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

Wayne State University

Eugene Applebaum College of Pharmacy and Department of Pharmacy Practice Health Sciences

11-10-2018

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

Paul Evan Kilgore Wayne State University, [email protected]

Follow this and additional works at: https://digitalcommons.wayne.edu/pharm_practice

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Recommended Citation GBD 2017 Population and Fertility Collaborators and Kilgore, Paul Evan, "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" (2018). Department of Pharmacy Practice. 3. https://digitalcommons.wayne.edu/pharm_practice/3

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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

GBD 2017 Population and Fertility Collaborators*

Summary Background Population estimates underpin demographic and epidemiological research and are used to track progress Lancet 2018; 392: 1995–2051 on numerous international indicators of health and development. To date, internationally available estimates of This online publication has been population and fertility, although useful, have not been produced with transparent and replicable methods and do not corrected. The corrected version use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility first appeared at thelancet.com on June 20, 2019 and population by sex with standardised and replicable methods. *Collaborators listed at the end of the paper Methods We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 Correspondence to: with standardised and replicable methods. We based the estimates on the demographic balancing equation, with Prof Christopher J L Murray, inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital Institute for Health Metrics and registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary Evaluation, Seattle, WA 98121, USA birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a [email protected] 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. www.thelancet.com Vol 392 November 10, 2018 1995 Global Health Metrics

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

Introduction generated by the UN Population Division at the Age-sex-specific estimates of population are a bedrock of Department of Economics and Social Affairs (UNPOP) epidemiological and economic analyses, and they are for population denominators,2,3 although it is not well integral to planning across several sectors of society. As documented how often these estimates are used by the denominator for most indicators, such estimates national governments. The UNPOP has produced permeate every aspect of our understanding of health population estimates since 1951, and it uses a de and development. Errors in population estimates affect centralised approach to estimation.4 For example, the national and international target tracking and time-series Latin American and Caribbean Demographic Centre and cross-country analyses of development outcomes. produces estimates for Latin America, whereas estimates The importance of accurate population estimates for for all other groups of countries are developed by analysts government planning cannot be overstated: population in New York. Although the UNPOP describes a general size, age, and composition dictate the national need for approach of examining data on fertility, mortality, infrastructure, housing, education, employment, health migration, and population and searching for consistency,5 care, care of older people, electoral representation, replicable statistical methods are not used. Decisions on provision of public health and services, food supply, and how to deal with inconsistency between the compo security.1 Similarly, fertility rates, both by maternal age nents of fertility, mortality, and migration within and overall, are key drivers of population growth and population counts are left to individual analysts, leading important social outcomes in their own right. to considerable heterogeneity in approaches across Many governments typically produce national popu countries. Accordingly, discrepancies between UNPOP lation estimates by age and sex for planning purposes. and nationally produced estimates—for instance, in Most international studies and comparative indicators, 2015, the population estimates for Mexico by UNPOP including the Millennium Development Goals and the were 4·6 million more than those of Mexico’s National Sustainable Development Goals, rely on the estimates Population Council (125·9 million by UNPOP vs

Research in context Evidence before this study transparent data and replicable analytical code, applying a Population estimates by age and sex are extensively used in all standardised approach to the estimation of population for each forms of epidemiological and demographic analysis. National single year of age for each calendar year from 1950 to 2017 for estimates of population and fertility for age and sex groups 195 countries and territories and for the globe. This study have been produced by the UN Population Division since 1951. provides improved population estimates that are internally The US Census Bureau produces revised demographic estimates consistent with the Global Burden of Diseases, Injuries, and Risk for 15 to 30 countries each year. Several national authorities Factors Study’s assessment of fertility and mortality, which are produce their own population estimates, particularly those in important inputs to other epidemiological research and high and middle Socio-demographic Index countries. These government planning. efforts are all based on the cohort-component method of Implications of all the available evidence population projection, namely that population in an age group Population counts by age and sex that are produced with a at a given time t must equal the population in that cohort at transparent and empirical approach will be useful for the start of the time period (t–1) plus new entrants and minus epidemiological and demographic analyses. The production of people exiting the population because of migration and death. annual estimates will also facilitate timely tracking of progress Although these estimates are based on the demographic on global indicators, including the Sustainable Development balancing equation, estimates are not based on standardised, Goals. In the future, the methods applied here can be used to transparent, or replicable statistical methods. enhance population estimation at the subnational level. 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

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121·3 million by National Population Council)—cannot The cohort-component method of population projection currently be resolved.4,6 extends this demographic balancing equation to estimate The US Census Bureau’s International Division internally consistent age-sex-specific populations. The periodically releases detailed population analyses for method requires estimates of ASFRs, sex ratio at birth, selected countries, with new revisions produced for age-sex-specific net migration, and age-sex-specific mor 15 to 30 countries per year.7 Other organisations, such as tality rates that are consistent with observed population the Population Reference Bureau,8 the World Bank,9 counts that have been corrected for underenumeration or the Wittgenstein Centre,10 and Gapminder Foundation11 overenumeration. GBD provides a consistent set of age- also release population estimates, but these are largely sex-specific mortality rates with standardised methods;15 in combinations of national estimates with selected UNPOP this analysis, we estimated the sex ratio at birth, ASFR, and or US Census Bureau analyses. Many of the organi age-sex-specific migration rates consistent with the sations who estimate or report on population also available population data to create a full time series of provide fertility estimates, which, in addition to affecting population estimates by age and sex. population trends, are used to monitor reproductive These estimates comply with GATHER (appendix 1 See Online for appendix 1 health service delivery in many locations. To our section 5). Analyses were done with R version 3.3.2, Python knowledge, global estimates of annual population by age version 2.7.14, or Stata version 13.1. Data and statistical and sex with underlying primary data and replicable code for all analyses are publicly available online. For the statistical code see computer code and statistical modelling details are not http://ghdx.healthdata.org/gbd- available from any source. Geographical units and time periods 2017 The Global Burden of Diseases, Injuries, and Risk We produced single calendar-year and single year-of- Factors Study (GBD) is committed to the Guidelines on age population estimates for 195 countries and territories Accurate and Transparent Health Estimates Reporting that were grouped into 21 regions and seven super- (GATHER).12 Continued use of the UNPOP population regions. The seven super-regions are central Europe, estimates in GBD is not compatible with GATHER eastern Europe, and central Asia; high income; because the methods used for UNPOP estimation are Latin America and the Caribbean; north Africa and the not transparent and uncertainty intervals are not Middle East; south Asia; southeast Asia, east Asia, and estimated for populations.4 Moreover, UNPOP population Oceania; and sub-Saharan Africa. Each year, GBD includes estimates, especially in years between or after a census, subnational analyses for a few new countries and are inconsistent with GBD estimates because there is a continues to provide subnational estimates for countries marked difference between UNPOP and GBD estimates that were added in previous cycles. Subnational estimation of age-specific mortality in many instances.13,14 For this in GBD 2017 includes five new countries (Ethiopia, Iran, GBD 2017 paper, we sought to produce population New Zealand, Norway, Russia) and countries previously estimates and associated fertility estimates for estimated at subnational levels (GBD 2013: China, Mexico, 195 countries and territories from 1950 to 2017 that were and the UK [regional level]; GBD 2015: Brazil, India, based on the available census or population registry data Japan, Kenya, South Africa, Sweden, and the USA; GBD and survey and census data on age-specific fertility rates 2016: Indonesia and the UK [local government authority (ASFR; ie, the annual number of livebirths to women of a level]). All analyses are at the first level of administrative specified age group per 1000 women in that age group) organisation within each country except for New Zealand by use of replicable methods, leveraging the previous (by Māori ethnicity), Sweden (by Stockholm and non- GBD work that estimated age-sex-specific mortality Stockholm), and the UK (by local government authorities). rates.15 To achieve this goal, we aimed to conduct All subnational estimates for these countries were systematic analyses of available sources that could incorporated into model development and evaluation as inform ASFR estimation and to systematically identify part of GBD 2017. To meet data use requirements, in this and extract census and population registry data. publication we present all subnational estimates excluding those pending publication (Brazil, India, Japan, Kenya, Methods Mexico, Sweden, the UK, and the USA); given space Overview constraints, these results are presented in appendix 2 See Online for appendix 2 As with all population estimation, the underlying instead of the main text. Subnational estimates for equation used for GBD is based on the demographic countries with populations of more than 200 million balancing equation16 people (assessed by use of our most recent year of published estimates) that have not yet been published N(T)=N(0) + B(0,T) – D(0,T) + G(0,T) elsewhere are presented wherever estimates are illus trated with maps but are not included in tables. Estimates where N (T) is the population at a given time, N (0) is the were produced for the years 1950–2017. 1950 was selected population at the start of the interval, B (0,T) is livebirths as the start year for the analysis because we were unable to during the interval, D (0,T) is deaths during the interval, locate sufficient data on ASFR, mortality, and population and G (0,T) is net migration during the interval. before 1950. www.thelancet.com Vol 392 November 10, 2018 1997 Global Health Metrics

Fertility schooling as a covariate. Spline knots were selected by Fertility data are obtained from vital registration systems, inspection of the data to identify where there was a complete birth histories, or summary birth histories. reversal in trend. The purpose of this approach was to Complete birth histories include the date of birth and, if capture an increase in fertility rates in women aged applicable, the dates of death of all children ever born 30 years or older while the ASFR for women aged alive to each woman that is interviewed, whereas 20–24 years decreased below a specific threshold. Given summary birth histories include the total number of that the point of inflection for the ASFR for women aged children ever born alive to each mother and the total 30 years or older relative to the ASFR for women number of those children born alive to each mother that aged 20–24 years varied by super-region, we fit the models have died. In countries with complete birth registration, separately for some GBD super-regions (high income; vital registration systems typically provide tabulations of sub-Saharan Africa; and central Europe, eastern Europe, births by age of the mother. From 1890,17 some censuses and central Asia) and modelled the rest of the super- asked about the number of children ever born to a regions together. The first step of the model also included woman, and this question has been widely asked in location-and-source-specific random effects to correct bias censuses and many household surveys in the past from non-sampling error in different source types, such 70 years. From the 1970s, fertility information has also as incomplete vital registration. Hyperparameters for the been collected through complete birth histories, model were selected on the basis of a measure of data beginning with the World Fertility Survey, then the density. Further details on this process are provided in Demographic and Health Surveys, and, in some appendix 1 (section 2). countries, the Multiple Indicator Cluster Surveys, In the second stage of the analysis, we used the sponsored by the UN Children’s Fund. We identified ASFR estimates from the first stage to process and 977 censuses and household surveys that had summary incorporate several forms of aggregated data. First, we birth history data, 429 household surveys that had split cumulative cohort fertility data (ie, children ever complete birth history data, and 7817 country-years of born) from summary birth history into period ASFR data. birth registration systems through searches of national For this split, we computed the ratio between reported statistical sources and the Demographic Yearbooks children ever born alive from each 5-year cohort of women produced by the UN Statistics Division from 1948 to represented in a given data source and the total fertility for present.18 The number and type of sources for each each of these cohorts that was implied by the first-stage location are provided in appendix 1 (section 5). The estimates of ASFR by location and year. This ratio For the Global Health Data Global Health Data Exchange provides the metadata for was applied as a scaling factor to our estimated cohort Exchange see http://ghdx. all these sources. ASFR at 5-year intervals (when all members of the cohort healthdata.org/ Given the hetergeneous nature of the data (vital all belong to a single 5-year GBD age group), to distribute registration, summary birth histories, complete birth experienced fertility (ie, from age 10 years until the date of histories), we used a two-stage approach to modelling the the survey in women interviewed from the cohorts ASFR for the age groups 15–19 years, 20–24 years, specified in the original data) back across age and time. 25–29 years, 30–34 years, 35–39 years, 40–44 years, and Additionally, we used the estimated age proportion of 45–49 years. The two-stage approach was designed to take livebirths from the first stage to distribute total reported advantage of the greater availability of some summary livebirths by the age of the mother. Lastly, for historical birth history data for the period 1950 to 1975 and to help location aggregates for which we had registry data (eg, the to compensate for the lower availability of complete birth Soviet Union), we used the estimated proportions of age- history data in some low-income countries. For the specific livebirths in constituent locations from the first fertility rates in those aged 10–14 years and 50–54 years, stage to allocate births back in time to their current which are much lower than in other age groups and for GBD geographies. This new set of methods allowed us to which only vital registration data were available, we used supplement the model with a substantial amount of a separate, simpler approach, described later in this additional information about the overall fertility. We then section. re-estimated ASFR as described, with all vital registration, In the first stage of our analysis, we used spatiotemporal complete birth history, and split data to produce final Gaussian process regression to analyse vital registration fertility estimates for women aged 15–49 years. and complete birth history data.15,19 For spatiotemporal In both the first and second stage, data were adjusted in Gaussian process regression, the prior was estimated the mixed-effects model on the basis of random separately for women aged 20–24 years, with average years effects values (appendix 1 section 2) by selecting a of schooling in women aged 20–24 years as the covariate. reference or benchmark source. In locations with For all other age groups, the prior was estimated with a complete child death registration (see previous GBD spline on the estimated ASFR for women aged 20–24 years analyses),15,20 vital registration was typically the benchmark and with the average years of schooling for the age group or reference source. In other locations, Demographic and of interest. The prior for GBD locations in the high- Health Survey complete birth history data were used as income super-region did not include average years of the reference source. If neither vital registration nor

