)

3·2

(2·8–3·6)

0·63

(0·52–0·75)

1·7

(1·4–1·9)

2184

(1908–2489)

1·49

(1·3–1·68)

Vanuatu 0·53

(0·23–

1·1)

51·2

(43·4–

61·2)

190·1

(164·6–

220·0)

187·2

(172·7–

204·0)

158·8

(141·5–

176·7)

109·5

(91·1–

128·8)

41·3

(32·3–

51·3)

8·0

(5·3–

11·3)

0·15

(0·15–

0·16)

3·7

(3·4–4·1)

1·2

(1·0–1·4)

1·6

(1·4–1·8)

8428

(7704–9314)

1·71

(1·56–1·88)

Southeast Asia 0·21

(0·09–

0·43)

32·0

(28·5–

36·3)

98·6

(83·7–

117·5)

118·2

(111·0–

127·0)

94·0

(86·3–

103·2)

54·1

(47·9–

61·5)

17·1

(14·8–

19·9)

1·8

(1·4–

2·2)

0·03

(0·03–

0·03)

2·1

(1·9–2·3)

0·65

(0·56–0·77)

0·84

(0·75–0·93)

10 983 387

(9 949 746–

12 233 978)

0·97

(0·88–1·08)

Cambodia 0·34

(0·15–

0·7)

43·6

(37·0–

52·2)

133·8

(114·2–

157·9)

150·7

(138·2–

165·5)

118·5

(104·6–

133·2)

75·4

(61·4–

90·7)

20·4

(16·0–

25·5)

4·1

(2·9–

5·6)

0·08

(0·08–

0·08)

2·7

(2·5–3·0)

0·89

(0·76–1·05)

1·1

(0·9–1·3)

377 406

(343 642–

419 172)

1·28

(1·17–1·42)

Indonesia 0·15

(0·06–

0·3)

27·7

(23·5–

32·6)

92·8

(76·9–

113·1)

113·7

(103·0–

126·6)

89·5

(77·7–

104·1)

51·7

(41·7–

64·8)

16·4

(12·7–

21·5)

1·8

(1·2–

2·5)

0·03

(0·03–

0·04)

2·0

(1·7–2·3)

0·6

(0·52–0·7)

0·8

(0·67–0·96)

4 032 914

(3 491 800–

4 705 431)

0·92

(0·8–1·08)

Laos 0·41

(0·18–

0·85)

62·9

(54·0–

74·2)

164·6

(143·4–

190·0)

147·0

(135·4–

159·0)

109·1

(96·9–

122·1)

65·9

(54·4–

78·6)

23·1

(18·1–

29·0)

7·7

(5·4–

10·5)

0·15

(0·14–

0·15)

2·9

(2·6–3·2)

1·1

(1·0–1·3)

1·0

(0·9–1·2)

176 836

(161 828–

194 522)

1·32

(1·21–1·45)

Malaysia 0·22

(0·09–

0·44)

11·1

(9·4–

13·1)

51·7

(44·2–

60·2)

122·2

(113·9–

130·8)

125·1

(114·1–

136·6)

72·6

(61·9–

84·2)

20·7

(16·4–

25·6)

1·4

(0·9–

2·2)

0·03

(0·03–

0·03)

2·0

(1·8–2·3)

0·32

(0·28–0·36)

1·1

(1·0–1·2)

508 960

(457 674–

566 097)

0·96

(0·86–1·07)

Maldives 0·12

(0·05–

0·24)

17·6

(14·7–

21·0)

96·6

(83·8–

110·2)

112·9

(104·0–

121·9)

83·1

(73·3–

93·7)

46·0

(36·9–

58·1)

15·8

(11·7–

21·1)

1·9

(1·2–

2·9)

0·04

(0·04–

0·04)

1·9

(1·7–2·0)

0·57

(0·5–0·64)

0·73

(0·66–0·82)

6844

(6312–7356)

0·9

(0·83–0·97)

Mauritius 0·46

(0·2–

0·93)

23·0

(19·6–

26·9)

61·0

(52·3–

70·6)

82·1

(75·2–

90·5)

63·0

(55·5–

72·2)

28·0

(22·4–

34·8)

6·8

(4·9–

9·4)

0·31

(0·19–

0·49)

0·01

(0·01–

0·01)

1·3

(1·2–1·4)

0·42

(0·36–0·49)

0·49

(0·43–0·55)

12 416

(11 454–

13 507)

0·64

(0·59–0·69)

Myanmar 0·25

(0·11–

0·51)

24·8

(20·9–

29·8)

84·7

(70·9–

102·3)

105·4

(95·7–

115·6)

94·6

(83·1–

106·9)

65·3

(53·9–

77·9)

25·5

(20·1–

31·8)

3·0

(2·1–

4·1)

0·06

(0·06–

0·06)

2·0

(1·9–2·2)

0·55

(0·46–0·66)

0·94

(0·84–1·06)

876 249

(804 806–

959 636)

0·92

(0·85–1·01)

Philippines 0·22

(0·1–

0·47)

54·2

(46·1–

64·6)

137·3

(116·6–

162·7)

162·4

(149·5–

177·5)

141·5

(126·5–

157·0)

92·2

(77·4–

107·9)

32·2

(26·0–

39·1)

3·5

(2·5–

4·7)

0·07

(0·06–

0·07)

3·1

(2·8–3·4)

0·96

(0·81–1·14)

1·3

(1·2–1·5)

2 526 359

(2 304 451–

2 800 024)

1·44

(1·31–1·59)

Sri Lanka 0·13

(0·06–

0·27)

18·5

(15·4–

22·2)

67·3

(54·0–

83·3)

109·6

(98·6–

121·7)

97·2

(84·2–

111·8)

51·3

(40·9–

63·7)

14·3

(10·9–

18·6)

0·81

(0·56–

1·17)

0·02

(0·02–

0·02)

1·8

(1·5–2·1)

0·43

(0·35–0·53)

0·82

(0·68–0·98)

292 833

(248 351–

344 723)

0·86

(0·73–1·01)

Seychelles 1·1

(0·5–

2·4)

58·0

(50·5–

66·6)

114·4

(98·8–

132·0)

111·8

(102·4–

122·0)

81·8

(73·8–

90·7)

48·8

(42·0–

56·6)

13·6

(11·1–

16·7)

0·5

(0·31–

0·78)

0·01

(0·01–

0·01)

2·1

(1·9–2·4)

0·87

(0·75–1·0)

0·72

(0·64–0·82)

1497

(1322–1693)

1·03

(0·91–1·16)

Thailand 0·36

(0·16–

0·73)

32·4

(27·1–

39·3)

64·6

(52·1–

81·1)

66·0

(58·6–

74·0)

48·6

(41·2–

57·0)

21·8

(16·8–

27·7)

7·6

(5·8–

9·9)

0·49

(0·34–

0·69)

0·01

(0·01–

0·01)

1·2

(1·1–1·4)

0·49

(0·4–0·6)

0·39

(0·32–0·48)

613 237

(539 713–

701 506)

0·58

(0·51–0·66)

Timor-Leste 0·4

(0·18–

0·84)

61·1

(51·9–

71·8)

176·0

(150·4–

204·0)

209·8

(195·1–

225·2)

194·0

(176·2–

212·3)

123·2

(103·5–

144·2)

52·5

(41·9–

64·4)

10·7

(7·4–

14·8)

0·21

(0·2–

0·21)

4·1

(3·6–4·7)

1·2

(1·0–1·4)

1·9

(1·6–2·2)

38 826

(34 156–

43 904)

1·92

(1·69–2·17)

Vietnam 0·25

(0·11–

0·51)

24·7

(20·9–

29·6)

107·8

(91·8–

127·8)

114·7

(105·5–

124·5)

77·0

(67·9–

86·8)

36·7

(30·4–

43·9)

8·8

(7·0–

10·8)

0·48

(0·34–

0·65)

0·01

(0·01–

0·01)

1·9

(1·7–2·0)

0·66

(0·56–0·79)

0·61

(0·53–0·71)

1 504 552

(1 372 351–

1 660 292)

0·88

(0·8–0·97)

Sub-Saharan Africa 2·1

(0·9–

4·3)

93·7

(84·2–

105·2)

199·0

(184·9–

215·8)

206·3

(198·2–

215·8)

190·1

(179·9–

201·1)

137·5

(125·6–

149·2)

71·2

(64·2–

78·1)

23·3

(21·2–

25·3)

0·44

(0·43–

0·46)

4·6

(4·3–4·9)

1·5

(1·4–1·6)

2·1

(2·0–2·3)

36 181 702

(34 016 504–

38 650 498)

2·02

(1·91–2·14)

(Table 1 continues on next page)

Global Health Metrics

www.thelancet.com Vol 392 November 10, 2018

2019

Age-speciﬁc fertility rate (livebirths per 1000 women annually) Total

fertility

rate

Total

fertility rate

under age

25 years

Total fertility

rate from ages

30 to 54 years

Number of

livebirths

Net

reproductive

rate

10–14

years

15–19

years

20–24

years

25–29

years

30–34

years

35–39

years

40–44

years

45–49

years

50–54

years

(Continued from previous page)

Central sub-Saharan

Africa

1·5

(0·6–

3·1)

94·1

(85·6–

103·0)

195·3

(178·9–

212·1)

209·5

(199·7–

219·6)

214·4

(202·8–

225·9)

159·2

(147·9–

169·6)

81·9

(75·8–

87·8)

19·0

(16·3–

21·9)

0·37

(0·35–

0·38)

4·9

(4·6–5·1)

1·5

(1·3–1·6)

2·4

(2·3–2·5)

4 318 103

(4 060 044–

4 568 273)

2·14

(2·04–2·23)

Angola 1·7

(0·7–

3·5)

120·6

(105·7–

137·3)

206·1

(179·5–

234·4)

212·7

(198·1–

228·0)

209·3

(192·1–

226·9)

162·3

(143·3–

182·6)

91·6

(81·2–

102·5)

20·0

(16·0–

24·9)

0·39

(0·37–

0·4)

5·1

(4·7–5·5)

1·6

(1·4–1·9)

2·4

(2·2–2·6)

1 052 695

(962 954–

1 146 611)

2·27

(2·09–2·46)

Central African

Republic

1·4

(0·6–

3·0)

87·9

(75·6–

101·8)

162·5

(137·3–

192·9)

127·3

(114·8–

142·3)

150·2

(132·6–

170·9)

104·0

(85·0–

126·8)

55·9

(45·0–

67·4)

22·9

(17·9–

28·1)

0·44

(0·42–

0·46)

3·6

(3·2–4·0)

1·3

(1·1–1·4)

1·7

(1·5–1·9)

133 353

(119 271–

149 763)

1·44

(1·31–1·58)

Congo

(Brazzaville)

1·2

(0·5–

2·6)

69·8

(60·5–

81·5)

130·4

(111·1–

154·2)

137·4

(125·8–

151·2)

157·4

(142·7–

172·5)

111·7

(92·5–

131·7)

41·4

(32·2–

51·6)

11·0

(7·8–

14·9)

0·21

(0·2–

0·22)

3·3

(3·0–3·7)

1·0

(0·9–1·2)

1·6

(1·4–1·9)

131 030

(119 585–

145 001)

1·48

(1·35–1·63)

Democratic

Republic of the

Congo

1·4

(0·6–

2·9)

87·4

(77·7–

97·9)

200·0

(178·9–

221·6)

221·3

(208·0–

234·6)

227·4

(213·0–

241·5)

168·1

(153·2–

182·2)

85·1

(76·9–

93·1)

19·5

(15·8–

23·5)

0·38

(0·36–

0·39)

5·1

(4·7–5·4)

1·4

(1·3–1·6)

2·5

(2·4–2·6)

2 920 848

(2 712 396–

3 123 108)

2·21

(2·09–2·32)

Equatorial Guinea 1·6

(0·7–

3·3)

107·7

(93·4–

123·7)

167·1

(141·7–

195·1)

157·1

(143·3–

172·0)

161·5

(143·6–

180·4)

108·8

(89·5–

129·8)

58·0

(46·8–

70·2)

13·7

(9·7–

19·0)

0·26

(0·25–

0·27)

3·9

(3·4–4·4)

1·4

(1·2–1·6)

1·7

(1·5–2·0)

39 049

(33 922–

44 596)

1·74

(1·51–1·98)

Gabon 1·2

(0·5–

2·5)

62·9

(53·4–

75·0)

112·7

(92·5–

138·4)

123·9

(111·6–

138·7)

131·1

(114·4–

151·2)

91·4

(73·7–

110·5)

30·0

(22·6–

38·6)

4·8

(3·2–

7·0)

0·09

(0·09–

0·1)

2·8

(2·5–3·2)

0·88

(0·73–1·07)

1·3

(1·1–1·5)

41 125

(36 192–

47 105)

1·3

(1·15–1·48)

Eastern

sub-Saharan Africa

1·9

(0·8–

4·0)

92·8

(82·2–

105·6)

209·7

(192·1–

229·6)

203·1

(193·6–

213·9)

186·7

(176·0–

198·8)

138·2

(125·4–

151·0)

73·1

(65·6–

80·2)

23·9

(21·4–

26·2)

0·46

(0·44–

0·47)

4·6

(4·4–5·0)

1·5

(1·4–1·7)

2·1

(2·0–2·3)

13 995 648

(13 041 912–

15 084 874)

2·07

(1·95–2·2)

Burundi 1·4

(0·6–

2·9)

52·6

(45·0–

62·2)

206·7

(184·8–

232·0)

238·1

(224·9–

253·1)

243·2

(228·8–

258·6)

198·9

(185·5–

211·0)

96·7

(88·1–

104·7)

23·0

(18·9–

27·1)

0·44

(0·43–

0·46)

5·3

(5·0–5·6)

1·3

(1·2–1·5)

2·8

(2·7–2·9)

406 276

(379 690–

436 338)

2·32

(2·2–2·46)

Comoros 1·3

(0·6–

2·7)

43·7

(37·0–

51·6)

123·3

(102·5–

147·0)

144·1

(131·0–

158·4)

169·2

(151·5–

187·9)

120·1

(100·7–

140·8)

49·5

(39·4–

60·9)

25·5

(20·4–

30·8)

0·49

(0·47–

0·51)

3·4

(2·9–3·9)

0·84

(0·7–1·0)

1·8

(1·6–2·1)

18 191

(15 712–

20 908)

1·54

(1·34–1·77)

Djibouti 1·4

(0·6–

2·8)

49·0

(41·3–

58·0)

135·0

(112·3–

160·8)

147·7

(134·3–

162·3)

200·5

(182·8–

218·7)

128·1

(108·0–

149·3)

61·0

(49·6–

73·4)

39·8

(36·6–

42·5)

0·77

(0·74–

0·79)

3·8

(3·3–4·3)

0·93

(0·77–1·1)

2·2

(1·9–2·4)

34 700

(30 383–

39 346)

1·72

(1·5–1·95)

Eritrea 1·3

(0·6–

2·8)

48·2

(40·6–

58·1)

146·7

(122·7–

176·3)

128·6

(116·0–

143·7)

185·8

(167·6–

206·4)

167·4

(148·5–

187·4)

82·5

(70·8–

95·1)

43·8

(41·8–

45·5)

0·84

(0·81–

0·88)

4·0

(3·6–4·6)

0·98

(0·82–1·18)

2·4

(2·1–2·7)

177 412

(155 541–

203 011)

1·82

(1·61–2·04)

Ethiopia 1·9

(0·8–

3·9)

89·0

(77·4–

103·5)

202·7

(178·6–

230·7)

207·2

(193·0–

223·4)

200·2

(183·4–

218·9)

151·3

(133·3–

168·9)

75·3

(65·7–

84·9)

29·4

(25·1–

33·4)

0·57

(0·54–

0·59)

4·8

(4·4–5·2)

1·5

(1·3–1·7)

2·3

(2·1–2·5)

3 714 299

(3 402 189–

4 069 663)

2·15

(2·0–2·32)

Kenya 1·5

(0·6–

3·1)

70·9

(60·6–

83·9)

184·0

(158·3–

211·0)

153·7

(140·2–

167·8)

139·0

(122·1–

156·5)

85·0

(68·2–

103·3)

35·2

(27·0–

44·6)

6·4

(4·3–

8·9)

0·12

(0·12–

0·13)

3·4

(3·0–3·8)

1·3

(1·1–1·4)

1·3

(1·1–1·6)

1 365 160

(1 208 543–

1 535 478)

1·54

(1·34–1·75)

Madagascar 2·4

(1·0–

5·0)

128·0

(113·1–

144·4)

230·2

(204·3–

257·3)

215·6

(201·2–

230·7)

175·1

(157·7–

193·5)

134·5

(115·5–

154·3)

75·9

(65·5–

86·6)

16·8

(13·2–

21·0)

0·32

(0·31–

0·34)

4·9

(4·4–5·5)

1·8

(1·6–2·0)

2·0

(1·8–2·3)

975 570

(871 322–

1 083 828)

2·16

(1·93–2·4)

Malawi 2·7

(1·2–

5·6)

110·3

(98·7–

124·4)

220·2

(199·4–

244·0)

191·9

(179·5–

206·2)

154·1

(140·3–

168·2)

107·7

(93·9–

122·2)

69·9

(60·9–

78·9)

33·9

(30·3–

37·2)

0·65

(0·63–

0·68)

4·5

(4·2–4·8)

1·7

(1·5–1·9)

1·8

(1·6–2·0)

612 862

(571 079–

660 504)

1·99

(1·86–2·12)

Mozambique 2·0

(0·8–

4·1)

98·2

(87·6–

111·0)

187·4

(167·5–

208·0)

160·7

(149·2–

172·6)

157·7

(143·6–

172·0)

114·7

(99·8–

130·2)

74·4

(65·4–

83·3)

36·5

(33·0–

39·6)

0·7

(0·68–

0·73)

4·2

(3·8–4·5)

1·4

(1·3–1·6)

1·9

(1·8–2·1)

988 056

(912 263–

1 068 141)

1·78

(1·66–1·91)

Rwanda 1·1

(0·5–

2·2)

30·1

(25·5–

36·2)

167·6

(143·9–

196·1)

205·2

(190·6–

222·0)

219·1

(202·2–

237·6)

158·4

(139·9–

178·5)

83·4

(72·8–

93·5)

20·9

(16·1–

26·7)

0·4

(0·39–

0·42)

4·4

(4·0–4·9)

0·99

(0·85–1·17)

2·4

(2·2–2·6)

423 424

(381 994–

470 006)

2·03

(1·85–2·24)

Somalia 1·9

(0·8–

4·1)

96·4

(83·1–

112·9)

248·7

(221·2–

279·3)

273·3

(259·9–

288·1)

251·7

(236·9–

267·4)

202·6

(188·7–

214·9)

101·6

(92·0–

110·1)

42·3

(39·8–

44·3)

0·81

(0·78–

0·85)

6·1

(5·7–6·5)

1·7

(1·5–2·0)

3·0

(2·8–3·1)

685 515

(638 214–

737 921)

2·56

(2·44–2·69)

(Table 1 continues on next page)

Global Health Metrics

2020

www.thelancet.com Vol 392 November 10, 2018

Age-speciﬁc fertility rate (livebirths per 1000 women annually) Total

fertility

rate

Total

fertility rate

under age

25 years

Total fertility

rate from ages

30 to 54 years

Number of

livebirths

Net

reproductive

rate

10–14

years

15–19

years

20–24

years

25–29

years

30–34

years

35–39

years

40–44

years

45–49

years

50–54

years

(Continued from previous page)

South Sudan 2·3

(1·0–

4·8)

129·6

(113·5–

149·1)

271·7

(245·0–

300·8)

262·4

(248·5–

277·7)

251·7

(237·0–

265·6)

163·5

(144·6–

181·5)

78·4

(66·6–

90·0)

25·7

(20·4–

31·2)

0·49

(0·48–

0·51)

5·9

(5·6–6·3)

2·0

(1·8–2·3)

2·6

(2·3–2·8)

413 783

(387 551–

444 396)

2·5

(2·36–2·66)

Tanzania 2·0

(0·9–

4·1)

99·2

(87·4–

113·8)

220·7

(197·7–

247·1)

211·7

(198·2–

227·1)

183·2

(167·3–

201·4)

138·7

(122·5–

155·0)

79·4

(71·0–

87·8)

21·9

(17·9–

26·0)

0·42

(0·41–

0·44)

4·8

(4·4–5·2)

1·6

(1·4–1·8)

2·1

(2·0–2·3)

1 986 281

(1 828 505–

2 163 767)

2·16

(2·02–2·32)

Uganda 2·1

(0·9–

4·4)

108·5

(97·4–

121·9)

246·8

(226·3–

269·9)

246·1

(234·2–

257·9)

198·9

(185·1–

212·7)

146·0

(131·7–

160·0)

80·2

(73·0–

87·4)

19·2

(15·7–

23·0)

0·37

(0·36–

0·38)

5·2

(5·0–5·5)

1·8

(1·6–2·0)

2·2

(2·1–2·4)

1 550 366

(1 471 798–

1 638 382)

2·37

(2·26–2·48)

Zambia 2·0

(0·9–

4·3)

104·8

(91·1–

121·6)

206·2

(180·0–

236·7)

199·3

(184·8–

216·1)

183·5

(165·8–

203·6)

136·6

(116·9–

158·8)

81·0

(69·8–

91·8)

23·1

(18·0–

28·3)

0·44

(0·43–

0·46)

4·7

(4·2–5·2)

1·6

(1·4–1·8)

2·1

(1·9–2·3)

634 965

(568 251–

710 865)

2·1

(1·9–2·31)

Southern

sub-Saharan Africa

0·77

(0·34–

1·61)

69·2

(59·8–

81·1)

124·3

(109·2–

143·1)

137·5

(127·6–

149·0)

100·3

(88·0–

113·7)

66·1

(54·1–

79·6)

22·8

(18·7–

27·5)

2·9

(2·1–

3·8)

0·05

(0·05–

0·06)

2·6

(2·4–2·9)

0·97

(0·85–1·12)

0·96

(0·82–1·12)

1 748 266

(1 595 640–

1 938 810)

1·19

(1·09–1·32)

Botswana 0·5

(0·22–

1·03)

45·8

(39·3–

54·0)

115·1

(102·5–

130·6)

119·0

(111·2–

127·2)

95·3

(87·3–

103·8)

66·5

(58·7–

74·8)

24·9

(21·5–

28·7)

4·3

(3·3–

5·5)

0·08

(0·08–

0·09)

2·4

(2·2–2·5)

0·81

(0·71–0·92)

0·96

(0·87–1·04)

48 644

(45 386–

52 258)

1·1

(1·03–1·19)

Lesotho 1·1

(0·5–

2·2)

70·2

(61·2–

81·3)

150·9

(132·5–

173·1)

123·4

(113·2–

135·7)

108·8

(97·0–

121·3)

73·9

(61·8–

87·2)

38·4

(32·1–

45·3)

6·5

(4·5–

8·9)

0·12

(0·12–

0·13)

2·9

(2·6–3·2)

1·1

(1·0–1·3)

1·1

(1·0–1·3)

48 751

(44 699–

53 717)

1·23

(1·12–1·36)

Namibia 3·1

(1·4–

6·4)

54·5

(47·6–

63·1)

129·4

(114·1–

147·9)

146·9

(136·8–

157·4)

128·9

(116·4–

142·0)

95·5

(82·0–

109·8)

37·3

(30·6–

44·8)

7·0

(4·9–

9·8)

0·14

(0·13–

0·14)

3·0

(2·8–3·3)

0·93

(0·82–1·07)

1·3

(1·2–1·5)

59 520

(54 862–

64 829)

1·4

(1·28–1·52)

South Africa 0·59

(0·26–

1·24)

59·3

(50·2–

70·9)

102·9

(84·1–

126·9)

129·6

(117·0–

144·8)

89·0

(76·0–

103·3)

56·7

(44·2–

71·1)

18·1

(13·4–

23·7)

1·9

(1·3–

2·8)

0·04

(0·04–

0·04)

2·3

(2·0–2·6)

0·81

(0·67–0·99)

0·83

(0·68–1·0)

1 091 574

(976 081–

1 238 233)

1·05

(0·94–1·19)

Swaziland

(eSwatini)

1·2

(0·5–

2·4)

73·3

(63·7–

85·2)

153·0

(133·0–

177·1)

129·3

(117·9–

143·0)

125·5

(111·1–

142·7)

83·8

(69·3–

101·8)

35·8

(28·4–

45·3)

6·5

(4·4–

9·1)

0·12

(0·12–

0·13)

3·0

(2·7–3·5)

1·1

(1·0–1·3)

1·3

(1·1–1·5)

30 680

(26 863–

35 270)

1·36

(1·18–1·56)

Zimbabwe 0·86

(0·38–

1·82)

100·7

(89·6–

114·2)

192·2

(176·1–

208·8)

176·1

(166·7–

185·8)

143·4

(132·6–

154·6)

98·3

(87·1–

109·9)

39·0

(32·7–

45·8)

6·1

(4·8–

7·8)

0·12

(0·11–

0·12)

3·8

(3·5–4·0)

1·5

(1·4–1·6)

1·4

(1·3–1·6)

469 094

(438 268–

501 128)

1·7

(1·6–1·81)

Western

sub-Saharan Africa

2·5

(1·1–

5·3)

98·0

(86·9–

111·6)

203·0

(187·9–

219·9)

222·8

(214·1–

232·4)

207·2

(196·6–

218·2)

147·2

(133·7–

160·6)

78·0

(69·4–

86·6)

29·1

(26·2–

31·8)

0·56

(0·54–

0·58)

4·9

(4·6–5·3)

1·5

(1·4–1·7)

2·3

(2·1–2·5)

16 119 684

(15 142 476–

17 204 806)

2·12

(2·01–2·25)

Benin 6·2

(2·7–

12·9)

79·3

(69·3–

91·6)

204·3

(182·9–

229·0)

224·2

(210·5–

239·8)

203·1

(186·6–

221·6)

134·7

(116·5–

153·0)

74·0

(63·7–

84·3)

28·9

(23·9–

33·7)

0·56

(0·54–

0·58)

4·8

(4·4–5·2)

1·4

(1·3–1·6)

2·2

(2·0–2·4)

420 926

(387 743–

457 500)

2·09

(1·96–2·24)

Burkina Faso 2·4

(1·0–

5·0)

98·0

(86·9–

111·5)

235·6

(214·1–

260·0)

247·3

(234·4–

261·7)

217·2

(201·6–

234·4)

164·4

(148·0–

180·2)

85·9

(76·6–

94·9)

29·0

(25·1–

32·7)

0·56

(0·54–

0·58)

5·4

(5·1–5·8)

1·7

(1·5–1·9)

2·5

(2·3–2·7)

850 128

(792 781–

913 615)

2·31

(2·2–2·44)

Cameroon 2·3

(1·0–

4·9)

91·9

(80·0–

106·7)

172·9

(150·4–

199·7)

164·5

(151·4–

179·8)

166·3

(149·8–

185·4)

115·4

(97·5–

136·4)

55·3

(45·8–

65·5)

18·0

(14·4–

21·9)

0·35

(0·33–

0·36)

3·9

(3·5–4·4)

1·3

(1·2–1·5)

1·8

(1·6–2·0)

860 875

(767 156–

970 019)

1·73

(1·54–1·94)

Cape Verde 1·4

(0·6–

2·9)

35·3

(29·6–

42·1)

88·8

(71·9–

108·7)

113·3

(101·8–

126·1)

89·1

(75·8–

104·2)

63·4

(49·3–

80·1)

26·2

(19·5–

34·6)

19·4

(14·4–

25·0)

0·37

(0·36–

0·39)

2·2

(1·8–2·6)

0·63

(0·51–0·76)

0·99

(0·8–1·22)

9895

(8296–11 738)

1·04

(0·86–1·24)

Chad 3·2

(1·4–

6·9)

172·7

(155·9–

192·3)

294·7

(271·9–

319·2)

306·6

(295·9–

317·0)

270·3

(258·4–

281·4)

188·0

(173·9–

201·0)

84·4

(75·2–

93·3)

24·2

(20·2–

28·2)

0·47

(0·45–

0·48)

6·7

(6·4–7·0)

2·4

(2·2–2·6)

2·8

(2·7–3·0)

716 150

(684 354–

753 893)

2·81

(2·71–2·92)

Côte d’Ivoire 3·1

(1·4–

6·6)

99·1

(87·1–

113·9)

187·7

(163·1–

216·8)

184·2

(169·9–

200·9)

183·6

(166·2–

203·5)

136·2

(117·3–

155·2)

77·9

(67·8–

87·9)

28·1

(23·4–

32·6)

0·54

(0·52–

0·56)

4·5

(4·1–4·9)

1·4

(1·3–1·7)

2·1

(1·9–2·3)

863 669

(785 916–

951 705)

1·97

(1·8–2·16)

(Table 1 continues on next page)

Global Health Metrics

www.thelancet.com Vol 392 November 10, 2018

2021

single calendar years and single-year age groups

compared with previous assessments that reported

results for 5-year age groups.4 The global population

increased nearly three-fold between 1950 and 2017, from

2·6 billion (2·5–2·6) people in 1950 to 7·6 billion

(7·4–7·9) people in 2017. Although global popu lation

growth rates have declined from a peak of 2·0% in 1964

to 1·1% in 2017, the size of the global population has

steadily been increasing by more than 80 million people

annually since 1985. These global estimates mask huge

country variation, with 35 countries showing decreasing

populations in 2017 whereas 57 countries had population

growth at a rate higher than 2·0%. Country variation in

population growth rates is driven to a large extent by

wide variations in fertility rates and to a lesser extent by

migration rates.

Of the 59 countries with a TFR of more than

three livebirths per woman in 2017 (ﬁgure 9), 41 are in

sub-Saharan Africa. Of the remainder, six countries are

in north Africa and the Middle East. These continuous

Age-speciﬁc fertility rate (livebirths per 1000 women annually) Total

fertility

rate

Total

fertility rate

under age

25 years

Total fertility

rate from ages

30 to 54 years

Number of

livebirths

Net

reproductive

rate

10–14

years

15–19

years

20–24

years

25–29

years

30–34

years

35–39

years

40–44

years

45–49

years

50–54

years

(Continued from previous page)

The Gambia 2·0

(0·9–

4·3)

72·1

(61·5–

85·5)

163·5

(138·3–

193·9)

168·6

(154·4–

185·4)

181·9

(163·8–

202·5)

136·7

(116·6–

159·4)

73·9

(62·4–

85·5)

29·4

(24·3–

34·3)

0·57

(0·54–

0·59)

4·1

(3·7–4·7)

1·2

(1·0–1·4)

2·1

(1·9–2·3)

68 878

(60 884–

78 170)

1·88

(1·68–2·09)

Ghana 1·2

(0·5–

2·4)

49·5

(41·8–

58·4)

131·5

(109·5–

156·4)

160·4

(146·5–

175·3)

160·0

(142·4–

178·9)

111·6

(92·4–

132·5)

55·1

(44·2–

67·1)

25·3

(20·2–

31·0)

0·49

(0·47–

0·51)

3·5

(3·0–4·0)

0·91

(0·76–1·08)

1·8

(1·5–2·0)

876 967

(760 322–

1 005 644)

1·56

(1·36–1·79)

Guinea 4·3

(1·9–

9·0)

116·9

(105·2–

131·1)

202·6

(182·7–

223·0)

195·2

(182·1–

210·2)

177·4

(162·0–

193·0)

122·4

(106·1–

138·9)

73·0

(64·2–

81·8)

33·1

(29·6–

36·3)

0·64

(0·61–

0·66)

4·6

(4·4–4·9)

1·6

(1·5–1·7)

2·0

(1·9–2·2)

434 559

(409 818–

461 664)

1·98

(1·88–2·08)

Guinea-Bissau 2·3

(1·0–

4·8)

90·5

(78·0–

104·7)

189·0

(162·6–

217·5)

185·6

(171·1–

201·1)

191·3

(173·5–

209·8)

141·5

(121·4–

163·8)

83·7

(72·4–

96·0)

40·8

(37·9–

43·5)

0·79

(0·76–

0·82)

4·6

(4·2–5·0)

1·4

(1·2–1·6)

2·3

(2·1–2·5)

68 623

(62 211–

75 432)

2·01

(1·84–2·19)

Liberia 1·3

(0·6–

2·8)

99·9

(87·7–

115·0)

183·5

(160·3–

210·8)

165·9

(152·5–

181·6)

168·1

(151·7–

187·1)

125·4

(107·7–

145·7)

76·0

(65·3–

86·4)

28·5

(23·8–

32·9)

0·55

(0·53–

0·57)

4·2

(3·8–4·7)

1·4

(1·2–1·6)

2·0

(1·8–2·2)

154 182

(138 357–

172 510)

1·85

(1·68–2·04)

Mali 3·1

(1·3–

6·6)

145·8

(132·4–

161·6)

254·1

(232·4–

278·4)

266·1

(253·6–

279·9)

234·7

(220·5–

248·4)

178·5

(164·1–

192·1)

89·6

(81·0–

97·9)

32·1

(28·2–

35·6)

0·62

(0·59–

0·64)

6·0

(5·7–6·4)

2·0

(1·8–2·2)

2·7

(2·5–2·9)

877 747

(829 520–

932 043)

2·52

(2·41–2·63)

Mauritania 2·0

(0·9–

4·2)

69·7

(60·1–

81·8)

155·0

(134·5–

179·7)

164·2

(151·4–

179·2)

192·3

(177·0–

207·5)

146·2

(129·5–

162·7)

68·9

(59·8–

78·2)

32·1

(27·8–

36·1)

0·62

(0·59–

0·64)

4·2

(3·8–4·5)

1·1

(1·0–1·3)

2·2

(2·0–2·4)

118 860

(109 956–

129 685)

1·91

(1·77–2·06)

Niger 3·2

(1·4–

6·9)

174·9

(158·1–

194·4)

303·5

(282·6–

326·0)

315·5

(305·2–

326·6)

278·4

(267·0–

290·3)

201·2

(188·6–

212·6)

101·9

(92·9–

110·0)

37·3

(33·7–

40·5)

0·72

(0·69–

0·75)

7·1

(6·8–7·4)

2·4

(2·2–2·6)

3·1

(3·0–3·2)

1 005 868

(952 540–

1 063 380)

3·0

(2·9–3·1)

Nigeria 2·3

(1·0–

4·9)

91·5

(80·1–

105·7)

202·4

(179·3–

226·2)

239·9

(226·4–

253·2)

219·2

(204·0–

234·1)

152·4

(136·0–

168·5)

82·5

(73·2–

91·7)

30·4

(26·0–

34·5)

0·58

(0·56–

0·61)

5·1

(4·7–5·5)

1·5

(1·3–1·6)

2·4

(2·2–2·6)

7 798 484

(7 206 652–

8 409 904)

2·17

(2·02–2·32)

São Tomé and

Príncipe

1·0

(0·4–

2·1)

57·3

(49·8–

66·0)

145·8

(126·3–

167·4)

114·6

(103·7–

126·5)

139·2

(123·5–

156·2)

104·1

(88·0–

121·7)

69·1

(58·1–

80·6)

18·6

(14·8–

22·8)

0·36

(0·34–

0·37)

3·3

(2·8–3·7)

1·0

(0·9–1·2)

1·7

(1·4–1·9)

4948

(4317–5639)

1·52

(1·33–1·72)

Senegal 2·1

(0·9–

4·4)

74·9

(64·2–

88·4)

182·9

(157·4–

213·2)

198·4

(183·7–

215·2)

201·3

(184·0–

218·4)

151·3

(132·3–

169·9)

80·1

(69·0–

91·0)

23·3

(18·3–

28·4)

0·45

(0·43–

0·47)

4·6

(4·2–5·0)

1·3

(1·1–1·5)

2·3

(2·0–2·5)

496 713

(457 701–

543 020)

2·1

(1·94–2·27)

Sierra Leone 2·4

(1·0–

5·0)

98·0

(85·6–

113·3)

183·8

(159·9–

208·7)

172·8

(159·1–

186·9)

174·5

(157·6–

191·7)

122·8

(104·8–

141·3)

67·7

(57·4–

78·2)

28·2

(23·7–

32·5)

0·54

(0·52–

0·56)

4·3

(3·8–4·7)

1·4

(1·3–1·6)

2·0

(1·7–2·2)

269 005

(243 337–

296 085)

1·79

(1·64–1·95)

Togo 1·7

(0·8–

3·6)

51·8

(45·3–

60·1)

149·0

(130·6–

171·3)

151·0

(139·2–

165·0)

177·5

(162·7–

192·5)

131·0

(114·8–

147·2)

63·1

(54·2–

72·3)

37·3

(34·3–

39·9)

0·72

(0·69–

0·75)

3·8

(3·5–4·1)

1·0

(0·9–1·2)

2·0

(1·8–2·3)

223 039

(207 346–

241 916)

1·69

(1·58–1·81)

95% uncertainty intervals are in parentheses. Data are presented to the number of decimal places as accuracy of these data allows. Super-regions, regions, and countries are listed alphabetically. Total fertility rate is

the number of livebirths expected per woman in each age group if she were to survive through the reproductive years (10–54 years) under the age-speciﬁc fertility rates at that timepoint. Net reproductive rate is

the number of female livebirths expected per woman, given the observed age-speciﬁc mortality and fertility rates. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study. SDI=Socio-demographic Index.

Table 1: Age-speciﬁc fertility rates, total fertility rate, total fertility up to a maternal age of 25 years and during ages 30–54 years; the number of livebirths; and net reproductive rate,

globally and for the SDI groups, GBD regions, super-regions, countries, and territories, 2017

Global Health Metrics

2022

www.thelancet.com Vol 392 November 10, 2018

Persian Gulf

Caribbean LCA

Dominica

ATG

TTO

Grenada

VCT

TLS

Maldives

Barbados

Seychelles

Mauritius

Comoros

West Africa Eastern

Mediterranean

Malta

Singapore Balkan Peninsula Tonga

Samoa

FSM

Fiji

Solomon Isl

Marshall Isl

Vanuatu

Kiribati

Persian Gulf

Caribbean LCA

Dominica

ATG

TTO

Grenada

VCT

TLS

Maldives

Barbados

Seychelles

Mauritius

Comoros

West Africa Eastern

Mediterranean

Malta

Singapore Balkan Peninsula Tonga

Samoa

FSM

Fiji

Solomon Isl

Marshall Isl

Vanuatu

Kiribati

≤0·24

0·25–0·49

0·50–0·74

0·75–0·99

1·00–1·24

1·25–1·49

1·50–1·74

1·75–1·99

≥2·00

<0·50

0·50–0·99

1·00–1·49

1·50–1·74

1·75–1·99

2·00–2·24

2·25–2·49

2·50–2·99

≥3·00

Livebirths per woman

Figure 6: Total fertility rates under age 25 years (A) and total fertility rate over age 30 years (B), in 2017, by location

Data are the number of livebirths expected for a hypothetical woman by age 25 years (A) or ageing from 30 to 54 years (B) who survived the age group and was exposed to current ASFRs. ATG=Antigua

and Barbuda. FSM=Federated States of Micronesia. Isl=Islands. LCA=Saint Lucia. TLS=Timor-Leste. TTO=Trinidad and Tobago. VCT=Saint Vincent and the Grenadines.

Global Health Metrics

www.thelancet.com Vol 392 November 10, 2018

2023

Persian Gulf

Caribbean LCA

Dominica

ATG

TTO

Grenada

VCT

TLS

Maldives

Barbados

Seychelles

Mauritius

Comoros

West Africa Eastern

Mediterranean

Malta

Singapore Balkan Peninsula Tonga

Samoa

FSM

Fiji

Solomon Isl

Marshall Isl

Vanuatu

Kiribati

Persian Gulf

Caribbean LCA

Dominica

ATG

TTO

Grenada

VCT

TLS

Maldives

Barbados

Seychelles

Mauritius

Comoros

West Africa Eastern

Mediterranean

Malta

Singapore Balkan Peninsula Tonga

Samoa

FSM

Fiji

Solomon Isl

Marshall Isl

Vanuatu

Kiribati

<−60%

−60% to −41%

−40% to −21%

−20% to −11%

−10% to 9%

10% to 19%

20% to 39%

40% to 59%

≥60%

Change in total fertility rate

<1·07

1·07–1·09

1·10–1·12

1·13–1·15

>1·15

Ratio of males to females at birth

Figure 7: Percentage change in total fertility rates from 1975 to 2017 for women aged 30–54 years (A) and sex ratio at birth in 2017 (B), by location

Data are the number of livebirths expected for a hypothetical woman ageing from 30 to 54 years who survived the age group and was exposed to current age-speciﬁc fertility rates (A) and the ratio of males

to females at birth (B). ATG=Antigua and Barbuda. FSM=Federated States of Micronesia. Isl=Islands. LCA=Saint Lucia. TLS=Timor-Leste. TTO=Trinidad and Tobago. VCT=Saint Vincent and the Grenadines.

Global Health Metrics

2024

www.thelancet.com Vol 392 November 10, 2018

Persian Gulf

Caribbean LCA

Dominica

ATG

TTO

Grenada

VCT

TLS

Maldives

Barbados

Seychelles

Mauritius

Comoros

West Africa Eastern

Mediterranean

Malta

Singapore Balkan Peninsula Tonga

Samoa

FSM

Fiji

Solomon Isl

Marshall Isl

Vanuatu

Kiribati

<–1·50

−1·50 to −1·0

−0·99 to −0·50

−0·49 to 0

0 to 0·49

0·50 to 0·99

1·00 to 1·49

1·50 to 1·99

≥2·00

Population growth rate (%)

Figure 8: Population growth rate from 2010 to 2017, by location

ATG=Antigua and Barbuda. FSM=Federated States of Micronesia. Isl=Islands. LCA=Saint Lucia. TLS=Timor-Leste. TTO=Trinidad and Tobago. VCT=Saint Vincent and the Grenadines.





















Andorra Bangladesh

Bosnia and Herzegovina

Brazil

China

Indonesia

India

Jordan

Japan

Kuwait

Luxembourg

Maldives

Northern Mariana Islands

Nigeria

Pakistan

Qatar

Russia

Saudi Arabia

South Sudan

USA

Samoa

−2

−1

1234567

Total fertility rate (livebirths per woman)

Population growth rate (%)

GBD super-region

Central Europe, eastern Europe, and central Asia High income Latin America and Caribbean North Africa and Middle East

South Asia Southeast Asia, east Asia, and Oceania Sub-Saharan Africa

Figure 9: Relationship between total fertility rates and the population growth rate, 2017

Total fertility rate is the average number of children a woman would bear if she survived through the end of the reproductive age span (age 10–54 years) and experienced at each age a particular set of

age-speciﬁc fertility rates observed in the year of interest. Each dot represents a single country or territory. A vertical line is shown at the total fertility rate of 2·05, representing the replacement value,

and a horizontal line is shown at a population growth rate of zero.

Global Health Metrics

www.thelancet.com Vol 392 November 10, 2018

2025

1950 1960 1970 1980 1990 2000 2010 2017

Global 2 571 129

(2 518 739–

2 623 555)

3 097 198

(3 016 341–

3 175 666)

3 775 519

(3 666 324–

3 878 482)

4 546 838

(4 435 753–

4 651 568)

5 394 707

(5 276 054–

5 506 245)

6 189 102

(6 054 565–

6 317 018)

7 032 925

(6 888 938–

7 176 044)

7 640 466

(7 394 579–

7 863 850)

Low SDI 286 098

(274 890–

297 338)

349 546

(336 103–

363 341)

437 907

(422 312–

454 314)

550 926

(532 322–

569 634)

697 444

(674 783–

720 903)

884 141

(854 560–

913 871)

1 111 397

(1 073 601–

1 150 583)

1 289 721

(1 232 696–

1 350 886)

Low-middle SDI 428 432

(409 330–

447 205)

525 533

(502 817–

547 923)

664 708

(640 177–

690 260)

842 355

(812 779–

870 675)

1 044 178

(1 009 021–

1 079 083)

1 267 751

(1 225 483–

1 309 059)

1 512 969

(1 462 697–

1 561 490)

1 704 731

(1 638 487–

1 773 613)

Middle SDI 621 890

(600 571–

645 428)

777 263

(742 412–

812 838)

999 618

(947 545–

1 048 416)

1 265 828

(1 216 449–

1 313 285)

1 551 201

(1 501 707–

1 605 269)

1 769 031

(1 711 246–

1 829 574)

1 962 750

(1 906 995–

2 020 809)

2 090 439

(1 993 635–

2 188 823)

High-middle SDI 565 495

(547 267–

585 066)

682 199

(652 794–

713 287)

819 425

(777 362–

859 588)

960 873

(923 189–

999 256)

1 111 992

(1 075 191–

1 151 041)

1 217 799

(1 176 410–

1 260 107)

1 319 712

(1 279 281–

1 364 508)

1 387 317

(1 310 630–

1 462 683)

High SDI 660 034

(642 623–

675 897)

749 699

(733 577–

766 867)

836 408

(818 207–

855 201)

905 978

(885 242–

926 388)

965 963

(945 156–

987 885)

1 024 486

(1 001 540–

1 048 430)

1 098 420

(1 074 557–

1 122 783)

1 139 825

(1 098 829–

1 181 331)

Central Europe, eastern

Europe, and central Asia

279 682

(271 221–

287 601)

321 818

(311 502–

331 710)

360 299

(349 492–

370 047)

392 771

(380 737–

404 444)

420 814

(407 203–

433 044)

416 949

(402 995–

430 201)

411 243

(397 887–

423 691)

415 928

(395 177–

435 487)

Central Asia 28 227

(27 431–29 009)

35 702

(34 598–36 847)

47 868

(46 467–49 262)

58 719

(56 970–60 513)

69 756

(67 616–71 961)

74 835

(71 158–78 628)

82 351

(76 500–88 059)

90 925

(83 164–99 015)

Armenia 1453

(1352–1554)

1888

(1750–2024)

2571

(2408–2743)

3171

(2936–3426)

3419

(3161–3672)

3321

(3071–3555)

3105

(2872–3340)

3027

(2705–3349)

Azerbaijan 3134

(2934–3343)

3946

(3651–4245)

5273

(4910–5604)

6292

(5851–6752)

7330

(6767–7855)

8245

(7597–8878)

9300

(8577–9979)

10 225

(8964–11 430)

Georgia 3698

(3444–3957)

4225

(3904–4523)

4807

(4478–5152)

5171

(4766–5548)

5508

(5117–5908)

4691

(4326–5071)

3971

(3600–4344)

3691

(3373–4045)

Kazakhstan 7859

(7340–8388)

9966

(9216–10 796)

13 419

(12 438–14 356)

15 318

(14 126–16 430)

16 843

(15 523–18 040)

15 357

(14 214–16 541)

16 204

(16 114–16 287)

17 904

(16 485–19 230)

Kyrgyzstan 1765

(1641–1884)

2215

(2049–2378)

3029

(2811–3249)

3700

(3433–3979)

4462

(4138–4795)

5024

(4639–5413)

5639

(5251–6040)

6368

(5587–7101)

Mongolia 809

(758–860)

967

(883–1050)

1276

(1182–1370)

1693

(1572–1814)

2152

(1999–2314)

2440

(2269–2607)

2826

(2638–3023)

3251

(2870–3619)

Tajikistan 1667

(1558–1776)

2133

(1972–2288)

3015

(2809–3221)

4074

(3766–4359)

5376

(4988–5804)

6365

(5933–6844)

7818

(7339–8327)

9243

(8191–10 251)

Turkmenistan 1252

(1171–1332)

1619

(1496–1746)

2228

(2080–2377)

2920

(2714–3137)

3701

(3426–3980)

4202

(3659–4764)

4559

(4096–5030)

4976

(4563–5397)

Uzbekistan 6588

(6129–7015)

8738

(8063–9404)

12 248

(11 433–13 117)

16 375

(15 242–17 475)

20 961

(19 367–22 595)

25 186

(21 683–28 853)

28 925

(23 041–34 641)

32 236

(24 584–39 887)

Central Europe 88 946

(86 759–91 285)

101 568

(98 788–

104 692)

110 731

(107 678–

114 171)

120 005

(116 244–

124 011)

124 127

(120 615–

128 090)

121 176

(117 460–

125 149)

117 167

(115 229–

119 104)

114 803

(112 042–

117 477)

Albania 1268

(1186–1359)

1688

(1576–1807)

2196

(2035–2357)

2737

(2531–2941)

3307

(3048–3568)

3192

(2968–3432)

2889

(2674–3108)

2766

(2469–3068)

Bosnia and Herzegovina 2831

(2636–3025)

3352

(3101–3613)

3819

(3536–4087)

4230

(3925–4531)

4509

(4160–4853)

4085

(3584–4617)

3768

(3427–4101)

3399

(3089–3720)

Bulgaria 7348

(6835–7871)

8150

(7389–8939)

8741

(7893–9674)

9160

(8212–10 013)

8914

(8183–9640)

7965

(7422–8598)

7442

(7396–7486)

7052

(6530–7576)

Croatia 3904

(3625–4192)

4227

(3905–4550)

4513

(4143–4853)

4856

(4497–5199)

4898

(4527–5281)

4560

(4235–4888)

4364

(4058–4676)

4275

(3838–4725)

Czech Republic 8850

(8186–9456)

9495

(8814–10 191)

9802

(9168–10 485)

10 275

(9535–11 013)

10 279

(9458–11 050)

10 216

(10 145–10 288)

10 470

(10 397–10 548)

10 592

(10 516–10 668)

Hungary 9325

(8708–9957)

10 021

(9354–10 715)

10 302

(9603–11 011)

10 638

(9973–11 385)

10 457

(9702–11 197)

10 195

(9432–10 949)

9930

(9176–10 656)

9727

(8739–10 785)

Macedonia 1311

(1223–1406)

1434

(1324–1541)

1666

(1547–1788)

1943

(1793–2083)

2010

(1836–2200)

2021

(1863–2186)

2130

(1870–2379)

2174

(1825–2523)

Montenegro 410

(380–438)

478

(445–510)

537

(497–575)

592

(549–633)

625

(582–673)

635

(578–693)

631

(583–678)

626

(558–693)

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Poland 25 291

(23 602–26 937)

30 308

(28 285–32 322)

33 452

(31 175–35 829)

36 651

(33 547–39 682)

39 059

(35 959–42 058)

38 898

(35 689–41 955)

38 439

(38 177–38 707)

38 393

(38 118–38 672)

Romania 16 508

(15 328–17 597)

18 917

(17 237–20 689)

20 767

(18 850–22 784)

22 690

(20 777–24 679)

23 394

(21 570–25 252)

22 389

(20 571–24 271)

20 649

(19 122–22 276)

19 433

(17 350–21 520)

Serbia 6946

(6491–7434)

7795

(7188–8364)

8627

(7948–9310)

9324

(8636–9966)

9400

(8633–10 120)

9642

(8860–10 444)

9010

(8348–9664)

8874

(7849–9837)

Slovakia 3436

(3420–3451)

4073

(4055–4091)

4540

(4518–4561)

4980

(4956–5005)

5277

(5248–5303)

5385

(5352–5418)

5402

(5364–5442)

5419

(5006–5820)

Slovenia 1513

(1406–1613)

1623

(1496–1747)

1764

(1639–1905)

1922

(1775–2083)

1991

(1776–2203)

1989

(1976–2003)

2036

(2020–2052)

2068

(2053–2085)

Eastern Europe 162 508

(154 850–

170 367)

184 547

(175 249–

194 678)

201 699

(191 753–

211 238)

214 047

(203 174–

225 685)

226 929

(214 331–

239 070)

220 936

(208 467–

233 519)

211 724

(200 353–

222 893)

210 199

(192 574–

228 244)

Belarus 7418

(6920–7900)

8422

(7787–9053)

9277

(8680–9865)

9857

(9137–10 563)

10 455

(9656–11 248)

10 225

(9467–10 988)

9658

(8899–10 409)

9491

(8380–10 549)

Estonia 1031

(1026–1035)

1204

(1198–1210)

1352

(1345–1359)

1472

(1465–1480)

1568

(1559–1576)

1393

(1385–1402)

1332

(1322–1341)

1314

(1304–1324)

Latvia 1952

(1817–2084)

2178

(2014–2333)

2424

(2256–2581)

2582

(2393–2780)

2718

(2518–2922)

2431

(2250–2592)

2117

(2103–2131)

1945

(1931–1959)

Lithuania 2473

(2299–2649)

2825

(2629–3034)

3207

(2994–3419)

3497

(3245–3756)

3752

(3473–4030)

3593

(3328–3853)

3136

(2882–3359)

2847

(2828–2870)

Moldova 2520

(2346–2691)

3056

(2824–3277)

3684

(3451–3937)

4112

(3825–4411)

4463

(4140–4790)

4202

(3802–4597)

3870

(3450–4290)

3721

(3151–4276)

Russia 108 890

(101 648–

116 491)

123 122

(114 311–

132 472)

133 296

(123 706–

142 273)

141 389

(131 139–

152 459)

151 280

(139 269–

162 850)

149 335

(137 504–

161 416)

145 342

(135 464–

155 198)

146 189

(129 997–

162 390)

Ukraine 38 222

(35 486–40 820)

43 737

(40 535–46 886)

48 457

(45 206–51 901)

51 135

(47 072–54 769)

52 691

(48 740–56 440)

49 754

(46 128–53 518)

46 266

(40 680–51 959)

44 689

(37 188–51 843)

High income 624 261

(607 829–

640 001)

704 358

(687 585–

721 417)

784 499

(765 553–

803 595)

852 184

(830 617–

872 817)

909 777

(888 581–

930 669)

968 090

(945 346–

991 026)

1 036 657

(1 012 835–

1 060 283)

1 074 889

(1 033 559–

1 116 701)

Australasia 10 593

(9938–11 222)

12 947

(12 097–13 753)

15 656

(14 634–16 627)

17 897

(16 758–19 054)

20 271

(18 932–21 552)

22 664

(21 155–24 069)

25 864

(24 172–27 407)

28 391

(26 427–30 166)

Australia 8636

(8016–9252)

10 511

(9697–11 300)

12 761

(11 805–13 698)

14 651

(13 589–15 804)

16 854

(15 599–18 087)

18 878

(17 440–20 289)

21 598

(20 005–23 097)

23 943

(22 091–25 629)

New Zealand 1957

(1827–2087)

2435

(2254–2613)

2895

(2687–3099)

3245

(3009–3468)

3417

(3159–3674)

3785

(3504–4067)

4265

(3899–4656)

4448

(4042–4847)

High-income Asia Paciﬁc 107 077

(100 965–

112 694)

123 516

(116 754–

130 199)

141 788

(133 926–

149 440)

160 667

(151 955–

169 179)

173 560

(164 314–

182 570)

180 344

(170 747–

189 636)

184 713

(174 519–

194 370)

187 034

(175 679–

198 805)

Brunei 62

(58–66)

(80–92)

134

(124–144)

191

(177–205)

258

(239–277)

331

(305–356)

394

(363–424)

432

(388–477)

Japan 85 811

(79 862–91 233)

95 915

(89 659–102 233)

106 925

(99 115–114 411)

119 572

(111 399–

127 742)

125 857

(117 086–

134 191)

129 002

(120 122–

137 746)

129 954

(120 333–

138 917)

128 363

(118 345–

139 043)

Singapore 1183

(1104–1264)

1666

(1517–1806)

2132

(1985–2270)

2474

(2319–2633)

3175

(2972–3390)

4167

(3873–4449)

5020

(4674–5367)

5568

(4906–6188)

South Korea 20 019

(18 685–21 355)

25 848

(24 052–27 742)

32 595

(30 313–34 885)

38 429

(35 734–40 903)

44 268

(41 115–47 098)

46 842

(43 520–49 950)

49 343

(45 894–52 866)

52 670

(48 472–56 781)

High-income North America 167 071

(156 028–

177 729)

200 987

(188 815–

213 853)

230 418

(216 084–

244 897)

253 712

(237 019–

269 916)

280 718

(263 127–

298 908)

310 870

(291 015–

330 560)

342 507

(321 270–

364 211)

360 884

(324 630–

398 446)

Canada 14 028

(13 129–14 957)

18 300

(16 943–19 638)

21 732

(20 163–23 312)

24 473

(22 762–26 217)

27 242

(25 184–29 399)

30 301

(28 135–32 397)

33 563

(30 968–35 916)

35 982

(33 302–38 581)

Greenland 24

(22–25)

(31–36)

(44–50)

(49–50)

(55–55)

(55–56)

(56–56)

(55–56)

USA 153 014

(141 989–

163 572)

182 647

(170 786–

195 050)

208 632

(194 348–

222 510)

229 183

(212 518–

245 136)

253 413

(236 114–

271 078)

280 506

(260 887–

299 946)

308 881

(287 626–

330 134)

324 839

(288 772–

362 239)

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2027

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Southern Latin America 25 759

(24 521–27 013)

30 864

(29 278–32 252)

36 133

(34 286–37 829)

42 943

(40 694–45 029)

49 550

(46 839–52 051)

55 204

(52 208–58 135)

61 228

(58 049–64 578)

65 608

(60 307–70 557)

Argentina 17 644

(16 522–18 909)

20 665

(19 199–21 994)

24 120

(22 465–25 726)

28 791

(26 865–30 710)

33 125

(30 785–35 434)

36 784

(34 178–39 630)

41 101

(38 531–43 887)

44 265

(39 144–49 229)

Chile 5865

(5464–6251)

7614

(7135–8153)

9203

(8564–9843)

11 194

(10 266–12 121)

13 282

(12 242–14 355)

15 120

(13 857–16 323)

16 762

(14 885–18 566)

17 918

(16 679–19 069)

Uruguay 2246

(2088–2398)

2582

(2353–2818)

2806

(2530–3094)

2955

(2663–3243)

3139

(2824–3483)

3297

(2988–3604)

3360

(3106–3600)

3421

(3059–3767)

Western Europe 313 759

(302 436–

325 156)

336 042

(327 368–

344 888)

360 501

(351 707–

369 451)

376 964

(367 690–

386 532)

385 678

(378 299–

393 326)

399 006

(391 457–

406 653)

422 344

(416 202–

428 409)

432 969

(421 014–

445 856)

Andorra 5

(5–5)

(8–10)

(17–20)

(31–37)

(53–54)

(65–66)

(83–84)

(79–80)

Austria 6922

(6482–7400)

7044

(6537–7546)

7431

(6891–7997)

7541

(7017–8108)

7765

(7224–8321)

8017

(7447–8594)

8368

(8301–8430)

8793

(8730–8855)

Belgium 8663

(8082–9214)

9127

(8464–9784)

9649

(9004–10 304)

9832

(9080–10 615)

9977

(9169–10 739)

10 252

(9503–11 028)

10 861

(10 784–10 942)

11 319

(11 226–11 408)

Cyprus 488

(453–521)

590

(550–633)

641

(557–724)

669

(612–726)

775

(716–836)

915

(848–980)

1120

(1033–1205)

1262

(1138–1391)

Denmark 4270

(3976–4562)

4587

(4281–4909)

4934

(4585–5269)

5115

(5080–5148)

5139

(5101–5177)

5329

(5288–5372)

5529

(5483–5574)

5732

(5682–5779)

Finland 4028

(3743–4316)

4433

(4132–4726)

4629

(4605–4656)

4796

(4764–4827)

5001

(4969–5034)

5182

(5146–5220)

5375

(5335–5416)

5517

(5474–5561)

France 43 137

(40 160–46 060)

46 780

(43 056–50 537)

51 885

(47 890–56 018)

54 904

(50 517–59 115)

57 712

(53 593–61 378)

59 846

(55 427–64 284)

63 693

(59 476–67 922)

65 712

(59 712–71 552)

Germany 71 934

(62 172–82 007)

75 192

(69 750–80 580)

79 263

(73 661–84 235)

80 311

(75 192–85 827)

80 041

(79 562–80 550)

82 317

(81 737–82 927)

81 692

(81 091–82 343)

83 294

(74 704–91 872)

Greece 7766

(7251–8272)

8583

(7943–9231)

8930

(8259–9581)

9841

(9137–10 578)

10 418

(9642–11 205)

11 073

(10 256–11 901)

11 034

(10 265–11 774)

10 402

(9301–11 460)

Iceland 141

(140–141)

173

(172–174)

203

(202–204)

227

(225–228)

253

(252–255)

279

(277–281)

318

(315–320)

337

(334–340)

Ireland 3048

(2852–3245)

2900

(2684–3118)

3030

(2801–3274)

3487

(3215–3754)

3599

(3331–3858)

3862

(3555–4164)

4595

(4230–4972)

4860

(4519–5217)

Israel 1556

(1451–1667)

2168

(1999–2324)

3037

(2802–3282)

3875

(3561–4213)

4963

(4474–5456)

6388

(5759–7071)

7841

(7191–8497)

8949

(7824–10 109)

Italy 46 697

(43 475–49 705)

50 891

(46 782–54 804)

53 853

(49 819–57 792)

56 424

(52 179–60 406)

56 799

(52 808–60 687)

56 661

(52 418–60 671)

60 328

(59 854–60 768)

60 597

(60 155–61 024)

Luxembourg 307

(286–327)

322

(300–343)

347

(324–370)

368

(339–396)

387

(357–414)

433

(401–466)

502

(498–506)

590

(585–595)

Malta 333

(311–355)

328

(301–357)

321

(293–351)

339

(306–373)

369

(331–407)

400

(361–440)

422

(389–453)

434

(392–480)

Netherlands 10 035

(9980–10 086)

11 414

(11 353–11 475)

12 972

(12 903–13 048)

14 083

(13 985–14 174)

14 914

(14 810–15 021)

15 875

(15 751–16 002)

16 585

(16 442–16 731)

17 029

(16 889–17 177)

Norway 3277

(3060–3501)

3590

(3344–3820)

3885

(3621–4154)

4094

(3840–4381)

4233

(4205–4262)

4472

(4439–4507)

4858

(4821–4899)

5263

(5219–5310)

Portugal 8749

(8131–9348)

9189

(8582–9837)

8894

(8270–9519)

10 007

(9248–10 726)

10 123

(9342–10 866)

10 518

(9764–11 278)

10 771

(10 010–11 517)

10 681

(9534–11 855)

Spain 28 823

(26 809–30 811)

31 464

(29 402–33 634)

35 014

(32 739–37 502)

38 402

(35 587–41 263)

39 659

(37 010–42 676)

40 803

(40 523–41 063)

46 980

(46 656–47 300)

46 389

(42 868–49 868)

Sweden 7038

(6547–7532)

7500

(7009–8008)

8046

(8000–8089)

8304

(8256–8355)

8575

(8521–8630)

8892

(8827–8957)

9404

(9331–9468)

10 044

(9340–10 726)

Switzerland 4812

(4468–5149)

5536

(5148–5914)

6374

(5930–6794)

6494

(6069–6939)

6971

(6517–7430)

7401

(6916–7870)

7950

(7887–8009)

8593

(7909–9209)

UK 51 455

(48 480–54 194)

53 936

(50 656–57 264)

56 820

(53 576–60 255)

57 464

(53 834–60 763)

57 567

(53 983–61 179)

59 617

(55 956–63 260)

63 595

(59 545–67 590)

66 635

(60 812–72 583)

England 42 108

(39 171–44 851)

44 433

(41 093–47 763)

47 051

(43 853–50 449)

47 867

(44 155–51 133)

47 955

(44 409–51 589)

49 796

(46 122–53 444)

53 318

(49 243–57 349)

56 042

(50 152–61 990)

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(Continued from previous page)

Northern Ireland 1408

(1312–1500)

1461

(1350–1566)

1568

(1450–1692)

1526

(1420–1647)

1596

(1484–1715)

1705

(1575–1845)

1826

(1693–1962)

1914

(1711–2112)

Scotland 5257

(4919–5591)

5313

(4922–5705)

5392

(4985–5808)

5186

(4812–5570)

5112

(4718–5475)

5159

(4778–5536)

5362

(4954–5775)

5501

(4880–6075)

Wales 2681

(2493–2853)

2727

(2527–2939)

2807

(2591–3016)

2885

(2664–3103)

2902

(2681–3116)

2956

(2738–3188)

3087

(2855–3326)

3176

(2865–3512)

Latin America and Caribbean 141 013

(136 721–

145 145)

187 699

(181 895–

193 122)

249 570

(242 059–

256 807)

320 251

(310 191–

329 706)

391 272

(378 561–

403 097)

465 311

(451 038–

478 794)

534 453

(517 913–

550 186)

581 946

(553 278–

607 679)

Andean Latin America 13 876

(13 314–14 475)

18 187

(17 282–19 164)

23 910

(22 641–25 227)