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Demographic and Health Survey complete birth histories assuming a time-invariant prior for the mean because, in were available, other complete birth history sources were the absence of sex-selective abortion, we would not expect used as the reference. If no vital registration or complete the sex ratio at birth to deviate significantly from its birth history data were used, then the average of all natural equilibrium. Hyperparameters for spatiotemporal remaining summary birth history sources were used as smoothing and Gaussian process regression were chosen reference. Where sources were inconsistent or on the basis of data-density scores, taking into account implausible time trends were identified, some reference both the quantity and quality of available data. Our source designations were modified; the final choice of analysis only produced national estimates of sex ratio at reference sources for each location are provided in the birth—including for Hong Kong and Macau—for all appendix 1 (section 5). years from 1950 to 2017; thus, we assume that subnational Many household surveys on fertility excluded women in sex ratio at birth equals the national sex ratio at birth. the age groups 10–14 years and 50–54 years, and With additional data seeking and extraction, we will these data were limited to 3947 country-years of vital extend the analysis to all GBD locations in the next GBD registration data. To estimate fertility in girls aged study. Further details regarding sex ratio at birth 10–14 years, we used a linear regression of the log of the estimation are shown in appendix 1 (section 2). 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 Population aged 15–19 years. For women aged 50–54 years, we found To determine national and subnational populations, we no covariates that predicted variation in the ratio of ASFR searched the Integrated Public Use Microdata Series for women aged 50–54 years to the ASFR for those aged questionnaires, the UN Demographic Yearbook, the UN 45–49 years. In this case, we assumed the ratio of ASFR census programme census dates, and the International for women aged 50–54 years to ASFR for women aged Population Census Biography to identify all censuses 45–49 years was constant across locations and over time. conducted between 1950 and 2017 and available popu Our analysis generated a full set of ASFRs for each lation registers.22–25 We included 1233 censuses and location and year from 1950 to 2017; we used these ASFRs 26 population registers that contained 730 location-years to compute the total fertility rate (TFR), which is the of census or population registry data. In some cases, average number of children a woman would bear if she the same census was reported by different sources survived through the end of the reproductive age span in different years. We resolved these inconsistencies (age 10–54 years) and experienced at each age a particular through a review of available documentation. A list of set of ASFRs observed in the year of interest. We also all confirmed censuses isshown in the appendix 1 estimated the total fertility rate under age 25 years (section 5). We obtained population counts that were age- (TFU25; number of livebirths expected by age 25 years for sex-specific from 1171 censuses and only by sex from a hypothetical woman who survived the age group and 62 censuses. We sought to identify whether the counts in was exposed to current ASFRs) and the total fertility in each census were de facto (allocated to the place of women older than 30 years (TFO30; number of livebirths enumeration) or de jure (allocated to the place of regular expected for a hypothetical woman ageing from 30 to or legal residence). Our basis for population estimation 54 years who survived the age group and was exposed to is the de-facto population and, where both counts were current ASFRs). These age ranges were computed available, we used de-facto counts. Where only de-jure because nearly all locations show decreases in the TFU25 counts were available—typically in lower Socio- over time, with few or no reversals. In women aged 30 demographic Index (SDI) countries—we assumed that years or older, there is a clear U-shaped curve, with de-jure and de-facto populations were similar. The main decreases followed by sustained increases; in women difference between the counts at the national level is the aged 25–29 years, the pattern is less consistent. The exclusion of some migrant workers in some de-jure fertility rate in girls aged 10–19 years is a Sustainable counts; where migrant workers are known to be an Development Goal (SDG) indicator for goal 3, target 3.7: important fraction of the population and de-facto counts ensure universal access to sexual and reproductive health- were not available, we searched directly for data on care services, including for family planning, information documented migration. and education, and the integration of reproductive health In several cases, the UN does not recognise admin into national strategies and programmes.21 istrative splits in territories, including Kosovo and We estimated the sex ratio at birth with 4690 unique Serbia, Transnistria and Moldova, and the so-called location-years of registered livebirths by sex, 1756 location- Turkish Republic of Northern Cyprus and Cyprus.26 years of census and population registry counts that In these cases, we obtained census counts for the included children younger than 1 year and younger than components and interpolated to generate census counts 5 years by sex, and 2490 location-years of the proportion for the full territory. For east and west Germany before of live-born males from complete birth history. These unification, as the input to the model, we used census data informed a spatiotemporal Gaussian process counts for each component and interpolation to regression model of the proportion of live-born males, generate estimates of joint census counts in years www.thelancet.com Vol 392 November 10, 2018 1999 Global Health Metrics

closest to the censuses in both locations. We were able More details on the age-heaping corrections are shown in to obtain census counts for five of the six constituent appendix 1 (section 2). For all censuses in low and middle components that made up Yugoslavia; for Serbia we SDI countries, we did not use the census count of split aggregate Yugoslavia census data with previous children younger than 5 years in our model estimation. population estimates. For Singapore, we estimated the In other words, population estimates in these age groups population for residents and non-resident workers were driven by fertility and mortality estimates and combined (appendix 1 section 2). Of the 1963 location- consistency with the later census counts for the same years of census or population registry data, cohort. Systematic overestimation of age, particularly in 72 location-years were identified as outliers that were some countries in sub-Saharan Africa and Latin America, inconsistent with adjacent data, model analysis, or was apparent in the data; for example, census counts excluded subpopulations. could only be explained by large immigration of Census counts are typically undercounts of the actual populations at older ages, which appears implausible. population, although there are known cases in which We were unable to correct the data for these issues and censuses have overcounted the population.27–29 Post- used the modelling strategy that is subsequently enumeration surveys (PESs) aim to identify instances of described to deal with these challenges. overcounts or undercounts by comparing data. Many, if Our approach requires an estimate of the population not most, PESs are not published or are only reported in in 1950 in all locations for detailed age and sex groups; government releases, presentations, or online reports. only 54 countries had a census count in 1950. For PESs themselves are subject to considerable error, whether most other locations, we used backwards application of they use a direct or indirect method of estimating census the cohort-component method of population projection by completeness. We searched for all available PES results use of the oldest available census and the reverse and supplemented these results with publications or application of estimated mortality rates and an assumption presentations that provided summaries of other PESs.30–34 of zero net migration (appendix 1 section 2). As sub We identified 165 PESs, although it is likely that many sequently noted, in our GBD Bayesian demographic more were done that did not publicly report their results. balancing modelling framework, the baseline population We analysed the 165 PESs to generate a general model of is assumed to be measured with substantial error, and census completeness as a function of SDI. Because of the model produced posterior estimates that varied variable quality of PESs, we assumed that, in aggregate, considerably from this initial baseline. the 165 PESs provided an unbiased view of the association We used the estimates of population by location and between enumeration completeness and SDI, so we year for each single year of age to generate other adjusted census counts by the predictions from this model. summary measures, including population growth rates We used nationally reported PES results to adjust census that assumed logarithmic growth and the proportion counts in high SDI countries and used the estimated of the population that was of working age, which is census completeness to adjust data in other settings. defined by the Organisation for Economic Co-operation To account for systematic age variation in census and Development and the World Bank as those aged enumeration, we input age-sex-specific PES results into 15–64 years.41,42 DisMod-MR 2.1, a Bayesian meta-regression tool, to estimate a global age pattern of enumeration. This age Mortality pattern was then used to adjust the overall predicted The GBD mortality process produced annual abridged life enumeration to vary by age (appendix 1 section 2). tables that comprised 24 age groups: younger than 1 year, As has been extensively noted in the demographic 1–4 years, and then 5-year age groups up to age 110 years literature, census counts have several common problems: or older.13 To project populations forwards in time with the undercounts (particularly of children younger than cohort-component method of population projection, we 5 years), a tendency to exaggerate age at older ages, and needed annual period life tables with single-year age age heaping (reporting ages rounded to the nearest groups up to 95 years or older. For ages 15–99 years, we 35–38 5 or 10 years). The population counts from interpolated abridged lx values (the number of people still four different censuses, illustrating the different types of alive at age x for a hypothetical cohort in a period life table) age heaping and undercounts, are shown in figure 1. We by use of a monotone cubic spline with Hyman filtering.43,44 evaluated the age structure and consistency of census For people younger than 15 years and older than 100 years, data by calculating sex and age ratios for each census. we applied regression coefficients to predict single-year These ratios were then used to calculate sex and age ratio age group probability of death values. The Human scores, which were combined into a joint score. The joint Mortality Database provided 4557 empirical full-period life score was used to determine whether to apply a correction tables for 48 locations. We excluded 1280 of the life tables to the census counts or not. For census counts available because they were identified by the Human Mortality in 1-year age groups, we used the Feeney correction; Database as problematic or occurred during time periods for counts available in 5-year or 10-year age groups, we with extremely high mortality, such as World War 2 or used either the Arriaga or Arriaga strong correction.39,40 the 1918 influenza pandemic. To predict probability of

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ata rocessi ste 3 000 000 PES-corrected data 2 000 000 Un-age-heaped data Raw data orce Model posterior Datapoints 1 500 000 2 000 000

1 000 000

1 000 000 Population count/age group interval length 500 000

0 0

300 000 1 500 000

200 000 1 000 000

100 000 500 000 Population count/age group interval length

0 0 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 Middle of age group (years) 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.

death qx at age x for single-year age groups, we fit the age groups. The regression coefficients were applied to following separate linear regression by single-year age children younger than 15 years because of the unique group between ages zero and 110: patterns of single-year mortality younger than 15 years and to adults older than 100 years because of instability log( q )=β + β log( q ) + ε 1 xf 0 1 5 xa xf caused by low lx values at older ages. To mitigate instability caused by spikes in mortality due to fatal discontinuities where 1qxf is the single-year age group qx value from the full- such as wars and natural disasters, full-period life tables period life table, β0 is the coefficient for the intercept,1 β is were first generated based on abridged life tables without the coefficient for the slope, εxf is the error term, and 5qxa is fatal discontinuities, and then fatal discontinuities were the correponding abridged life-table age group’s qx value. added to nmx (the death rate in age group x to x + 1 for a

These predicted 1qxf values were scaled to the GBD abridged hypothetical cohort in a period life table) assuming a life-table 5qx values for consistency. constant death rate for fatal discontinuities within each For those aged 15–99 years, the non-parametric spline age group. To produce full life tables with the complete approach did not require rescaling to match the abridged set of single-year age group 1qx values, we assumed 1ax (the

5qx values and, consequently, produced smooth steps average number of years lived in age group x to x + 1 by in mortality across single-year ages and between 5-year people who died during the interval for a hypothetical www.thelancet.com Vol 392 November 10, 2018 2001 Global Health Metrics

cohort in a period life table) was 0·5 in all age groups algorithm often chose the model version that did not except for those younger than 1 year and older than exclude any of the census data for older ages but, in other 110 years; these groups were assumed to be identical to regions, the population estimates at older ages were