30 722

(29 309–32 279)

38 359

(36 434–40 371)

46 462

(43 869–49 307)

53 990

(51 448–56 723)

61 448

(59 143–63 649)

Bolivia 2850

(2648–3046)

3518

(3079–3973)

4329

(3849–4806)

5241

(4767–5738)

6455

(5906–6982)

8384

(7758–9008)

10 124

(9280–10 974)

11 542

(10 295–12 716)

Ecuador 3301

(3059–3523)

4436

(4087–4795)

6012

(5453–6537)

7818

(7186–8436)

10 022

(9345–10 688)

12 377

(11 445–13 332)

14 906

(13 941–15 882)

16 686

(14 871–18 474)

Peru 7725

(7207–8238)

10 232

(9441–10 956)

13 568

(12 427–14 616)

17 662

(16 407–18 988)

21 882

(19 972–23 707)

25 700

(23 134–28 464)

28 959

(26 635–31 236)

33 219

(33 065–33 364)

Caribbean 17 715

(17 167–18 255)

21 587

(20 614–22 550)

26 151

(25 327–26 952)

30 749

(29 698–31 808)

35 316

(33 544–37 048)

40 172

(38 761–41 590)

43 926

(42 256–45 624)

46 265

(43 663–48 895)

Antigua and Barbuda 46

(43–49)

(52–60)

(60–69)

(52–68)

(55–64)

(70–82)

(79–92)

(79–98)

The Bahamas 79

(74–85)

118

(108–129)

169

(158–180)

212

(197–228)

257

(239–275)

310

(290–332)

354

(330–380)

375

(331–415)

Barbados 233

(216–248)

240

(225–256)

243

(226–259)

251

(234–268)

253

(236–271)

256

(240–273)

281

(262–299)

295

(264–330)

Belize 69

(64–74)

(88–100)

124

(116–133)

150

(140–160)

188

(175–202)

239

(222–256)

329

(308–351)

394

(348–439)

Bermuda 37

(34–40)

(41–47)

(50–57)

(51–59)

(54–63)

(59–67)

(60–69)

(58–73)

Cuba 5704

(5330–6068)

6873

(6156–7637)

8630

(8064–9196)

9952

(9226–10 687)

10 836

(9518–12 097)

11 377

(10 476–12 256)

11 435

(10 572–12 351)

11 376

(10 251–12 434)

Dominica 53

(49–56)

(57–66)

(66–75)

(69–80)

(68–79)

(65–75)

(64–75)

(61–76)

Dominican Republic 2301

(2137–2457)

3201

(2984–3425)

4251

(3970–4548)

5730

(5328–6143)

7201

(6555–7836)

8659

(7953–9316)

9752

(9076–10 389)

10 451

(9310–11 553)

Grenada 87

(81–93)

(86–97)

(89–102)

(87–101)

(80–93)

102

(94–110)

110

(102–118)

110

(98–122)

Guyana 429

(400–457)

581

(542–620)

728

(678–777)

795

(741–848)

779

(721–831)

781

(721–844)

752

(695–812)

742

(670–823)

Haiti 3282

(3053–3521)

3906

(3400–4444)

4455

(4116–4804)

5063

(4651–5478)

6376

(5598–7140)

8203

(7482–8886)

10 263

(9170–11 395)

11 824

(9880–13 736)

Jamaica 1453

(1357–1550)

1668

(1549–1778)

1868

(1745–2001)

2216

(2036–2397)

2372

(2195–2552)

2641

(2457–2847)

2766

(2568–2977)

2779

(2466–3081)

Puerto Rico 2209

(2058–2360)

2426

(2269–2585)

2792

(2599–2987)

3280

(3070–3491)

3612

(3356–3875)

3876

(3613–4125)

3799

(3540–4062)

3665

(3246–4091)

Saint Lucia 77

(71–82)

(84–96)

102

(95–109)

118

(110–126)

136

(126–146)

155

(144–166)

169

(158–181)

176

(156–197)

Saint Vincent and the

Grenadines

(70–80)

(78–89)

(83–95)

101

(95–108)

110

(101–118)

110

(102–118)

112

(103–120)

114

(102–125)

Suriname 193

(181–206)

287

(259–314)

390

(362–419)

367

(322–409)

388

(339–433)

449

(418–479)

537

(493–579)

572

(516–627)

Trinidad and Tobago 671

(626–715)

860

(804–918)

969

(904–1030)

1087

(1012–1157)

1206

(1124–1287)

1296

(1208–1383)

1351

(1246–1453)

1391

(1241–1546)

Virgin Islands 27

(25–29)

(31–35)

(62–71)

(92–106)

106

(99–112)

111

(104–118)

108

(101–115)

104

(93–117)

Central Latin America 53 305

(51 222–55 418)

72 777

(69 750–75 647)

100 359

(96 254–104 267)

132 448

(126 681–

137 827)

164 144

(157 392–

170 819)

199 489

(191 315–

207 476)

232 490

(223 115–

241 788)

255 488

(238 702–

271 354)

Colombia 11 518

(10 713–12 274)

16 035

(14 502–17 505)

22 096

(20 140–23 979)

26 989

(24 200–29 733)

32 643

(29 711–35 561)

39 822

(35 746–43 843)

46 396

(42 095–51 038)

50 606

(43 109–58 074)

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2029

1950 1960 1970 1980 1990 2000 2010 2017

(Continued from previous page)

Costa Rica 845

(785–902)

1242

(1134–1347)

1789

(1639–1932)

2273

(2057–2487)

3041

(2732–3356)

3914

(3646–4170)

4398

(4093–4732)

4653

(4190–5146)

El Salvador 1920

(1789–2050)

2581

(2399–2774)

3610

(3351–3881)

4622

(4048–5164)

5243

(4825–5659)

5793

(5178–6471)

5957

(5422–6523)

6086

(5315–6826)

Guatemala 2963

(2763–3175)

4030

(3679–4398)

5115

(4688–5547)

6327

(5859–6800)

8007

(7215–8749)

10 939

(10 111–11 825)

14 427

(12 778–16 107)

16 924

(14 243–19 628)

Honduras 1463

(1364–1561)

1910

(1772–2050)

2497

(2271–2746)

3413

(3049–3754)

4706

(4334–5095)

6191

(5739–6687)

7996

(7316–8682)

9498

(8567–10 397)

Mexico 27 378

(25 506–29 357)

36 830

(34 236–39 260)

51 009

(47 429–54 370)

69 563

(64 691–73 998)

85 439

(79 728–91 534)

101 772

(94 994–

108 972)

116 291

(108 390–

123 903)

126 569

(112 520–

141 480)

Nicaragua 1120

(1047–1198)

1486

(1354–1617)

1953

(1810–2100)

2741

(2383–3078)

3893

(3496–4268)

4951

(4482–5429)

5781

(5208–6370)

6396

(5487–7334)

Panama 815

(762–871)

1110

(1033–1189)

1483

(1374–1578)

1876

(1748–2000)

2387

(2209–2545)

2907

(2723–3115)

3491

(3256–3735)

3921

(3485–4377)

Venezuela 5280

(4906–5656)

7550

(6970–8118)

10 803

(10 017–11 580)

14 640

(13 601–15 719)

18 781

(17 422–20 136)

23 197

(21 380–24 971)

27 749

(25 774–29 771)

30 831

(27 589–34 127)

Tropical Latin America 56 114

(52 441–59 899)

75 146

(70 400–80 193)

99 149

(92 501–105 855)

126 331

(117 933–

134 844)

153 452

(142 917–

164 089)

179 186

(167 179–

191 447)

204 046

(190 090–

218 128)

218 743

(195 334–

242 050)

Brazil 54 761

(51 039–58 521)

73 360

(68 585–78 366)

96 804

(90 169–103 453)

123 307

(114 851–

131 752)

149 420

(138 774–

159 951)

174 058

(161 715–

186 328)

197 908

(183 737–

211 808)

211 812

(187 982–

234 855)

Paraguay 1353

(1262–1446)

1786

(1640–1921)

2345

(2157–2533)

3024

(2790–3260)

4031

(3682–4369)

5128

(4711–5564)

6138

(5381–6897)

6931

(5885–8046)

North Africa and Middle East 115 959

(112 279–

119 565)

148 453

(143 729–

153 233)

193 718

(187 700–

199 829)

257 208

(249 717–

264 577)

340 904

(330 888–

350 735)

426 468

(412 356–

440 350)

527 903

(512 116–

544 418)

600 182

(579 215–

621 820)

Afghanistan 7681

(5541–9575)

9465

(7772–11 203)

11 629

(10 087–13 133)

12 052

(11 180–12 917)

10 006

(8643–11 335)

17 928

(14 299–21 554)

26 294

(19 416–33 390)

32 854

(22 892–42 005)

Algeria 8799

(8222–9375)

11 234

(10 036–12 402)

13 781

(12 541–15 031)

18 525

(16 936–20 235)

25 463

(23 280–27 514)

31 508

(29 092–33 981)

36 293

(33 467–39 148)

40 463

(35 851–45 748)

Bahrain 116

(107–124)

155

(144–166)

216

(199–232)

345

(320–368)

507

(471–545)

651

(606–700)

1257

(1170–1344)

1470

(1305–1638)

Egypt 20 786

(19 371–22 122)

27 091

(25 383–28 856)

34 251

(31 139–37 552)

43 063

(39 177–46 961)

54 991

(49 913–60 135)

66 897

(61 131–72 575)

83 106

(75 937–90 743)

96 484

(90 094–102 841)

Iran 16 731

(15 621–17 904)

21 780

(19 732–23 814)

29 030

(26 396–31 568)

40 335

(36 967–44 296)

57 866

(52 672–62 812)

67 498

(61 587–73 597)

76 594

(71 133–82 082)

82 176

(75 839–88 022)

Iraq 5377

(5048–5724)

7156

(6535–7761)

9710

(8716–10 707)

13 627

(12 253–14 787)

17 444

(15 844–19 013)

26 408

(22 685–30 551)

34 359

(26 137–41 960)

43 304

(31 839–54 011)

Jordan 441

(335–550)

736

(602–871)

1300

(1133–1475)

2282

(2116–2453)

3739

(3401–4095)

4849

(4413–5301)

7534

(6787–8274)

10 648

(9754–11 559)

Kuwait 94

(84–104)

283

(263–305)

772

(720–824)

1403

(1312–1495)

1773

(1591–1959)

1978

(1776–2176)

3010

(2780–3238)

4262

(3821–4708)

Lebanon 1335

(1243–1421)

1750

(1538–1967)

2285

(2128–2449)

3202

(2787–3626)

4109

(3347–4867)

5270

(4041–6636)

6510

(4425–8615)

8511

(5685–11 791)

Libya 1070

(994–1142)

1427

(1294–1568)

1915

(1742–2079)

3078

(2787–3357)

4184

(3769–4614)

5035

(4540–5535)

6188

(5601–6770)

6908

(5974–7823)

Morocco 9176

(8574–9848)

11 890

(11 090–12 712)

15 497

(14 336–16 617)

20 157

(18 632–21 698)

25 207

(22 885–27 584)

29 532

(26 635–32 424)

33 167

(30 016–36 275)

35 488

(32 624–38 856)

Oman 442

(290–590)

614

(451–776)

897

(705–1087)

1343

(1145–1550)

1917

(1747–2092)

2301

(2095–2500)

2850

(2664–3039)

4535

(4508–4563)

Palestine 926

(777–1083)

973

(865–1083)

1102

(1005–1203)

1430

(1229–1635)

2037

(1810–2269)

3036

(2768–3312)

4175

(3822–4524)

4852

(4536–5156)

Qatar 26

(18–33)

(43–69)

131

(109–152)

273

(243–301)

443

(401–483)

592

(538–643)

1741

(1622–1859)

2747

(2525–2976)

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(Continued from previous page)

Saudi Arabia 4329

(4036–4638)

4644

(4032–5254)

5956

(5386–6526)

9691

(8731–10 787)

16 386

(14 964–17 729)

21 143

(19 108–23 200)

28 053

(26 153–30 133)

34 444

(30 598–38 365)

Sudan 6013

(5610–6390)

7146

(6463–7843)

10 351

(9412–11 273)

14 602

(13 374–15 958)

20 209

(18 414–21 941)

27 119

(24 040–30 238)

34 285

(31 632–37 135)

40 255

(34 770–45 494)

Syria 3400

(3173–3633)

4708

(4377–5039)

6530

(6094–6946)

9087

(8429–9740)

12 687

(11 444–13 866)

16 588

(14 961–18 057)

22 738

(20 396–25 034)

18 131

(15 317–20 564)

Tunisia 3691

(3431–3942)

4302

(3922–4704)

5117

(4656–5619)

6562

(5955–7194)

8412

(7628–9214)

9901

(8986–10 817)

10 810

(9827–11 809)

11 442

(10 350–12 472)

Turkey 21 175

(19 749–22 566)

27 605

(25 702–29 512)

36 107

(33 578–38 511)

45 410

(42 172–48 627)

57 681

(53 805–61 370)

65 949

(58 509–73 185)

74 297

(73 904–74 694)

80 456

(80 023–80 937)

United Arab Emirates 73

(59–86)

105

(93–117)

250

(229–270)

1075

(1004–1146)

1887

(1706–2073)

3251

(2922–3575)

8958

(8048–9894)

9734

(8433–11 170)

Yemen 4254

(2729–5807)

5291

(3764–6756)

6804

(5262–8251)

9499

(8020–10 922)

13 726

(12 427–14 966)

18 706

(17 088–20 302)

25 182

(22 469–27 784)

30 449

(25 793–35 167)

South Asia 457 107

(430 732–

483 061)

552 631

(517 605–

586 189)

698 004

(656 913–

739 771)

891 598

(838 523–

941 440)

1108 770

(1043 283–

1175 270)

1346 782

(1265 595–

1426 290)

1605 324

(1508 063–

1700 357)

1782 677

(1638 317–

1941 429)

Bangladesh 41 397

(38 577–44 053)

48 333

(44 678–51 917)

65 862

(59 840–71 907)

83 984

(77 577–90 506)

108 900

(101 213–

116 979)

128 604

(119 080–

137 940)

145 626

(134 711–

156 550)

156 981

(140 228–

173 145)

Bhutan 181

(169–194)

221

(193–249)

293

(237–350)

404

(315–489)

562

(475–649)

603

(543–665)

789

(715–869)

957

(826–1094)

India 372 174

(346 875–

397 889)

454 421

(420 507–

487 432)

561 030

(520 907–

600 806)

708 230

(657 702–

757 375)

871 428

(805 834–

934 597)

1052 960

(971 762–

1131 565)

1249 523

(1156 683–

1341 804)

1380 560

(1236 095–

1534 340)

Nepal 8346

(7781–8884)

9837

(9139–10 572)

11 976

(11 092–12 844)

15 574

(14 479–16 722)

19 373

(17 882–20 852)

23 878

(22 183–25 498)

27 649

(25 630–29 701)

29 891

(26 626–32 797)

Pakistan 35 007

(32 485–37 379)

39 815

(36 728–42 755)

58 840

(54 193–63 438)

83 404

(77 107–89 378)

108 505

(96 417–120 410)

140 735

(129 490–

152 140)

181 734

(161 683–

201 652)

214 287

(199 020–

228 949)

Southeast Asia, east Asia,

and Oceania

774 843

(736 072–

814 983)

957 155

(890 929–

1021 806)

1201 660

(1101 819–

1287 271)

1460 435

(1369 642–

1543 503)

1731 863

(1642 563–

1818 200)

1921 127

(1821 758–

2016 695)

2068 109

(1975 307–

2162 325)

2158 800

(1981 518–

2320 037)

East Asia 583 744

(547 376–

625 484)

712 646

(650 162–

777 420)

891 338

(796 510–

980 025)

1073 817

(986 653–

1157 549)

1258 648

(1176 009–

1347 979)

1366 510

(1275 694–

1459 970)

1439 061

(1351 366–

1531 406)

1485 714

(1316 627–

1646 304)

China 557 744

(520 768–

597 524)

678 243

(616 756–

742 010)

846 255

(752 128–

933 401)

1019 880

(933 340–

1101 322)

1196 979

(1115 557–

1286 245)

1298 681

(1208 608–

1389 466)

1367 214

(1280 251–

1457 810)

1412 480

(1245 008–

1569 141)

North Korea 10 681

(7186–14 481)

12 431

(9222–15 787)

15 201

(12 024–18 317)

17 633

(14 984–20 053)

20 296

(18 578–22 146)

23 188

(20 485–25 862)

25 160

(23 167–27 154)

25 716

(22 826–28 768)

Taiwan (province of China) 7575

(7535–7617)

10 805

(10 751–10 858)

14 617

(14 553–14 681)

17 908

(17 828–17 986)

20 402

(20 294–20 517)

22 286

(22 152–22 417)

23 191

(23 025–23 360)

23 583

(23 397–23 769)

Oceania 2656

(2330–2976)

3236

(3024–3465)

4072

(3870–4281)

5115

(4879–5339)

6457

(5883–7021)

8325

(7924–8715)

10 685

(10 105–11 292)

12 602

(11 585–13 653)

American Samoa 19

(18–20)

(19–22)

(25–29)

(30–35)

(45–51)

(54–62)

(52–60)

(49–61)

Federated States of

Micronesia

(36–41)

(44–57)

(52–78)

(72–98)

103

(94–114)

109

(102–116)

105

(98–112)

103

(93–115)

Fiji 297

(276–317)

408

(372–446)

542

(489–594)

659

(599–722)

762

(693–833)

818

(741–895)

875

(798–949)

906

(846–970)

Guam 61

(57–65)

(65–73)

(81–93)

108

(101–115)

136

(127–146)

159

(148–169)

163

(153–175)

167

(148–186)

Kiribati 30

(27–32)

(33–40)

(42–50)

(57–67)

(69–79)

(81–94)

107

(100–114)

118

(108–128)

Marshall Islands 11

(7–14)

(12–20)

(19–27)

(30–38)

(42–49)

(48–56)

(50–58)

(50–62)

Northern Mariana Islands 4

(4–4)

(5–7)

(8–10)

(15–18)

(42–48)

(67–77)

(51–58)

(40–49)

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Papua New Guinea 1726

(1417–2022)

2031

(1833–2241)

2499

(2321–2693)

3156

(2956–3362)

4064

(3520–4594)

5525

(5151–5884)

7543

(7002–8110)

9227

(8264–10 220)

Samoa 86

(80–92)

114

(106–122)

147

(136–157)

160

(148–173)

163

(151–175)

178

(164–190)

192

(178–206)

198

(183–212)

Solomon Islands 105

(98–112)

135

(125–145)

170

(159–181)

233

(212–254)

337

(307–369)

444

(411–479)

552

(509–593)

637

(565–710)

Tonga 49

(46–53)

(59–72)

(77–93)

(86–104)

(87–105)

100

(91–110)

106

(98–113)

102

(95–110)

Vanuatu 49

(46–53)

(57–72)

(80–95)

118

(109–126)

150

(139–161)

192

(178–205)

247

(229–265)

287

(266–308)

Southeast Asia 188 442

(182 191–

194 754)

241 272

(232 260–

251 232)

306 249

(295 068–

318 483)

381 501

(369 792–

393 749)

466 758

(451 577–

483 000)

546 290

(515 395–

576 571)

618 362

(598 861–

638 911)

660 484

(625 637–

694 223)

Cambodia 4438

(4137–4750)

5901

(5400–6403)

7554

(6695–8435)

7938

(6417–9346)

10 428

(9236–11 681)

12 634

(11 624–13 711)

14 560

(13 337–15 756)

16 122

(14 157–18 177)

Indonesia 79 537

(74 213–84 967)

98 406

(91 399–105 742)

123 056

(113 430–

132 056)

153 254

(143 916–

162 920)

185 784

(173 237–

198 423)

213 339

(184 326–

242 359)

241 532

(225 765–

257 592)

258 134

(228 486–

286 754)

Laos 1694

(1214–2166)

2107

(1651–2593)

2632

(2232–3062)

3302

(2966–3630)

4136

(3704–4539)

5330

(4800–5868)

6360

(5725–6943)

6970

(6442–7469)

Malaysia 6249

(5441–7015)

8316

(7729–8849)

10 703

(9952–11 389)

13 557

(12 638–14 483)

17 639

(16 264–18 971)

23 837

(22 268–25 477)

28 119

(26 310–30 148)

30 639

(27 083–34 101)

Maldives 77

(72–83)

(85–100)

120

(110–130)

162

(148–176)

219

(204–234)

278

(259–298)

352

(320–385)

458

(420–497)

Mauritius 490

(460–524)

668

(614–723)

837

(770–902)

991

(902–1083)

1098

(1028–1173)

1213

(1128–1300)

1267

(1176–1365)

1272

(1147–1397)

Myanmar 19 282

(17 833–20 583)

22 719

(19 724–25 734)

27 646

(25 258–30 089)

33 907

(31 033–36 686)

40 438

(36 067–44 754)

45 959

(38 921–53 049)

50 146

(45 580–55 132)

52 795

(48 406–57 281)

Philippines 20 331

(18 972–21 688)

28 707

(26 687–30 602)

38 593

(36 063–41 123)

49 864

(46 687–52 939)

63 333

(59 158–67 655)

79 807

(74 205–85 456)

95 885

(89 486–102 745)

103 470

(94 554–111 888)

Sri Lanka 7860

(7357–8423)

10 193

(9265–11 080)

12 930

(11 976–13 919)

15 187

(14 082–16 304)

17 179

(14 962–19 266)

18 798

(16 243–21 314)

20 524

(18 983–22 141)

21 596

(19 459–23 802)

Seychelles 34

(32–37)

(40–46)

(50–58)

(60–72)

(66–79)

(74–88)

(87–99)

100

(90–112)

Thailand 20 403

(18 913–21 794)

27 525

(25 618–29 354)

35 509

(33 009–37 896)

46 425

(43 256–49 679)

57 028

(53 286–60 983)

62 993

(58 922–67 354)

67 779

(63 187–72 386)

70 626

(62 645–78 551)

Timor-Leste 413

(360–467)

543

(505–579)

560

(491–630)

580

(541–622)

781

(726–835)

912

(832–996)

1109

(1034–1180)

1287

(1188–1391)

Vietnam 27 356

(25 495–29 238)

35 681

(31 167–40 285)

45 566

(39 978–51 388)

55 740

(51 473–59 718)

67 997

(62 530–73 389)

80 359

(74 668–86 543)

89 793

(83 334–96 170)

96 140

(84 738–108 043)

Sub-Saharan Africa 178 260

(164 732–

191 802)

225 081

(211 487–

239 434)

287 767

(275 293–

299 920)

372 388

(360 384–

384 066)

491 304

(479 290–

502 499)

644 373

(625 722–

662 472)

849 233

(824 168–

875 493)

1026 040

(988 588–

1062 587)

Central sub-Saharan Africa 19 588

(18 634–20 532)

25 453

(23 155–27 713)

32 835

(31 174–34 531)

41 915

(38 838–44 872)

55 023

(50 322–59 723)

73 396

(65 208–82 601)

99 517

(84 702–115 702)

121 670

(99 121–143 192)

Angola 4393

(4097–4705)

5152

(4780–5526)

5934

(5534–6338)

7508

(6519–8450)

10 246

(8354–12 310)

14 687

(12 582–16 858)

21 784

(19 754–24 078)

28 202

(25 993–30 710)

Central African Republic 1348

(1048–1648)

1630

(1378–1902)

2062

(1856–2266)

2294

(2078–2515)

2734

(2521–2971)

3612

(3317–3931)

4404

(3944–4879)

4622

(3945–5323)

Congo (Brazzaville) 821

(644–1015)

1034

(878–1190)

1322

(1198–1449)

1768

(1598–1929)

2428

(2157–2683)

3173

(2811–3475)

4185

(3840–4520)

4913

(4244–5607)

Democratic Republic of the

Congo

12 459

(11 684–13 238)

16 949

(14 650–19 201)

22 683

(21 041–24 305)

29 288

(26 433–32 003)

38 211

(34 046–42 323)

50 035

(42 266–58 951)

66 608

(51 014–83 629)

80 884

(57 964–102 607)

Equatorial Guinea 196

(183–210)

217

(189–243)

245

(210–281)

300

(274–328)

423

(378–470)

653

(543–757)

1034

(932–1138)

1345

(1236–1454)

Gabon 369

(342–393)

470

(438–501)

587

(513–662)

754

(645–869)

980

(897–1076)

1233

(1092–1376)

1500

(1372–1624)

1702

(1546–1857)

(Table 2 continues on next page)

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1950 1960 1970 1980 1990 2000 2010 2017

(Continued from previous page)

Eastern sub-Saharan Africa 63 017

(57 728–68 369)

81 437

(76 298–87 231)

107 317

(102 585–

112 025)

142 590

(138 124–

147 178)

191 563

(185 668–

197 939)

248 306

(240 183–

257 027)

326 270

(315 878–

336 860)

393 180

(375 866–

410 737)

Burundi 2391

(1831–3025)

3015

(2482–3612)

3632

(3181–4089)

4439

(4094–4783)

5500

(5136–5863)

6265

(5496–6987)

8976

(8277–9684)

10 905

(9535–12 329)

Comoros 158

(132–184)

189

(169–209)

254

(231–277)

355

(330–379)

462

(429–495)

551

(503–599)

650

(575–731)

718

(608–828)

Djibouti 62

(46–76)

(76–118)

156

(132–179)

299

(272–326)

498

(443–553)

648

(571–729)

902

(838–970)

1113

(984–1234)

Eritrea 1114

(786–1436)

1467

(1142–1783)

1938

(1639–2239)

2568

(2326–2805)

2893

(2577–3200)

3499

(2958–4084)

5191

(3910–6431)

5859

(4233–7490)

Ethiopia 17 731

(12 350–22 674)

22 150

(17 344–27 306)

27 867

(23 496–32 244)

34 702

(31 667–38 187)

51 404

(47 067–56 618)

68 429

(61 781–75 440)

86 259

(78 817–93 383)

102 883

(89 646–

116 198)

Kenya 5537

(5181–5896)

7901

(7298–8545)

11 965

(11 037–12 807)

16 750

(15 514–18 068)

23 198

(21 587–24 928)

30 893

(28 565–33 142)

40 694

(37 600–43 784)

48 326

(42 513–53 790)

Madagascar 4302

(3997–4576)

5483

(4766–6173)

7099

(6407–7831)

9269

(8376–10 167)

11 955

(10 899–12 981)

15 858

(14 259–17 526)

21 285

(17 762–24 979)

26 108

(20 426–31 770)

Malawi 2941

(2736–3146)

3705

(3315–4097)

4776

(4343–5210)

6416

(5840–6967)

9667

(8850–10 466)

11 168

(10 248–12 018)

14 338

(13 150–15 501)

17 191

(14 949–19 275)

Mozambique 6069

(5643–6494)

7218

(6746–7709)

9096

(8487–9740)

12 285

(11 388–13 163)

14 401

(12 777–16 071)

17 315

(15 781–18 860)

23 491

(21 500–25 621)

30 035

(27 827–31 998)

Rwanda 2515

(2350–2695)

3173

(2753–3574)

4067

(3618–4519)

5341

(4914–5777)

7266

(6758–7812)

8139

(7443–8811)

10 374

(9574–11 208)

12 554

(11 271–13 772)

Somalia 2336

(2176–2488)

2906

(2519–3290)

3829

(3456–4206)

6424

(5728–7071)

7175

(6579–7781)

9738

(8336–11 203)

13 574

(10 638–16 499)

16 880

(12 489–21 415)

South Sudan 2617

(2347–2884)

3169

(2887–3481)

3931

(3315–4535)

4861

(4437–5260)

5883

(5198–6573)

7288

(6440–8110)

9497

(8689–10 238)

9941

(8738–11 240)

Tanzania 7566

(7030–8058)

10 278

(9380–11 168)

13 870

(12 628–15 021)

19 434

(17 797–21 093)

25 888

(23 767–27 993)

34 172

(31 362–36 995)

44 584

(41 315–47 884)

53 973

(48 580–59 610)

Uganda 5291

(4920–5642)

7368

(6864–7877)

10 330

(9552–11 147)

13 374

(11 491–15 263)

17 349

(16 088–18 628)

24 305

(22 203–26 327)

32 574

(29 492–35 541)

39 078

(35 694–42 446)

Zambia 2368

(2218–2525)

3285

(2919–3697)

4463

(4126–4793)

6010

(5628–6376)

7919

(7360–8483)

9881

(9175–10 573)

13 670

(12 838–14 542)

17 364

(15 312–19 457)

Southern sub-Saharan Africa 17 644

(16 546–18 863)

22 982

(21 717–24 414)

30 803

(29 257–32 561)

40 678

(36 735–44 712)

52 481

(48 570–56 500)

64 122

(60 418–67 632)

70 987

(67 220–74 904)

77 373

(71 350–83 396)

Botswana 392

(366–419)

515

(467–562)

666

(595–739)

920

(850–991)

1310

(1211–1404)

1692

(1575–1815)

2008

(1859–2159)

2281

(2052–2527)

Lesotho 576

(537–616)

762

(688–831)

1045

(943–1150)

1450

(1306–1586)

1806

(1644–1967)

1978

(1795–2177)

1919

(1745–2095)

1947

(1675–2215)

Namibia 448

(418–477)

574

(536–612)

777

(720–833)

1049

(913–1188)

1415

(1310–1523)

1844

(1713–1972)

2118

(1964–2275)

2353

(2114–2595)

South Africa 13 151

(12 211–14 119)

16 925

(15 767–18 024)

22 606

(21 186–24 070)

29 233

(25 330–33 144)

36 773

(32 941–40 675)

45 632

(42 015–49 033)

50 861

(47 239–54 509)

54 952

(49 033–60 617)

Swaziland (eSwatini) 254

(236–271)

334

(299–369)

437

(398–481)

587

(533–639)

807

(732–885)

1011

(922–1102)

1068

(978–1165)

1124

(1047–1201)

Zimbabwe 2821

(2014–3579)

3870

(3104–4685)

5269

(4550–5981)

7437

(6855–8030)

10 366

(9528–11 160)

11 961

(10 986–12 887)

13 011

(11 915–14 005)

14 713

(13 330–16 032)

Western sub-Saharan Africa 78 009

(65 663–90 262)

95 207

(82 616–108 235)

116 810

(105 261–

127 661)

147 204

(137 869–

157 367)

192 235

(184 599–

199 575)

258 547

(245 286–

271 665)

352 458

(336 581–

367 819)

433 815

(413 644–

453 718)

Benin 2288

(1723–2882)

2413

(1926–2838)

2718

(2400–3048)

3459

(3209–3717)

4842

(4456–5242)

6698

(6148–7243)

9333

(8506–10 124)

11 585

(10 516–12 737)

Burkina Faso 4325

(3282–5330)

4758

(3933–5616)

5482

(4907–6024)

7164

(6481–7830)

9562

(8610–10 525)

12 301

(11 148–13 532)

16 868

(15 293–18 437)

21 121

(18 146–24 118)

Cameroon 4563

(3495–5578)

5571

(4638–6460)

6691

(5980–7402)

8017

(7253–8752)

10 355

(9454–11 280)

14 965

(13 507–16 519)

22 201

(19 903–24 366)

27 769

(23 792–31 860)

Cape Verde 155

(145–164)

214

(199–228)

283

(264–303)

301

(281–323)

351

(326–376)

448

(418–479)

508

(472–542)

545

(484–606)

(Table 2 continues on next page)

Global Health Metrics

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2033

1950 1960 1970 1980 1990 2000 2010 2017

(Continued from previous page)

Chad 2559

(1731–3399)

3093

(2239–3948)

3703

(2909–4507)

4621

(3926–5314)

6037

(5517–6569)

8267

(7338–9195)

11 803

(10 925–12 663)

15 222

(13 380–17 036)

Côte d’Ivoire 3101

(2883–3309)

4147

(3600–4660)

5864

(5263–6443)

8286

(7495–9127)

12 251

(11 216–13 262)

17 112

(15 825–18 420)

21 621

(19 620–23 447)

24 965

(22 783–27 055)

The Gambia 264

(247–280)

303

(275–327)

456

(418–495)

664

(606–720)

986

(903–1071)

1347

(1237–1455)

1765

(1607–1924)

2132

(1932–2334)

Ghana 5099

(4782–5444)

6894

(6446–7342)

8985

(8378–9584)

11 690

(10 660–12 748)

14 936

(13 332–16 513)

19 143

(17 828–20 371)

25 227

(23 528–26 958)

30 205

(26 660–33 569)

Guinea 2945

(2747–3149)

3369

(2978–3764)

3967

(3593–4331)

4655

(4258–5057)

6148

(5475–6817)

8121

(7407–8826)

9983

(9053–10 971)

11 819

(10 848–12 828)

Guinea-Bissau 536

(498–571)

570

(531–611)

647

(571–729)

813

(751–869)

1009

(936–1083)

1248

(1085–1411)

1571

(1450–1685)

1855

(1636–2071)

Liberia 909

(778–1043)

1079

(999–1166)

1412

(1283–1535)

1965

(1779–2138)

1985

(1776–2196)

2928

(2573–3288)

4051

(3722–4404)

4722

(4138–5272)

Mali 3847

(3584–4118)

4708

(4136–5331)

5939

(5316–6580)

7233

(6536–7897)

8662

(7915–9459)

11 028

(10 142–11 941)

15 896

(14 642–17 132)

20 253

(17 822–22 672)

Mauritania 659

(504–815)

874

(720–1019)

1150

(1019–1278)

1551

(1408–1684)

2071

(1903–2244)

2613

(2437–2792)

3336

(3058–3620)

3913

(3560–4285)

Niger 2562

(1964–3134)

3359

(2785–3919)

4476

(3984–4964)

5955

(5447–6466)

8025

(7371–8642)

11 245

(10 391–12 091)

16 397

(15 090–17 678)

21 375

(19 349–23 648)

Nigeria 38 269

(25 767–50 494)

46 573

(34 104–59 648)

55 844

(44 421–66 586)

69 128

(60 148–79 038)

89 790

(82 940–96 408)

121 832

(109 542–

134 557)

166 431

(152 067–

181 236)

206 087

(188 405–

224 287)

São Tomé and Principe 62

(58–66)

(63–74)

(71–81)

(88–103)

121

(112–130)

142

(132–153)

174

(160–188)

200

(180–219)

Senegal 2529

(1953–3162)

3397

(2851–3959)

4523

(4020–5024)

5860

(5341–6380)

7624

(7011–8229)

9910

(9164–10 652)

12 556

(11 482–13 626)

14 688

(13 261–16 099)

Sierra Leone 1923

(1640–2196)

2193

(2009–2385)

2621

(2378–2874)

3070

(2770–3384)

3781

(3413–4161)

4311

(3917–4728)

6348

(5717–6990)

7829

(7207–8482)

Togo 1401

(1245–1567)

1609

(1477–1734)

1959

(1823–2091)

2662

(2483–2861)

3685

(3262–4111)

4874

(4289–5493)

6375

(5963–6813)

7516

(6726–8351)

Data are thousands of people (95% uncertainty intervals) for all ages and both sexes. Super-regions, regions, and countries are listed alphabetically. Estimates are de-facto population estimates. GBD=Global

Burden of Diseases, Injuries, and Risk Factors Study. SDI=Socio-demographic Index.