the abridged life-table 1ax values. driven by the census counts for younger ages and the mortality estimates that aged those people forwards in Migration time (appendix 1 section 2). Real data on age-specific net migration are more difficult An example of the fit to the available population data for to obtain than data on fertility, population, and mortality. the eight largest populations in 2017 is shown in figure 2. Net migration includes any change in the de-facto Overall, the in-sample fit of the model for age-sex-specific population that is not accounted for by births or deaths; population log space had an R² value of 0·99. These fits this number would include refugees and temporary show that the model closely tracks the available corrected workers. For most country-years, documented net census counts for all ages combined and by age. Code migration data are not reported and undocumented net for the GBD Bayesian demographic balancing model migration is not estimated. For some high-SDI countries, is available at the Global Health Data Exchange. The net migration is tracked and reported,45 and the UN High population estimates and census and registry data for all Commission for Refugees (UNHCR) reports the stock of 195 countries and territories are shown in appendix 2. refugees (the count of people not born in the country that they currently live in) in each country by country of origin The cohort-component method of population at the end of year. In more recent census rounds, census projection and uncertainty questions on the number of foreign-born individuals We produced final population estimates by single year and living in a country have been used, as have assumptions by single-year age groups with the cohort-component on differential survival to estimate when migration method of population projection.16 The population in occurred;46 however, these approaches, especially for the each single-year age group in each year was estimated period before 2000, have considerable uncertainty on the basis of the estimated starting population and associated with them and are heavily dependent on single-year, single-age rates of migration, fertility, and fertility and mortality assumptions for migrants. mortality. Uncertainty in population estimates comes We developed and applied the GBD Bayesian demo from two fundamental sources: uncertainty about the graphic balancing model to estimate net migration by completeness of a census count in a census year and single year of age and single calendar year, consistent uncertainty between censuses due to errors in estimates with our estimates of age-sex-specific mortality and of migration, fertility, and mortality. Uncertainty in the ASFR and the observed population data. Our model was counts was estimated by sampling the variance-covariance developed on the basis of the work of Wheldon and matrix of the model that predicted census completeness. colleagues47–49 but includes important modifications, such We estimated the uncertainty between counts by use as correlation of migration rates across ages and over of out-of-sample predictive validity. We held out data time and single-year, single-age estimation. Details on and estimated the error in estimates as a function of our GBD Bayesian demographic balancing model, the minimum of the number of years to the next developed in Template Model Builder, an open-source or previous census. We combined these two sources of statistical package for R,50 are shown in the appendix 1 uncertainty and generated 1000 draws of percentage error (section 2). in the population for each location-year. The 1000 draws of In applying the model, we dealt with known issues of percentage error in the population and the population age misreporting by including larger input data variance mean, generated by the GBD Bayesian demographic for population counts at the youngest ages and input balancing model, were then combined to create 1000 draws variance that steadily increases after age 45 years. The of population by age, sex, location, and year. 95% uncer choice of data variance was based on testing of a range of tainty intervals (UIs) were calculated with the 2·5th and variance assumptions; variance assumptions only change 97·5th percentiles. Details of this out-of-sample estimation the point estimates of the results in settings where there of uncertainty are shown in appendix 1 (section 2). Out-of- is substantial inconsistency between adjacent census sample estimates of uncertainty yielded larger uncertainty counts or between census counts (or both) and in the than in-sample methods because of the nearly perfect key inputs. To address age misreporting in the oldest inverse correlation between migration and death rates, ages, we ran several model versions for each location. For which was conditional on census counts with low error. each model version, we excluded census counts above a A dot plot comparison of our total population counts by given maximum age from the model fitting process country for different age groups in 2017 with UNPOP (appendix 1 section 5). We then selected the best model estimates is shown in appendix 2. version by prioritising versions that used the highest maximum age, predicted low absolute values of migration SDI in the age groups older than 55 years, and had good GBD 2015 developed the SDI as a composite measure of in-sample fits. In high-income locations, the selection TFR in a population, lag-distributed income per capita,

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Males eales Males eales 800 orce 800 Zero migration prior Model posterior

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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 measure of development as a proxy for the status of than 15 years.15,20 Each component was rescaled to a value women in society; other plausible measures capturing between 0 and 1, and the SDI was derived from their the status of women are not available for all countries geometric mean. The TFR was used in this overall over a long time period. Our analysis of detailed ASFR

5·0 e ro ears 50−54 45−49 4·5 40−44 35−39 30−34 4·0 25−29 20−24 15−19 3·5 10−14

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100

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0 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2017 Year

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.

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revealed in many countries that, through the process of 1980, the global population increased exponentially at an development the TFO30 generally decreased and then annualised rate of 1·9% (95% UI 1·88–1·92). From increased. For example, in the USA, the TFO30 has 1981 to 2017, however, the pace of the global popu increased steadily from 1975. In exploratory analysis, we lation increase has been largely linear, increasing by found that the TFU25 did not show this U-shaped pattern 83·6 million (79·8–87·5) people per year. Over the past as countries develop. For GBD 2017, we have recalculated 10 years (2007–17), the average annual increase in the SDI by use of the TFU25 as a better proxy for the population has been by 87·2 million (80·8–93·2) people, status of women in society. The TFU25 not only does compared with 81·5 million (79·0–84·5) people per year not show a U-shaped pattern with development but in the previous 10 years (1997–2007). The global also remains highly correlated with under-5 mortality population increased by 197·2% (95% UI 193·3–200·8), (Pearson correlation coefficient r=0·873) and other from 2·6 billion (2·5–2·6) people in 1950 to 7·6 billion mortality measures. The revised method for computing (7·4–7·9) people in 2017. Over this period, the SDI compared with the GBD 2016 method is correlated composition of the world’s population changed with the GBD 2017 method (r=0·992). Detailed substantially. In 1950, the high-income, central Europe, comparisons of the GBD 2015 and GBD 2016 methods eastern Europe, and central Asia GBD super-regions compared with the approach we used are shown in accounted for 35·2% of the global population but, in appendix 1 (section 3). 2017, the populations of these countries accounted for 19·5% of the global population. Large increases occurred Role of the funding source in the proportion of the world’s population living in The funder of the study had no role in study design, data south Asia, sub-Saharan Africa, Latin America and collection, data analysis, data interpretation, or writing of the Caribbean, and north Africa and the Middle East. the report. All authors had full access to all the data in the The annual population growth rate between 1950 and study and had final responsibility for the decision to 2017, globally and for the GBD super-regions, is shown submit for publication. in figure 4. Growth of the global population increased in the 1950s and reached 2·0% per year in 1964, then Results slowly decreased to 1·1% in 2017. The slow shift in the Global global population growth rate is determined by The global TFR by maternal age group from 1950 to 2017 markedly different trends by super-region. Growth of is shown in figure 3. In 1950, the TFR was 4·7 livebirths the population in north Africa and the Middle East (95% UI 4·5–4·9) and, by 2017, the TFR had decreased by increased until the 1970s, and it has remained quite 49·4% (46·4–52·0) to 2·4 livebirths (2·2–2·5). From 1950 high, at 1·7% in 2017. Population growth rates in sub- to 1995, the TFR within all 5-year maternal age groups Saharan Africa increased from 1950 to 1985, decreased decreased: the greatest decrease in terms of contribution during 1985–1993, increased again until 1997, and then to TFR was in women aged 20–24 years (who showed a plateaued; at 2·7% in 2017, population growth rates decrease of 0·42 livebirths), 25–29 years (0·52 livebirths), were almost the highest rates ever recorded in this and 30–34 years (0·38 livebirths). Since 1995, decreases in region. The most substantial changes to population the contribution to TFR from women aged 30–34 years, growth rates were in the southeast Asia, east Asia, and 35–39 years, and 40–44 years effectively plateaued at the Oceania super-region, where the population growth global level, whereas decreases in women at younger ages rate decreased from 2·5% in 1963 to 0·7% in 2017. The continued. This slowing trend in reductions in the large reduction in the population growth rate for this number of livebirths per woman in these age groups super-region around 1960 was due to the Great Leap masks marked heterogeneity across countries, as we Forward in China. In central Europe, eastern Europe, subsequently discuss. Of the total livebirths globally in and central Asia, the population growth rate dropped 2017, 9·4% occurred in teenage mothers, which is a rapidly after 1987 and was negative from 1993 to 2008. reduction from 9·9% of livebirths to teenage mothers in Growth rates in the high-income super-region have 1950. The age-specific fertility rate per 1000 women aged changed the least, starting at 1·2% in 1950 and reaching 10–19 years decreased from 37 livebirths (34–40) per 0·4% in 2017. 1000 women in 1950 to 22 livebirths (19–24) per 1000 Global population pyramids in 1950, 1975, 2000, and women in 2017. The number of livebirths globally 2017 are shown in figure 5. As the world’s population increased from 92·6 million livebirths (88·9–96·4 has grown, not only has the distribution of the global million) in 1950 to a peak of 141·7 million livebirths population shifted toward sub-Saharan Africa and (135·8–147·3 million) in 2012. Over the past 35 years, the south Asia, but the age structure of the global population number of livebirths annually has varied within a has also changed considerably. In 1950, the global mean relatively narrow range of 133·2 million (130·1–136·2) age of a person was 26·6 years, decreasing to 26·0 years, livebirths to 141·7 million (135·8–147·3) livebirths. in 1975, then increasing to 29·0 years in 2000 and The trend in world population from 1950 to 2017 by 32·1 years in 2017. Demographic change has economic GBD super-region is shown in figure 4. From 1950 to consequences, and the proportion of the population that www.thelancet.com Vol 392 November 10, 2018 2005 Global Health Metrics

G serreio Central Europe, eastern Europe, and central Asia Latin America and Caribbean South Asia Sub-Saharan Africa High income North Africa and Middle East Southeast Asia, east Asia, and Oceania 1·0

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Population growth rate (%) 1

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

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.

is of working age (15–64 years) decreased from 59·9% in population is the proportion of the population that is 1950 to 57·1% in 1975, then increased to 62·9% in 2000 female, which decreased from 50·1% to 49·8% over the and 65·3% in 2017. Another dimension of the global 67-year period.

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National less than 0·25 livebirths included many in western Fertility rates vary substantially across countries and Europe, Japan, South Korea, and Taiwan (province of over time (table 1; appendix 2). In 1950, TFR ranged China). TFU25 exceeded 1·5 livebirths in many parts from a low of 1·7 livebirths (95% UI 1·4–2·0) in Andorra of western, eastern, and central sub-Saharan Africa and to a high of 8·9 livebirths (8·7–9·0) in Jordan. The TFR in Afghanistan. Trends in TFO30 are more complex; decreased in all 195 countries and territories between decreases in fertility rate are observed at earlier stages of 1950 and 2017, and 102 countries and territories showed development, and there are sustained increases in a decrease of more than 50%. By 2017, the TFR ranged fertility rate at higher levels of development due to from a low of 1·0 livebirths (0·9–1·2) in Cyprus to a women delaying childbearing. TFO30 ranged from a high of 7·1 livebirths (6·8–7·4) in Niger. Although a low of 0·3 livebirths (0·3–0·4) in Puerto Rico to a high useful summary, the TFR masks variation in trends in of 3·1 livebirths (3·0–3·2) in Niger. In 2017, 145 countries fertility at different ages in many countries. The global showed higher fertility in women older than 30 years decrease in median ASFRs from 1950 to 2017 was than in women younger than 25 years. The geographical 43·4% in women aged 15–19 years and 49·4% in women pattern shows low fertility in women older than 30 years aged 20–24 years, which contrasts with the observed in disparate settings: central and eastern Europe, China, decreases in the median ASFR in older age groups of India, many parts of Latin America, and in some parts mothers of 59·4% in women aged 40–44 years, 65·6% in of the Middle East. North America, western Europe, women aged 45–49 years, and 68·7% in women aged central Europe, eastern Europe, Australasia, and high- 50–54 years. income Asia Pacific had a higher TFO30 in 2017 than In 2017, the TFU25 ranged from 0·08 livebirths in 1975, with a mean of 60·2% higher TFO30 in (95% UI 0·07–0·09) in South Korea to 2·4 livebirths these regions. (2·2–2·6) in Niger (figure 6), which is 31 times higher. Figure 7 shows the areas where the TFO30 has Countries and territories where the TFU25 was been increasing since 1975; increases of more than

100 e Female mean Female median Male mean 75 Male median e Females Males 50 Age (years)

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50 Age (years)

400 300 200 100 0 100 200 300 400 400 300 200 100 0 100 200 300 400 Population (millions) Population (millions)

Figure 5: Global population pyramids for females and males by age, in 1950, 1975, 2000, and 2017 www.thelancet.com Vol 392 November 10, 2018 2007 Global Health Metrics