Table 2: The global population and the populations of SDI groups, GBD regions and super-regions, countries, and territories, 1950–2017

high rates of total fertility are associated with high

rates of population growth in sub-Saharan Africa and

north Africa and the Middle East. The proportion of

women whose contraceptive needs are being met

through the provision of reproductive health services is

46·5% (95% UI 45·2–47·6) in sub-Saharan Africa and

69·0% (67·5–70·5) in north Africa and the Middle East.54

Given that the economic beneﬁts of the demographic

dividend are estimated to occur when the working-age

population represents more than 65% of the population,53

government action to meet the need for family planning

and to raise the educational attainment of women

are two potential pathways towards faster economic

growth. Notably, less than 55% of the population in

sub-Saharan Africa, on average, are of working age, and

this proportion is only slowly increasing. Fast economic

growth in sub-Saharan Africa from 2002 to 2014 shows

the potential for economic transition in the region;

capitalising on the demographic dividend might add to

this potential in the future. Policy options that focus on

educating young girls, providing access to reproductive

health services, and continued scale-up of eective

interventions for child mortality are available to

accelerate decreases in TFR and demographic change.

By contrast, 33 countries are in overall population

decline since 2010, including Estonia, Ukraine, Belarus,

Greece, Georgia, Bulgaria, Romania, and Spain. Many

other countries are also likely to have decreasing

populations as the size of their birth cohorts reduces.

Population decline and the associated shift to an older

population has profound cultural, economic, and social

implications. One early measure of this trend is the

percentage change in the number of livebirths over time;

in 89 countries, the size of the birth cohort has decreased

since 2000. The options in these countries to deal with

the social and economic consequences of population

decline include pro-natalist policies, liberal immigration

policies, and increasing the retirement age. Pro-natalist

policies have been pursued in more than a dozen

countries but the eects on fertility rates have not been

large.55–58 Liberal immigration policies have been eective

in sustaining population numbers in several countries,

Global Health Metrics

2034

www.thelancet.com Vol 392 November 10, 2018

but such policies have been accom panied by social and

political challenges in some. Dealing with population

decline will be a central policy challenge for a substantial

number of countries over the next few decades.

In high-income countries, the proportion of the

population that is of working age has also decreased in

the past 5 years, and this trend is likely to continue

for the foreseeable future. This demographic shift toward

an older population has a broad range of consequences,

from reductions in economic growth, decreasing tax

revenue, greater use of social security with fewer

contributors, and increasing health-care and other

demands prompted by an ageing population.59–65 This

shift is advanced in several high-income countries, with

one of the earliest examples being Japan.66 Our estimates

show that more than 20% of the population is older than

65 years in eight countries, implying that the challenges

of dealing eectively with ageing populations have

already advanced in these settings. Similarly to overall

population decline, several policy options have been

debated and implemented, ranging from immigration,

increasing retirement ages, pension reform, a focus on

disease prevention, and investments in human capital,

such as higher-level skill and knowledge building in a

shrinking workforce.63,64 In these same regions, the eects

of decreases in the proportion of the population aged

15–64 years on economic productivity could be mitigated

by individuals working far beyond age 65 years. This shift

to later retirement is already occurring in many countries,

including the USA, Australia, and Japan.67–72

The fertility rates in children and adolescents aged

10–19 years is an SDG indicator for goal 3, target 3.7. To

our knowledge, our analysis provides the ﬁrst annual

time series of fertility rates in these age groups. Fertility

rates in ages 15–19 years typically decrease with a

country’s development but the trends in those aged

10–14 years are less clear. In addition to the global

patterns in fertility rates in children and adolescents,

there are marked variations across countries at similar

levels of development. Within SDI bands, the ratio of

highest to lowest adolescent fertility rates is often more

than an order of magnitude, highlighting that many

factors other than development status contribute to the

fertility rate in children and adolescents. Some countries

have been able to reduce adolescent fertility rates faster

than expected. A detailed analysis of the determinants of

the variation in fertility rate among children and

adolescents across SDI bands, including policy factors, is

beyond the scope of this study, but this ﬁnding suggests

that such research is urgently needed.

The population decline that we found in Syria indicates

the potentially important role of conﬂict on both fertility

and migration rates. Conﬂict in some settings, such as in

Kuwait during the ﬁrst Persian Gulf War, can reduce

fertility rates, but other examples have been found where

conﬂict has led to younger marriage and increased

fertility rates.73 We explored adding the death rate from

conﬂict as a covariate to the fertility estimation model

but we found that this variable, on average, did not

predict changes in fertility; this ﬁnding is consistent with

1950

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2017

Proportion of the population who are working-age (%)

Year

GBD super-region

Central Europe, eastern Europe, and central Asia

High income

Latin America and Caribbean

North Africa and Middle East

South Asia

Southeast Asia, east Asia, and Oceania

Sub-Saharan Africa

Global

Figure 10: Proportion of the population that is of working age, globally and for GBD super-regions, 1950–2017

Working age is deﬁned as 15–64 years. Data are for both sexes combined from 1950 to 2017. GBD= Global Burden of Diseases, Injuries, and Risk Factors Study.

Global Health Metrics

www.thelancet.com Vol 392 November 10, 2018

2035

examples of increases and decreases in fertility in

individual countries. Conﬂict is also associated with large

migration ﬂows; many of these are captured in the

UNHCR migrant stock and derived ﬂow data. Given the

large-scale migration seen during the conﬂict in Syria, a

deeper understanding of what determines the magnitude

of migration before, during, and after conﬂict would be

useful in planning public health, social, and policy

interventions to ameliorate the eects of migration on

individuals and families.

Sex ratios in most countries remain in the narrow band

of 1·03–1·07 male livebirths for every female livebirth.

We found in some countries, most notably India and

China, that since the availability of ultrasonography in the

early 1980s, the ratio of males to females has increased. In

China, the sex ratios in 2017 were in excess of 1·16 males

for every female. These ratios imply very substantial

sex-selective abortion and even the possibility of female

infanticide. The eect of such pronounced sex ratios

on patterns of social interaction might be substantial in

future gener ations. From the perspective of demographic

growth, high sex ratios at birth reduce the net reproductive

rate to below that predicted from the TFR alone. In China,

low TFR and high sex ratios led to a net reproductive rate

of 0·69 female livebirths expected per woman.

Cross-cutting themes

An important debate in the medical literature about the

decreases in fertility has been regarding the relative

contribution of declines in the under-5 mortality rate,

women’s educational attainment, and the availability

of reproductive health services, particularly modern

contraception methods.74–79 There is a strong correlation

between estimated TFR and maternal education

(r=–0·886), the met contraceptive need (r=–0·799), and the

under-5 mortality rate (r=0·800), which are consistent over

decades and across SDI quintiles. Nevertheless, use of

time series of cross-sectional data to estimate causal

relationships is particularly challenging given that all

three of these measures are highly correlated. Under-

standing the magnitude of these dierent drivers and their

complex interconnections is important to understand the

future trajectory of ASFR. Fertility over the next few

decades is hard to forecast in regions such as western

sub-Saharan Africa, where fertility rates remain high,

progress on educational attainment has been relatively

modest, met need for contraception remains low (despite

some recent improvements), and under-5 mortality has

considerably decreased. Our more detailed time series of

these drivers could provide opportunities for future studies

to disentangle the contribution of these dierent factors.

Many factors other than maternal education, repro-

ductive health services, and under-5 mortality rates

inﬂuence annual fertility rates. The data compiled for

our study show that there has been marked variation

in fertility rates annually or over shorter durations in

response to events with cultural signiﬁcance or policy

change. For example, the TFR in Singapore increased

from 2·01 livebirths in 1999 to 2·39 livebirths in 2000,

whereas in Japan in 1966—the year of the Fire Horse,

during which giving birth to females was deemed

unlucky80—the TFR decreased by 13% in a single year.

Local legislation can also lead to an abrupt increase in the

TFR: the introduction of a ban on abortion in Romania in

1966 increased TFR from 2·72 livebirths to 3·53 livebirths

in the year following the ban. This ban on abortion also

led to increases in the maternal mortality rate. The recent

change from the one-child policy in China to a policy

that allows second births was associated with an

11·7% increase in total livebirths from 2014 to 2017.

These abrupt variations in fertility rates highlight the

importance of understanding the magnitude of policy

changes on fertility rates, especially in settings where

fertility rates might have decreased far below the

replacement value.

Over the past 25 years, annual livebirths globally have

remained between 133·5 million and 141·7 million

livebirths per year. This comparative stability has occurred

even during marked changes in the population of women

of repro ductive age and highly heterogeneous trends in

fertility rates. With each year, a larger proportion of the

birth cohort is represented in regions with lower incomes

and lower educational attainment because of dierent

speeds of changing fertility in dierent locations, creating

a phenomenon known as demographic headwinds.61,64,65

As more births occur in increasingly dicult circum-

stances, the challenge of meeting the ambitious SDG

targets will become more dicult. We would expect the

pace of reductions in the global under-5 mortality rate to

slow due to the changes in the birth cohort, and similar

global slowing might be expected for other indicators

such as childhood vaccination. Other changes, such as

the slower rates of decrease in neonatal mortality than in

mortality in post-neonatal infants (age 28–365 days) and

children aged 1–4 years, might slow the decrease in

overall child mortality. Evaluating global progress will

need to take into account these important compositional

shifts in the global birth cohort in terms of income and

educational attainment.

Estimation challenges

The biggest challenge in creating population estimates

that are consistent with observed population counts and

with data on ASFR and age-speciﬁc mortality is the poor

data available in many countries regarding net migration.

We used the GBD Bayesian demographic balancing model

to eectively infer net migration from the dierence

between the population expected from fertility and

mortality rates and that observed in census or registry data.

For some countries, the model has been informed with

reported data on documented migration and UNHCR data

on stocks and ﬂows of refugees. Nevertheless, the only

data that are increasingly available for many low-income

and middle-income countries are stocks of migrants

Global Health Metrics

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www.thelancet.com Vol 392 November 10, 2018

reported at the time of the census. Although these data are

clearly useful, dierent assumptions about mortality rates

and the timing of migration can lead to very dierent

estimates of past migration ﬂows, leading to the same

observed stock of migrants in each country being estimated

for. Even within these data, some temporary migrants who

move for employment opportunities might not be

recorded. More transparent estimates of population with

standardised methods, such as the methods that we have

presented, will hopefully drive a more extensive debate on

data sources for assessing migration and how to improve

them in the future.

We identiﬁed and extracted results from national PESs

in only 165 censuses, although it is likely that many more

have been done but their results have not been publicly

released. PESs use direct or indirect methods: direct

PESs match the records of individuals with actual census

records to estimate census completeness, whereas

indirect methods ask PES respondents if they participated

in the census. Direct matching is more reliable but much

harder to conduct. Censuses and PESs can miss certain

populations such as homeless people in some countries

or excluded minorities. The absence of PESs for most

censuses in most countries means that the actual

population count in many countries is uncertain. To

avoid systematic bias, we estimated census completeness

in all countries. The issue of census completeness

remains a major challenge and one that cannot easily be

addressed for past censuses. It is unlikely, for example,

that we will empirically resolve debates on census

completeness for many censuses in the 1950s–2000s.

At best, we can adequately represent this uncertainty in

our results. Moving forward, standardising the reporting

of PES results so that some form of systematic analysis

can be done will aid in future assessments.

Age misreporting, including age heaping, is a sub stantial

challenge in use of data from many censuses, particularly

in locations where numeracy of the res pondents is

relatively low.35,37 In fact, some education research has used

age heaping as a proxy measure of the quality of

mathematics education in a country.81 We detected age

misreporting in many earlier censuses in many countries,

often manifested by implausible immigration rates

required to match census counts in the oldest age groups.

We mitigated the eect of age misreporting by excluding

some data in the oldest age groups so that the estimates

are driven by census data at younger age groups and

mortality estimates, and by increasing the variance of

population counts at older ages, but this approach does not

remove all the eects of systematic age misreporting. For

age heaping, we used the Feeney, Arriaga, and Arriaga

strong corrections, dependent on the details of age group

available and the degree of age heaping. These approaches

have helped to mitigate age misreporting and age-heaping

issues, but further work on how to analyse these complex

error patterns in the data will be helpful to improve future

estimates.

Demographers have long recognised that population

estimates are necessary for planning, regardless of the

availability and quality of the data. The challenge for

demographers is to produce the most plausible estimates

of population that can be used, rather than simply

cataloguing all the limitations of the available data or the

potential for error. This approach was part of the original

inspiration for GBD. However, demographic estimation

has also remained quite operator dependent: analytical

choices by dierent demographers can lead to con-

siderable dierences in estimates for the same country.

The dierences between UNPOP estimates, US Census

Bureau estimates, and national government estimates for

many countries is one illustration of this analyst

dependence. Demographic estimation has only recently

started to examine statistical methods that generate

uncertainty intervals,47–49,82–85 but these have not been

widely used by UNPOP, the US Census Bureau, or by

most national authorities for population estimation, and

these methods remain primarily a research interest. To

our knowledge, we have generated the ﬁrst complete time

series of the population size (with uncertainty intervals)

for all countries by use of such methods; however, there

are still many analytical choices that have been made that

could arguably be changed in future eorts. These might

include the choice of age-heaping smoother, the decision

to exclude some census counts as outliers, or inclusion of

documented migration estimates from various sources.

We hope that this eort will stimulate vigorous debate on

the analysis of population size for dierent countries.

Limitations

This study has many limitations, some of which—

including the paucity of direct measurement of net

migration—have already been identiﬁed, whereas others

need to be articulated. First, the GBD Bayesian demo-

graphic balancing model for population and migration

estimation includes a number of hyperpriors. The results

of the estimation are sensitive to the choice of these

hyperpriors, such as the correlation of migration over

time. We have largely used the same hyperpriors for all

locations, but we have modiﬁed the hyperpriors in some

locations to improve the ﬁt of the model. Second, we

sought to estimate de-facto population counts, but in

some low-income and middle-income locations, only de-

jure counts were available as inputs. De-jure counts could,

in some countries, exclude temporary migrants; we

identiﬁed and included migration data in locations where

large labour migration is known to occur, but the use of

de-jure counts in other settings could overestimate or

underestimate de-facto counts. Third, we assume that the

estimates of age-speciﬁc mortality from the GBD study

and ASFR from this study are accurate. Any systematic

errors in either would aect our estimates of migration

and of population in years that are further from a census.

Fourth, the estimation method requires a baseline

estimate of the population in 1950 for detailed age groups,

Global Health Metrics

www.thelancet.com Vol 392 November 10, 2018

2037

and any errors in this baseline based on a backwards

cohort-component method of population projection will

have a sustained eect on the population estimates from

the baseline until at least the ﬁrst census after 1950. Major

errors in the baseline can also have an eect after the ﬁrst

census. Fifth, we were unable to obtain census counts by

sex from ten known censuses and could not obtain age-

speciﬁc population data in 62 censuses. Inclusion of this

unpublished information could substantially change the

results for those locations. Sixth, uncertainty in our

current results is based on the uncertainty in population

counts and the time since the last population count and,

implicitly, errors in fertility and mortality estimation. We

used an out-of-sample approach to estimate uncertainty in

the population size in years without a census count, and

we used uncertainty in the PES model prediction of

completeness to estimate uncertainty in the years with

and without a census count. The out-of-sample method

provides a robust approach to estimating uncertainty but

does not provide draws of migration, fertility, and mortality

associated with each draw of population. We also assumed

that years where registry counts are available only have

uncertainty in the PES model prediction of completeness

and zero uncertainty from the out-of-sample approach.

This approach to estimating population uncertainty also

does not incorporate any spatial correlation of uncertainty

across countries and assumes complete correlation of

uncertainty by age. Uncertainty at the country level could

be exaggerated by this approach. Seventh, age-speciﬁc

migration estimates can be aected by age-speciﬁc

variation in census completeness. In our analysis, we have

included the average age pattern of enumeration

completeness, as detected in our analysis of PESs,

but country-speciﬁc variation in the age pattern of

enumeration is possible. Eighth, refugee ﬂows might be

misenumerated by UNHCR in some settings, leading to

underestimates of migrants. Ninth, alternative hyper-

parameters could be selected and could change the results,

although we believe that our selection of hyperparameters,

which were based on several rounds of testing, provide

sensible results. Tenth, we analysed each location

independently, without imposing global constraints on

global net migration. As a con sequence, in some years,

our estimates imply global net migration, which is not

possible. For example, in 2015, our estimate of global net

migration was 14 709 people. Finally, our model for fertility

in girls aged 10–14 years is based on a simple linear

regression of the ratio of fertility in those aged 10–14 years

versus those aged 15–19 years, on the fertility rate in those

aged 15–19 years and 50–54 years was estimated as a ﬁxed

fraction of the fertility rate in women aged 45–49 years

because, even in the linear regression, the coecient was

not signiﬁcant. This regression is based on locations with

complete vital registration data, which tend to be high-SDI

and middle-SDI countries. Other factors might drive

fertility at these extreme ages that are not captured in our

models or the available data.

Future directions

There are many ways in which our estimation of population

by age, sex, location, and year can be improved and made

more useful for diverse applications. We currently use the

GBD Bayesian demographic balancing model to estimate

age-sex-year-speciﬁc migration, consistent with our

estimated fertility and mortality rates and observed

population numbers. In settings where direct measure-

ment of migration is possible, it could be useful to use a

version of the same model that allows the posterior values

for fertility, mortality, and migration to change relative to

the prior. This approach is conceptually appealing,

allowing incon sistencies between fertility, mortality, and

migration to be resolved through shifts in some or all of

these inputs. However, our early testing of this approach

showed considerable instability given that the same

observed population count can be exactly explained by an

inﬁnite set of combinations of deaths and migration. This

instability in the full Bayesian model led to estimates of

implausible shifts in the age and time pattern of mortality.

In some settings, it might be possible to provide more

information on the credible age structure of death and

migration to stabilise such a version of the model. A second

improvement in the modelling approach would be to

address how to ensure that the global net migration in any

age-sex-year group is zero. Joint estimation of all locations

simultaneously is unlikely to be computationally feasible

given the complexity of the model for just one location at a

time. Two-stage processes can be explored that might

accommodate the logical requirement for global net

migration to be zero. Another avenue that warrants

investigation is the inclusion in the analysis of household

age structure from household surveys; there is a very wide

array of these surveys, and methods to use this information

with appropriately wider data variance than a census could

improve estimation in census-poor locations. We currently

adjust data for age heaping with the three correction

methods (Feeney, Arriaga, and Arriaga strong), but there

could be other ways to incorporate age-heaping corrections

directly into the GBD Bayesian demographic balancing

model likelihood. In future analyses of fertility and

population, the im portant role of urbanisation should be

explored. Given the drive in many GBD-related analyses

toward 5 × 5 km estimation,86,87 the logical extension of our

analysis will be to generate population estimates at a

detailed local level. Such eorts will need to leverage

similarly ﬁne-grained assessments of fertility, mortality,

and available population counts, supplemented with

satellite imagery where feasible.

Conclusion

Population size and age structure have substantial

consequences on every aspect of social and economic life

in every location. Over the past 70 years, there have

been huge changes in ASFR, mortality, and migration

that have reshaped popu lation structures. Trends have

not been homogeneous across and within countries and,

Global Health Metrics

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www.thelancet.com Vol 392 November 10, 2018

although global population growth rates have decreased,

the absolute increase in global population every year

has remained notably constant for many decades.

Linear growth in the global population is occurring

despite population decreases in some parts of the world,

particularly eastern Europe, and large population

increases in sub-Saharan Africa. Demographic changes

will continue to have substantial social and economic

eects, highlighting the impor tance of close monitoring

and analysis of fertility and population at the local level.

The statistical methods for estimation that we present

will hopefully facilitate this need, providing the essential

demographic intelligence for countries to reliably inform

their health and social development strategies.

GBD 2017 Population and Fertility Collaborators

Christopher J L Murray, Charlton S K H Callender, Xie Rachel Kuliko,

Vinay Srinivasan, Degu Abate, Kalkidan Hassen Abate, Solomon M Abay,

Nooshin Abbasi, Hedayat Abbastabar, Jemal Abdela, Ahmed Abdelalim,

Omar Abdel-Rahman, Alireza Abdi, Nasrin Abdoli, Ibrahim Abdollahpour,

Rizwan Suliankatchi Abdulkader, Haftom Temesgen Abebe, Molla Abebe,

Zegeye Abebe, Teshome Abuka Abebo, Ayenew Negesse Abejie,

Victor Aboyans, Haftom Niguse Abraha, Daisy Maria Xavier Abreu,

Aklilu Roba Abrham, Laith Jamal Abu-Raddad, Niveen M E Abu-Rmeileh,

Manfred Mario Kokou Accrombessi, Pawan Acharya, Abdu A Adamu,

Oladimeji M Adebayo, Isaac Akinkunmi Adedeji, Victor Adekanmbi,

Olatunji O Adetokunboh, Beyene Meressa Adhena, Tara Ballav Adhikari,

Mina G Adib, Arsène Kouablan Adou, Jose C Adsuar, Mohsen Afarideh,

Ashkan Afshin, Gina Agarwal, Kareha M Agesa, Sargis Aghasi Aghayan,

Sutapa Agrawal, Alireza Ahmadi, Mehdi Ahmadi, Muktar Beshir Ahmed,

Sayem Ahmed, Amani Nidhal Aichour, Ibtihel Aichour,

Miloud Taki Eddine Aichour, Ali S Akanda, Mohammad Esmaeil Akbari,

Mohammed Akibu, Rufus Olusola Akinyemi, Tomi Akinyemiju,

Nadia Akseer, Fares Alahdab, Ziyad Al-Aly, Khurshid Alam,

Animut Alebel, Alicia V Aleman, Kefyalew Addis Alene,

Ayman Al-Eyadhy, Raghib Ali, Mehran Alijanzadeh,

Reza Alizadeh-Navaei, Syed Mohamed Aljunid, Ala’a Alkerwi,

François Alla, Peter Allebeck, Ali Almasi, Jordi Alonso,

Rajaa M Al-Raddadi, Ubai Alsharif, Khalid Altirkawi,

Nelson Alvis-Guzman, Azmeraw T Amare, Walid Ammar,

Nahla Hamed Anber, Catalina Liliana Andrei, Soﬁa Androudi,

Megbaru Debalkie Animut, Hossein Ansari, Mustafa Geleto Ansha,

Carl Abelardo T Antonio, Seth Christopher Yaw Appiah, Olatunde Aremu,

Habtamu Abera Areri, Nicholas Arian, Johan Ärnlöv, Al Artaman,

Krishna K Aryal, Hamid Asayesh, Ephrem Tsegay Asfaw,

Solomon Weldegebreal Asgedom, Reza Assadi,

Tesfay Mehari Mehari Atey, Suleman Atique, Madhu Sudhan Atteraya,

Marcel Ausloos, Euripide F G A Avokpaho, Ashish Awasthi,

Beatriz Paulina Ayala Quintanilla, Yohanes Ayele, Rakesh Ayer,

Tambe B Ayuk, Peter S Azzopardi, Tesleem Kayode Babalola,

Arefeh Babazadeh, Hamid Badali, Alaa Badawi, Ayele Geleto Bali,

Maciej Banach, Suzanne Lyn Barker-Collo, Till Winfried Bärnighausen,

Lope H Barrero, Huda Basaleem, Quique Bassat, Arindam Basu,

Bernhard T Baune, Habtamu Wondifraw Baynes, Ettore Beghi,

Masoud Behzadifar, Meysam Behzadifar, Bayu Begashaw Bekele,

Abate Bekele Belachew, Aregawi Gebreyesus Belay, Ezra Belay,

Saba Abraham Belay, Yihalem Abebe Belay, Michelle L Bell,

Aminu K Bello, Derrick A Bennett, Isabela M Bensenor, Gilles Bergeron,

Adugnaw Berhane, Adam E Berman, Eduardo Bernabe,

Robert S Bernstein, Gregory J Bertolacci, Mircea Beuran, Suraj Bhattarai,

Soumyadeep Bhaumik, Zulﬁqar A Bhutta, Belete Biadgo, Ali Bijani,

Boris Bikbov, Nigus Bililign, Muhammad Shahdaat Bin Sayeed,

Sait Mentes Birlik, Charles Birungi, Tuhin Biswas, Hailemichael Bizuneh,

Archie Bleyer, Berrak Bora Basara, Cristina Bosetti, Souﬁane Boufous,

Oliver J Brady, Nicola Luigi Bragazzi, Michael Brainin,

Alexandra Brazinova, Nicholas J K Breitborde, Hermann Brenner,

Jerry D Brewer, Paul Svitil Briant, Gabrielle Britton, Roy Burstein,

Reinhard Busse, Zahid A Butt, Lucero Cahuana-Hurtado,

Ismael R Campos-Nonato, Julio Cesar Campuzano Rincon, Jorge Cano,

Mate Car, Rosario Cárdenas, Juan J Carrero, Félix Carvalho,

Carlos A Castañeda-Orjuela, Jacqueline Castillo Rivas, Franz Castro,

Ferrán Catalá-López, Alanur Çavlin, Ester Cerin, Julian Chalek,

Hsing-Yi Chang, Jung-Chen Chang, Aparajita Chattopadhyay,

Pankaj Chaturvedi, Peggy Pei-Chia Chiang, Ken Lee Chin,

Vesper Hichilombwe Chisumpa, Abdulaal Chitheer, Jee-Young J Choi,

Rajiv Chowdhury, Devasahayam J Christopher, Flavia M Cicuttini,

Liliana G Ciobanu, Massimo Cirillo, Rafael M Claro,

Daniel Collado-Mateo, Maria-Magdalena Constantin, Sara Conti,

Cyrus Cooper, Leslie Trumbull Cooper, Leslie Cornaby,

Paolo Angelo Cortesi, Monica Cortinovis, Megan Costa,

Elizabeth A Cromwell, Christopher Stephen Crowe, Petra Cukelj,

Matthew Cunningham, Alemneh Kabeta Daba, Berihun Assefa Dachew,

Lalit Dandona, Rakhi Dandona, Paul I Dargan, Ahmad Daryani,

Rajat Das Gupta, José Das Neves, Tamirat Tesfaye Dasa,

Aditya Prasad Dash, Nicole Davis Weaver, Dragos Virgil Davitoiu,

Kairat Davletov, Diego De Leo, Jan-Walter De Neve, Meaza Girma Degefa,

Louisa Degenhardt, Tizta Tilahun Degﬁe, Selina Deiparine,

Gebre Teklemariam Demoz, Balem Demtsu, Edgar Denova-Gutiérrez,

Kebede Deribe, Nikolaos Dervenis, Don C Des Jarlais,

Getenet Ayalew Dessie, Samath D Dharmaratne, Meghnath Dhimal,

Daniel Dicker, Eric L Ding, Girmaye Deye Dinsa, Shirin Djalalinia,

Huyen Phuc Do, Klara Dokova, David Teye Doku, Kate A Dolan,

Kerrie E Doyle, Tim R Driscoll, Manisha Dubey, Eleonora Dubljanin,

Eyasu Ejeta Duken, Andre R Duraes, Soheil Ebrahimpour,

David Edvardsson, Charbel El Bcheraoui, Ziad El-Khatib, Iqbal Rf Elyazar,

Ahmadali Enayati, Aman Yesuf Endries, Sergey Petrovich Ermakov,

Babak Eshrati, Sharareh Eskandarieh, Reza Esmaeili, Alireza Esteghamati,

Sadaf Esteghamati, Kara Estep, Hamed Fakhim, Tamer Farag,

Mahbobeh Faramarzi, Mohammad Fareed, Carla Soﬁa E Sá Farinha,

Andre Faro, Maryam S Farvid, Farshad Farzadfar,

Mohammad Hosein Farzaei, Kairsten A Fay, Mir Sohail Fazeli,

Valery L Feigin, Andrea B Feigl, Fariba Feizy, Ama P Fenny,

Netsanet Fentahun, Seyed-Mohammad Fereshtehnejad,

Eduarda Fernandes, Garumma Tolu Feyissa, Irina Filip, Samuel Finegold,

Florian Fischer, Luisa Sorio Flor, Nataliya A Foigt, Kyle J Foreman,

Carla Fornari, Thomas Fürst, Takeshi Fukumoto, John E Fuller,

Nancy Fullman, Emmanuela Gakidou, Silvano Gallus,

Amiran Gamkrelidze, Morsaleh Ganji, Fortune Gbetoho Gankpe,

Gregory M Garcia, Miguel Á Garcia-Gordillo, Abadi Kahsu Gebre,

Teshome Gebre, Gebremedhin Berhe Gebregergs,

Tsegaye Tewelde Gebrehiwot, Amanuel Tesfay Gebremedhin,

Tilayie Feto Gelano, Yalemzewod Assefa Gelaw, Johanna M Geleijnse,

Ricard Genova-Maleras, Peter Gething, Kebede Embaye Gezae,

Mohammad Rasoul Ghadami, Reza Ghadimi, Keyghobad Ghadiri,

Khalil Ghasemi Falavarjani, Maryam Ghasemi-Kasman,

Hesam Ghiasvand, Mamata Ghimire, Aloke Gopal Ghoshal,

Paramjit Singh Gill, Tiany K Gill, Giorgia Giussani,

Elena V Gnedovskaya, Srinivas Goli, Ricardo Santiago Gomez,

Hector Gómez-Dantés, Philimon N Gona, Amador Goodridge,

Sameer Vali Gopalani, Alessandra C Goulart,

Bárbara Niegia Garcia Goulart, Ayman Grada, Giuseppe Grosso,

Harish Chander C Gugnani, Jingwen Guo, Yuming Guo,

Prakash C Gupta, Rahul Gupta, Rajeev Gupta, Tanush Gupta,

Juanita A Haagsma, Vladimir Hachinski, Nima Hafezi-Nejad,

Tekleberhan B Hagos, Tewodros Tesfa Hailegiyorgis,

Gessessew Bugssa Hailu, Arvin Haj-Mirzaian, Arya Haj-Mirzaian,

Randah R Hamadeh, Samer Hamidi, Alexis J Handal, Graeme J Hankey,

Yuantao Hao, Hilda L Harb, Hamidreza Haririan, Josep Maria Haro,

Mehedi Hasan, Hadi Hassankhani, Hamid Yimam Hassen,

Rasmus Havmoeller, Simon I Hay, Yihua He,

Akbar Hedayatizadeh-Omran, Mohamed I Hegazy, Behzad Heibati,

Behnam Heidari, Delia Hendrie, Andualem Henok, Nathaniel J Henry,

Claudiu Herteliu, Fatemeh Heydarpour, Desalegn T Hibstu,

Michael K Hole, Enayatollah Homaie Rad, Praveen Hoogar,

H Dean Hosgood, Seyed Mostafa Hosseini,

Meimanat M Hosseini Chavoshi, Mehdi Hosseinzadeh, Mihaela Hostiuc,

Sorin Hostiuc, Mohamed Hsairi, Thomas Hsiao, Guoqing Hu,

John J Huang, Kim Moesgaard Iburg, Ehimario U Igumbor,

Chad Thomas Ikeda, Olayinka Stephen Ilesanmi, Usman Iqbal,

Global Health Metrics

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Asnake Ararsa Irenso, Seyed Sina Naghibi Irvani,

Oluwaseyi Oluwakemi Isehunwa, Sheikh Mohammed Shariful Islam,

Leila Jahangiry, Nader Jahanmehr, Sudhir Kumar Jain, Mihajlo Jakovljevic,

Moti Tolera Jalu, Spencer L James, Simerjot K Jassal, Mehdi Javanbakht,

Achala Upendra Jayatilleke, Panniyammakal Jeemon, Ravi Prakash Jha,

Vivekanand Jha, John S Ji, Jost B Jonas, Jacek Jerzy Jozwiak,

Suresh Banayya Jungari, Mikk Jürisson, Zubair Kabir, Rajendra Kadel,

Amaha Kahsay, Rizwan Kalani, Umesh Kapil, Manoochehr Karami,

Behzad Karami Matin, André Karch, Corine Karema, Seyed M Karimi,

Amir Kasaeian, Dessalegn H Kassa, Getachew Mullu Kassa,

Tesfaye Dessale Kassa, Zemenu Yohannes Kassa, Nicholas J Kassebaum,

Anshul Kastor, Srinivasa Vittal Katikireddi, Anil Kaul, Norito Kawakami,

Ali Kazemi Karyani, Seifu Kebede, Peter Njenga Keiyoro,

Grant Rodgers Kemp, Andre Pascal Kengne, Andre Keren,

Maia Kereselidze, Yousef Saleh Khader, Morteza Abdullatif Khafaie,

Alireza Khajavi, Nauman Khalid, Ibrahim A Khalil, Ejaz Ahmad Khan,

Muhammad Shahzeb Khan, Young-Ho Khang, Tripti Khanna,

Mona M Khater, Alireza Khatony, Zahra Khazaeipour, Habibolah Khazaie,

Abdullah T Khoja, Ardeshir Khosravi, Mohammad Hossein Khosravi,

Getiye D Kibret, Zelalem Teklemariam Kidanemariam, Daniel N Kiirithio,

Paul Evan Kilgore, Daniel Kim, Jun Y Kim, Young-Eun Kim, Yun Jin Kim,

Ruth W Kimokoti, Yohannes Kinfu, Sanjay Kinra, Adnan Kisa,

Mika Kivimäki, Sonali Kochhar, Yoshihiro Kokubo, Tufa Kolola,

Jacek A Kopec, Margaret N Kosek, Soewarta Kosen, Parvaiz A Koul,

Ai Koyanagi, Kewal Krishan, Sanjay Krishnaswami, Kristopher J Krohn,

Barthelemy Kuate Defo, Burcu Kucuk Bicer, G Anil Kumar,

Manasi Kumar, Pushpendra Kumar, Fekede Asefa Kumsa, Michael J Kutz,

Sheetal D Lad, Alessandra Lafranconi, Dharmesh Kumar Lal,

Ratilal Lalloo, Hilton Lam, Faris Hasan Lami, Justin J Lang, Sonia Lansky,

Van C Lansingh, Dennis Odai Laryea, Zohra S Lassi, Arman Latiﬁ,

Avula Laxmaiah, Jerey V Lazarus, James B Lee, Paul H Lee, James Leigh,

Cheru Tesema Leshargie, Samson Leta, Miriam Levi, Shanshan Li,

Xiaohong Li, Yichong Li, Juan Liang, Xiaofeng Liang,

Misgan Legesse Liben, Lee-Ling Lim, Miteku Andualem Limenih,

Shai Linn, Shiwei Liu, Stefan Lorkowski, Paulo A Lotufo, Rafael Lozano,

Raimundas Lunevicius, Crispin Mabika Mabika,

Erlyn Rachelle King Macarayan, Mark T Mackay, Fabiana Madotto,

Tarek Abd Elaziz Mahmood, Narayan Bahadur Mahotra, Marek Majdan,

Reza Majdzadeh, Azeem Majeed, Reza Malekzadeh,

Manzoor Ahmad Malik, Abdullah A Mamun, Wondimu Ayele Manamo,

Ana-Laura Manda, Srikanth Mangalam, Mohammad Ali Mansournia,

Lorenzo Giovanni Mantovani, Chabila Christopher Mapoma,

Dadi Marami, Joemer C Maravilla, Wagner Marcenes,

Shakhnazarova Marina, Francisco Rogerlândio Martins-Melo,

Winfried März, Melvin B Marzan, Tivani Phosa Mashamba-Thompson,

Felix Masiye, Amanda J Mason-Jones, Benjamin Ballard Massenburg,

Manu Raj Mathur, Pallab K Maulik, Mohsen Mazidi, John J McGrath,

Suresh Mehata, Sanjay Madhav Mehendale, Man Mohan Mehndiratta,

Ravi Mehrotra, Saeed Mehrzadi, Kala M Mehta, Varshil Mehta,

Tefera C Mekonnen, Hagazi Gebre Meles, Kidanu Gebremariam Meles,

Addisu Melese, Mulugeta Melku, Peter T N Memiah, Ziad A Memish,

Walter Mendoza, Melkamu Merid Mengesha, Desalegn Tadese Mengistu,

Getnet Mengistu, George A Mensah, Seid Tiku Mereta, Atte Meretoja,

Tuomo J Meretoja, Tomislav Mestrovic, Haftay Berhane Mezgebe,

Yode Miangotar, Bartosz Miazgowski, Tomasz Miazgowski, Ted R Miller,

Molly Katherine Miller-Petrie, G K Mini, Parvaneh Mirabi,

Andreea Mirica, Erkin M Mirrakhimov, Awoke Temesgen Misganaw,

Babak Moazen, Karzan Abdulmuhsin Mohammad, Moslem Mohammadi,

Noushin Mohammadifard, Maryam Mohammadi-Khanaposhtani,

Mohammed A Mohammed, Shaﬁu Mohammed, Ali H Mokdad,

Glen Dl Mola, Mariam Molokhia, Lorenzo Monasta,

Julio Cesar Montañez, Ghobad Moradi, Mahmoudreza Moradi,

Maziar Moradi-Lakeh, Mehdi Moradinazar, Paula Moraga,

Joana Morgado-Da-Costa, Rintaro Mori, Shane Douglas Morrison,

Abbas Mosapour, Marilita M Moschos, Seyyed Meysam Mousavi,

Achenef Asmamaw Muche, Kindie Fentahun Muchie,

Ulrich Otto Mueller, Satinath Mukhopadhyay, Tasha B Murphy,

Kate Muller, G V S Murthy, Jonah Musa, Kamarul Imran Musa,

Ghulam Mustafa, Saravanan Muthupandian, Jean B Nachega,

Gabriele Nagel, Mohsen Naghavi, Aliya Naheed, Azin Nahvijou,

Gurudatta Naik, Paulami Naik, Farid Najaﬁ, Luigi Naldi, Vinay Nangia,

Jobert Richie Nansseu, Bruno Ramos Nascimento, Haseeb Nawaz,

Busisiwe P Ncama, Nahid Neamati, Ionut Negoi, Ruxandra Irina Negoi,

Subas Neupane, Charles Richard James Newton, Frida N Ngalesoni,

Josephine W Ngunjiri, Grant Nguyen, Long Hoang Nguyen,

Trang Huyen Nguyen, Dina Nur Anggraini Ningrum,

Yirga Legesse Nirayo, Muhammad Imran Nisar, Molly R Nixon,

Shuhei Nomura, Mehdi Noroozi, Jean Jacques Noubiap,

Hamid Reza Nouri, Malihe Nourollahpour Shiadeh,

Mohammad Reza Nowroozi, Alypio Nyandwi, Peter S Nyasulu,

Christopher M Odell, Richard Ofori-Asenso, Okechukwu Samuel Ogah,

Felix Akpojene Ogbo, In-Hwan Oh, Anselm Okoro,

Olanrewaju Oladimeji, Andrew T Olagunju, Tinuke O Olagunju,

Pedro R Olivares, Bolajoko Olubukunola Olusanya,

Jacob Olusegun Olusanya, Sok King Ong, Alberto Ortiz,

Aaron Osgood-Zimmerman, Erika Ota, Brenda Achieng Otieno,

Stanislav S Otstavnov, Mayowa Ojo Owolabi, Abayomi Samuel Oyekale,

Mahesh P A, Smita Pakhale, Abhijit P Pakhare, Adrian Pana,

Basant Kumar Panda, Songhomitra Panda-Jonas, Achyut Raj Pandey,

Eun-Kee Park, Hadi Parsian, Shanti Patel, Snehal T Patil, Ajay Patle,

George C Patton, Vishnupriya Rao Paturi, Deepak Paudel,

Marcel Moraes Pedroso, Emmanuel K Peprah, David M Pereira,

Norberto Perico, Konrad Pesudovs, William A Petri, Max Petzold,

Maxwell Pierce, David M Pigott, Julian David Pillay, Meghdad Pirsaheb,

Guilherme V Polanczyk, Maarten J Postma, Farshad Pourmalek,

Akram Pourshams, Hossein Poustchi, Swayam Prakash, Narayan Prasad,

Caroline A Purcell, Manorama B Purwar, Mostafa Qorbani,

Reginald Quansah, Amir Radfar, Anwar Rafay, Alireza Raﬁei,

Fakher Rahim, Afarin Rahimi-Movaghar, Vafa Rahimi-Movaghar,

Mahfuzar Rahman, Md Shaﬁur Rahman, Mohammad Hifz Ur Rahman,

Muhammad Aziz Rahman, Sajjad Ur Rahman, Rajesh Kumar Rai,

Fatemeh Rajati, Sasa Rajsic, Usha Ram, Chhabi Lal Ranabhat,

Prabhat Ranjan, David Laith Rawaf, Salman Rawaf, Sarah E Ray,

Christian Razo-García, Robert C Reiner, Cesar Reis, Giuseppe Remuzzi,

Andre M N Renzaho, Serge Resniko, Satar Rezaei, Shahab Rezaeian,

Mohammad Sadegh Rezai, Seyed Mohammad Riahi,

Maria Jesus Rios-Blancas, Kedir Teji Roba, Nicholas L S Roberts,

Leonardo Roever, Luca Ronfani, Gholamreza Roshandel, Ali Rostami,

Enrico Rubagotti, George Mugambage Ruhago, Yogesh Damodar Sabde,

Perminder S Sachdev, Basema Saddik, Sahar Saeedi Moghaddam,

Hosein Safari, Yahya Safari, Roya Safari-Faramani, Mahdi Safdarian,

Sare Saﬁ, Saeid Saﬁri, Rajesh Sagar, Amirhossein Sahebkar,

Mohammad Ali Sahraian, Haniye Sadat Sajadi, Mohamadreza Salahshoor,

Nasir Salam, Joseph S Salama, Payman Salamati,

Raphael De Freitas Saldanha, Zikria Saleem, Yahya Salimi,

Hamideh Salimzadeh, Joshua A Salomon, Sundeep Santosh Salvi,

Inbal Salz, Evanson Zondani Sambala, Abdallah M Samy, Juan Sanabria,

Maria Dolores Sanchez-Niño, Itamar S Santos,

Milena M Santric Milicevic, Bruno Piassi Sao Jose, Mayank Sardana,

Abdur Razzaque Sarker, Rodrigo Sarmiento-Suárez, Satish Saroshe,

Nizal Sarrafzadegan, Benn Sartorius, Shahabeddin Sarvi, Brijesh Sathian,

Maheswar Satpathy, Arundhati R Sawant, Monika Sawhney, Sonia Saxena,

Elke Schaener, Kathryn Schelonka, Ione J C Schneider,

David C Schwebel, Falk Schwendicke, Soraya Seedat, Mario Sekerija,

Sadaf G Sepanlou, Edson Serván-Mori, Hosein Shabaninejad,

Katya Anne Shackelford, Azadeh Shaﬁeesabet, Amira A Shaheen,

Masood Ali Shaikh, Raad A Shakir, Mehran Shams-Beyranvand,

Mohammadbagher Shamsi, Morteza Shamsizadeh, Heidar Sharaﬁ,

Kiomars Sharaﬁ, Mehdi Sharif, Mahdi Sharif-Alhoseini, Jayendra Sharma,

Rajesh Sharma, Jun She, Aziz Sheikh, Peilin Shi, Kenji Shibuya,

Mika Shigematsu, Rahman Shiri, Reza Shirkoohi, Ivy Shiue,

Farhad Shokraneh, Sharvari Rahul Shukla, Si Si, Soraya Siabani,

Abla Mehio Sibai, Tariq J Siddiqi, Inga Dora Sigfusdottir,

Rannveig Sigurvinsdottir, Naris Silpakit, Diego Augusto Santos Silva,

João Pedro Silva, Dayane Gabriele Alves Silveira,

Narayana Sarma Venkata Singam, Jasvinder A Singh, Narinder Pal Singh,

Virendra Singh, Dhirendra Narain Sinha, Karen Sliwa,

Adauto Martins Soares Filho, Badr Hasan Sobaih, Soheila Sobhani,

Moslem Sooﬁ, Joan B Soriano, Ireneous N Soyiri,

Chandrashekhar T Sreeramareddy, Vladimir I Starodubov, Caitlyn Steiner,

Leo G Stewart, Mark A Stokes, Mark Strong, Michelle L Subart,

Mu’awiyyah Babale Suﬁyan, Gerhard Sulo, Bruno F Sunguya,

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Patrick John Sur, Ipsita Sutradhar, Bryan L Sykes, P N Sylaja,

Dillon O Sylte, Cassandra E I Szoeke, Rafael Tabarés-Seisdedos,

Karen M Tabb, Santosh Kumar Tadakamadla, Nikhil Tandon,

Aberash Abay Tassew, Segen Gebremeskel Tassew, Nuno Taveira,

Nega Yimer Tawye, Arash Tehrani-Banihashemi, Tigist Gashaw Tekalign,

Merhawi Gebremedhin Tekle, Mohamad-Hani Temsah,

Abdullah Sulieman Terkawi, Manaye Yihune Teshale, Belay Tessema,

Mebrahtu Teweldemedhin, Jarnail Singh Thakur,

Kavumpurathu Raman Thankappan, Sathish Thirunavukkarasu,

Nihal Thomas, Alan J Thomson, Binyam Tilahun, Quyen G To,

Marcello Tonelli, Roman Topor-Madry, Anna E Torre,

Miguel Tortajada-Girbés, Marcos Roberto Tovani-Palone,

Hideaki Toyoshima, Bach Xuan Tran, Khanh Bao Tran,

Srikanth Prasad Tripathy, Thomas Clement Truelsen, Nu Thi Truong,

Afewerki Gebremeskel Tsadik, Amanuel Tsegay, Nikolaos Tsilimparis,

Lorainne Tudor Car, Kingsley N Ukwaja, Irfan Ullah,

Muhammad Shariq Usman, Olalekan A Uthman, Selen Begüm Uzun,

Muthiah Vaduganathan, Afsane Vaezi, Gaurang Vaidya, Pascual R Valdez,

Elena Varavikova, Santosh Varughese, Tommi Juhani Vasankari,

Ana Maria Nogales Vasconcelos, Narayanaswamy Venketasubramanian,

Santos Villafaina, Francesco S Violante,

Sergey Konstantinovitch Vladimirov, Vasily Vlassov, Stein Emil Vollset,

Theo Vos, Kia Vosoughi, Isidora S Vujcic, Fasil Shiferaw Wagnew,

Yasir Waheed, Judd L Walson, Yanping Wang, Yuan-Pang Wang,

Elisabete Weiderpass, Robert G Weintraub, Kidu Gidey Weldegwergs,

Andrea Werdecker, Ronny Westerman, Harvey Whiteford,

Justyna Widecka, Katarzyna Widecka, Tissa Wijeratne,

Andrea Sylvia Winkler, Charles Shey Wiysonge, Charles D A Wolfe,

Shouling Wu, Grant M A Wyper, Gelin Xu, Tomohide Yamada,

Yuichiro Yano, Mehdi Yaseri, Yasin Jemal Yasin, Pengpeng Ye,

Gökalp Kadri Yentür, Alex Yeshaneh, Ebrahim M Yimer, Paul Yip,

Engida Yisma, Naohiro Yonemoto, Seok-Jun Yoon, Marcel Yotebieng,

Mustafa Z Younis, Mahmoud Youseﬁfard, Chuanhua Yu, Vesna Zadnik,

Zoubida Zaidi, Sojib Bin Zaman, Mohammad Zamani, Zohreh Zare,

Mulugeta Molla Zeleke, Zerihun Menlkalew Zenebe,

Taddese Alemu Zerfu, Xueying Zhang, Xiu-Ju Zhao, Maigeng Zhou,

Jun Zhu, Stephanie R M Zimsen, Sanjay Zodpey, Leo Zoeckler,

Alan D Lopez, Stephen S Lim.

Aﬃliations

Institute for Health Metrics and Evaluation (Prof C J L Murray DPhil,

C S Callender BS, X R Kuliko BA, V Srinivasan BA, A Afshin MD,

K M Agesa BA, N Arian BA, G J Bertolacci BS, P S Briant BS,

R Burstein BA, J Chalek BS, L Cornaby BS, E A Cromwell PhD,

M Cunningham MSc, Prof L Dandona MD, Prof R Dandona PhD,

N Davis Weaver MPH, L Degenhardt PhD, S Deiparine BA,

S D Dharmaratne MD, D Dicker BS, C El Bcheraoui PhD, K Estep MPA,

T Farag PhD, K A Fay BS, Prof V L Feigin PhD, S Finegold BS,

K J Foreman PhD, J E Fuller MLIS, N Fullman MPH,

Prof E Gakidou PhD, G M Garcia BS, J Guo BS, Prof S I Hay FMedSci,

Y He MS, N J Henry BS, T Hsiao BS, C T Ikeda BS, S L James MD,

N J Kassebaum MD, G R Kemp BA, I A Khalil MD, J Y Kim BS,

K J Krohn MPH, M J Kutz BS, J B Lee BS, Prof R Lozano MD,

F Masiye PhD, M K Miller-Petrie MSc, A T Misganaw PhD,

Prof A H Mokdad PhD, K Muller MPH, Prof M Naghavi MD,

P Naik MSPH, G Nguyen MPH, M R Nixon PhD, C M Odell MPP,

A Osgood-Zimmerman MS, M Pierce, D M Pigott DPhil,

C A Purcell BA, S E Ray BA, R C Reiner PhD, N L S Roberts BS,

J S Salama MSc, K Schelonka BA, K A Shackelford BA, N Silpakit BS,

C Steiner MPH, L G Stewart BS, M L Subart BA, P J Sur MPH,

D O Sylte BA, A E Torre BS, Prof S E Vollset DrPH, Prof T Vos PhD,

H A Whiteford PhD, S R M Zimsen MA, L Zoeckler BA,

Prof A D Lopez PhD, Prof S S Lim PhD), Department of Health Metrics

Sciences (Prof C J L Murray DPhil, A Afshin MD,

E A Cromwell PhD, C El Bcheraoui PhD, Prof E Gakidou PhD,

Prof S I Hay FMedSci, I A Khalil MD, Prof R Lozano MD,

A T Misganaw PhD, Prof A H Mokdad PhD, Prof M Naghavi MD,

D M Pigott DPhil, R C Reiner PhD, Prof S E Vollset DrPH,

Prof T Vos PhD, Prof S S Lim PhD), Department of Neurology

(R Kalani MD), Department of Global Health (S Kochhar MD,

Prof J L Walson MD), Department of Surgery (S D Morrison MD),

Division of Plastic Surgery (C S Crowe MD, B B Massenburg MD),

School of Social Work (T B Murphy PhD), University of Washington,

Seattle, WA, USA; College of Health and Medical Sciences

(Z T Kidanemariam MSc), Department of Epidemiology and Biostatistics

(M Mengesha MPH), Department of Medical Laboratory Science

(D Marami MSc), Department of Pediatrics (A R Abrham MSc), School

of Nursing and Midwifery (T T Dasa MSc, K T Roba PhD), School of

Pharmacy (J Abdela MSc, Y Ayele MSc, G Mengistu MSc,

M M Zeleke MSc), School of Public Health (A G Bali MPH,

A Irenso MPH, F A Kumsa MPH, M G Tekle MPH), Haramaya

University, Harar, Ethiopia (D Abate MSc, T F Gelano MSc,

T Hailegiyorgis MSc, M T Jalu MPH, T G Tekalign MS); Department of

Environmental Health Sciences and Technology (S Mereta PhD),

Department of Epidemiology (M B Ahmed MPH,

T T Gebrehiwot MPH), Department of Health Education & Behavioral

Sciences (G T Feyissa MPH), Department of Population and Family

Health (K H Abate PhD, A T Gebremedhin MPH), Mycobacteriology

Research Center (E Duken MSc), Jimma University, Jimma, Ethiopia;

Department of Pharmacology and Clinical Pharmacy (S M Abay PhD),

School of Allied Health Sciences (E Yisma MPH), School of Nursing and

Midwifery (H A Areri MSc), School of Public Health (A Berhane PhD,

K Deribe PhD, W A Manamo MS, Y J Yasin MPH), Addis Ababa

University, Addis Ababa, Ethiopia (G T Demoz MSc, S Leta MSc); Brain

and Spinal Cord Injury Research Center (Z Khazaeipour MD), Cancer

Biology Research Center (R Shirkoohi PhD), Cancer Research Center

(A Nahvijou PhD, R Shirkoohi PhD), Community-Based Participatory

Research Center (Prof R Majdzadeh PhD), Department of Anatomy

(S Sobhani MD), Department of Epidemiology and Biostatistics

(Prof S Hosseini PhD, M Mansournia PhD, M Yaseri PhD), Department

of Health (H Abbastabar PhD), Department of Health Management and

Economics (S Mousavi PhD), Department of Pharmacology

(A Haj-Mirzaian MD, A Haj-Mirzaian MD), Digestive Diseases Research

Institute (Prof R Malekzadeh MD, Prof A Pourshams MD,

H Poustchi PhD, G Roshandel PhD, H Salimzadeh PhD,

S G Sepanlou MD), Endocrine Research Center (S Esteghamati MD),

Endocrinology and Metabolism Research Center (M Afarideh MD,

Prof A Esteghamati MD, M Ganji MD), Hematologic Malignancies

Research Center (A Kasaeian PhD), Hematology-Oncology and Stem

Cell Transplantation Research Center (A Kasaeian PhD), Iran National

Institute of Health Research (H S Sajadi PhD), Iranian National Center

for Addiction Studies (Prof A Rahimi-Movaghar MD), Knowledge

Utilization Research Center (Prof R Majdzadeh PhD), Multiple Sclerosis

Research Center (S Eskandarieh PhD, Prof M Sahraian MD), Non-

communicable Diseases Research Center (N Abbasi MD,

F Farzadfar MD, S N Irvani MD, S Saeedi Moghaddam MSc,

M Shams-Beyranvand MSc), School of Medicine (N Hafezi-Nejad MD),

Sina Trauma and Surgery Research Center

(Prof V Rahimi-Movaghar MD, M Safdarian MD, Prof P Salamati MD,

M Sharif-Alhoseini PhD), Uro-Oncology Research Center

(M Nowroozi MD), Tehran University of Medical Sciences, Tehran, Iran;

Montreal Neuroimaging Center (N Abbasi MD), Montreal Neurological

Institute (S Fereshtehnejad PhD), McGill University, Montreal, QC,

Canada; Department of Medical Parasitology (M M Khater MD),

Department of Neurology (Prof A Abdelalim MD, M I Hegazy PhD),

Cairo University, Cairo, Egypt; Department of Oncology

(O Abdel-Rahman MD), Department of Medicine (Prof M Tonelli MD),

University of Calgary, Calgary, AB, Canada; Department of Entomology

(A M Samy PhD), Department of Oncology (O Abdel-Rahman MD),

Ain Shams University, Cairo, Egypt; Department of Anesthesiology

(A Ahmadi PhD), Department of Environmental Health Engineering

(Prof A Almasi PhD), Department of Epidemiology & Biostatistics

(Prof F Najaﬁ PhD, Y Salimi PhD), Department of Health Education &

Promotion (F Rajati PhD), Department of Psychiatry

(Prof H Khazaie MD), Department of Traditional and Complementary

Medicine (M Farzaei PhD), Department of Urology (Prof M Moradi MD),

Environmental Determinants of Health Research Center (S Rezaei PhD,

M Sooﬁ PhD), Faculty of Nursing and Midwifery (A Abdi PhD), Faculty

of Nutrition and Food Sciences (F Heydarpour PhD), Faculty of Public

Health (B Karami Matin PhD, A Kazemi Karyani PhD,

R Safari-Faramani PhD), Imam Ali Cardiovascular Research Center

(S Siabani PhD), Pharmaceutical Sciences Research Center

(M Farzaei PhD), Research Center for Environmental Determinants of

Global Health Metrics

www.thelancet.com Vol 392 November 10, 2018

2041

Health (M Moradinazar PhD), Sleep Disorders Research Center

(M Ghadami MD), Sports Medicine & Rehabilitation (M Shamsi PhD),

Kermanshah University of Medical Sciences, Kermanshah, Iran

(N Abdoli PsyD, K Ghadiri BEP, A Khatony PhD, Prof M Pirsaheb PhD,

S Rezaeian PhD, Y Safari PhD, K Sharaﬁ PhD); Department of

Epidemiology (I Abdollahpour PhD), Arak University of Medical

Sciences, Arak, Iran; Multiple Sclerosis Research Center, Tehran, Iran

(I Abdollahpour PhD); Department of Statistics (R S Abdulkader MD),

Manonmaniam Sundaranar University, Tirunelveli, India; Anatomy Unit

(T B Hagos MSc), Biomedical Sciences Division (G B Hailu MSc),

Clinical Pharmacy Unit (H N Abraha MSc, T D Kassa MSc,

Y L Nirayo MS, K G Weldegwergs MSc), College of Health Sciences

(H T Abebe PhD, A Tsegay MSc), Department of Biostatistics

(K Gezae MSc), Department of Epidemiology (A G Belay MPH),

Department of Microbiology and Immunology (S Muthupandian PhD),

Department of Midwifery (Z M Zenebe MSc), Department of Nutrition

and Dietetics (M G Degefa BSc, A Kahsay MPH), Institute of Biomedical

Science (E T Asfaw MSc), School of Medicine (D T Mengistu MSc),

School of Pharmacy (S W Asgedom MSc, T M Atey MS, A K Gebre MSc,

A G Tsadik MSc, E M Yimer MSc), School of Public Health

(B M Adhena MPH, A B Belachew MSc, G B Gebregergs MPH), Mekelle

University, Mekelle, Ethiopia (E Belay MSc, B D Demtsu MSc,

H G Meles MPH, K G Meles MPH, S G Tassew MSc); Department of

Clinical Chemistry (M Abebe MSc, B Biadgo MSc), Department of

Medical Microbiology (B Tessema PhD), Human Nutrition Department

(Z Abebe MSc), Institute of Public Health (K A Alene MPH,

B Bekele MPH, B A Dachew MPH, Y A Gelaw MPH, M A Limenih MSc,

M Melku MSc, A A Muche MPH, K Muchie MSc, A A Tassew MPH,

B Tilahun PhD), University of Gondar, Gondar, Ethiopia

(H W Baynes MSc); College of Medicine and Health Sciences

(T A Abebo MPH, A K Daba MSc), Department of Reproductive Health

(D T Hibstu MPH), School of Nursing and Midwifery (Z Y Kassa MSc),

Hawassa University, Hawassa, Ethiopia; College of Health Sciences

(G M Kassa MSc), Department of Nursing (A Alebel MSc,

G A Dessie MSc, D H Kassa MSc, F S Wagnew MSc), Department of

Public Health (Y A Belay MPH, G D Kibret MPH, C T Leshargie MPH),

Debre Markos University, Debre Markos, Ethiopia (A N Abejie MPH);

Department of Cardiology (Prof V Aboyans MD), Dupuytren University

Hospital, Limoges, France; Institute of Epidemiology

(Prof V Aboyans MD), University of Limoges, Limoges, France;

Department of Surgery (Prof R S Gomez PhD), Education Center in

Public Health (D M Abreu DSc), Hospital of the Federal University of

Minas Gerais (B R Nascimento PhD), Nutrition Department

(Prof R M Claro PhD), Post-Graduate Program in Infectious Diseases

and Tropical Medicine (B P Sao Jose PhD), Federal University of Minas

Gerais, Belo Horizonte, Brazil; Department of Healthcare Policy and

Research (Prof L J Abu-Raddad PhD), Weill Cornell Medical College in

Qatar, Doha, Qatar; Institute of Community and Public Health

(N M Abu-Rmeileh PhD), Birzeit University, Birzeit, Palestine; Bénin

Clinical Research Institute (IRCB), Cotonou, Benin

(M M K Accrombessi PhD, E F A Avokpaho MD); Nepal Development

Society, Pokhara, Nepal (P Acharya MPH); Department of Global Health

(A A Adamu MSc, O O Adetokunboh MD, Prof C S Wiysonge MD),

Department of Psychiatry (Prof S Seedat PhD), Faculty of Medicine &

Health Sciences (Prof P S Nyasulu PhD), Stellenbosch University, Cape

Town, South Africa; Cochrane South Africa (A A Adamu MSc,

O O Adetokunboh MD), South African Medical Research Council, Cape

Town, South Africa; Department of Medicine (O M Adebayo MD,

O S Ogah PhD), University College Hospital, Ibadan, Nigeria;

Department of Sociology (I A Adedeji PhD), Olabisi Onabanjo

University, Ago-Iwoye, Nigeria; School of Medicine (V Adekanmbi PhD),

Cardi University, Cardi, UK; Nepal Health Research Environment

(T B Adhikari MPH), Center for Social Science and Public Health

Research Nepal, Lalitpur, Nepal; Unit for Health Promotion Research

(T B Adhikari MPH), University of Southern Denmark, Esbjerg,

Denmark; Emergency Department (M G Adib MD), Saint Mark

Hospital, Alexandria, Egypt; Ivorian Association for Family Welfare,

Abidjan, Côte d’Ivoire (A K Adou MD); Sport Science Department

(J C Adsuar PhD, S Villafaina MSc), University of Extremadura, Cáceres,

Spain (D Collado-Mateo MSc); Department of Family Medicine

(G Agarwal MD), Department of Pathology and Molecular Medicine

(T O Olagunju MD), McMaster University, Hamilton, ON, Canada;

Department of Zoology (S A Aghayan PhD), Yerevan State University,

Yerevan, Armenia; Research Group of Molecular Parasitology

(S A Aghayan PhD), Scientiﬁc Center of Zoology and Hydroecology,

Yerevan, Armenia; Indian Institute of Public Health

(Prof S Zodpey PhD), Indian Institute of Public Health – Hyderabad

(Prof G Murthy MD), Public Health Foundation of India, Gurugram,

India (S Agrawal PhD, A Awasthi PhD, Prof L Dandona MD,

Prof R Dandona PhD, G Kumar PhD, D K Lal MD, M R Mathur PhD);