50% have been observed in most of western Europe, as observed in many Persian Gulf nations. A comparison high-income North America, Australasia, and high- of the 2017 population growth rate versus the TFR is income Asia Pacific. The correlation of the ASFR over shown in figure 9, which highlights countries in which maternal age groups is shown in appendix 2. In 2017, the TFR is less than the replacement value but where 169 countries had a sex ratio of less than 1·07 males the population is still growing. The countries where the per female at birth. Countries with higher sex ratios at population is declining are also shown. Countries fall birth varied geographically (figure 7). For example, into four quadrants, defined as a TFR of more than Greenland, Tunisia, and Afghanistan had sex ratios or less than the replacement value and a population between 1·07 and 1·10 males per female at birth, and growth rate of more than or less than zero. Divergence India had a sex ratio at birth of 1·10 males per female. between these two measures, as noted, is a function of Three countries had higher sex ratios at birth: Armenia lags between period TFR and growth rate (population (1·14 males per female), Azerbaijan (1·15 males per momentum) or net migration. female), and China (1·17 males per female). High sex Population estimates by country since 1950 are shown ratios at birth lower the effective net reproductive rate in table 2. Age-sex-specific detail for these same years is (the number of female livebirths expected per woman, provided in appendix 2. Single-year, single-age population given observed age-specific death and fertility rates) estimates for the entire period of 1950–2017 are available even more than the TFR. Estimates of the net from the Global Health Data Exchange. reproductive rate are shown in table 1. Net reproductive The proportion of the population that was of working rate in 2017 ranged from 0·48 female livebirths age from 1950 to 2017 by GBD super-region is shown in (0·42–0·56) expected per woman in Cyprus to figure 10. Studies of economic growth have identified 3·00 female livebirths (2·90–3·10) expected per woman the potential for a demographic dividend when the in Niger. 95 countries had a net reproductive rate of less proportion of the population that is of working age than 1 meaning that, without changes in fertility, death reaches more than 65%.53 In high-income countries, the rates, or net immigration, populations in those proportion of the population that is of working age countries will eventually decrease. increased from the 1960s, crossed the 65% threshold in The population growth rate from 2010 to 2017 is shown the late 1970s, and was relatively constant during in figure 8. 33 countries had a negative population the 1980s and 1990s. In 2005, this proportion began to growth rate, most of which were located in central, decrease and was only just more than the 65% threshold eastern, and western Europe and the Caribbean. in 2017. 12 of 34 high-income countries now have a Outside Europe, negative growth rates were observed proportion of the population of working age that is less in 14 countries, and the largest negative growth rates than 65%, and Japan has a working-age proportion of were observed in Syria, the Northern Mariana Islands, less than 60%. Other than sub-Saharan Africa and high- Georgia, Puerto Rico, and the Virgin Islands. Cyprus income countries, the GBD super-regions have had a (which has a growth rate of 1·7%), Israel (1·9%), and substantially increasing proportion of the population of Luxembourg (2·3%) are notable in the GBD western working age from the mid-1960s to the present day; in Europe region because they are the only countries with a 2017, Latin American and the Caribbean, north Africa growth rate greater than 1·2%. Population growth rates and the Middle East, south Asia, and central Europe, in North America, Latin America, and the Caribbean eastern Europe, and central Asia all had proportions of ranged from –0·5% in Puerto Rico to 2·6% in Belize. the population that are of working age between 64% and Population growth rates of more than 2·0% were 71%. The most pronounced increase in the working-age seen in 33 of 46 countries in sub-Saharan Africa. population occurred in southeast Asia, east Asia, and The Persian Gulf states, with the exception of the Oceania, which increased from 54·2% of the population United Arab Emirates, all had growth rates of more than in 1965 to 72·2% in 2011. Sub-Saharan Africa is the clear 2·2%, mostly due to the migration of workers, not outlier among GBD super-regions; the proportion of the fertility rates. Australia is of note among the GBD population of working age in this region has remained high-income super-region in the southern hemisphere, at or less than 55% during the entire time period, with a high population growth rate of 1·5%. although this proportion has more recently increased. Even when countries have a TFR of less than the In sub-Saharan Africa, the proportion of the population replacement value (the TFR at which a population that is of working age was less than 50% in Mali replaces itself from generation to generation, assuming (49·7%), Chad (46·6%), and Niger (46·1%) in 2017. no migration; generally estimated to be 2·05),51 popu lations can continue to grow because of population Discussion momentum: the phenomenon by which the past growth Main findings of birth cohorts leads to more women of childbearing To our knowledge, this study presents the first estimates age and increased births relative to deaths, even though of population by location from 1950 to 2017 that are the TFR for a time period is less than the replacement based on transparent data and replicable analytical value.52 Populations can also grow due to immigration, code. Annual population estimates are provided for

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Age-specific fertility rate (livebirths per 1000 women annually) Total Total Total fertility Number of Net fertility fertility rate rate from ages livebirths reproductive rate under age 30 to 54 years rate 25 years 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 years years years years years years years years years Global 0·81 42·9 129·4 131·9 96·8 52·4 17·2 3·4 0·06 2·4 0·87 0·85 138 810 622 1·08 (0·35– (38·6– (117·8– (125·9– (91·4– (47·7– (15·4– (3·1– (0·06– (2·2–2·5) (0·78–0·96) (0·79–0·92) (129 960 385– (1·02–1·16) 1·69) 48·0) 142·7) 138·7) 102·9) 57·5) 19·2) 3·8) 0·06) 149 058 367) Low SDI 1·4 71·9 202·6 188·4 147·0 93·4 44·6 15·1 0·29 3·8 1·4 1·5 37 891 965 1·68 (0·6– (65·0– (181·7– (177·3– (136·9– (83·6– (39·5– (13·7– (0·28– (3·6–4·1) (1·2–1·5) (1·4–1·6) (35 159 071– (1·58–1·81) 2·9) 80·0) 225·9) 200·9) 158·2) 103·5) 49·9) 16·7) 0·3) 41 108 482) Low-middle SDI 0·88 51·7 156·6 152·0 112·4 63·8 24·1 6·4 0·12 2·8 1·0 1·0 40 394 490 1·28 (0·39– (45·5– (141·0– (143·4– (104·4– (57·0– (21·0– (5·5– (0·11– (2·6–3·1) (0·9–1·2) (0·9–1·1) (37 088 216– (1·18–1·4) 1·85) 59·3) 174·3) 161·4) 121·8) 72·0) 27·7) 7·5) 0·12) 44 296 344) Middle SDI 0·61 33·5 112·4 120·1 79·2 39·0 11·3 1·4 0·03 2·0 0·73 0·65 26 502 966 0·83 (0·27– (30·1– (100·6– (113·3– (73·7– (34·4– (9·9– (1·1– (0·03– (1·8–2·2) (0·66–0·82) (0·6–0·72) (24 536 281– (0·77–0·9) 1·27) 37·6) 125·9) 127·9) 85·3) 44·2) 13·1) 1·6) 0·03) 28 871 941) High-middle SDI 0·42 19·8 84·2 107·0 69·4 32·1 8·0 0·63 0·01 1·6 0·52 0·55 22 028 156 0·86 (0·18– (18·3– (79·0– (103·4– (65·8– (29·2– (7·2– (0·53– (0·01– (1·5–1·7) (0·49–0·56) (0·51–0·59) (20 983 021– (0·81–0·9) 0·86) 21·6) 89·9) 110·7) 73·1) 35·3) 8·8) 0·74) 0·01) 23 184 413) High SDI 0·25 12·5 49·6 89·5 98·6 51·8 11·1 0·63 0·01 1·6 0·31 0·81 11 638 396 0·76 (0·11– (11·3– (44·4– (84·3– (91·2– (45·2– (9·4– (0·55– (0·01– (1·4–1·7) (0·28–0·35) (0·73–0·9) (10 631 265– (0·69–0·83) 0·5) 14·0) 55·7) 95·2) 106·8) 59·4) 13·2) 0·72) 0·01) 12 780 564) Central 0·08 27·0 102·9 110·0 77·0 32·6 6·3 0·29 0·01 1·8 0·65 0·58 5 224 690 0·84 Europe, eastern (0·03– (23·5– (89·3– (102·9– (70·1– (27·8– (5·3– (0·24– (0·01– (1·6–2·0) (0·56–0·75) (0·52–0·66) (4 687 984– (0·76–0·94) Europe, and 0·15) 31·2) 118·2) 117·7) 84·8) 38·2) 7·5) 0·35) 0·01) 5 805 610) central Asia Central Asia 0·05 35·8 172·3 145·6 91·2 39·3 9·6 0·53 0·01 2·5 1·0 0·7 1 910 928 1·15 (0·02– (30·7– (151·6– (137·2– (82·3– (33·4– (7·8– (0·37– (0·01– (2·3–2·7) (0·9–1·2) (0·62–0·81) (1 754 242– (1·05–1·25) 0·1) 41·8) 195·2) 154·7) 102·1) 47·0) 12·0) 0·79) 0·01) 2 076 808) Armenia 0·04 24·8 113·7 103·4 50·3 20·5 3·7 0·2 0·0 1·6 0·69 0·37 38 128 0·73 (0·02– (21·3– (99·3– (95·0– (44·0– (16·9– (2·9– (0·13– (0·0– (1·4–1·7) (0·6–0·79) (0·33–0·42) (34 976– (0·67–0·8) 0·09) 28·8) 129·9) 113·6) 58·1) 24·9) 4·6) 0·31) 0·0) 41 387) Azerbaijan 0·01 44·1 148·3 118·6 55·6 21·0 4·5 0·46 0·01 2·0 0·96 0·41 173 728 0·87 (0·0– (37·4– (128·8– (108·9– (48·9– (17·5– (3·6– (0·31– (0·01– (1·7–2·2) (0·83–1·11) (0·35–0·47) (153 488– (0·77–0·99) 0·02) 52·0) 169·9) 129·2) 63·2) 25·2) 5·5) 0·72) 0·01) 196 430) Georgia 0·26 46·3 126·8 119·8 71·1 35·8 9·0 0·89 0·02 2·0 0·87 0·58 50 298 0·97 (0·11– (39·3– (109·2– (109·9– (62·7– (29·6– (7·3– (0·67– (0·02– (1·9–2·2) (0·74–1·01) (0·5–0·69) (45 798– (0·88–1·07) 0·54) 54·6) 146·5) 131·5) 81·3) 43·9) 11·3) 1·21) 0·02) 55 247) Kazakhstan 0·05 30·1 140·1 149·2 93·7 52·5 12·4 0·5 0·01 2·4 0·85 0·8 347 980 1·13 (0·02– (26·5– (120·0– (137·1– (81·5– (41·6– (9·3– (0·31– (0·01– (2·2–2·6) (0·73–0·99) (0·66–0·97) (315 168– (1·03–1·25) 0·1) 34·4) 162·7) 162·3) 108·8) 66·6) 16·8) 0·81) 0·01) 381 856) Kyrgyzstan 0·01 38·2 151·2 171·9 119·1 57·0 18·0 0·26 0·0 2·8 0·95 0·97 151 035 1·31 (0·01– (32·6– (132·1– (159·8– (107·2– (48·0– (14·6– (0·16– (0·0– (2·6–3·0) (0·82–1·09) (0·85–1·1) (141 013– (1·22–1·4) 0·03) 44·7) 172·4) 186·0) 131·6) 66·9) 22·0) 0·39) 0·01) 161 162) Mongolia 0·21 27·2 147·3 159·7 114·5 69·6 20·1 1·5 0·03 2·7 0·87 1·0 75 835 1·27 (0·09– (23·9– (130·5– (148·4– (103·9– (60·3– (16·6– (1·0– (0·03– (2·5–2·9) (0·77–0·98) (0·9–1·2) (70 120– (1·17–1·37) 0·43) 30·8) 165·0) 171·4) 125·6) 79·7) 24·1) 2·1) 0·03) 81 639) Tajikistan 0·06 55·7 226·8 208·3 128·9 68·3 19·2 2·1 0·04 3·5 1·4 1·1 285 161 1·62 (0·03– (47·3– (199·4– (193·6– (112·4– (53·5– (14·0– (1·4– (0·04– (3·2–3·9) (1·2–1·6) (0·9–1·3) (259 803– (1·47–1·77) 0·13) 65·4) 255·5) 223·8) 148·6) 87·5) 26·4) 3·4) 0·04) 310 494) Turkmenistan 0·04 19·2 156·1 190·9 125·8 49·1 10·4 0·01 0·0 2·8 0·88 0·93 109 634 1·29 (0·02– (16·4– (135·1– (176·5– (113·3– (41·1– (8·3– (0·01– (0·0– (2·5–3·1) (0·77–1·0) (0·81–1·07) (98 243– (1·15–1·45) 0·07) 22·5) 181·5) 207·5) 140·8) 59·4) 13·2) 0·01) 0·0) 123 307) Uzbekistan 0·03 32·3 194·3 127·8 85·2 25·4 5·1 0·2 0·0 2·4 1·1 0·58 679 125 1·09 (0·01– (27·4– (169·4– (115·6– (74·9– (20·8– (3·9– (0·13– (0·0– (2·1–2·6) (1·0–1·3) (0·5–0·68) (619 142– (1·0–1·19) 0·06) 38·0) 221·0) 142·5) 97·8) 31·5) 6·7) 0·31) 0·0) 740 880) Central Europe 0·19 19·5 57·2 93·1 78·0 31·9 5·5 0·24 0·0 1·4 0·38 0·58 1 066 904 0·69 (0·08– (17·5– (49·7– (86·7– (70·9– (27·0– (4·8– (0·2– (0·0– (1·3–1·6) (0·34–0·44) (0·52–0·65) (960 814– (0·62–0·76) 0·39) 21·6) 66·0) 99·8) 86·2) 37·8) 6·4) 0·28) 0·0) 1 187 258) (Table 1 continues on next page)

www.thelancet.com Vol 392 November 10, 2018 2009 Global Health Metrics

Age-specific fertility rate (livebirths per 1000 women annually) Total Total Total fertility Number of Net fertility fertility rate rate from ages livebirths reproductive rate under age 30 to 54 years rate 25 years 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 years years years years years years years years years (Continued from previous page) Albania 0·07 19·0 104·0 144·9 73·8 28·4 5·9 0·37 0·01 1·9 0·62 0·54 37 047 0·88 (0·03– (15·9– (86·8– (132·5– (63·5– (21·9– (4·2– (0·26– (0·01– (1·6–2·2) (0·51–0·73) (0·45–0·65) (32 029– (0·76–1·02) 0·13) 22·7) 123·8) 158·3) 85·5) 36·5) 8·1) 0·52) 0·01) 42 830) Bosnia and 0·05 10·1 48·3 87·1 74·0 27·3 4·8 0·32 0·01 1·3 0·29 0·53 27 688 0·6 Herzegovina (0·02– (8·6– (42·7– (81·7– (67·7– (23·2– (3·9– (0·23– (0·01– (1·2–1·4) (0·26–0·33) (0·49–0·58) (25 627– (0·56–0·65) 0·11) 11·7) 54·6) 92·8) 80·7) 31·9) 5·9) 0·45) 0·01) 29 913) Bulgaria 0·74 39·3 72·2 87·0 65·4 25·7 3·9 0·2 0·0 1·5 0·56 0·48 58 874 0·71 (0·33– (34·2– (61·9– (79·8– (57·6– (20·8– (3·0– (0·14– (0·0– (1·3–1·7) (0·48–0·65) (0·41–0·55) (51 873– (0·62–0·8) 1·54) 45·4) 84·0) 94·9) 74·1) 31·7) 4·9) 0·27) 0·0) 66 693) Croatia 0·06 9·8 45·6 87·7 85·9 38·4 6·4 0·32 0·01 1·4 0·28 0·66 36 549 0·66 (0·03– (8·5– (40·5– (82·7– (79·4– (33·5– (5·2– (0·23– (0·01– (1·3–1·4) (0·25–0·31) (0·59–0·72) (34 544– (0·63–0·7) 0·12) 11·6) 51·3) 93·0) 92·6) 43·8) 7·7) 0·44) 0·01) 38 688) Czech Republic 0·03 12·6 51·1 99·4 103·0 43·4 6·2 0·2 0·0 1·6 0·32 0·76 104 681 0·76 (0·01– (10·7– (44·9– (93·5– (95·5– (37·6– (4·7– (0·12– (0·0– (1·4–1·7) (0·28–0·37) (0·7–0·84) (95 942– (0·7–0·84) 0·06) 14·8) 58·2) 105·9) 111·2) 50·0) 7·9) 0·32) 0·0) 114 456) Hungary 0·28 21·7 47·0 82·4 86·4 39·9 7·2 0·22 0·0 1·4 0·35 0·67 86 143 0·69 (0·12– (18·7– (40·0– (75·7– (77·5– (33·2– (5·6– (0·15– (0·0– (1·3–1·6) (0·29–0·4) (0·59–0·76) (76 294– (0·61–0·78) 0·56) 25·3) 55·3) 89·7) 96·4) 47·7) 9·5) 0·3) 0·0) 97 319) Macedonia 0·27 15·9 64·9 104·8 81·5 29·4 4·2 0·26 0·01 1·5 0·41 0·58 23 593 0·71 (0·12– (13·7– (58·5– (99·2– (75·4– (25·1– (3·1– (0·17– (0·0– (1·4–1·6) (0·37–0·44) (0·54–0·62) (22 076– (0·67–0·76) 0·54) 18·5) 71·7) 110·7) 87·9) 34·1) 5·6) 0·38) 0·01) 25 167) Montenegro 0·12 11·3 63·3 112·5 95·1 42·9 8·8 0·45 0·01 1·7 0·37 0·74 7069 0·79 (0·05– (9·6– (56·4– (106·2– (88·3– (37·2– (6·9– (0·29– (0·01– (1·6–1·8) (0·33–0·43) (0·69–0·79) (6742–7432) (0·76–0·84) 0·24) 13·5) 71·9) 119·1) 102·2) 49·4) 11·4) 0·69) 0·01) Poland 0·05 12·7 49·8 89·9 73·5 29·7 5·7 0·23 0·0 1·3 0·31 0·55 355 970 0·63 (0·02– (10·9– (42·6– (83·1– (65·7– (24·5– (4·5– (0·17– (0·0– (1·2–1·5) (0·27–0·37) (0·47–0·63) (315 476– (0·56–0·71) 0·11) 14·9) 58·2) 97·3) 82·2) 35·8) 7·1) 0·31) 0·0) 402 395) Romania 0·38 34·9 71·9 97·4 73·7 28·9 4·7 0·22 0·0 1·6 0·54 0·54 177 010 0·75 (0·17– (30·5– (61·9– (90·0– (65·7– (23·6– (3·6– (0·16– (0·0– (1·4–1·7) (0·48–0·6) (0·47–0·62) (158 216– (0·67–0·84) 0·79) 40·6) 83·5) 105·5) 82·8) 35·3) 6·1) 0·29) 0·0) 198 220) Serbia 0·2 15·4 60·1 90·8 74·7 28·5 4·4 0·32 0·01 1·4 0·38 0·54 80 547 0·66 (0·09– (13·3– (51·0– (83·4– (66·3– (23·4– (3·4– (0·22– (0·01– (1·2–1·6) (0·32–0·44) (0·47–0·62) (71 021– (0·58–0·75) 0·4) 18·0) 70·7) 99·0) 84·2) 34·6) 5·7) 0·45) 0·01) 91 372) Slovakia 0·13 23·1 54·5 87·0 76·7 31·8 5·3 0·21 0·0 1·4 0·39 0·57 52 596 0·67 (0·06– (20·2– (46·4– (79·9– (68·2– (26·1– (4·1– (0·15– (0·0– (1·2–1·6) (0·33–0·45) (0·5–0·65) (46 603– (0·59–0·76) 0·27) 26·5) 63·9) 94·8) 86·3) 38·6) 6·7) 0·28) 0·0) 59 441) Slovenia 0·03 5·0 41·5 108·1 103·0 39·7 5·9 0·23 0·0 1·5 0·23 0·74 19 132 0·73 (0·01– (4·1– (34·9– (100·3– (93·7– (33·0– (4·3– (0·14– (0·0– (1·4–1·7) (0·2–0·28) (0·66–0·84) (17 463– (0·67–0·8) 0·07) 6·0) 49·4) 117·2) 114·0) 48·5) 8·0) 0·36) 0·0) 21 101) Eastern Europe 0·03 25·3 80·1 98·6 70·6 30·4 5·6 0·23 0·0 1·6 0·53 0·53 2 246 857 0·74 (0·01– (21·8– (68·1– (90·2– (61·7– (24·2– (4·3– (0·17– (0·0– (1·4–1·8) (0·45–0·62) (0·45–0·63) (1 958 844– (0·64–0·85) 0·07) 29·4) 94·0) 107·8) 80·6) 37·8) 7·3) 0·3) 0·0) 2 577 202) Belarus 0·02 19·2 84·7 104·3 72·3 29·3 4·8 0·16 0·0 1·6 0·52 0·53 101 939 0·75 (0·01– (16·5– (74·5– (95·9– (64·1– (24·3– (3·8– (0·11– (0·0– (1·4–1·8) (0·46–0·59) (0·46–0·62) (90 523– (0·67–0·85) 0·03) 22·2) 96·3) 113·6) 81·4) 35·3) 6·1) 0·23) 0·0) 114 916) Estonia 0·04 14·1 52·8 98·6 90·7 46·9 10·1 0·3 0·01 1·6 0·33 0·74 13 446 0·75 (0·02– (11·8– (44·6– (91·1– (81·6– (38·9– (7·5– (0·18– (0·01– (1·4–1·8) (0·28–0·4) (0·64–0·85) (11 863– (0·66–0·86) 0·07) 16·9) 62·5) 106·9) 100·8) 56·2) 13·5) 0·46) 0·01) 15 268) Latvia 0·03 18·7 62·8 100·2 84·4 41·2 8·2 0·32 0·01 1·6 0·41 0·67 19 399 0·76 (0·01– (15·9– (53·0– (92·2– (74·8– (33·8– (6·4– (0·22– (0·01– (1·4–1·8) (0·34–0·48) (0·59–0·77) (17 182– (0·67–0·86) 0·06) 22·0) 74·2) 109·0) 95·1) 50·0) 10·3) 0·43) 0·01) 21 920) Lithuania 0·03 15·8 61·1 114·5 89·8 36·2 6·3 0·24 0·0 1·6 0·39 0·66 29 108 0·78 (0·02– (13·6– (51·8– (105·7– (80·0– (29·7– (5·0– (0·17– (0·0– (1·4–1·8) (0·33–0·45) (0·58–0·75) (25 844– (0·69–0·88) 0·07) 18·5) 72·0) 124·2) 100·8) 43·8) 7·7) 0·31) 0·0) 32 717) (Table 1 continues on next page)

2010 www.thelancet.com Vol 392 November 10, 2018 Global Health Metrics

Age-specific fertility rate (livebirths per 1000 women annually) Total Total Total fertility Number of Net fertility fertility rate rate from ages livebirths reproductive rate under age 30 to 54 years rate 25 years 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 years years years years years years years years years (Continued from previous page) Moldova 0·05 23·5 75·7 82·2 53·7 21·6 4·1 0·13 0·0 1·3 0·5 0·4 35 612 0·62 (0·02– (20·3– (65·1– (74·9– (47·3– (17·9– (3·4– (0·09– (0·0– (1·2–1·5) (0·44–0·56) (0·34–0·47) (31 581– (0·55–0·71) 0·1) 27·3) 89·2) 91·0) 61·6) 26·5) 5·2) 0·2) 0·0) 40 581) Russia 0·03 25·6 81·7 101·1 74·1 32·3 6·0 0·23 0·0 1·6 0·54 0·56 1 622 870 0·77 (0·01– (22·0– (68·9– (92·5– (64·7– (25·6– (4·4– (0·15– (0·0– (1·4–1·9) (0·46–0·63) (0·48–0·67) (1 410 393– (0·66–0·88) 0·07) 29·9) 96·4) 110·7) 84·8) 40·5) 7·9) 0·33) 0·0) 1 868 353) Ukraine 0·03 26·8 77·7 89·4 57·8 23·7 4·6 0·24 0·0 1·4 0·52 0·43 424 480 0·66 (0·01– (23·3– (66·2– (81·5– (50·3– (19·0– (3·5– (0·17– (0·0– (1·2–1·6) (0·45–0·61) (0·37–0·51) (369 821– (0·58–0·76) 0·06) 30·9) 90·9) 98·1) 66·5) 29·5) 5·8) 0·34) 0·0) 487 645) High income 0·36 16·2 53·5 91·0 104·4 56·1 11·6 0·65 0·01 1·7 0·35 0·86 11 470 352 0·81 (0·16– (14·8– (47·9– (85·6– (95·9– (48·6– (9·6– (0·57– (0·01– (1·5–1·8) (0·32–0·39) (0·77–0·97) (10 419 059– (0·73–0·89) 0·73) 17·8) 60·1) 96·9) 113·6) 64·8) 14·1) 0·75) 0·01) 12 658 766) Australasia 0·22 14·5 51·9 101·1 125·9 69·8 14·4 0·81 0·02 1·9 0·33 1·1 373 680 0·91 (0·1– (12·8– (45·4– (94·5– (116·1– (60·6– (11·7– (0·51– (0·01– (1·7–2·1) (0·29–0·38) (0·9–1·2) (338 110– (0·83–1·01) 0·45) 16·5) 59·2) 108·1) 136·3) 80·0) 17·8) 1·31) 0·02) 413 048) Australia 0·15 13·3 49·2 98·9 125·0 69·4 14·4 0·82 0·02 1·9 0·31 1·0 313 630 0·89 (0·06– (11·3– (41·8– (91·5– (113·8– (58·9– (11·1– (0·51– (0·02– (1·6–2·1) (0·27–0·37) (0·9–1·2) (278 661– (0·79–1·01) 0·3) 15·6) 57·8) 107·1) 137·1) 81·4) 18·4) 1·33) 0·02) 353 002) New Zealand 0·59 20·1 66·4 114·1 131·7 72·0 14·9 0·74 0·01 2·1 0·44 1·1 60 050 1·01 (0·25– (17·2– (58·4– (106·9– (122·3– (63·0– (11·7– (0·46– (0·01– (1·9–2·3) (0·38–0·5) (1·0–1·2) (54 692– (0·92–1·12) 1·2) 23·9) 76·4) 122·5) 142·7) 83·0) 18·6) 1·2) 0·01) 66 222) High-income Asia 0·01 3·4 23·8 74·1 103·3 47·4 7·5 0·22 0·0 1·3 0·14 0·79 1 427 130 0·63 Pacific (0·01– (2·9– (19·5– (67·8– (93·5– (39·7– (5·8– (0·15– (0·0– (1·2–1·5) (0·11–0·17) (0·69–0·9) (1 260 959– (0·56–0·71) 0·03) 4·0) 29·1) 80·8) 114·1) 56·5) 9·8) 0·31) 0·0) 1 623 740) Brunei 0·28 12·4 53·7 107·4 111·8 70·1 19·8 0·55 0·01 1·9 0·33 1·0 7093 0·89 (0·12– (10·3– (43·8– (97·8– (98·6– (56·8– (14·6– (0·34– (0·01– (1·7–2·0) (0·27–0·4) (0·9–1·1) (6568–7631) (0·82–0·96) 0·57) 14·9) 65·4) 117·5) 125·8) 87·0) 26·3) 0·86) 0·01) Japan 0·01 4·0 29·6 81·9 96·7 46·3 8·0 0·21 0·0 1·3 0·17 0·76 922 225 0·65 (0·0– (3·3– (23·1– (72·7– (82·6– (35·2– (5·6– (0·13– (0·0– (1·1–1·6) (0·13–0·21) (0·62–0·92) (767 131– (0·54–0·77) 0·01) 4·9) 37·6) 92·2) 112·5) 59·9) 11·1) 0·31) 0·0) 1 106 046) Singapore 0·08 4·4 20·4 65·0 101·4 51·7 9·6 0·37 0·01 1·3 0·12 0·82 64 836 0·61 (0·03– (3·6– (15·8– (57·4– (86·9– (39·5– (6·8– (0·23– (0·01– (1·1–1·5) (0·1–0·16) (0·67–0·99) (54 182– (0·51–0·73) 0·16) 5·3) 26·0) 73·7) 117·8) 66·5) 13·3) 0·6) 0·01) 77 378) South Korea 0·02 1·7 13·5 60·6 117·4 48·9 6·1 0·24 0·0 1·2 0·08 0·86 432 974 0·6 (0·01– (1·4– (11·7– (57·3– (111·6– (44·2– (4·9– (0·15– (0·0– (1·2–1·3) (0·07–0·09) (0·81–0·92) (412 109– (0·57–0·63) 0·04) 2·1) 15·5) 64·1) 123·3) 53·8) 7·4) 0·39) 0·0) 453 553) High-income North 0·55 20·7 70·7 99·4 103·3 52·5 11·0 0·74 0·01 1·8 0·46 0·84 4 314 373 0·86 America (0·24– (18·8– (64·2– (94·4– (96·4– (46·5– (9·3– (0·55– (0·01– (1·7–1·9) (0·42–0·51) (0·76–0·92) (3 982 175– (0·8–0·94) 1·11) 22·7) 77·9) 104·8) 110·8) 59·2) 13·1) 0·99) 0·01) 4 683 089) Canada 0·15 12·8 46·8 99·2 111·9 51·8 9·5 0·42 0·01 1·7 0·3 0·87 390 262 0·8 (0·07– (10·7– (37·8– (89·7– (98·2– (40·8– (7·0– (0·28– (0·01– (1·4–1·9) (0·24–0·37) (0·73–1·02) (334 379– (0·69–0·94) 0·31) 15·3) 57·7) 109·7) 127·0) 64·8) 12·7) 0·62) 0·01) 455 010) Greenland 0·62 42·5 104·5 119·1 87·0 42·9 6·5 0·05 0·0 2·0 0·74 0·68 817 0·94 (0·27– (35·8– (87·6– (107·8– (74·8– (33·6– (4·6– (0·03– (0·0– (1·8–2·3) (0·65–0·84) (0·57–0·81) (728–910) (0·84–1·06) 1·28) 51·1) 123·9) 132·7) 100·8) 54·2) 9·2) 0·08) 0·0) USA 0·58 21·4 73·1 99·4 102·3 52·5 11·2 0·78 0·01 1·8 0·48 0·83 3 923 218 0·87 (0·25– (19·6– (66·9– (94·9– (96·2– (47·2– (9·6– (0·56– (0·01– (1·7–1·9) (0·43–0·52) (0·77–0·91) (3 646 761– (0·81–0·94) 1·19) 23·4) 80·0) 104·3) 108·9) 58·5) 13·2) 1·05) 0·02) 4 226 835) Southern Latin 1·5 53·7 92·0 96·7 90·6 61·8 15·6 0·99 0·02 2·1 0·74 0·84 1 041 669 1·0 America (0·7– (49·0– (82·4– (91·3– (81·3– (52·1– (12·2– (0·64– (0·02– (1·9–2·2) (0·68–0·79) (0·73–0·98) (958 720– (0·91–1·08) 3·2) 59·3) 102·6) 102·8) 100·9) 72·9) 19·8) 1·49) 0·02) 1 130 812) Argentina 1·7 58·2 100·2 101·3 92·0 63·8 15·8 1·0 0·02 2·2 0·8 0·86 747 539 1·04 (0·7– (51·9– (87·8– (93·9– (82·7– (54·1– (12·4– (0·7– (0·02– (2·0–2·3) (0·73–0·88) (0·75–0·99) (695 353– (0·97–1·12) 3·5) 66·0) 113·6) 110·2) 102·2) 75·0) 19·8) 1·6) 0·02) 801 816) (Table 1 continues on next page)