Vital Strategies, Gurugram, India (S Agrawal PhD); Department of

Neurosurgery (H Safari MD), Department of Public Health

(M A Khafaie PhD), Environmental Technologies Research Center

(M Ahmadi PhD), Thalassemia and Hemoglobinopathy Research Center

(F Rahim PhD), Ahvaz Jundishapur University of Medical Sciences,

Ahvaz, Iran; Health Economics and Financing Research Group

(A R Sarker MHE), Health Systems and Population Studies Division

(S Ahmed MSc), Initiative for Non Communicable Diseases

(A Naheed PhD), Maternal and Child Health Division (S Zaman MPH),

International Centre for Diarrhoeal Disease Research, Bangladesh,

Dhaka, Bangladesh; Department of Learning, Informatics, Management,

and Ethics (S Ahmed MSc), Department of Medical Epidemiology and

Biostatistics (J J Carrero PhD, Prof E Weiderpass PhD), Department of

Neurobiology (Prof J Ärnlöv PhD), Department of Neurobiology, Care

Sciences and Society (S Fereshtehnejad PhD), Department of Public

Health Sciences (Prof P Allebeck MD, Z El-Khatib PhD), Karolinska

Institute, Stockholm, Sweden; University Ferhat Abbas of Setif, Setif,

Algeria (A Aichour BMedSc, I Aichour BPharm); Higher National

School of Veterinary Medicine, Algiers, Algeria (M Aichour MA);

Department of Civil and Environmental Engineering (A S Akanda PhD),

University of Rhode Island, Kingston, RI, USA; Cancer Research Center

(Prof M Akbari MD), Department of Biostatistics (A Khajavi MSc),

Department of Epidemiology (S Riahi PhD), Ophthalmic Epidemiology

Research Center (S Saﬁ PhD), Ophthalmic Research Center (S Saﬁ PhD,

M Yaseri PhD), Research Institute for Endocrine Sciences

(A Haj-Mirzaian MD, S N Irvani MD), Safety Promotion and Injury

Prevention Research Center (N Jahanmehr PhD), School of Public

Health (N Jahanmehr PhD), Shahid Beheshti University of Medical

Sciences, Tehran, Iran; Department of Midwifery (M Akibu MSc),

Department of Public Health (M G Ansha MPH, T Kolola MPH), Debre

Berhan University, Debre Berhan, Ethiopia; Institute for Advanced

Medical Research and Training (R O Akinyemi PhD,

Prof M O Owolabi DrM), University of Ibadan, Ibadan, Nigeria;

Department of Epidemiology (T Akinyemiju PhD), University of

Kentucky, Lexington, KY, USA; Department of Nutritional Sciences

(A Badawi PhD), The Hospital for Sick Children (N Akseer PhD,

Prof Z A Bhutta PhD), University of Toronto, Toronto, ON, Canada;

Evidence Based Practice Center (F Alahdab MD), Mayo Clinic

Foundation for Medical Education and Research, Rochester, MN, USA;

Research Committee (F Alahdab MD), Syrian American Medical Society,

Washington, DC, USA; Internal Medicine Department (Z Al-Aly MD),

Washington University in St Louis, St Louis, MO, USA; Clinical

Epidemiology Center, VA St Louis Health Care System (Z Al-Aly MD),

Department of Internal Medicine (S K Jassal MD), Department of

Veterans Aairs, St Louis, MO, USA; School of Medicine

(Prof G J Hankey MD), School of Population and Global Health

(K Alam PhD), University of Western Australia, Perth, WA, Australia;

Department of Preventive Medicine (A V Aleman MD), University of the

Republic, Montevideo, Uruguay; National Centre for Epidemiology and

Population Health (M Bin Sayeed MSPS), Research School of Population

Health (K A Alene MPH), Australian National University, Canberra,

ACT, Australia; Department of Pediatrics (B H Sobaih MD,

M Temsah MD), Pediatric Intensive Care Unit (A Al-Eyadhy MD),

King Saud University, Riyadh, Saudi Arabia (K Altirkawi MD); Public

Health Research Center (R Ali MPH), New York University Abu Dhabi,

Abu Dhabi, United Arab Emirates; Big Data Institute

(Prof P W Gething PhD), Department of Psychiatry

(Prof C R J Newton MD), Nueld Department of Population Health

(R Ali MPH, D A Bennett PhD), University of Oxford, Oxford, UK

(Prof V Jha MD); Qazvin University of Medical Sciences, Qazvin, Iran

(M Alijanzadeh PhD); Department of Immunology (Prof A Raﬁei PhD),

Department of Medical Mycology (H Badali PhD), Department of

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Medical Mycology and Parasitology (A Vaezi PhD), Department of

Pediatrics (M Rezai MD), Department of Physiology and Pharmacology

(M Mohammadi PhD), Gastrointestinal Cancer Research Center

(R Alizadeh-Navaei PhD), Molecular and Cell Biology Research Center

(Prof A Raﬁei PhD), School of Public Health (Prof A Enayati PhD),

Toxoplasmosis Research Center (Prof A Daryani PhD, S Sarvi PhD),

Mazandaran University of Medical Sciences, Sari, Iran

(A Hedayatizadeh-Omran PhD, M Nourollahpour Shiadeh PhD,

Z Zare PhD); Department of Health Policy and Management

(Prof S M Aljunid PhD), Kuwait University, Safat, Kuwait; International

Centre for Casemix and Clinical Coding (Prof S M Aljunid PhD),

National University of Malaysia, Bandar Tun Razak, Malaysia;

Department of Population Health (A Alkerwi PhD), Luxembourg

Institute of Health, Strassen, Luxembourg; University of Bordeaux,

Bordeaux, France (Prof F Alla PhD); Swedish Research Council for

Health, Working Life, and Welfare, Stockholm, Sweden

(Prof P Allebeck MD); Research Program in Epidemiology & Public

Health (Prof J Alonso MD), Hospital del Mar Medical Research Institute,

Barcelona, Spain; Department of Experimental and Health Sciences

(Prof J Alonso MD), Pompeu Fabra University, Barcelona, Spain;

Department of Family and Community Medicine

(Prof R M Al-Raddadi PhD), King Abdulaziz University, Jeddah, Saudi

Arabia; Department of Operative and Preventive Dentistry

(Prof F Schwendicke MPH), Institute of Public Health

(Prof R Busse PhD, Prof E Schaener MD), Charité University Medical

Center Berlin, Berlin, Germany (U Alsharif MD); Research Group on

Health Economics (Prof N Alvis-Guzman PhD), University of Cartagena,

Cartagena, Colombia; Research Group in Hospital Management and

Health Policies (Prof N Alvis-Guzman PhD), University of the Coast,

Barranquilla, Colombia; Sansom Institute (A Amare PhD),

Wardliparingga Aboriginal Research Unit (P S Azzopardi PhD),

South Australian Health and Medical Research Institute, Adelaide, SA,

Australia; Department of Public Health Nutrition (N Fentahun PhD),

Bahir Dar University, Bahir Dar, Ethiopia (A Amare PhD); Federal

Ministry of Health, Beirut, Lebanon (Prof W Ammar PhD); Department

of Epidemiology and Population Health (Prof A M Sibai PhD), Faculty of

Health Sciences (Prof W Ammar PhD), American University of Beirut,

Beirut, Lebanon; Faculty of Medicine (N H Anber PhD), Mansoura

University, Mansoura, Egypt (N H Anber PhD); Anatomy and

Embryology Department (R I Negoi PhD), Department of General

Surgery (D V Davitoiu PhD, M Hostiuc PhD), Department of Legal

Medicine and Bioethics (S Hostiuc PhD), Emergency Hospital of

Bucharest (Prof M Beuran PhD, I Negoi PhD), 2nd Department of

Dermatology (M Constantin MD), Carol Davila University of Medicine

and Pharmacy, Bucharest, Romania (C Andrei PhD); Department of

Medicine (S Androudi PhD), University of Thessaly, Volos, Greece;

Department of Public Health (M Y Teshale MPH), Arba Minch

University, Arba Minch, Ethiopia (M D Animut MPH); Zahedan

University of Medical Sciences, Zahedan, Iran (H Ansari PhD);

Department of Health Policy and Administration (C T Antonio MD),

Development and Communication Studies (E K Macarayan PhD),

University of the Philippines Manila, Manila, Philippines; Department

of Applied Social Sciences (C T Antonio MD), School of Nursing

(P H Lee PhD), Hong Kong Polytechnic University, Hong Kong, China;

Department of Sociology and Social Work (S Appiah MD), Kwame

Nkrumah University of Science and Technology, Kumasi, Ghana; Center

for International Health (S Appiah MD, D Paudel PhD), Ludwig

Maximilians University, Munich, Germany; School of Health Sciences

(O Aremu PhD), Birmingham City University, Birmingham, UK; School

of Health and Social Studies (Prof J Ärnlöv PhD), Dalarna University,

Falun, Sweden; Department of Community Health Sciences

(A Artaman PhD), University of Manitoba, Winnipeg, MB, Canada;

Monitoring Evaluation and Operational Research Project

(K K Aryal PhD), Abt Associates Nepal, Lalitpur, Nepal; Qom University

of Medical Sciences, Qom, Iran (H Asayesh MSc); Department of

Medical Biotechnology (A Sahebkar PhD), Education Development

Center (R Assadi PhD), Mashhad University of Medical Sciences,

Mashhad, Iran; University Institute of Public Health (S Atique PhD),

The University of Lahore, Lahore, Pakistan; Public Health Department

(S Atique PhD), University of Hail, Hail, Saudi Arabia; Department of

Social Welfare (M S Atteraya PhD), Keimyung University, Daegu, South

Korea; School of Business (Prof M Ausloos PhD), University of Leicester,

Leicester, UK; Contrôle des Maladies Infectieuses (E F A Avokpaho MD),

Non Communicable Disease Department (F G Gankpe MD), Laboratory

of Studies and Research-Action in Health, Porto Novo, Benin; Indian

Institute of Public Health, Gandhinagar, India (A Awasthi PhD); Austin

Clinical School of Nursing (M Rahman PhD), Department of Psychology

and Counselling (Prof T Wijeratne MD), School of Nursing and

Midwifery (Prof D Edvardsson PhD), The Judith Lumley Centre

(B Ayala Quintanilla PhD), La Trobe University, Melbourne, VIC,

Australia; General Oce for Research and Technological Transfer

(B Ayala Quintanilla PhD), Peruvian National Institute of Health, Lima,

Peru; Department of Community and Global Health (R Ayer MHSc),

Department of Diabetes and Metabolic Diseases (T Yamada MD),

Department of Global Health Policy (S Nomura MSc, M Rahman MHS,

Prof K Shibuya MD), Department of Mental Health

(Prof N Kawakami PhD), University of Tokyo, Tokyo, Japan; Centre for

Food and Nutrition Research (T B Ayuk PhD), Institute of Medical

Research and Medicinal Plant Studies, Yaounde, Cameroon; Department

of Health studies (T B Ayuk PhD), University of South Africa, Pretoria,

South Africa; Global Adolescent Health Group (P S Azzopardi PhD),

Burnet Institute, Melbourne, VIC, Australia; Department of Public

Health Medicine (T K Babalola MSc, T P Mashamba-Thompson PhD,

Prof B P Ncama PhD, Prof B Sartorius PhD), University of KwaZulu-

Natal, Durban, South Africa; Department of Community Health and

Primary Care (T K Babalola MSc), Department of Psychiatry

(A T Olagunju MD), University of Lagos, Lagos, Nigeria; Center for

Infectious Diseases Research, Babol, Iran (A Babazadeh MD,

S Ebrahimpour PhD); Health Promotion and Chronic Disease

Prevention Branch (J J Lang PhD), Public Health Risk Sciences Division

(A Badawi PhD), Public Health Agency of Canada, Toronto, ON, Canada;

Department of Hypertension (Prof M Banach PhD), Medical University

of Lodz, Lodz, Poland; Polish Mothers’ Memorial Hospital Research

Institute, Lodz, Poland (Prof M Banach PhD); Molecular Medicine and

Pathology (K B Tran MD), School of Psychology

(Prof S L Barker-Collo PhD), University of Auckland, Auckland,

New Zealand; Augenpraxis Jonas (S Panda-Jonas MD), Department of

Ophthalmology (Prof J B Jonas MD), Institute of Public Health

(Prof T W Bärnighausen MD, Prof J De Neve MD, B Moazen MSc,

S Mohammed PhD), Medical Clinic V (Prof W März MD), Heidelberg

University, Heidelberg, Germany; Ariadne Labs (E K Macarayan PhD),

Department of Global Health and Population

(Prof T W Bärnighausen MD, A B Feigl PhD), Department of Nutrition

(E L Ding DSc, M S Farvid PhD), Division of General Internal Medicine

and Primary Care (Prof A Sheikh MD), Fenot Project (G D Dinsa PhD),

Heart and Vascular Center (M Vaduganathan MD), T H Chan School of

Public Health (G D Dinsa PhD, P C Gupta DSc), Harvard University,

Boston, MA, USA; Department of Industrial Engineering

(Prof L H Barrero DSc), Pontiﬁcal Javeriana University, Bogota,

Colombia; University of Aden, Aden, Yemen (H Basaleem PhD);

Barcelona Institute for Global Health, Barcelona, Spain

(Prof Q Bassat MD, Prof J V Lazarus PhD); Manhiça Health Research

Center, Manhiça, Mozambique (Prof Q Bassat MD); School of Health

Sciences (A Basu PhD), University of Canterbury, Christchurch, New

Zealand; Melbourne Medical School, Melbourne, VIC, Australia

(Prof B T Baune PhD); Department of Environmental Health Science

(S Gallus DSc), Department of Neuroscience (E Beghi MD,

G Giussani PhD), Department of Oncology (C Bosetti PhD,

M Cortinovis PhD), Department of Renal Medicine (B Bikbov MD,

N Perico MD), Mario Negri Institute for Pharmacological Research,

Milan, Italy (Prof G Remuzzi MD); Air Pollution Research Center

(B Heibati PhD), Department of Community Medicine

(A Tehrani-Banihashemi PhD), Department of Health Policy

(H Shabaninejad PhD), Department of Neuroscience (M Safdarian MD),

Department of Ophthalmology (K Ghasemi Falavarjani MD), Health

Management and Economics Research Center (M Behzadifar PhD),

Pharmacology Department (S Mehrzadi PhD), Physiology Research

Center (M Youseﬁfard PhD), Preventive Medicine and Public Health

Research Center (M Moradi-Lakeh MD, A Tehrani-Banihashemi PhD,

K Vosoughi MD), Iran University of Medical Sciences, Tehran, Iran

(M Hosseinzadeh PhD); Social Determinants of Health Research Center

(M Behzadifar PhD), Lorestan University of Medical Sciences,

Global Health Metrics

www.thelancet.com Vol 392 November 10, 2018

2043

Khorramabad, Iran (M Behzadifar MS); Public Health Department

(B Bekele MPH, H Y Hassen MPH), Mizan-Tepi University, Teppi,

Ethiopia (A Henok MPH); Dr Tewelde Legesse Health Sciences College,

Mekelle, Ethiopia (S A Belay MPH); Department of Ophthalmology and

Visual Science (Prof J J Huang MD), School of Forestry and

Environmental Studies (Prof M L Bell PhD), Yale University, New

Haven, CT, USA; Department of Medicine (A K Bello PhD), University

of Alberta, Edmonton, AB, Canada; Center for Clinical and

Epidemiological Research (A C Goulart PhD), Department of Internal

Medicine (I M Bensenor PhD, Prof I S Santos PhD), Department of

Medicine (Prof P A Lotufo DrPH), Department of Pathology and Legal

Medicine (M R Tovani-Palone MSc), Department of Psychiatry

(G V Polanczyk MD, Y Wang PhD), University Hospital, Internal

Medicine Department (A C Goulart PhD), University of São Paulo, São

Paulo, Brazil; Sackler Institute for Nutrition Science (G Bergeron PhD),

New York Academy of Sciences, New York, NY, USA; Division of

Cardiology (Prof A E Berman MD), Medical College of Georgia at

Augusta University, Augusta, GA, USA; Department of Health Policy

(Prof A E Berman MD), Personal Social Services Research Unit

(R Kadel MPH), London School of Economics and Political Science,

London, UK; Dental Institute (E Bernabe PhD), Division of Patient and

Population (Prof W Marcenes PhD), Faculty of Life Sciences and

Medicine (Prof P I Dargan MB, M Molokhia PhD), School of Population

Health & Environmental Sciences (Prof C D A Wolfe MD), King’s

College London, London, UK; Hubert Department of Global Health

(R S Bernstein MD), Emory University, Atlanta, GA, USA; Department

of Global Health (R S Bernstein MD), University of South Florida,

Tampa, FL, USA; Department of Disease Control (J Cano PhD),

Department of Infectious Disease Epidemiology (O J Brady PhD),

Department of Non-communicable Disease Epidemiology

(Prof S Kinra PhD), London School of Hygiene & Tropical Medicine,

London, UK (S Bhattarai MD); Nepal Academy of Science & Technology,

Patan, Nepal (S Bhattarai MD); The George Institute for Global Health,

New Delhi, India (S Bhaumik MBBS, Prof V Jha MD, P K Maulik PhD);

Center of Excellence in Women and Child Health

(Prof Z A Bhutta PhD), Department of Pediatrics & Child Health

(M Nisar MSc), Aga Khan University, Karachi, Pakistan; Cellular and

Molecular Biology Research Center (H Nouri PhD), Department of

Clinical Biochemistry (A Mosapour PhD, N Neamati MSc,

H Parsian PhD), Department of Pharmacology

(M Mohammadi-Khanaposhtani PhD), Fatemeh Zahra Infertility and

Reproductive Health Center (P Mirabi PhD), Health Research Institute

(R Ghadimi PhD, M Ghasemi-Kasman PhD), Infectious Diseases and

Tropical Medicine Research Center (A Rostami PhD), Social

Determinants of Health Research Center (A Bijani PhD), Student

Research Committee (M Zamani MD), Babol University of Medical

Sciences, Babol, Iran (M Faramarzi PhD); Woldia University, Woldia,

Ethiopia (N Bililign BHlthSci); Department of Clinical Pharmacy and

Pharmacology (M Bin Sayeed MSPS), University of Dhaka, Ramna,

Bangladesh; Department of Medical and Surgical Sciences

(Prof F S Violante MPH), University of Bologna, Bologna, Italy

(S M Birlik MBA); Liaison of Turkey (S M Birlik MBA), Guillain-Barré

Syndrome/Chronic Inﬂammatory Demyelinating Polyneuropathy

Foundation International, Conshohocken, PA, USA; Department of

Epidemiology and Public Health (Prof M Kivimäki PhD,

M R Mathur PhD), Department of Psychology (M Kumar PhD), The

UCL Centre for Global Health Economics (C Birungi MSc), University

College London, London, UK; Fast-Track Implementation Department

(C Birungi MSc), United Nations Programme on HIV/AIDS (UNAIDS),

Gaborone, Botswana; Department of Health Sciences (I Filip MD),

A T Still University, Brisbane, QLD, Australia (T Biswas MPH,

A Radfar MD); Department of Public Health (H Bizuneh MPH),

St Paul’s Hospital Millennium Medical College, Addis Ababa, Ethiopia;

Department of Surgery (S Krishnaswami MD), Radiation Medicine

(A Bleyer MD), Oregon Health and Science University, Portland, OR,

USA; Department of Pediatrics (A Bleyer MD), University of Texas,

Houston, TX, USA (X Zhang PhD); General Directorate of Health

Information Systems (B Bora Basara PhD), Department of Health

Statistics (G K Yentür MSc), Ministry of Health, Ankara, Turkey

(M Car PhD, A Chitheer MD); National Drug and Alcohol Research

Centre (Prof L Degenhardt PhD), School of Medicine (P K Maulik PhD),

School of Psychiatry (Prof P S Sachdev MD), Transport and Road Safety

Research (S Boufous PhD), University of New South Wales, Sydney,

NSW, Australia (Prof K A Dolan PhD); University of Genoa, Genoa, Italy

(N L Bragazzi PhD); Department for Clinical Neurosciences and

Preventive Medicine (Prof M Brainin MD), Danube University Krems,

Krems, Austria; Institute of Epidemiology (A Brazinova MD), Comenius

University, Bratislava, Slovakia; College of Public Health

(M Yotebieng PhD), Department of Psychology

(Prof N J K Breitborde PhD), Psychiatry and Behavioral Health

Department (Prof N J K Breitborde PhD), The Ohio State University,

Columbus, OH, USA; Division of Clinical Epidemiology and Aging

Research (Prof H Brenner MD), German Cancer Research Center,

Heidelberg, Germany; Department of Cardiovascular Medicine

(L T Cooper MD), Department of Dermatology (J D Brewer MD), Mayo

Clinic, Rochester, MN, USA; Tuberculosis Biomarker Research Unit

(A Goodridge PhD), Institute for Scientiﬁc Research and High

Technology Services, City of Knowledge, Panama (G Britton PhD);

Department of Research and Health Technology Assessment

(F Castro MD), Gorgas Memorial Institute for Health Studies, Panama,

Panama (G Britton PhD); School of Population and Public Health

(Z A Butt PhD, F Pourmalek PhD, Prof N Sarrafzadegan MD),

University of British Columbia, Vancouver, BC, Canada (J A Kopec PhD);

Al Shifa School of Public Health (Z A Butt PhD), Al Shifa Trust Eye

Hospital, Rawalpindi, Pakistan; Center for Health Systems Research

(L Cahuana-Hurtado PhD, H Gómez-Dantés MSc, M Rios-Blancas MPH,

Prof E Serván-Mori DSc), Center for Nutrition and Health Research

(E Denova-Gutiérrez DSc), Center for Population Health Research

(C Razo-García MSc), National Institute of Public Health, Cuernavaca,

Mexico (I R Campos-Nonato PhD, J Campuzano Rincon PhD); School of

Medicine (J Campuzano Rincon PhD), University of the Valley of

Cuernavaca, Cuernavaca, Mexico; Department of Primary Care and

Public Health (M Car PhD, Prof A Majeed MD, Prof S Rawaf PhD),

Division of Brain Sciences (Prof R A Shakir MD), School of Public

Health (Prof S Saxena MD), WHO Collaborating Centre for Public

Health Education and Training (D L Rawaf MD), Imperial College

London, London, UK; Department of Population and Health

(Prof R Cárdenas DSc), Metropolitan Autonomous University, Mexico

City, Mexico; Applied Molecular Biosciences Unit (Prof F Carvalho PhD),

Institute for Research and Innovation in Health (i3S) (J das Neves PhD),

Institute of Biomedical Engineering (J das Neves PhD), Institute of

Public Health (Prof F Carvalho PhD), REQUIMTE/LAQV

(Prof E Fernandes PhD, Prof D M Pereira PhD), UCIBIO

(J P Silva PhD), University of Porto, Porto, Portugal; Colombian National

Health Observatory (C A Castañeda-Orjuela MD), National Institute of

Health, Bogota, Colombia; Epidemiology and Public Health Evaluation

Group (C A Castañeda-Orjuela MD), National University of Colombia,

Bogota, Colombia; Area de Estadística, Dirección Actuarial

(Prof J Castillo Rivas MSc), Costa Rican Department of Social Security,

San Jose, Costa Rica; School of Dentistry (Prof J Castillo Rivas MSc),

University of Costa Rica, San Pedro, Costa Rica; Department of Health

Planning and Economics (F Catalá-López PhD), Institute of Health

Carlos III, Madrid, Spain; Department of Public Health

(B Kucuk Bicer BEP), Institute of Population Studies (A Çavlin PhD),

Hacettepe University, Ankara, Turkey; Mary MacKillop Institute for

Health Research (Prof E Cerin PhD), The Brain Institute

(Prof C E I Szoeke PhD), Australian Catholic University, Melbourne,

VIC, Australia; Centre for Suicide Research and Prevention

(Prof P Yip PhD), School of Public Health (Prof E Cerin PhD),

University of Hong Kong, Hong Kong, China (Prof P Yip PhD); Health

Systems Research Center (Prof J C Montañez MSc), Institute of

Population Health Sciences (Prof H Chang DrPH), National Health

Research Institutes, Zhunan Township, Taiwan; College of Medicine

(J Chang PhD), National Taiwan University, Taipei, Taiwan; Department

of Development Studies (A Chattopadhyay PhD, M A Malik MPhil),

Department of Fertility Studies (A Kastor MPhil, B K Panda MA),

Department of Population Studies (A Patle MPH), Department of Public

Health & Mortality Studies (M H Rahman MPhil, Prof U Ram PhD),

International Institute for Population Sciences, Mumbai, India

(S Goli PhD, P Kumar PhD); Surgical Oncology (Prof P Chaturvedi MD),

Tata Memorial Hospital, Mumbai, India; Clinical Governance

(P P Chiang PhD), Gold Coast Health, Gold Coast, QLD, Australia;

Global Health Metrics

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Centre of Cardiovascular Research and Education in Therapeutics

(R Ofori-Asenso MSc), Department of Epidemiology and Preventive

Medicine (K L Chin PhD), School of Public Health and Preventive

Medicine (Prof F M Cicuttini PhD, Prof Y Guo PhD, S Li PhD,

S Si PhD), Monash University, Melbourne, VIC, Australia; Department

of Economics (F Masiye PhD), Department of Population Studies

(V H Chisumpa PhD, C Mapoma PhD), University of Zambia, Lusaka,

Zambia; Demography and Population Studies (V H Chisumpa PhD),

University of the Witwatersrand, Johannesburg, South Africa;

Biochemistry, Biomedical Science (J J Choi PhD), Seoul National

University Hospital, Seoul, South Korea; Department of Public Health

and Primary Care (R Chowdhury PhD), University of Cambridge,

Cambridge, UK; Department of Endocrinology (Prof N Thomas PhD),

Department of Pulmonary Medicine (Prof D J Christopher MD),

Christian Medical College and Hospital (CMC), Vellore, India

(Prof S Varughese MD); Adelaide Medical School (L G Ciobanu PhD,

T K Gill PhD), Robinson Research Institute (Z S Lassi PhD), University

of Adelaide, Adelaide, SA, Australia (A T Olagunju MD); Scuola Medica

Salernitana (M Cirillo MD), University of Salerno, Baronissi, Italy;

Faculty of Business and Management (M Á Garcia-Gordillo PhD),

Faculty of Education (D Collado-Mateo MSc), Institute of Physical

Activity and Health (Prof P R Olivares PhD), Autonomous University of

Chile, Talca, Chile; School of Medicine and Surgery (S Conti PhD,

P A Cortesi PhD, A Lafranconi MD, F Madotto PhD,

Prof L G Mantovani DSc), University of Milan Bicocca, Monza, Italy;

NIHR Oxford Biomedical Research Centre (Prof C Cooper MEd),

University of Southampton, Southampton, UK (Prof C Cooper MEd);

T Denny Sanford School of Social and Family Dynamics (M Costa PhD),

Arizona State University, Tempe, AZ, USA; Division of Reproductive

Health (M Costa PhD), Centers for Disease Control and Prevention

(CDC), Atlanta, GA, USA; Division of Epidemiology and Prevention of

Chronic Noncommunicable Diseases (P Cukelj MA, M Sekerija PhD),

Croatian Institute of Public Health, Zagreb, Croatia; Division of

Epidemiology and Biostatistics, School of Public Health

(Y A Gelaw MPH), Institute for Social Science Research

(A A Mamun PhD, J C Maravilla PhD), Queensland Brain Institute

(Prof J J McGrath MD), School of Dentistry (R Lalloo PhD), School of

Public health (B A Dachew MPH), The University of Queensland,

Brisbane, QLD, Australia (Prof H A Whiteford PhD); Biomedical

Research Council (Prof C D A Wolfe MD), Clinical Toxicology Service

(Prof P I Dargan MB), Guy’s and St. Thomas’ NHS Foundation Trust,

London, UK; James P Grant School of Public Health

(R Das Gupta MPH, M Hasan MPH, I Sutradhar MPH), Research and

Evaluation Division (M Rahman PhD), BRAC University, Dhaka,

Bangladesh; Central University of Tamil Nadu (Prof A P Dash DSc),

Thiruvarur, India; Department of Surgery (D V Davitoiu PhD), Clinical

Emergency Hospital Sf Pantelimon, Bucharest, Romania; Kazakh

National Medical University, Almaty, Kazakhstan (K Davletov PhD);

Australian Institute for Suicide Research and Prevention

(Prof D De Leo DSc), Menzies Health Institute Queensland

(S K Tadakamadla PhD), Grith University, Mount Gravatt, QLD,

Australia; Maternal and Child Wellbeing Unit (T A Zerfu PhD),

Population Dynamics and Reproductive Health Unit (T T Degﬁe PhD),

African Population Health Research Centre, Nairobi, Kenya; Department

of Clinical Pharmacy (G T Demoz MSc), Department of Medical

Laboratory Sciences (M Teweldemedhin MSc), Aksum University,

Aksum, Ethiopia; Department of Global Health and Infection

(K Deribe PhD), Brighton and Sussex Medical School, Brighton, UK;

Information Services Division (G M A Wyper MSc), National Health

Service Scotland, Edinburgh, UK (N Dervenis MD); Aristotle University

of Thessaloniki, Thessaloniki, Greece (N Dervenis MD); Department of

Psychiatry (Prof D C Des Jarlais PhD), Icahn School of Medicine at

Mount Sinai, New York, NY, USA; Department of Community Medicine

(S D Dharmaratne MD), University of Peradeniya, Peradeniya,

Sri Lanka; Health Research Section (M Dhimal PhD), Research Section

(A R Pandey MPH), Nepal Health Research Council, Kathmandu, Nepal;

Center of Communicable Disease Control (B Eshrati PhD), Deputy of

Research and Technology (S Djalalinia PhD), Ministry of Health and

Medical Education, Tehran, Iran (A Khosravi PhD); Institute for Global

Health Innovations (H P Do PhD, L H Nguyen PhD,

T H Nguyen BMedSc), Nguyen Tat Thanh University, Hanoi, Vietnam;

Department of Social Medicine and Health Care Organisation

(K Dokova PhD), Medical University of Varna, Varna, Bulgaria;

Department of Population and Health (D T Doku PhD), University of

Cape Coast, Cape Coast, Ghana; Faculty of Social Sciences

(D T Doku PhD), Faculty of Health Sciences (S Neupane PhD),

University of Tampere, Tampere, Finland; School of Health and

Biomedical Sciences (Prof K E Doyle PhD), Royal Melbourne Institute of

Technology University, Bundoora, VIC, Australia; Asbestos Diseases

Research Institute (J Leigh MD), Sydney Medical School (S Islam PhD),

Sydney School of Public Health (Prof T R Driscoll PhD), University of

Sydney, Sydney, NSW, Australia (M A Mohammed PhD); United Nations

World Food Programme, New Delhi, India (M Dubey PhD); Centre

School of Public Health and Health Management

(Prof M M Santric Milicevic PhD), Faculty of Medicine

(E Dubljanin PhD), Faculty of Medicine Institute of Epidemiology

(I S Vujcic PhD), University of Belgrade, Belgrade, Serbia; Department

of Health Sciences (E Duken MSc), Wollega University, Nekemte,

Ethiopia; School of Medicine (Prof A R Duraes PhD), Federal University

of Bahia, Salvador, Brazil; Diretoria Médica (Prof A R Duraes PhD),

Roberto Santos General Hospital, Salvador, Brazil; Department of

Nursing (Prof D Edvardsson PhD), Umeå University, Umeå, Sweden;

Eijkman-Oxford Clinical Research Unit (I R Elyazar PhD), Eijkman

Institute for Molecular Biology, Jakarta, Indonesia; Public Health

Department (A Y Y Endries MPH), Saint Paul’s Hospital Millennium

Medical College, Addis Ababa, Ethiopia; Laboratory for Socio-economic

Issues of Human Development and Quality of Life

(Prof S P Ermakov DSc), Russian Academy of Sciences, Moscow, Russia;

Central Research Institute of Cytology and Genetics (E Varavikova PhD),

Department of Medical Statistics and Documentary

(Prof S P Ermakov DSc), Federal Research Institute for Health

Organization and Informatics of the Ministry of Health, Moscow, Russia

(Prof V I Starodubov DSc, S K Vladimirov PhD); Department of Public

Health (R Esmaeili PhD), Gonabad University of Medical Sciences,

Gonabad, Iran; Department of Medical Parasitology and Mycology

(H Fakhim PhD), Urmia University of Medical Science, Urmia, Iran;