www.thelancet.com Vol 392 November 10, 2018 2011 Global Health Metrics

Age-specific fertility rate (livebirths per 1000 women annually) Total Total Total fertility Number of Net fertility fertility rate rate from ages livebirths reproductive rate under age 30 to 54 years rate 25 years 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 years years years years years years years years years (Continued from previous page) Chile 1·2 40·9 72·3 86·3 87·5 57·4 15·6 0·88 0·02 1·8 0·57 0·81 245 912 0·88 (0·5– (35·9– (62·3– (79·5– (78·2– (48·0– (12·1– (0·55– (0·02– (1·6–2·1) (0·5–0·66) (0·69–0·94) (215 928– (0·77–1·0) 2·4) 46·6) 83·9) 93·9) 97·8) 68·3) 19·9) 1·36) 0·02) 279 946) Uruguay 1·2 53·9 86·9 93·6 87·8 56·1 14·1 1·0 0·02 2·0 0·71 0·8 48 170 0·95 (0·5– (47·1– (74·5– (85·6– (77·7– (46·0– (10·5– (0·7– (0·02– (1·7–2·3) (0·61–0·82) (0·68–0·93) (41 869– (0·82–1·09) 2·6) 61·6) 101·2) 102·5) 99·2) 67·9) 18·6) 1·5) 0·02) 55 322) Western Europe 0·05 8·7 40·6 87·8 106·6 61·2 13·3 0·73 0·01 1·6 0·25 0·91 4 313 498 0·77 (0·02– (7·5– (35·0– (81·6– (97·6– (52·7– (11·1– (0·64– (0·01– (1·4–1·8) (0·21–0·29) (0·81–1·02) (3 871 044– (0·69–0·86) 0·1) 10·0) 47·2) 94·5) 116·6) 70·8) 15·8) 0·82) 0·01) 4 807 568) Andorra 0·22 4·9 26·7 57·2 85·1 52·1 12·6 0·96 0·02 1·2 0·16 0·75 642 0·58 (0·09– (4·2– (22·6– (52·2– (76·5– (45·5– (10·4– (0·59– (0·02– (1·1–1·3) (0·13–0·19) (0·67–0·85) (567–724) (0·51–0·65) 0·45) 5·6) 31·3) 62·6) 94·1) 59·3) 15·1) 1·45) 0·02) Austria 0·05 9·1 42·8 87·4 99·3 53·5 10·1 0·46 0·01 1·5 0·26 0·82 86 756 0·73 (0·02– (7·7– (37·4– (81·8– (91·8– (46·7– (8·2– (0·32– (0·01– (1·4–1·7) (0·23–0·3) (0·75–0·9) (79 382– (0·67–0·8) 0·11) 10·6) 49·0) 93·4) 107·5) 61·2) 12·4) 0·63) 0·01) 94 860) Belgium 0·04 7·6 43·6 114·3 111·8 49·7 9·7 0·51 0·01 1·7 0·26 0·86 121 588 0·82 (0·02– (6·4– (36·9– (106·5– (101·9– (41·7– (7·8– (0·36– (0·01– (1·5–1·9) (0·22–0·3) (0·77–0·96) (109 546– (0·74–0·91) 0·07) 9·1) 51·5) 122·7) 122·7) 59·0) 12·0) 0·71) 0·01) 134 907) Cyprus 0·03 4·0 24·5 58·8 68·3 35·9 9·3 1·1 0·02 1·0 0·14 0·57 10 788 0·48 (0·01– (3·3– (19·6– (52·8– (59·7– (28·7– (6·9– (0·7– (0·02– (0·9–1·2) (0·11–0·18) (0·49–0·67) (9310–12 496) (0·42–0·56) 0·06) 4·8) 30·6) 65·5) 78·1) 44·6) 12·6) 1·6) 0·02) Denmark 0·01 4·6 35·7 111·4 128·2 58·3 11·0 0·49 0·01 1·7 0·2 0·99 60 724 0·84 (0·01– (3·9– (29·6– (103·0– (116·8– (49·4– (8·8– (0·33– (0·01– (1·6–1·9) (0·17–0·24) (0·89–1·1) (54 681– (0·76–0·94) 0·03) 5·6) 43·0) 120·6) 140·5) 68·6) 13·6) 0·7) 0·01) 67 563) Finland 0·02 7·0 45·0 95·5 109·9 57·1 12·8 0·69 0·01 1·6 0·26 0·9 55 235 0·8 (0·01– (5·9– (38·5– (88·2– (99·5– (48·1– (10·3– (0·51– (0·01– (1·5–1·8) (0·22–0·3) (0·81–1·01) (49 589– (0·71–0·89) 0·03) 8·3) 52·6) 103·5) 121·3) 67·3) 15·8) 0·93) 0·01) 61 618) France 0·03 7·6 47·4 117·2 120·6 61·5 13·8 0·77 0·01 1·8 0·28 0·98 737 405 0·89 (0·01– (6·6– (41·0– (109·7– (110·6– (52·6– (11·1– (0·55– (0·01– (1·7–2·0) (0·24–0·32) (0·88–1·1) (664 102– (0·81–0·99) 0·06) 8·8) 54·8) 125·4) 131·4) 71·7) 17·1) 1·03) 0·02) 819 651) Germany 0·04 8·0 34·3 75·2 97·3 53·6 9·9 0·4 0·01 1·4 0·21 0·81 710 634 0·67 (0·02– (6·9– (29·4– (69·5– (88·5– (45·4– (7·9– (0·28– (0·01– (1·2–1·6) (0·18–0·25) (0·71–0·91) (633 455– (0·6–0·76) 0·07) 9·3) 40·1) 81·5) 107·1) 63·0) 12·3) 0·53) 0·01) 798 663) Greece 0·14 9·1 32·1 74·5 100·8 55·6 11·2 1·3 0·03 1·4 0·21 0·84 89 713 0·69 (0·06– (7·9– (27·0– (68·4– (91·0– (46·7– (8·6– (0·9– (0·02– (1·3–1·6) (0·18–0·24) (0·75–0·95) (79 740– (0·61–0·77) 0·29) 10·6) 38·2) 81·1) 111·5) 65·8) 14·6) 1·9) 0·03) 100 854) Iceland 0·07 9·6 54·5 109·1 112·7 64·9 15·3 0·5 0·01 1·8 0·32 0·97 4250 0·89 (0·03– (8·1– (46·5– (100·9– (102·0– (55·2– (12·2– (0·31– (0·01– (1·7–2·0) (0·28–0·37) (0·88–1·05) (3897–4639) (0·82–0·98) 0·14) 11·3) 63·9) 118·1) 124·6) 75·5) 19·2) 0·81) 0·01) Ireland 0·05 10·6 40·5 77·1 123·4 93·4 22·0 1·2 0·02 1·8 0·26 1·2 64 902 0·89 (0·02– (9·2– (34·4– (71·0– (112·4– (80·9– (17·6– (0·8– (0·02– (1·6–2·1) (0·22–0·29) (1·1–1·4) (57 702– (0·79–1·0) 0·09) 12·6) 47·6) 84·0) 135·4) 107·2) 27·2) 1·7) 0·02) 72 962) Israel 0·03 11·3 101·1 171·3 169·7 100·8 24·1 1·7 0·03 2·9 0·56 1·5 177 148 1·4 (0·01– (9·6– (88·1– (161·6– (157·9– (88·7– (19·8– (1·2– (0·03– (2·6–3·2) (0·49–0·65) (1·3–1·6) (161 025– (1·27–1·54) 0·06) 13·5) 115·8) 181·8) 182·1) 114·0) 29·1) 2·3) 0·03) 194 812) Italy 0·02 5·6 28·4 66·2 91·6 58·5 14·9 0·92 0·02 1·3 0·17 0·83 464 442 0·64 (0·01– (4·7– (24·3– (61·0– (83·0– (49·8– (12·0– (0·64– (0·02– (1·2–1·5) (0·15–0·2) (0·73–0·94) (410 461– (0·57–0·73) 0·03) 6·6) 33·2) 71·9) 101·1) 68·5) 18·5) 1·27) 0·02) 526 027) Luxembourg 0·08 6·2 34·4 75·7 103·8 62·2 13·4 0·61 0·01 1·5 0·2 0·9 6407 0·72 (0·04– (5·1– (28·3– (69·3– (94·0– (52·7– (10·5– (0·38– (0·01– (1·4–1·6) (0·17–0·24) (0·83–0·97) (5861–6965) (0·65–0·78) 0·17) 7·4) 41·8) 82·8) 114·6) 72·6) 17·0) 0·92) 0·01) Malta 0·19 12·6 38·1 89·2 101·3 47·0 9·0 0·44 0·01 1·5 0·25 0·79 4311 0·71 (0·08– (10·9– (32·2– (82·0– (90·6– (38·6– (7·0– (0·3– (0·01– (1·3–1·7) (0·22–0·29) (0·68–0·91) (3812–4880) (0·63–0·81) 0·39) 14·4) 45·2) 97·0) 113·2) 56·9) 11·5) 0·65) 0·01) (Table 1 continues on next page)

2012 www.thelancet.com Vol 392 November 10, 2018 Global Health Metrics

Age-specific fertility rate (livebirths per 1000 women annually) Total Total Total fertility Number of Net fertility fertility rate rate from ages livebirths reproductive rate under age 30 to 54 years rate 25 years 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 years years years years years years years years years (Continued from previous page) Netherlands 0·02 4·1 30·0 98·2 129·2 60·5 9·7 0·43 0·01 1·7 0·17 1·0 172 472 0·8 (0·01– (3·4– (25·1– (91·1– (118·1– (51·0– (7·5– (0·29– (0·01– (1·5–1·8) (0·14–0·2) (0·9–1·11) (155 190– (0·72–0·89) 0·05) 4·9) 35·7) 105·9) 141·3) 71·4) 12·9) 0·59) 0·01) 191 686) Norway 0·01 5·7 42·2 106·9 120·4 59·9 12·0 0·64 0·01 1·7 0·24 0·96 60 329 0·84 (0·01– (4·7– (36·3– (100·2– (111·6– (51·9– (9·2– (0·4– (0·01– (1·6–1·9) (0·21–0·28) (0·88–1·05) (55 230– (0·77–0·92) 0·03) 6·9) 49·0) 114·1) 129·9) 68·9) 15·4) 0·98) 0·01) 66 015) Portugal 0·12 10·2 31·2 64·2 89·9 51·5 11·1 0·65 0·01 1·3 0·21 0·77 85 589 0·63 (0·05– (8·7– (26·0– (58·8– (80·9– (43·2– (8·8– (0·45– (0·01– (1·1–1·5) (0·17–0·25) (0·67–0·88) (74 860– (0·55–0·72) 0·25) 12·0) 37·3) 70·2) 99·9) 61·2) 14·0) 0·88) 0·01) 97 921) Spain 0·08 8·3 26·0 57·8 95·1 66·5 15·7 0·87 0·02 1·4 0·17 0·89 407 088 0·65 (0·04– (7·0– (22·7– (54·1– (88·7– (59·8– (13·2– (0·6– (0·02– (1·2–1·5) (0·15–0·2) (0·81–0·98) (370 674– (0·59–0·72) 0·17) 9·8) 29·8) 61·9) 102·1) 73·9) 18·6) 1·23) 0·02) 447 913) Sweden 0·02 5·2 43·6 108·8 125·7 68·7 14·4 0·73 0·01 1·8 0·24 1·0 118 087 0·88 (0·01– (4·4– (38·1– (102·7– (117·3– (60·7– (11·4– (0·46– (0·01– (1·7–2·0) (0·21–0·28) (1·0–1·1) (109 169– (0·82–0·96) 0·04) 6·3) 49·9) 115·5) 134·7) 77·6) 17·8) 1·09) 0·01) 127 819) Switzerland 0·01 3·4 28·3 77·0 111·8 65·1 13·1 0·64 0·01 1·5 0·16 0·95 87 282 0·72 (0·01– (2·8– (23·3– (70·4– (101·6– (55·4– (10·6– (0·45– (0·01– (1·3–1·7) (0·13–0·19) (0·85–1·06) (78 003– (0·64–0·81) 0·03) 4·1) 34·3) 84·3) 123·0) 76·2) 16·1) 0·85) 0·01) 97 459) UK 0·11 15·3 54·1 91·0 107·1 64·7 13·5 0·8 0·02 1·7 0·35 0·93 783 225 0·84 (0·05– (13·5– (46·8– (84·7– (97·8– (55·9– (11·0– (0·58– (0·01– (1·6–1·9) (0·3–0·4) (0·83–1·05) (703 221– (0·75–0·93) 0·21) 17·4) 62·4) 97·9) 117·3) 74·7) 16·5) 1·07) 0·02) 873 164) England 0·1 14·9 54·7 92·4 108·5 66·1 14·0 0·82 0·02 1·8 0·35 0·95 672 857 0·85 (0·05– (13·1– (47·5– (86·2– (99·2– (57·2– (11·2– (0·61– (0·02– (1·6–2·0) (0·3–0·4) (0·84–1·06) (604 801– (0·76–0·95) 0·21) 17·0) 63·0) 99·3) 118·7) 76·2) 17·2) 1·09) 0·02) 749 278) Northern 0·13 15·2 53·4 95·7 114·6 67·3 13·0 0·59 0·01 1·8 0·34 0·98 23 589 0·87 Ireland (0·06– (13·1– (44·9– (87·9– (103·4– (56·7– (9·7– (0·31– (0·01– (1·6–2·0) (0·29–0·41) (0·85–1·12) (20 766– (0·76–0·99) 0·27) 17·6) 63·4) 104·3) 126·9) 79·4) 16·9) 1·04) 0·01) 26 808) Scotland 0·11 18·9 44·8 72·1 93·1 55·4 10·1 0·62 0·01 1·5 0·32 0·8 53 451 0·71 (0·05– (16·9– (38·8– (66·9– (84·6– (47·8– (8·1– (0·31– (0·01– (1·3–1·6) (0·28–0·37) (0·72–0·88) (48 281– (0·64–0·79) 0·22) 21·2) 51·8) 77·8) 102·5) 64·0) 12·4) 1·16) 0·01) 59 123) Wales 0·11 16·1 59·8 96·1 100·2 52·4 10·0 0·76 0·01 1·7 0·38 0·82 33 470 0·81 (0·05– (14·2– (51·0– (88·7– (90·1– (43·9– (7·8– (0·47– (0·01– (1·5–1·9) (0·33–0·44) (0·71–0·94) (29 639– (0·72–0·92) 0·22) 18·3) 70·0) 104·2) 111·4) 62·3) 12·6) 1·16) 0·02) 37 854) Latin America and 2·0 63·5 112·9 104·2 85·7 51·5 15·5 1·3 0·02 2·2 0·89 0·77 10 393 604 1·04 Caribbean (0·9– (57·1– (100·1– (96·7– (79·2– (45·2– (13·2– (1·1– (0·02– (2·0–2·4) (0·8–1·0) (0·69–0·85) (9 469 048– (0·95–1·14) 4·2) 71·0) 127·8) 112·6) 92·9) 58·3) 18·1) 1·5) 0·02) 11 430 456) Andean Latin 1·4 71·4 138·2 132·8 114·3 76·8 27·0 2·7 0·05 2·8 1·1 1·1 1 386 395 1·34 America (0·6– (63·9– (121·6– (123·2– (102·9– (65·8– (22·1– (2·1– (0·05– (2·6–3·1) (0·9–1·2) (1·0–1·2) (1 260 285– (1·22–1·47) 2·9) 79·5) 157·2) 143·6) 127·4) 88·4) 32·5) 3·4) 0·05) 1 526 533) Bolivia 2·2 71·9 156·2 154·6 136·2 92·4 30·9 4·3 0·08 3·2 1·2 1·3 301 119 1·52 (1·0– (61·8– (132·9– (141·1– (119·8– (75·8– (23·9– (2·9– (0·08– (2·9–3·6) (1·0–1·3) (1·2–1·5) (271 239– (1·37–1·69) 4·6) 83·4) 184·4) 170·5) 155·6) 111·0) 39·4) 6·3) 0·09) 334 416) Ecuador 0·76 60·5 134·3 112·9 86·2 46·3 12·5 1·2 0·02 2·3 0·98 0·73 315 984 1·08 (0·33– (51·5– (112·3– (101·6– (73·8– (36·0– (9·3– (0·8– (0·02– (1·9–2·7) (0·82–1·15) (0·6–0·89) (268 796– (0·92–1·26) 1·58) 70·9) 159·1) 125·3) 100·2) 58·7) 16·6) 1·8) 0·02) 370 024) Peru 1·5 78·0 133·5 135·0 120·9 87·0 32·9 3·0 0·06 3·0 1·1 1·2 769 292 1·42 (0·6– (67·0– (113·1– (123·0– (106·5– (72·2– (25·8– (2·0– (0·05– (2·6–3·3) (0·9–1·3) (1·1–1·4) (687 072– (1·26–1·59) 3·1) 91·9) 158·8) 149·3) 138·2) 103·1) 41·0) 4·2) 0·06) 866 600) Caribbean 1·2 58·1 119·3 112·8 86·0 52·7 15·3 1·9 0·03 2·2 0·89 0·78 815 882 1·04 (0·5– (51·1– (108·3– (105·8– (78·5– (46·0– (12·6– (1·5– (0·03– (2·0–2·4) (0·81–0·99) (0·7–0·86) (746 824– (0·95–1·13) 2·5) 66·0) 131·3) 120·1) 93·7) 60·0) 18·1) 2·3) 0·03) 889 894) Antigua and 2·5 50·1 81·5 75·7 57·8 27·5 6·9 0·08 0·0 1·5 0·67 0·46 1071 0·73 Barbuda (1·1– (43·2– (67·2– (67·8– (49·2– (21·1– (5·1– (0·05– (0·0– (1·3–1·8) (0·56–0·79) (0·38–0·56) (905–1261) (0·62–0·86) 5·1) 58·2) 98·3) 84·6) 67·8) 35·5) 9·3) 0·13) 0·0) (Table 1 continues on next page)