College of Medicine (M Fareed PhD), Department of Public Health

(A T Khoja MD), Imam Muhammad Ibn Saud Islamic University,

Riyadh, Saudi Arabia; National Statistical Oce, Lisbon, Portugal

(C S e Farinha MSc); Department of Psychology (Prof A Faro PhD),

Federal University of Sergipe, Sao Cristovao, Brazil; Doctor Evidence,

Santa Monica, CA, USA (M Fazeli PhD); National Institute for Stroke

and Applied Neurosciences (Prof V L Feigin PhD), Auckland University

of Technology, Auckland, New Zealand; Health Division (A B Feigl PhD),

Organisation for Economic Co-operation and Development, Paris,

France; Fertility & Infertility, Sarem Fertility & Infertility Research

Center, Tehran, Iran (Prof F Feizy MD); Institute of Statistical, Social and

Economic Research (A P Fenny PhD), School of Public Health

(R Quansah PhD), University of Ghana, Legon, Ghana; Psychiatry

(I Filip MD), Kaiser Permanente, Fontana, CA, USA; Department of

Public Health Medicine (F Fischer PhD), Bielefeld University, Bielefeld,

Germany; Sergio Arouca National School of Public Health, Rio de

Janeiro, Brazil (L S Flor MPH); Federal University of Espírito Santo,

Vitoria, Brazil (L S Flor MPH); Institute of Gerontology (N A Foigt PhD),

National Academy of Medical Sciences of Ukraine, Kyiv, Ukraine;

Department of Medicine and Surgery (C Fornari PhD), University of

Milano – Bicocca, Monza, Italy; Epidemiology and Public Health

(T Fürst PhD); Malaria Vaccines (C Karema MPH), Swiss Tropical and

Public Health Institute, Basel, Switzerland; University of Basel, Basel,

Switzerland (T Fürst PhD); Gene Expression & Regulation Program

(T Fukumoto PhD), Cancer Institute, Philadelphia, PA, USA;

Department of Dermatology (T Fukumoto PhD), Kobe University, Kobe,

Japan; Medical Statistics (S Marina MS), National Centre for Disease

Control, Tbilisi, Georgia (Prof A Gamkrelidze PhD, M Kereselidze MD);

Faculty of Medicine and Pharmacy of Fez (F G Gankpe MD), University

Sidi Mohammed Ben Abdellah, Fez, Morocco; International Trachoma

Initiative (T Gebre PhD), Task Force for Global Health, Decatur, GA,

USA; School of Public Health (A T Gebremedhin MPH, D Hendrie PhD,

T R Miller PhD), Curtin University, Perth, WA, Australia; Division of

Human Nutrition and Health (Prof J M Geleijnse PhD), Wageningen

University & Research, Wageningen, Netherlands; Directorate General

for Public Health (R Genova-Maleras MSc), Regional Health Council,

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Madrid, Spain; Social Determinants of Health Research Center

(H Ghiasvand PhD), University of Social Welfare and Rehabilitation

Sciences, Tehran, Iran (M Noroozi PhD); Department of Health Care

Policy and Management (M Ghimire MA), University of Tsukuba,

Tsukuba, Japan; Department of Respiratory Medicine

(Prof A G Ghoshal MD), National Allergy, Asthma, and Bronchitis

Institute, Kolkota, India; Department of Respiratory Medicine

(Prof A G Ghoshal MD), Fortis Hospital, Kolkata, India; Division of

Health Sciences (O A Uthman PhD), Unit of Academic Primary Care

(Prof P S Gill DM), University of Warwick, Coventry, UK; Research

Center of Neurology, Moscow, Russia (E V Gnedovskaya PhD); Center

for the Study of Regional Development (S Goli PhD), Centre for Ethics

(T Khanna PhD), Jawahar Lal Nehru University, New Delhi, India;

Nursing and Health Sciences Department (P N Gona PhD), University

of Massachusetts Boston, Boston, MA, USA; Department of Biostatistics

and Epidemiology (S V Gopalani MPH), University of Oklahoma,

Oklahoma City, OK, USA; Department of Health and Social Aairs

(S V Gopalani MPH), Government of the Federated States of Micronesia,

Palikir, Federated States of Micronesia; Postgraduate Program in

Epidemiology (Prof B N G Goulart DSc), Federal University of Rio

Grande do Sul, Porto Alegre, Brazil; School of Medicine (A Grada MD),

School of Public Health (O O Isehunwa MD), Boston University, Boston,

MA, USA; Registro Tumori Integrato (G Grosso PhD), Vittorio

Emanuele University Hospital Polyclinic, Catania, Italy; Department of

Epidemiology (Prof H C C Gugnani PhD), Department of Microbiology

(Prof H C C Gugnani PhD), Saint James School of Medicine, The Valley,

Anguilla; Department of Epidemiology (P C Gupta DSc,

D N Sinha PhD), Healis Sekhsaria Institute for Public Health, Mumbai,

India; Commissioner of Public Health (Prof R Gupta MD), West Virginia

Bureau for Public Health, Charleston, WV, USA; Department of Health

Policy, Management & Leadership (Prof R Gupta MD), West Virginia

University School of Public Health, Morgantown, WV, USA; Academics

and Research (Prof R Gupta MD), Rajasthan University of Health

Sciences, Jaipur, India; Department of Preventive Cardiology

(Prof R Gupta MD), Eternal Heart Care Centre & Research Institute,

Jaipur, India; Department of Cardiology (T Gupta MD), Monteﬁore

Medical Center, Bronx, NY, USA; Department of Epidemiology and

Population Health (H Hosgood PhD), Albert Einstein College of

Medicine, Bronx, NY, USA (T Gupta MD); Department of Public Health

(J A Haagsma PhD, S Kochhar MD), Erasmus University Medical

Center, Rotterdam, Netherlands; Department of Clinical Neurological

Sciences (V Hachinski DSc), The University of Western Ontario,

London, ON, Canada; Lawson Health Research Institute, London, ON,

Canada (V Hachinski DSc); Department of Epidemiology

(Prof J B Nachega PhD), Department of Gastroenterology and

Hepatology (K Vosoughi MD), Department of Health Policy and

Management (A T Khoja MD), Department of International Health

(M N Kosek MD), Department of Radiology (N Hafezi-Nejad MD,

A Haj-Mirzaian MD), Johns Hopkins University, Baltimore, MD, USA;

Department of Family and Community Medicine

(Prof R R Hamadeh DPhil), Arabian Gulf University, Manama, Bahrain;

School of Health and Environmental Studies (Prof S Hamidi DrPH),

Hamdan Bin Mohammed Smart University, Dubai, United Arab

Emirates; Population Health Department (A J Handal PhD), University

of New Mexico, Albuquerque, NM, USA; Neurology Department

(Prof G J Hankey MD), Sir Charles Gairdner Hospital, Perth, WA,

Australia; Department of Medical Statistics and Epidemiology

(Prof Y Hao PhD), Sun Yat-sen Global Health Institute

(Prof Y Hao PhD), Sun Yat-sen University, China; Department of

Disease, Epidemics, and Pandemics Control (J Nansseu MD),

Department of Vital and Health Statistics (H L Harb MPH), Ministry of

Public Health, Beirut, Lebanon; Health Education and Health Promotion

Department (L Jahangiry PhD), Tabriz University of Medical Sciences,

Tabriz, Iran (H Haririan PhD, H Hassankhani PhD); Research and

Development Unit (Prof J M Haro MD, A Koyanagi MD), San Juan de

Dios Sanitary Park, Sant Boi de Llobregat, Spain; Department of

Medicine (Prof J M Haro MD), University of Barcelona, Barcelona,

Spain; Independent Consultant, Tabriz, Iran (H Hassankhani PhD);

Unit of Epidemiology and Social Medicine (H Y Hassen MPH),

University Hospital Antwerp, Wilrijk, Belgium; Clinical Sciences

(R Havmoeller PhD), Karolinska University Hospital, Stockholm,

Sweden; Endocrinology and Metabolism Research Center

(B Heidari MD), Teikyo University School of Medicine, Tehran, Iran;

Department of Statistics and Econometrics (Prof C Herteliu PhD,

A Mirica PhD, A Pana MD), Bucharest University of Economic Studies,

Bucharest, Romania; University of Texas Austin, Austin, TX, USA

(M K Hole MD); Guilan Road Trauma Research Center

(E Homaie Rad PhD), School of Health (E Homaie Rad PhD), Guilan

University of Medical Sciences, Rasht, Iran; Transdisciplinary Centre for

Qualitative Methods (P Hoogar PhD), Manipal University, Manipal,

India; Department of Medicine (Prof T Wijeratne MD), Department of

Paediatrics (M T Mackay PhD, Prof G C Patton MD), School of Health

Sciences (A Meretoja MD, Prof C E I Szoeke PhD), School of Population

and Global Health (M M Hosseini Chavoshi PhD), University of

Melbourne, Melbourne, VIC, Australia (Prof A D Lopez PhD);

Department of Computer Science (M Hosseinzadeh PhD), University of

Human Development, Sulaimaniyah, Iraq; Department of Internal

Medicine (M Hostiuc PhD), Bucharest Emergency Hospital, Bucharest,

Romania; Clinical Legal Medicine (S Hostiuc PhD), National Institute of

Legal Medicine Mina Minovici, Bucharest, Romania; Faculty of Medicine

Tunis (Prof M Hsairi MPH), Medicine School of Tunis, Baab Saadoun,

Tunisia; Department of Epidemiology and Health Statistics

(Prof G Hu PhD), Central South University, Changsha, China;

Department of Public Health (K M Iburg PhD), National Centre for

Aarhus, Denmark; School of Public Health (Prof E U Igumbor PhD),

University of the Western Cape, Bellville, Cape Town, South Africa;

Department of Public Health (Prof E U Igumbor PhD), Walter Sisulu

University, Mthatha, South Africa; Department of Public Health and

Community Medicine (O S Ilesanmi PhD), University of Liberia,

Monrovia, Liberia; Global Health and Development Department

(Prof U Iqbal PhD), Graduate Institute of Biomedical Informatics

(D N A Ningrum MPH), Taipei Medical University, Taipei City, Taiwan,

Taiwan; School of Public Health (O O Isehunwa MD), University of

Memphis, Memphis, TN, USA; Department of Psychology

(M A Stokes PhD), Institute for Physical Activity and Nutrition

(S Islam PhD), School of Medicine (M Rahman PhD), Deakin

University, Burwood, VIC, Australia; Department of Parasitic Diseases

(S K Jain MD), National Centre for Disease Control Delhi, Delhi, India;

Medical Sciences Department (Prof M Jakovljevic PhD), University of

Kragujevac, Kragujevac, Serbia; Department of Internal Medicine

(S K Jassal MD), University of California San Diego, San Diego, CA,

USA; Newcastle University, Tyne, UK (M Javanbakht PhD); Faculty of

Graduate Studies (A U Jayatilleke PhD), Institute of Medicine

(A U Jayatilleke PhD), University of Colombo, Colombo, Sri Lanka;

Achutha Menon Centre for Health Science Studies (P Jeemon PhD,

G K Mini PhD, Prof K R Thankappan MD), Neurology Department

(Prof P Sylaja MD), Sree Chitra Tirunal Institute for Medical Sciences

and Technology, Trivandrum, India (Prof P Sylaja MD); Department of

Community Medicine (R P Jha MSc), Banaras Hindu University,

Varanasi, India; Environmental Research Center (J S Ji DSc), Duke

Kunshan University, Kunshan, China; Beijing Institute of

Ophthalmology (Prof J B Jonas MD), Beijing Tongren Hospital, Beijing,

China; Institution of Health and Nutrition Sciences (J J Jozwiak PhD),

Czestochowa University of Technology, Czestochowa, Poland; Faculty of

Medicine and Health Sciences (J J Jozwiak PhD), University of Opole,

Opole, Poland; School of Health Sciences (S B Jungari MA), Savitribai

Phule Pune University, Pune, India; Institute of Family Medicine and

Public Health (M Jürisson PhD), University of Tartu, Tartu, Estonia;

School of Public Health (Z Kabir PhD), University College Cork, Cork,

UK; A C S Medical College and Hospital, New Delhi, India

(Prof U Kapil MD, M Salahshoor PhD); Chronic Diseases (Home Care)

Research Center (M Shamsizadeh MSc), Department of Epidemiology

(M Karami PhD), Hamadan University of Medical Sciences, Hamadan,

Iran; Department for Epidemiology (A Karch MD), Helmholtz Centre

for Infection Research, Braunschweig, Germany; Quality and Equity

Health Care, Kigali, Rwanda (C Karema MPH); School of

Interdisciplinary Arts and Sciences (S Karimi PhD), University of

Washington Tacoma, Tacoma, WA, USA; Department of Anesthesiology

& Pain Medicine (N J Kassebaum MD), Seattle Children’s Hospital,

Seattle, WA, USA (T B Murphy PhD); MRC/CSO Social and Public

Health Sciences Unit (S V Katikireddi PhD), University of Glasgow,

Global Health Metrics

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Glasgow, UK; School of Health Care Administration (Prof A Kaul MD),

Oklahoma State University, Tulsa, OK, USA; Health Care Delivery

Sciences (Prof A Kaul MD), University of Tulsa, Tulsa, OK, USA;

Midwifery Program (S Kebede MSc), Salale University, Fiche, Ethiopia;

ODeL campus (Prof P N Keiyoro PhD), University of Nairobi

(M Kumar PhD), Nairobi, Kenya; Department of Linguistics and

Germanic, Slavic, Asian, and African Languages (G R Kemp BA),

Michigan State University, East Lansing, MI, USA; Cochrane South

Africa (E Z Sambala PhD, Prof C S Wiysonge MD), Non-Communicable

Diseases Research Unit (Prof A P Kengne PhD), Medical Research

Council South Africa, Cape Town, South Africa; Department of Medicine

(Prof A P Kengne PhD, G A Mensah MD, J Noubiap MD,

Prof K Sliwa MD), University of Cape Town, Cape Town, South Africa;

Institute of Cardiology (Prof A Keren MD), Assuta Hospital, Tel Aviv

Yao, Israel; Heart Failure and Cardiomyopathies Center

(Prof A Keren MD), Hadassah Hebrew University Hospital, Jerusalem,

Israel; Department of Public Health and Community Medicine

(Prof Y S Khader PhD), Jordan University of Science and Technology,

Ramtha, Jordan; School of Food and Agricultural Sciences

(N Khalid PhD), University of Management and Technology, Lahore,

Pakistan; Epidemiology and Biostatistics Department (E A Khan MPH),

Health Services Academy, Islamabad, Pakistan; Department of Internal

Medicine (M S Khan MD), John H Stroger, Jr Hospital of Cook County,

Chicago, IL, USA; Department of Internal Medicine (M S Khan MD,

T J Siddiqi MB, M S Usman MB), Dow University of Health Sciences,

Karachi, Pakistan; Department of Health Policy and Management

(Prof Y Khang MD), Institute of Health Policy and Management

(Prof Y Khang MD), Seoul National University, Seoul, South Korea;

Department of Health Research (T Khanna PhD), National Institute for

Research in Environmental Health (Y D Sabde MD), National Institute

of Nutrition (Prof A Laxmaiah PhD), Indian Council of Medical

Research, New Delhi, India (S M Mehendale MD); Student Research

Committee (M Khosravi MD), Baqiyatallah University of Medical

Sciences, Tehran, Iran; International Otorhinolaryngology Research

Association, Tehran, Iran (M Khosravi MD); Research Department

(D N Kiirithio MSc), Kenya Revenue Authority, Nairobi, Kenya; Research

and Data Solutions (D N Kiirithio MSc), Synotech Consultant, Nairobi,

Kenya; Departments of Pharmacy Practice and Public Health Sciences

(P E Kilgore MD), Wayne State University, Detroit, MI, USA;

Department of Health Sciences (Prof D Kim DrPH), Northeastern

University, Boston, MA, USA; Department of Preventive Medicine

(Y Kim PhD, Prof S Yoon PhD), Korea University, Seoul, South Korea;

School of Medicine (Y Kim PhD), Xiamen University Malaysia, Sepang,

Malaysia; Department of Nutrition (R W Kimokoti MD), Simmons

College, Boston, MA, USA; Faculty of Health (Y Kinfu PhD), University

of Canberra, Canberra, ACT, Australia; Department of Health

Management and Health Economics (Prof A Kisa PhD), Institute of

Health and Society (A S Winkler PhD), University of Oslo, Oslo, Norway;

Department of Global Community Health and Behavioral Sciences

(Prof A Kisa PhD), Tulane University, New Orleans, LA, USA;

Department of Public Health (Prof M Kivimäki PhD), University of

Helsinki, Helsinki, Finland (T J Meretoja MD); Department of

Preventive Cardiology (Prof Y Kokubo PhD), National Cerebral and

Cardiovascular Center, Suita, Japan; Arthritis Research Canada,

Richmond, BC, Canada (J A Kopec PhD); Independent Consultant,

Jakarta, Indonesia (S Kosen MD); Department of Internal and

Pulmonary Medicine (Prof P A Koul MD), Sheri Kashmir Institute of

Medical Sciences, Srinagar, India; Department of Anthropology

(K Krishan PhD), Panjab University, Chandigarh, India; Department of

Demography (Prof B Kuate Defo PhD), Department of Social and

Preventive Medicine (Prof B Kuate Defo PhD), University of Montreal,

Montreal, QC, Canada; Department of Public Health

(B Kucuk Bicer BEP), Yuksek Ihtisas University, Ankara, Turkey; Center

for Midwifery, Child and Family Health (F A Kumsa MPH), School of

Health (S Siabani PhD), University of Technology Sydney, Sydney, NSW,

Australia; Department of Pediatrics (S D Lad MD), School of Public

Health (Prof J S Thakur MD, Prof J S Thakur MD), Post Graduate

Institute of Medical Education and Research, Chandigarh, India; Center

for Translation Research and Implementation Science

(G A Mensah MD), Institute of Health Policy and Development Studies

(Prof H Lam PhD), National Heart, Lung, and Blood Institute

(E K Peprah PhD), National Institutes of Health, Manila, Philippines;

Department of Community and Family Medicine (F H Lami PhD),

Academy of Medical Science, Baghdad, Iraq; HelpMeSee, New York, NY,

USA (Prof V C Lansingh PhD); International Relations

(Prof V C Lansingh PhD), Mexican Institute of Ophthalmology,

Queretaro, Mexico; Belo Horizonte City Hall, Municipal Health

Department of Belo Horizonte, Belo Horizonte, Brazil

(Prof S Lansky PhD); Disease Control Department (D O Laryea MD),

Ghana Health Service, Accra, Ghana; Department of Public Health

(A Latiﬁ PhD), Managerial Epidemiology Research Center (S Saﬁri PhD),

Maragheh University of Medical Sciences, Maragheh, Iran; Regional

Centre for the Analysis of Data on Occupational and Work-related

Injuries and Diseases (M Levi PhD), Local Health Unit Tuscany Centre,

Florence, Italy; Department of Health Sciences (M Levi PhD), University

of Florence, Florence, Italy; West China Second University Hospital of

Sichuan University, Chengdu, China (X Li PhD); Department of Clinical

Research and Epidemiology (Y Li PhD, Y Li PhD), Shenzhen Sun

Yat-sen Cardiovascular Hospital, Shenzhen, China; National Oce for

Maternal and Child Health Surveillance, Chengdu, China

(Prof J Liang MD, Prof Y Wang MD, Prof J Zhu MD); National Center of

Birth Defects Monitoring of China, Chengdu, China (Prof J Liang MD,

Prof Y Wang MD); Division of Injury Prevention and Mental Health

Improvement (P Ye MPH), Non-communicable Disease Control and

Prevention Center (M Zhou PhD), Chinese Center for Disease Control

and Prevention, Beijing, China (Prof X Liang MD); Department of

Public Health (M L Liben MPH), Samara University, Samara, Ethiopia;

Department of Medicine (L Lim MD), University of Malaya, Kuala

Lumpur, Malaysia; Department of Medicine and Therapeutics

(L Lim MD), The Chinese University of Hong Kong, Shatin, China;

School of Public Health (Prof S Linn DrPH), University of Haifa, Haifa,

Israel; Centre for Chronic Disease Control, Beijing, China

(Prof S Liu PhD); Institute of Nutrition (Prof S Lorkowski PhD),

Friedrich Schiller University Jena, Jena, Germany; Competence Cluster

for Nutrition and Cardiovascular Health (nutriCARD), Jena, Germany

(Prof S Lorkowski PhD); General Surgery Department

(R Lunevicius PhD), Aintree University Hospital National Health Service

(NHS) Foundation Trust, LIverpool, UK; Surgery Department

(R Lunevicius PhD), University of Liverpool, LIverpool, UK; School of

Public Health (M Yotebieng PhD), University of Kinshasa, Kinshasa,

Democratic Republic of the Congo (Prof C Mabika Mabika PhD);

Cardiology Department (R G Weintraub MB), Neurology Department

(M T Mackay PhD), Royal Children’s Hospital, Melbourne, VIC,

Australia; Preventive Department (T A Mahmood MBBCH), Ministry of

Health and Population, Cairo, Egypt; Institute of Medicine

(N B Mahotra MD), Tribhuvan University, Kathmandu, Nepal;

Department of Public Health (M Majdan PhD), Trnava University,

Trnava, Slovakia; Non-Communicable Diseases Research Center

(Prof R Malekzadeh MD, S G Sepanlou MD), Shiraz University of

Medical Sciences, Shiraz, Iran; Department of Humanities and Social

Sciences (M A Malik MPhil), Indian Institute of Technology, Roorkee,

Haridwar, India; Surgery Department (A Manda MD), Emergency

University Hospital Bucharest, Bucharest, Romania; Public Risk

Management Institute, Mississauga, ON, Canada (S Mangalam MS);

Trade and Competitiveness (S Mangalam MS), World Bank, New York,

NY, USA; Campus Caucaia (F R Martins-Melo PhD), Federal Institute of

Education, Science and Technology of Ceará, Caucaia, Brazil; Clinical

Institute of Medical and Chemical Laboratory Diagnostics

(Prof W März MD), Medical University of Graz, Graz, Austria; Graduate

School (M B Marzan MSc), University of the East Ramon Magsaysay

Memorial Medical Center, Quezon City, Philippines; Department of

Health Sciences (A J Mason-Jones PhD), University of York, York, UK;

Department of Biology and Biological Engineering (M Mazidi PhD),

Chalmers University of Technology, Gothenburg, Sweden; Research,

Monitoring and Evaluation (S Mehata PhD), Ipas Nepal, Kathmandu,

Nepal; Neurology Department (Prof M Mehndiratta MD), Janakpuri

Super Specialty Hospital Society, New Delhi, India; Preventive Oncology

(Prof R Mehrotra PhD), National Institute of Cancer Prevention and

Research, Noida, India; Department of Epidemiology and Biostatistics

(K M Mehta DSc), University of California San Francisco, San Francisco,

CA, USA; Department of Internal Medicine (V Mehta MD), SevenHills

Hospital, Mumbai, India; Department of Adult Health Nursing

Global Health Metrics

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(N Y Tawye MSc), Department of Pharmacy (G Mengistu MSc),

Department of Public Health (T C Mekonnen MPH), Wollo University,

Dessie, Ethiopia; College of Health Sciences (A Melese MSc),

Department of Pharmacy (M M Zeleke MSc), Debre Tabor University,

Debre Tabor, Ethiopia; Department of Public Health

(P T N Memiah DrPH), University of West Florida, Pensacola, FL, USA;

Research Department Prince Mohammed Bin Abdulaziz Hospital

(Prof Z A Memish MD), Ministry of Health, Riyadh, Saudi Arabia;

College of Medicine (Prof Z A Memish MD, M Temsah MD), Alfaisal

University, Riyadh, Saudi Arabia; Peru Country Oce (W Mendoza MD),

United Nations Population Fund (UNFPA), Lima, Peru; Breast Surgery

Unit (T J Meretoja MD), Neurocenter (A Meretoja MD), Helsinki

University Hospital, Helsinki, Finland; Clinical Microbiology and

Parasitology Unit (T Mestrovic PhD), Dr Zora Profozic Polyclinic,

Zagreb, Croatia; University Centre Varazdin (T Mestrovic PhD),

University North, Varazdin, Croatia; Pharmacy (H B Mezgebe MSc),

Ethiopian Academy of Medical Science, Ethiopia; Faculty of Humanities

and Social Sciences (Y Miangotar PhD), University of N’Djaména,

N’Djaména, Chad; Department of Hypertension

(Prof T Miazgowski MD), Emergency Department (B Miazgowski MD),

Zdroje Hospital (J Widecka PhD), Pomeranian Medical University,

Szczecin, Poland (B Miazgowski MD, K Widecka PhD); Paciﬁc Institute

for Research & Evaluation, Calverton, MD, USA (T R Miller PhD);

President’s Oce (A Mirica PhD), National Institute of Statistics,

Bucharest, Romania; Faculty of General Medicine

(Prof E M Mirrakhimov MD), Kyrgyz State Medical Academy, Bishkek,

Kyrgyzstan; Department of Atherosclerosis and Coronary Heart Disease

(Prof E M Mirrakhimov MD), National Center of Cardiology and Internal

Disease, Bishkek, Kyrgyzstan; Institute of Addiction Research (ISFF)

(B Moazen MSc), Frankfurt University of Applied Sciences, Frankfurt,

Germany; Department of Biology (K A Mohammad PhD), Salahaddin

University, Erbil, Iraq; Erbil (K A Mohammad PhD), ISHIK University,

Erbil, Iraq; Cardiovascular Research Institute (N Mohammadifard PhD,

Prof N Sarrafzadegan MD), Isfahan University of Medical Sciences,

Isfahan, Iran; Department of Public Health (M A Mohammed PhD),

Jigjiga University, Jigjiga, Ethiopia (A A Tassew MPH); Department of

Community Medicine (M B Suﬁyan MD), Health Systems and Policy

Research Unit (S Mohammed PhD), Ahmadu Bello University, Zaria,

Nigeria; School of Medicine and Health Sciences, Obstetrics &

Gynecology Department (Prof G D Mola MB), University of Papua New

Guinea, Boroko, Papua New Guinea; Department of Obstetrics and

Gynaecology (Prof G D Mola MB), Port Moresby General Hospital,

Boroko, Port Moresby, Papua New Guinea; Clinical Epidemiology and

Public Health Research Unit (L Monasta DSc, L Ronfani PhD), Burlo

Garofolo Institute for Maternal and Child Health, Trieste, Italy;

Department of Epidemiology and Biostatistics (G Moradi PhD), Social

Determinants of Health Research Center (G Moradi PhD), Kurdistan

University of Medical Sciences, Sanandaj, Iran; Lancaster University,

Lancaster, UK (P Moraga PhD); Hospital de Sto António

(J Morgado-da-Costa MSc), Hospital Center of Porto, Porto, Portugal;

Department of Health Policy (Prof R Mori PhD), National Center for

Child Health and Development, Setagaya, Japan; Department of Clinical

Biochemistry (A Mosapour PhD), Tarbiat Modares University, Tehran,

Iran; 1st Department of Ophthalmology (M M Moschos PhD), University

of Athens, Athens, Greece; Biomedical Research Foundation

(M M Moschos PhD), Academy of Athens, Athens, Greece; Competence

Center Mortality-Follow-Up (R Westerman PhD), Demographic Change

and Ageing Research Area (A Werdecker PhD), Federal Institute for

Population Research, Wiesbaden, Germany (Prof U O Mueller MD);

Center for Population and Health, Wiesbaden, Germany

(Prof U O Mueller MD); Department of Endocrinology & Metabolism

(Prof S Mukhopadhyay MD), Institute of Post Graduate Medical

Education & Research, Kolkata, India; Department of Obstetrics and

Gynecology (J Musa MD), University of Jos, Jos, Nigeria; Center for

Global Health (J Musa MD), Department of Preventive Medicine

(Y Yano MD), Northwestern University, Chicago, IL, USA; School of

Medical Sciences (K Musa PhD), Science University of Malaysia, Kubang

Kerian, Malaysia; Pediatrics Department (Prof G Mustafa MD), Nishtar

Medical University, Multan, Pakistan; Pediatrics & Pediatric

Pulmonology (Prof G Mustafa MD), Institute of Mother & Child Care,

Multan, Pakistan; Department of Epidemiology (Prof J B Nachega PhD),

University of Pittsburgh, Pittsburgh, PA, USA; Institute of Epidemiology

and Medical Biometry (Prof G Nagel PhD), Ulm University, Ulm,

Germany; Department of Epidemiology (G Naik MPH, J A Singh MD),

Department of Medicine (P Ranjan PhD, J A Singh MD), Department of

Psychology (D C Schwebel PhD), University of Alabama at Birmingham,

Birmingham, AL, USA (A R Sawant MD); Department of Dermatology

(Prof L Naldi MD), San Bortolo Hospital, Vicenza, Italy; Direction

(Prof L Naldi MD), GISED Study Center, Bergamo, Italy; Suraj Eye

Institute, Nagpur, India (V Nangia MD); Department of Public Heath

(J Nansseu MD), University of Yaoundé I, Yaoundé, Cameroon; Mercy

Saint Vincent Medical Center, Toledo, OH, USA (H Nawaz MD);

Department of Cardiology (R I Negoi PhD), Cardio-Aid, Bucharest,

Romania; Kenya Medical Research Institute/Wellcome Trust Research

Programme, Kiliﬁ, Kenya (Prof C R J Newton MD); Ministry of Health,

Community Development, Gender, Elderly and Children, Dar es Salaam,

Tanzania (F N Ngalesoni PhD); Department of Biological Sciences

(J W Ngunjiri DrPH), University of Embu, Embu, Kenya; Public Health

Science Department (D N A Ningrum MPH), State University of

Semarang, Kota Semarang, Indonesia; Institute for Global Health Policy

Research (S Nomura MSc), National Center for Global Health and

Medicine, Shinjuku-ku, Japan; Directorate General of Planning,

Monitoring and Evolution (A Nyandwi MPH), Rwanda Ministry of

Health, Kigali, Rwanda; College of Medicine and Health Sciences

(A Nyandwi MPH), University of Rwanda, Kigali, Rwanda; Independent

Consultant, Accra, Ghana (R Ofori-Asenso MSc); Department of

Medicine (O S Ogah PhD), Abia State University, Uturu, Nigeria; School

of Social Sciences and Psychology (Prof A M N Renzaho PhD), Western

Sydney University, Penrith, NSW, Australia (F A Ogbo PhD);

Department of Preventive Medicine (I Oh PhD), Kyung Hee University,

Dongdaemun-gu, South Korea; Research, Measurement, and Results

(A Okoro MPH), Society for Family Health, Nigeria, Abuja, Nigeria;

Department of HIV/AIDS, STIs & TB (O Oladimeji MD), Human

Sciences Research Council, Durban, South Africa; School of Public

Health (O Oladimeji MD), University of Namibia, Oshakati Campus,

Namibia; Centre for Healthy Start Initiative, Ikoyi, Nigeria

(B O Olusanya PhD, J O Olusanya MBA); NCD Prevention & Control

Unit (S Ong MBBS), Ministry of Health, Bandar Seri Begawan, Brunei;

Institute of Health Science (S Ong MBBS), University of Brunei

Darussalam, Gadong, Brunei; Pneumology Service

(Prof J B Soriano MD), School of Medicine (Prof A Ortiz MD),

Autonomous University of Madrid, Madrid, Spain; Department of

Nephrology and Hypertension (Prof A Ortiz MD), The Institute for

Health Research Foundation Jiménez Díaz University Hospital, Madrid,

Spain; Department of Global Health Nursing (Prof E Ota PhD), St.

Luke’s International University, Chuo-ku, Japan; Research, Monitoring

and Evaluation (B A Otieno MPH), Kisumu Medical and Education

Trust, Kisumu, Kenya; Ministry of Health of the Russian Federation,

Moscow, Russia; Moscow Institute of Physics and Technology

(S S Otstavnov PhD), Moscow State University, Dolgoprudny, Russia;

Agricultural Economics Group (Prof A S Oyekale PhD), Department of

Pediatrics (Prof S u Rahman MBBS), North-West University, Maﬁkeng,

South Africa; Department of TB & Respiratory Medicine

(Prof M P A DNB), Jagadguru Sri Shivarathreeswara University, Mysore,

India; Department of Medicine (S Pakhale MD), University of Ottawa,

Ottawa, ON, Canada; Centre for Community Medicine

(A P Pakhare MD), Department of Endocrinology, Metabolism, &

Diabetes (Prof N Tandon PhD), Department of Psychiatry

(Prof R Sagar MD), All India Institute of Medical Sciences, New Delhi,

India; Health Outcomes (A Pana MD), Center for Health Outcomes &

Evaluation, Bucharest, Romania; Department of Medical Humanities

and Social Medicine (Prof E Park PhD), Kosin University, Busan, South

Korea; Department of Medicine (S Patel MD), Maimonides Medical

Center, Brooklyn, NY, USA; Krishna Institute of Medical Sciences

(S T Patil MBA), Deemed University, Karad, India; International

Institute of Health Management Research, New Delhi, India

(A Patle MPH); Population Health Group (Prof G C Patton MD),

Murdoch Childrens Research Institute, Melbourne, VIC, Australia

(R G Weintraub MB); Clinical Research Department

(Prof V R Paturi MD), Diabetes Research Society, Hyderabad, India;

Clinical Research Department (Prof V R Paturi MD), DiabetOmics,

Portland, OR, USA; Health, Nutrition, and HIV/AIDS Program

Global Health Metrics

2048

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(D Paudel PhD), Save the Children, Kathmandu, Nepal; Institute of

Scientiﬁc and Technological Communication and Information in Health

(M M Pedroso PhD, R d Saldanha MPH), Oswaldo Cruz Foundation,

Rio de Janeiro, Brazil; Cartagena University, Cartagena, Colombia

(Prof D M Pereira PhD); Independent Consultant, Glenelg, SA, Australia

(Prof K Pesudovs PhD); Anesthesiology Department (A S Terkawi MD),

School of Medicine (W A Petri MD), University of Virginia,

Charlottesville, VA, USA; Institute of Medicine (Prof M Petzold PhD),

University of Gothenburg, Gothenburg, Sweden; School of Public

Health (Prof M Petzold PhD), University of Witwatersrand,

Johannesburg, South Africa; Basic Medical Sciences Department

(J D Pillay PhD), Durban University of Technology, Durban, South

Africa; University Medical Center Groningen (Prof M J Postma PhD),

University of Groningen, Groningen, Netherlands; Department of

Nephrology (S Prakash PhD, Prof N Prasad MD), Sanjay Gandhi

Postgraduate Institute of Medical Sciences, Lucknow, India; Government

Medical College, Nagpur, India (Prof M B Purwar MD);

Non-communicable Diseases Research Center (M Qorbani PhD), Alborz

University of Medical Sciences, Karaj, Iran; Medichem, Barcelona, Spain

(A Radfar MD); Department of Epidemiology & Biostatistics

(A Rafay MS), Contech School of Public Health, Lahore, Pakistan;

Research Division (M Rahman MHS), Global Public Health Research

Foundation, Dhaka, Bangladesh; Department of Clinical Pediatrics

(Prof S u Rahman MBBS), Sweidi Hospital, Riyadh, Saudi Arabia;

Society for Health and Demographic Surveillance, Suri, India

(R Rai MPH); Department of Economics (R Rai MPH), University of

Göttingen, Göttingen, Germany; Medical University Innsbruck,

Innsbruck, Austria (S Rajsic MD); Institute for Poverty Alleviation and

International Development (C L Ranabhat PhD), Yonsei University,

Seoul, Korea; University College London Hospitals, London, UK

(D L Rawaf MD); Public Health England, London, UK

(Prof S Rawaf PhD); Department of Preventive Medicine and

Occupational Medicine (C Reis MD), Loma Linda University Medical

Center, Loma Linda, CA, USA; Brien Holden Vision Institute, Sydney,

NSW, Australia (Prof S Resniko MD); Organization for the Prevention

of Blindness, Paris, France (Prof S Resniko MD); Department of

Epidemiology (S Riahi PhD), Birjand University of Medical Sciences,

Birjand, Iran; Department of Clinical Research (L Roever PhD), Federal

University of Uberlândia, Uberlândia, Brazil; Golestan Research Center

of Gastroenterology and Hepatology (G Roshandel PhD), Golestan

University of Medical Sciences, Gorgan, Iran; Biotechnology

(E Rubagotti PhD), IKIAM Amazon Regional University, Ciudad de

Tena, Ecuador; Department of Ocean Science and Engineering

(E Rubagotti PhD), Southern University of Science and Technology,

Shenzhen, China; Department of Community Health

(B F Sunguya PhD), School of Public Health (G M Ruhago PhD),

Muhimbili University of Health and Allied Sciences, Dar es Salaam,

Tanzania (B F Sunguya PhD); Neuropsychiatric Institute

(Prof P S Sachdev MD), Prince of Wales Hospital, Randwick, NSW,

Australia; Medical Department (B Saddik PhD), University of Sharjah,

Sharjah, United Arab Emirates; College of Medicine (N Salam PhD), Al-

Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia;

School of Health and Policy Management, Faculty of Health

(Prof P Salamati MD), York University, Toronto, ON, Canada; Punjab

University College of Pharmacy, Anarkali, Pakistan (Z Saleem PharmD);

Center for Health Policy & Center for Primary Care and Outcomes

Research (Prof J A Salomon PhD), Stanford University, Stanford, CA,

USA; Clinical Research Division (Prof S S Salvi MD), Chest Research

Foundation, Pune, India; Department of Surgery (Prof J Sanabria MD),

Marshall University, Huntington, WV, USA; Health and Disability

Intelligence Group (I Salz MD), Ministry of Health, Wellington, New

Zealand; Department of Nutrition and Preventive Medicine

(Prof J Sanabria MD), Case Western Reserve University, Cleveland, OH,

USA; Nephrology Group (M Sanchez-Niño PhD), Jimenez Diaz

Foundation University Hospital Institute for Health Research, Madrid,

Spain; Department of Medicine (M Sardana MD), University of

Massachusetts Medical School, Worcester, MA, USA; Department of

Health and Society, Faculty of Medicine (Prof R Sarmiento-Suárez MPH),

University of Applied and Environmental Sciences, Bogotá, Colombia;

Department of Community Medicine (S Saroshe MD), Mahatma Gandhi

Memorial Medical College, Indore, India; Surgery Department

(B Sathian PhD), Hamad Medical Corporation, Doha, Qatar; Faculty of

Health & Social Sciences (B Sathian PhD), Bournemouth University,

Bournemouth, UK; UGC Centre of Advanced Study in Psychology

(M Satpathy PhD), Utkal University, Bhubaneswar, India; Udyam-Global

Association for Sustainable Development, Bhubaneswar, India

(M Satpathy PhD); Dr D Y Patil Vidyapeeth, Pune, India

(A R Sawant MD); Department of Public Health Sciences

(M Sawhney PhD), University of North Carolina at Charlotte, Charlotte,

NC, USA; School of Health Sciences (Prof I J C Schneider PhD,

Prof D A S Silva PhD), Federal University of Santa Catarina, Ararangua,

Brazil; Department of Medical Statistics, Epidemiology and Medical

Informatics (M Sekerija PhD), University of Zagreb, Zagreb, Croatia;

Langone Medical Center (A Shaﬁeesabet MD), New York University,

New York, NY, USA; Public Health Division (A A Shaheen PhD), An-

Najah National University, Nablus, Palestine; Department of Molecular

Hepatology (H Sharaﬁ PhD), Middle East Liver Disease Center, Tehran,

Iran; Independent Consultant, Karachi, Pakistan (M A Shaikh MD);

Department of Basic Sciences (Prof M Sharif PhD), Department of

Laboratory Sciences (Prof M Sharif PhD), Islamic Azad University, Sari,

Iran; Policy and Planning Division (J Sharma MPH), Ministry of Health,

Thimphi, Bhutan; University School of Management and

Entrepreneurship (R Sharma PhD), Delhi Technological University,

New Delhi, India; Department of Pulmonary Medicine (J She MD),

Fudan University, Shanghai, China; Usher Institute of Population

Health Sciences and Informatics (Prof A Sheikh MD, I N Soyiri PhD),

University of Edinburgh, Edinburgh, UK; Friedman School of Nutrition

Science and Policy (P Shi PhD), Tufts University, Boston, MA, USA;

National Institute of Infectious Diseases, Tokyo, Japan

(M Shigematsu PhD); Finnish Institute of Occupational Health,

Helsinki, Finland (R Shiri PhD); Institute of Medical Epidemiology

(I Shiue PhD), Martin Luther University Halle-Wittenberg, Halle,

Germany; School of Medicine (F Shokraneh MS), University of

Nottingham, Nottingham, UK; Symbiosis Institute of Health Sciences

(Prof S R Shukla PhD), Symbiosis International University, Pune, India;

Department of Psychology (Prof I D Sigfusdottir PhD,

R Sigurvinsdottir PhD), Reykjavik University, Reykjavik, Iceland;

Department of Health and Behavior Studies (Prof I D Sigfusdottir PhD),

Columbia University, New York, NY, USA; Brasília University, Brasília,

Brazil (Prof D A Silveira MD); Department of the Health Industrial

Complex and Innovation in Health (Prof D A Silveira MD), Department

of Diseases and Non-communicable Diseases and Health Promotion

(A M Soares Filho DSc), Federal Ministry of Health, Brasilia, Brazil;

Division of Cardiovascular Medicine (N V Singam MD, G Vaidya MD),

University of Louisville, Louisville, KY, USA; Max Hospital, Ghaziabad,

India (Prof N P Singh MD); Department of Pulmonary Medicine

(Prof V Singh MD), Asthma Bhawan, Jaipur, India; Department of

Epidemiology (D N Sinha PhD), School of Preventive Oncology, Patna,

India; Pediatric Department (B H Sobaih MD), King Khalid University

Hospital, Riyadh, Saudi Arabia; Service of Pulmonology

(Prof J B Soriano MD), Health Research Institute of the University

Hospital “de la Princesa”, Madrid, Spain; Hull York Medical School

(I N Soyiri PhD), University of Hull, Hull City, UK; Division of

Community Medicine (C T Sreeramareddy MD), International Medical

University, Kuala Lumpur, Malaysia; School of Health and Related

Research (M Strong PhD), University of Sheeld, Sheeld, UK;

Norwegian Institute of Public Health, Bergen, Norway (G Sulo PhD);

School of Medicine (P J Sur MPH), University of California Riverside,

Riverside, CA, USA; Department of Criminology, Law and Society

(Prof B L Sykes PhD), University of California Irvine, Irvine, CA, USA;

Department of Medicine (Prof R Tabarés-Seisdedos PhD), Department

of Pediatrics, Obstetrics and Gynecology (Prof M Tortajada-Girbés PhD),

University of Valencia, Valencia, Spain; Carlos III Health Institute

(Prof R Tabarés-Seisdedos PhD), Biomedical Research Networking

Center for Mental Health Network (CiberSAM), MADRID, Spain; School

of Social Work (Prof K M Tabb PhD), University of Illinois, Urbana, IL,

USA; University Institute “Egas Moniz”, Monte da Caparica, Portugal

(Prof N Taveira PhD); Research Institute for Medicines, Faculty of

Pharmacy of Lisbon (Prof N Taveira PhD), University of Lisbon, Lisbon,

Portugal; Selihom School of Nursing (N Y Tawye MSc), Alkan Health

Science, Business and Technology College, Dessie, Ethiopia; Syrian

Expatriate Medical Association, Charlottesville, VA, USA

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2049

(A S Terkawi MD); Lee Kong Chian School of Medicine

(L Tudor Car PhD), Nanyang Technological University, Singapore,

Singapore (S Thirunavukkarasu PhD); Department of Global Health

Research (A J Thomson PhD), Adaptive Knowledge Management,

Victoria, BC, Canada; School of Exercise and Nutrition Sciences

(Q G To PhD), Queensland University of Technology, Brisbane, QLD,

Australia; Institute of Public Health (R Topor-Madry PhD), Jagiellonian

University Medical College, Krakow, Poland; Agency for Health

Technology Assessment and Tari System, Warszawa, Poland

(R Topor-Madry PhD); Pediatric Department

(Prof M Tortajada-Girbés PhD), University Hospital Doctor Peset,

Valencia, Spain; Nagoya University, Nagoya, Japan

(Prof H Toyoshima PhD); Department of Health Economics

(B X Tran PhD), Hanoi Medical University, Hanoi, Vietnam; Clinical

Hematology and Toxicology (K B Tran MD), Military Medical University,

Hanoi, Vietnam; National Institute for Research in Tuberculosis, Chennai,

India (S P Tripathy MD); Department of Neurology (T C Truelsen PhD),

University of Copenhagen, Copenhagen, Denmark; Institute for Global

Health Innovations (N T Truong BHlthSci), Duy Tan University, Hanoi,

Vietnam; Department of Vascular Medicine (N Tsilimparis PhD),

University Heart Center of Hamburg, Hamburg, Germany; Department

of Internal Medicine (K N Ukwaja MD), Federal Teaching Hospital,

Abakaliki, Nigeria; Gomal Center of Biochemistry and Biotechnology

(I Ullah PhD), Gomal University, Dera Ismail Khan, Pakistan; TB Culture

Laboratory (I Ullah PhD), Mufti Mehmood Memorial Teaching Hospital,

Dera Ismail Khan, Pakistan; Ankara University, Ankara, Turkey

(S B Uzun MSc); Argentine Society of Medicine, Ciudad de Buenos Aires,

Argentina (Prof P R Valdez MEd); Velez Sarsﬁeld Hospital, Buenos Aires,

Argentina (Prof P R Valdez MEd); UKK Institute, Tampere, Finland

(Prof T J Vasankari MD); Department of Statistics

(Prof A N Vasconcelos PhD), University of Brasilia, Brasília, Brazil;

Directorate of Social Studies and Policies (Prof A N Vasconcelos PhD),

Federal District Planning Company, Brasília, Brazil; Raes Neuroscience

Centre (Prof N Venketasubramanian MBBS), Raes Hospital, Singapore,

Singapore; Yong Loo Lin School of Medicine

(Prof N Venketasubramanian MBBS), National University of Singapore,

Singapore, Singapore; Occupational Health Unit (Prof F S Violante MPH),

Sant’Orsola Malpighi Hospital, Bologna, Italy; Department of Information

and Internet Technologies (S K Vladimirov PhD), I M Sechenov First

Moscow State Medical University, Moscow, Russia; Department of Health

Care Administration and Economy (Prof V Vlassov MD), National Research

University Higher School of Economics, Moscow, Russia; Foundation

University Medical College (Y Waheed PhD), Foundation University,

Rawalpindi, Pakistan; Department of Research (Prof E Weiderpass PhD),

Cancer Registry of Norway, Oslo, Norway; Independent Consultant,

Staufenberg, Germany (A Werdecker PhD); Department of Neurology

(A S Winkler PhD), Technical University of Munich, Munich, Germany;

Kailuan General Hospital (Prof S Wu PhD), Kailuan General Hospital,

Tangshan, China; University of Strathclyde, Glasgow, UK

(G M A Wyper MSc); School of Medicine (Prof G Xu MD), Nanjing

University, Nanjing, China; Department of Earth Science (Y J Yasin MPH),

King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia;

Wolkite University, Wolkite, Ethiopia (A Yeshaneh BHlthSci); Department

of Biostatistics (N Yonemoto MPH), Kyoto University, Kyoto, Japan;

Department of Health Policy and Management (Prof M Z Younis DrPH),

Jackson State University, Jackson, MS, USA; Tsinghua University

(Prof M Z Younis DrPH), Tsinghua University, Beijing, China; Department

of Epidemiology and Biostatistics (Prof C Yu PhD), Global Health Institute

(Prof C Yu PhD), Wuhan University, Wuhan, China; Epidemiology and

Cancer Registry Sector (Prof V Zadnik PhD), Institute of Oncology

Ljubljana, Ljubljana, Slovenia; Department of Epidemiology

(Prof Z Zaidi PhD), University Hospital of Setif, Setif, Algeria; Public

Health Department (T A Zerfu PhD), Dilla University, Dilla, Ethiopia;

Wuhan Polytechnic University, Wuhan, China (X Zhao PhD)

Contributors

Please see appendix 1 for more detailed information about individual

authors’ contributions to the research, divided into the following

categories: managing the estimation process; writing the ﬁrst draft of the

manuscript; providing data or critical feedback on data sources;

developing methods or computational machinery; applying analytical

methods to produce estimates; providing critical feedback on methods or

results; drafting the work or revising it critically for important intellectual

content; extracting, cleaning, or cataloguing data; designing or coding

ﬁgures and tables; and managing the overall research enterprise.

Declaration of interests

Adam Berman reports personal fees from Philips. Cyrus Cooper reports

personal fees from Alliance for Better Bone Health, Amgen, Eli Lilly,

GSK, Medtronic, Merck, Novartis, Pﬁzer, Roche, Servier, Takeda, and

UCB. Mir Sohail Fazeli reports personal fees from Doctor Evidence LLC.

Panniyammakal Jeemon reports a Clinical and Public Health

Intermediate Fellowship from the Wellcome Trust-DBT India Alliance

(2015–20). Jacek Jóźwiak reports a grant from Valeant, personal fees

from Valeant, ALAB Laboratoria and Amgen, and non-ﬁnancial support

from Microlife and Servier. Nicholas Kassebaum reports personal fees

and other support from Vifor Pharmaceuticals, LLC. Srinivasa Vittal

Katikireddi reports grants from NHS Research Scotland (no.

SCAF/15/02), the Medical Research Council (MC_UU_12017/13 and

MC_UU_12017/15), and Scottish Government Chief Scientist Oce

(SPHSU13 and SPHSU15). Jerey Lazarus reports personal fees from

Janssen and CEPHEID and grants and personal fees from AbbVie,

Gilead Sciences, and MSD. Winfried März reports grants and personal

fees from Siemens Diagnostics, Aegerion Pharmaceuticals, Amgen,

AstraZeneca, Danone Research, Pﬁzer, BASF, Numares AG, and

Berline-Chemie; personal fees from Homann LaRoche, MSD, Sanoﬁ,

and Synageva; grants from Abbott Diagnostics; and other support from

Synlab Holding Deutschland GmbH. Walter Mendoza is currently a

Program Analyst for Population and Development at the Peru Country

Oce of the United Nations Population Fund (UNFPA), which does not

necessarily endorse this study. Ted Miller reports an evaluation contract

from AB InBev Foundation. Guilherme Polanczyk reports personal fees

from Shire, Teva, Medice, and Editora Manole. Maarten Postma reports

grants from Mundipharma, Bayer, BMS, AstraZeneca, ARTEG,

and AscA; grants and personal fees from Sigma Tau, MSD, GSK, Pﬁzer,

Boehringer-Ingelheim, Novavax, Ingress Health, AbbVie, and Sanoﬁ;

personal fees from Quintiles, Astellas, Mapi, OptumInsight, Novartis,

Swedish Orphan, Innoval, Jansen, Intercept, and Pharmerit, and stock

ownership in Ingress Health and Pharmacoeconomics Advice

Groningen. Kenji Shibuya reports grants from Ministry of Health,

Labour, and Welfare and from Ministry of Education, Culture, Sports,

Science, and Technology. Cassandra Szoeke reports a grant from the

National Medical Health Research Council, Lundbeck, Alzheimer’s

Association, and the Royal Australasian College of Practitioners;

she holds patent PCT/AU2008/001556. Muthiah Vaduganathan receives

research support from the NIH/NHLBI and serves as a consultant for

Bayer AG and Baxter Healthcare. Marcel Yotebieng reports grants from

the US National Institutes of Health. All remaining authors declare no

competing interests.

Data sharing

To download the data used in these analyses, please visit the Global

Health Data Exchange at http://ghdx.healthdata.org/gbd−2017.

Acknowledgments

Research reported in this publication was supported by the

Bill & Melinda Gates Foundation, the University of Melbourne, Public

Health England, the Norwegian Institute of Public Health, St Jude

Children’s Research Hospital, the National Institute on Ageing of the

National Institutes of Health (award no. P30AG047845), and the National

Institute of Mental Health of the National Institutes of Health

(R01MH110163). The content is solely the responsibility of the authors

and does not necessarily represent the ocial views of the funders.

Data for this research was provided by the Russia Longitudinal

Monitoring survey, conducted by the National Research University

Higher School of Economics, ZAO Demoscope, Carolina Population

Center, University of North Carolina at Chapel Hill, and the Institute of

Sociology RAS. This analysis uses data or information from the LASI

Pilot micro data and documentation. The development and release of the

LASI Pilot Study was funded by the National Institute on Ageing and

National Institutes of Health (R21AG032572, R03AG043052, and

R01AG030153). The Palestinian Central Bureau of Statistics granted the

researchers access to relevant data in accordance with license number

SLN2014-3-170, after subjecting data to processing aiming to preserve the

conﬁdentiality of individual data in accordance with the General

Global Health Metrics

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www.thelancet.com Vol 392 November 10, 2018

Statistics Law, 2000. The researchers are solely responsible for the

conclusions and inferences drawn upon available data.

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ResearchGate has not been able to resolve any citations for this publication.

Mapping child growth failure in Africa between 2000 and 2015

Article

Full-text available

Mar 2018
NATURE

Insufficient growth during childhood is associated with poor health outcomes and an increased risk of death. Between 2000 and 2015, nearly all African countries demonstrated improvements for children under 5 years old for stunting, wasting, and underweight, the core components of child growth failure. Here we show that striking subnational heterogeneity in levels and trends of child growth remains. If current rates of progress are sustained, many areas of Africa will meet the World Health Organization Global Targets 2025 to improve maternal, infant and young child nutrition, but high levels of growth failure will persist across the Sahel. At these rates, much, if not all of the continent will fail to meet the Sustainable Development Goal target—to end malnutrition by 2030. Geospatial estimates of child growth failure provide a baseline for measuring progress as well as a precision public health platform to target interventions to those populations with the greatest need, in order to reduce health disparities and accelerate progress.

Trends in Demographic and Health Survey data quality: An analysis of age heaping over time in 34 countries in Sub Saharan Africa between 1987 and 2015

Article

Full-text available

Dec 2017
BMC Res Notes

Objective: This paper evaluates one aspect of data quality within DHS surveys, the accuracy of age reporting as measured by age heaping. Other literature has explored this phenomenon, and this analysis build on previous work, expanding the analysis of the extent of age heaping across multiple countries, and across time. Results: This paper makes a comparison of the magnitude of Whipple's index of age heaping across all Demographic and Health Surveys from 1986 to 2015 in Sub-Saharan Africa. A random slope multilevel model is used to evaluate the trend in the proportion of respondents within each survey rounding their age to the nearest age with terminal digit 0 or 5. The trend in the proportion of misreported ages has remained flat, in the region of 5% of respondents misreporting their age. We find that Nigeria and Ghana have demonstrated considerable improvements in age reporting quality, but that a number of countries have considerable increases in the proportion of age misreported, most notably Mali and Ethiopia with demonstrate increases in excess of 10% points.

Mapping under-5 and neonatal mortality in Africa, 2000-15: A baseline analysis for the Sustainable Development Goals

Article

Full-text available

Nov 2017

Background: During the Millennium Development Goal (MDG) era, many countries in Africa achieved marked reductions in under-5 and neonatal mortality. Yet the pace of progress toward these goals substantially varied at the national level, demonstrating an essential need for tracking even more local trends in child mortality. With the adoption of the Sustainable Development Goals (SDGs) in 2015, which established ambitious targets for improving child survival by 2030, optimal intervention planning and targeting will require understanding of trends and rates of progress at a higher spatial resolution. In this study, we aimed to generate high-resolution estimates of under-5 and neonatal all-cause mortality across 46 countries in Africa. Methods: We assembled 235 geographically resolved household survey and census data sources on child deaths to produce estimates of under-5 and neonatal mortality at a resolution of 5 × 5 km grid cells across 46 African countries for 2000, 2005, 2010, and 2015. We used a Bayesian geostatistical analytical framework to generate these estimates, and implemented predictive validity tests. In addition to reporting 5 × 5 km estimates, we also aggregated results obtained from these estimates into three different levels-national, and subnational administrative levels 1 and 2-to provide the full range of geospatial resolution that local, national, and global decision makers might require. Findings: Amid improving child survival in Africa, there was substantial heterogeneity in absolute levels of under-5 and neonatal mortality in 2015, as well as the annualised rates of decline achieved from 2000 to 2015. Subnational areas in countries such as Botswana, Rwanda, and Ethiopia recorded some of the largest decreases in child mortality rates since 2000, positioning them well to achieve SDG targets by 2030 or earlier. Yet these places were the exception for Africa, since many areas, particularly in central and western Africa, must reduce under-5 mortality rates by at least 8·8% per year, between 2015 and 2030, to achieve the SDG 3.2 target for under-5 mortality by 2030. Interpretation: In the absence of unprecedented political commitment, financial support, and medical advances, the viability of SDG 3.2 achievement in Africa is precarious at best. By producing under-5 and neonatal mortality rates at multiple levels of geospatial resolution over time, this study provides key information for decision makers to target interventions at populations in the greatest need. In an era when precision public health increasingly has the potential to transform the design, implementation, and impact of health programmes, our 5 × 5 km estimates of child mortality in Africa provide a baseline against which local, national, and global stakeholders can map the pathways for ending preventable child deaths by 2030. Funding: Bill & Melinda Gates Foundation.

Measuring progress and projecting attainment on the basis of past trends of the health-related Sustainable Development Goals in 188 countries: An analysis from the Global Burden of Disease Study 2016

Article

Full-text available

Sep 2017

Background: The UN's Sustainable Development Goals (SDGs) are grounded in the global ambition of “leaving no one behind”. Understanding today's gains and gaps for the health-related SDGs is essential for decision makers as they aim to improve the health of populations. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016), we measured 37 of the 50 health-related SDG indicators over the period 1990–2016 for 188 countries, and then on the basis of these past trends, we projected indicators to 2030. Methods: We used standardised GBD 2016 methods to measure 37 health-related indicators from 1990 to 2016, an increase of four indicators since GBD 2015. We substantially revised the universal health coverage (UHC) measure, which focuses on coverage of essential health services, to also represent personal health-care access and quality for several non-communicable diseases. We transformed each indicator on a scale of 0–100, with 0 as the 2·5th percentile estimated between 1990 and 2030, and 100 as the 97·5th percentile during that time. An index representing all 37 health-related SDG indicators was constructed by taking the geometric mean of scaled indicators by target. On the basis of past trends, we produced projections of indicator values, using a weighted average of the indicator and country-specific annualised rates of change from 1990 to 2016 with weights for each annual rate of change based on out-of-sample validity. 24 of the currently measured health-related SDG indicators have defined SDG targets, against which we assessed attainment. Findings: Globally, the median health-related SDG index was 56·7 (IQR 31·9–66·8) in 2016 and country-level performance markedly varied, with Singapore (86·8, 95% uncertainty interval 84·6–88·9), Iceland (86·0, 84·1–87·6), and Sweden (85·6, 81·8–87·8) having the highest levels in 2016 and Afghanistan (10·9, 9·6–11·9), the Central African Republic (11·0, 8·8–13·8), and Somalia (11·3, 9·5–13·1) recording the lowest. Between 2000 and 2016, notable improvements in the UHC index were achieved by several countries, including Cambodia, Rwanda, Equatorial Guinea, Laos, Turkey, and China; however, a number of countries, such as Lesotho and the Central African Republic, but also high-income countries, such as the USA, showed minimal gains. Based on projections of past trends, the median number of SDG targets attained in 2030 was five (IQR 2–8) of the 24 defined targets currently measured. Globally, projected target attainment considerably varied by SDG indicator, ranging from more than 60% of countries projected to reach targets for under-5 mortality, neonatal mortality, maternal mortality ratio, and malaria, to less than 5% of countries projected to achieve targets linked to 11 indicator targets, including those for childhood overweight, tuberculosis, and road injury mortality. For several of the health-related SDGs, meeting defined targets hinges upon substantially faster progress than what most countries have achieved in the past. Interpretation: GBD 2016 provides an updated and expanded evidence base on where the world currently stands in terms of the health-related SDGs. Our improved measure of UHC offers a basis to monitor the expansion of health services necessary to meet the SDGs. Based on past rates of progress, many places are facing challenges in meeting defined health-related SDG targets, particularly among countries that are the worst off. In view of the early stages of SDG implementation, however, opportunity remains to take actions to accelerate progress, as shown by the catalytic effects of adopting the Millennium Development Goals after 2000. With the SDGs' broader, bolder development agenda, multisectoral commitments and investments are vital to make the health-related SDGs within reach of all populations.

bayesPop : Probabilistic Population Projections

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Full-text available

Dec 2016
J STAT SOFTW

We describe bayesPop, an R package for producing probabilistic population projections for all countries. This uses probabilistic projections of total fertility and life expectancy generated by Bayesian hierarchical models. It produces a sample from the joint posterior predictive distribution of future age-and sex-specific population counts, fertility rates and mortality rates, as well as future numbers of births and deaths. It provides graphical ways of summarizing this information, including trajectory plots and various kinds of probabilistic population pyramids. An expression language is introduced which allows the user to produce the predictive distribution of a wide variety of derived population quantities, such as the median age or the old age dependency ratio. The package produces aggregated projections for sets of countries, such as UN regions or trading blocs. The methodology has been used by the United Nations to produce their most recent official population projections for all countries, published in the World Population Prospects.

Rising Preferred Retirement Age in Europe: Are Europe's Future Pensioners Adapting to Pension System Reforms?

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Nov 2016
J Aging Soc Pol

Moritz Heß

This study investigates whether older workers have adapted their preferred retirement age to the pension reforms aimed at extending working life. Based on data from Eurobarometer and the European Social Survey in 12 European countries, the analysis shows that future pensioners have indeed increased their preferred retirement age and adjusted to the new credo of late retirement. However, the strength of the increase was found to vary between different groups of older workers: it is much stronger for the higher educated than for the lower educated. This finding supports recent concerns regarding the re-emergence of social inequality in the retirement process.

Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement

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Full-text available

Jun 2016
PLOS MED

Measurements of health indicators are rarely available for every population and period of interest, and available data may not be comparable. The Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) define best reporting practices for studies that calculate health estimates for multiple populations (in time or space) using multiple information sources. Health estimates that fall within the scope of GATHER include all quantitative population-level estimates (including global, regional, national, or subnational estimates) of health indicators, including indicators of health status, incidence and prevalence of diseases, injuries, and disability and functioning; and indicators of health determinants, including health behaviours and health exposures. GATHER comprises a checklist of 18 items that are essential for best reporting practice. A more detailed explanation and elaboration document, describing the interpretation and rationale of each reporting item along with examples of good reporting, is available on the GATHER website.

Concepts, Methods and Practical Applications in Applied Demography

Book

Jan 2018

Richard K. Thomas

This textbook offers a comprehensive overview of applied demography by presenting both basic concepts and methodological techniques. It allows students from the social and human sciences, demographers, consultants and anyone interested in applied demography to gain an understanding of a wide range of practical applications of demographic concepts, methods and techniques to real- world problems. Featured sidebars highlight relevant terms and concepts and case studies and exercises throughout the book offer first-hand exposure to demographic applications. Charts and graphs supplement the presentation of demographic concepts and a glossary provides an inventory of relevant terms. The first section reviews basic components of applied demography as a context for understanding and addressing societal issues. It details the methods, techniques and data sources applied by demographers in a variety of areas. Coverage includes cohort analysis, data standardization, population estimation, and the use of geographic in- formation systems (GIS). The second section focuses on the substantive areas in which demography is currently applied. The topics covered include business demography, health demography, political demography, educational demography, and applications to urban and regional planning. The book illustrates the many ways in which demographers contribute to the formulation of public policy and the resolution of societal issues.

The Demographic Dividend: A New Perspective on the Economic Consequences of Population Change

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Jan 2003

Estimates of Global Bilateral Migration Flows by Gender between 1960 and 2015

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Guy J. Abel

An indirect estimation method is used to derive country to country migration flows from changes in global bilateral stock data. Estimates are obtained over five- and 10-year periods between 1960 and 2015 by gender, providing a comprehensive picture of past migration patterns. The estimated total of global international migrant flows generally increases over the 55-year time frame. The global rate of migration over five- and 10-year periods fluctuate at around 0.65 and 1.25 percent of the population, respectively. The sensitivity of estimates to alternative input stock and demographic data are explored.

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