www.thelancet.com Vol 392 November 10, 2018 2013 Global Health Metrics

Age-specific fertility rate (livebirths per 1000 women annually) Total Total Total fertility Number of Net fertility fertility rate rate from ages livebirths reproductive rate under age 30 to 54 years rate 25 years 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 years years years years years years years years years (Continued from previous page) The Bahamas 0·76 33·5 73·4 82·4 65·8 41·5 10·8 0·37 0·01 1·5 0·54 0·59 4679 0·74 (0·33– (28·2– (59·3– (73·3– (55·4– (31·7– (7·9– (0·24– (0·01– (1·3–1·9) (0·44–0·65) (0·48–0·73) (3895–5611) (0·62–0·88) 1·56) 39·9) 90·2) 92·6) 77·8) 53·5) 14·6) 0·56) 0·01) Barbados 1·2 40·2 75·6 74·7 58·2 27·9 8·6 0·17 0·0 1·4 0·58 0·47 2850 0·68 (0·5– (34·3– (61·9– (66·6– (49·2– (21·2– (6·3– (0·11– (0·0– (1·2–1·7) (0·48–0·7) (0·38–0·58) (2393–3388) (0·58–0·81) 2·4) 47·1) 91·8) 83·7) 68·7) 36·2) 11·6) 0·25) 0·0) Belize 1·1 59·0 132·5 112·5 81·3 46·8 11·3 1·3 0·03 2·2 0·96 0·7 7843 1·06 (0·5– (51·1– (115·2– (102·9– (72·5– (39·2– (9·4– (1·1– (0·02– (2·0–2·5) (0·83–1·1) (0·61–0·81) (6904–8895) (0·94–1·2) 2·3) 68·2) 151·8) 123·1) 91·2) 55·6) 13·5) 1·5) 0·03) Bermuda 0·42 8·5 34·9 58·6 83·0 58·1 16·1 0·96 0·02 1·3 0·22 0·79 562 0·63 (0·18– (7·3– (30·0– (53·5– (74·8– (49·8– (13·3– (0·61– (0·02– (1·2–1·5) (0·19–0·25) (0·71–0·89) (502–631) (0·57–0·71) 0·86) 9·8) 40·6) 64·4) 92·2) 67·6) 19·7) 1·47) 0·02) Cuba 1·6 45·5 91·2 84·1 52·7 23·3 4·1 0·15 0·0 1·5 0·69 0·4 109 664 0·72 (0·7– (41·1– (82·5– (78·8– (47·5– (19·3– (3·1– (0·1– (0·0– (1·4–1·6) (0·64–0·75) (0·35–0·46) (103 731– (0·68–0·77) 3·3) 51·0) 100·4) 90·4) 58·4) 28·0) 5·5) 0·24) 0·0) 116 193) Dominica 1·4 44·6 84·3 79·4 66·6 34·5 8·0 0·23 0·0 1·6 0·65 0·55 801 0·75 (0·6– (38·0– (68·6– (70·7– (56·4– (27·7– (6·0– (0·15– (0·0– (1·3–1·9) (0·54–0·8) (0·45–0·67) (677–960) (0·63–0·89) 3·0) 53·2) 104·7) 90·2) 79·5) 43·3) 10·6) 0·36) 0·0) Dominican 0·91 90·9 153·6 123·0 69·6 29·5 6·2 0·65 0·01 2·4 1·2 0·53 216 514 1·12 Republic (0·4– (78·3– (131·2– (111·4– (58·8– (22·5– (4·5– (0·44– (0·01– (2·0–2·7) (1·1–1·4) (0·43–0·65) (186 677– (0·96–1·29) 1·92) 105·1) 178·5) 135·8) 82·1) 38·3) 8·4) 0·92) 0·01) 250 296) Grenada 0·92 48·2 88·0 84·3 85·3 53·3 16·1 0·69 0·01 1·9 0·69 0·78 1514 0·9 (0·41– (41·8– (72·1– (75·2– (73·2– (41·7– (12·2– (0·43– (0·01– (1·6–2·2) (0·57–0·82) (0·64–0·94) (1279–1786) (0·76–1·06) 1·91) 55·7) 106·7) 94·6) 99·2) 67·1) 21·1) 1·06) 0·01) Guyana 1·9 67·3 153·7 126·2 89·6 47·9 12·4 1·2 0·02 2·5 1·1 0·76 15 719 1·18 (0·8– (58·2– (131·8– (114·6– (77·8– (38·4– (9·6– (0·8– (0·02– (2·2–2·9) (1·0–1·3) (0·63–0·9) (13 597– (1·02–1·35) 3·9) 77·8) 178·1) 138·9) 102·8) 59·1) 15·9) 1·7) 0·02) 18 087) Haiti 1·3 49·9 128·6 147·9 139·7 107·9 43·5 8·3 0·16 3·1 0·9 1·5 325 281 1·41 (0·6– (42·6– (108·7– (135·1– (123·9– (90·8– (34·9– (6·3– (0·15– (2·8–3·5) (0·79–1·02) (1·3–1·7) (290 528– (1·25–1·59) 2·6) 58·3) 153·2) 163·1) 158·4) 128·2) 53·0) 10·7) 0·17) 365 513) Jamaica 1·1 41·0 81·8 75·7 62·7 39·2 12·8 0·93 0·02 1·6 0·62 0·58 38 063 0·76 (0·5– (35·9– (70·6– (69·2– (55·5– (33·7– (10·6– (0·7– (0·02– (1·4–1·8) (0·54–0·71) (0·5–0·67) (33 491– (0·67–0·86) 2·2) 46·9) 94·5) 82·9) 70·9) 45·5) 15·5) 1·22) 0·02) 43 222) Puerto Rico 0·74 30·9 75·5 65·2 43·5 21·0 4·7 0·15 0·0 1·2 0·54 0·35 29 896 0·57 (0·32– (26·9– (66·6– (59·9– (38·1– (16·8– (3·4– (0·1– (0·0– (1·1–1·3) (0·49–0·59) (0·29–0·41) (27 172– (0·52–0·63) 1·51) 35·5) 86·5) 71·2) 49·6) 26·2) 6·4) 0·21) 0·0) 32 946) Saint Lucia 0·94 44·5 84·2 71·9 60·4 34·6 10·3 0·52 0·01 1·5 0·65 0·53 2102 0·74 (0·41– (37·8– (68·5– (63·8– (51·0– (26·7– (7·5– (0·35– (0·01– (1·3–1·8) (0·53–0·78) (0·43–0·65) (1753–2513) (0·62–0·88) 1·95) 52·3) 102·8) 81·0) 71·2) 44·4) 14·0) 0·77) 0·01) Saint Vincent and 1·5 60·1 94·6 88·9 70·6 43·4 11·2 0·81 0·02 1·9 0·78 0·63 1551 0·88 the Grenadines (0·6– (51·4– (77·7– (79·4– (59·8– (33·3– (8·4– (0·5– (0·01– (1·6–2·2) (0·65–0·93) (0·51–0·77) (1302–1838) (0·74–1·05) 3·1) 70·3) 114·3) 99·5) 83·1) 55·8) 14·7) 1·3) 0·02) Suriname 3·4 55·6 112·7 115·0 91·2 49·7 12·3 0·84 0·02 2·2 0·86 0·77 9614 1·04 (1·5– (48·1– (95·7– (104·8– (80·1– (40·5– (9·5– (0·68– (0·02– (1·9–2·5) (0·73–1·0) (0·65–0·9) (8337–11 018) (0·9–1·18) 7·1) 64·1) 132·1) 126·2) 103·6) 60·6) 15·8) 1·03) 0·02) Trinidad and 0·74 39·4 96·7 92·3 68·8 34·6 7·9 0·49 0·01 1·7 0·68 0·56 17 521 0·81 Tobago (0·33– (34·2– (82·8– (84·3– (61·0– (28·8– (6·4– (0·37– (0·01– (1·5–1·9) (0·59–0·79) (0·48–0·64) (15 376– (0·71–0·92) 1·54) 45·3) 112·5) 101·1) 77·5) 41·4) 9·6) 0·65) 0·01) 19 960) Virgin Islands 1·6 52·8 121·5 113·0 77·3 36·3 4·8 0·07 0·0 2·0 0·88 0·59 1287 0·98 (0·7– (44·9– (100·8– (101·8– (66·0– (27·9– (3·6– (0·04– (0·0– (1·7–2·4) (0·73–1·04) (0·49–0·71) (1097–1507) (0·84–1·15) 3·4) 62·0) 145·1) 125·3) 90·1) 46·7) 6·3) 0·1) 0·0) Central Latin 1·8 72·5 129·6 118·0 87·4 47·9 12·3 1·4 0·03 2·4 1·0 0·75 5 004 522 1·12 America (0·8– (65·4– (115·3– (110·1– (78·9– (40·7– (10·0– (1·0– (0·02– (2·1–2·6) (0·9–1·1) (0·65–0·85) (4 502 598– (1·01–1·25) 3·7) 80·4) 145·5) 126·5) 97·1) 56·2) 15·1) 1·7) 0·03) 5 583 565) (Table 1 continues on next page)

2014 www.thelancet.com Vol 392 November 10, 2018 Global Health Metrics

Age-specific fertility rate (livebirths per 1000 women annually) Total Total Total fertility Number of Net fertility fertility rate rate from ages livebirths reproductive rate under age 30 to 54 years rate 25 years 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 years years years years years years years years years (Continued from previous page) Colombia 1·2 64·1 114·6 104·7 80·9 45·4 10·9 1·3 0·02 2·1 0·9 0·69 851 115 1·01 (0·5– (55·2– (97·0– (94·9– (70·3– (36·7– (8·4– (0·9– (0·02– (1·8–2·5) (0·77–1·05) (0·58–0·82) (733 293– (0·87–1·17) 2·5) 74·4) 134·7) 115·7) 92·9) 55·9) 14·0) 1·8) 0·03) 985 161) Costa Rica 1·3 53·8 92·1 86·6 69·7 37·0 9·6 0·71 0·01 1·8 0·74 0·58 69 820 0·85 (0·6– (48·4– (82·7– (80·7– (63·0– (31·1– (7·2– (0·51– (0·01– (1·6–1·9) (0·66–0·83) (0·53–0·64) (64 085– (0·78–0·93) 2·6) 60·5) 103·5) 93·7) 77·8) 43·9) 12·6) 0·99) 0·01) 76 591) El Salvador 1·1 63·6 105·5 93·7 72·4 40·1 11·7 1·0 0·02 1·9 0·85 0·63 107 660 0·93 (0·5– (55·9– (90·6– (85·4– (64·1– (33·2– (9·5– (0·72– (0·02– (1·7–2·2) (0·74–0·98) (0·54–0·73) (94 215– (0·81–1·06) 2·2) 72·3) 122·3) 103·0) 81·7) 48·2) 14·4) 1·36) 0·02) 122 854) Guatemala 1·5 75·9 138·9 134·4 105·7 72·4 26·4 4·7 0·09 2·8 1·1 1·0 430 775 1·33 (0·7– (65·7– (118·3– (122·4– (92·1– (58·5– (20·7– (3·3– (0·09– (2·4–3·2) (0·9–1·3) (0·9–1·2) (372 362– (1·15–1·53) 3·2) 87·5) 161·9) 147·5) 120·9) 88·4) 33·3) 6·6) 0·09) 496 213) Honduras 2·8 86·7 148·5 130·7 109·0 72·8 25·1 3·3 0·06 2·9 1·2 1·1 244 568 1·37 (1·2– (75·5– (127·8– (119·4– (95·7– (59·4– (19·6– (2·3– (0·06– (2·5–3·4) (1·0–1·4) (0·9–1·3) (212 956– (1·19–1·59) 6·0) 100·7) 173·7) 144·2) 125·1) 89·5) 32·3) 4·7) 0·07) 283 074) Mexico 1·7 70·0 137·1 125·9 90·0 46·9 11·0 1·1 0·02 2·4 1·0 0·74 2 518 031 1·16 (0·8– (59·9– (115·2– (113·7– (77·1– (36·5– (7·9– (0·8– (0·02– (2·1–2·9) (0·9–1·2) (0·61–0·91) (2 153 263– (0·99–1·37) 3·6) 82·8) 164·1) 140·5) 105·9) 60·7) 15·4) 1·5) 0·02) 2 972 894) Nicaragua 1·5 82·8 129·6 113·9 96·6 53·5 13·1 1·5 0·03 2·5 1·1 0·82 137 802 1·18 (0·6– (71·5– (108·9– (102·7– (83·1– (41·8– (9·7– (1·0– (0·03– (2·1–2·9) (0·9–1·2) (0·68–1·0) (117 837– (1·01–1·37) 3·0) 95·6) 153·1) 126·3) 111·8) 67·5) 17·5) 2·2) 0·03) 160 442) Panama 2·4 77·1 126·4 113·5 85·3 44·7 11·3 0·78 0·01 2·3 1·0 0·71 69 684 1·1 (1·1– (66·1– (105·5– (102·8– (73·8– (35·5– (8·6– (0·54– (0·01– (2·0–2·7) (0·9–1·2) (0·59–0·85) (59 588– (0·94–1·27) 5·1) 89·7) 150·2) 125·4) 98·2) 55·7) 14·7) 1·11) 0·02) 81 021) Venezuela 2·7 92·1 122·2 105·2 75·2 38·8 10·7 1·1 0·02 2·2 1·1 0·63 575 062 1·06 (1·2– (83·1– (107·4– (97·4– (66·8– (31·9– (8·2– (0·8– (0·02– (2·0–2·5) (1·0–1·2) (0·54–0·74) (511 153– (0·95–1·19) 5·8) 102·1) 138·7) 113·7) 84·5) 47·1) 13·9) 1·7) 0·02) 645 003) Tropical Latin 2·8 50·4 82·8 78·0 76·7 48·9 16·2 0·73 0·01 1·8 0·68 0·71 3 186 804 0·85 America (1·2– (43·8– (70·9– (70·8– (67·2– (39·5– (12·7– (0·54– (0·01– (1·6–2·0) (0·59–0·8) (0·6–0·84) (2 886 839– (0·77–0·95) 5·8) 58·9) 98·1) 86·7) 87·2) 59·9) 20·3) 0·96) 0·01) 3 554 625) Brazil 2·9 49·9 81·6 76·4 75·7 48·3 16·1 0·72 0·01 1·8 0·67 0·7 3 040 969 0·84 (1·3– (43·0– (69·2– (69·0– (66·0– (38·7– (12·5– (0·53– (0·01– (1·6–2·0) (0·57–0·79) (0·59–0·83) (2 742 060– (0·75–0·94) 6·0) 58·6) 97·4) 85·4) 86·2) 59·4) 20·2) 0·97) 0·01) 3 409 928) Paraguay 0·7 64·1 115·4 124·0 110·9 70·8 22·2 0·99 0·02 2·5 0·9 1·0 145 834 1·2 (0·31– (54·8– (96·9– (112·3– (96·6– (57·1– (16·9– (0·65– (0·02– (2·2–3·0) (0·76–1·06) (0·9–1·2) (125 161– (1·03–1·4) 1·45) 74·8) 136·6) 137·0) 126·8) 86·8) 28·8) 1·47) 0·02) 169 315) North Africa and 0·28 47·3 131·6 138·9 117·7 72·7 28·2 5·1 0·09 2·7 0·9 1·1 13 008 474 1·26 Middle East (0·13– (42·3– (121·0– (131·4– (108·3– (63·1– (25·1– (4·4– (0·09– (2·5–2·9) (0·82–0·98) (1·0–1·2) (12 060 286– (1·17–1·37) 0·59) 53·0) 143·2) 147·7) 128·6) 83·1) 31·8) 5·9) 0·09) 14 103 277) Afghanistan 0·38 97·5 279·2 314·6 248·6 166·7 70·9 24·0 0·46 6·0 1·9 2·6 1 376 280 2·64 (0·16– (84·5– (255·7– (304·2– (234·8– (150·2– (61·0– (20·1– (0·44– (5·7–6·3) (1·7–2·0) (2·4–2·7) (1 303 953– (2·52–2·76) 0·8) 113·6) 302·0) 324·5) 261·8) 182·4) 80·8) 27·9) 0·48) 1 448 446) Algeria 0·05 9·8 70·9 121·3 162·4 132·8 58·4 5·3 0·1 2·8 0·4 1·8 963 291 1·32 (0·02– (8·2– (59·1– (109·5– (145·4– (115·8– (49·7– (4·3– (0·1– (2·5–3·1) (0·34–0·48) (1·6–2·0) (855 732– (1·17–1·47) 0·09) 11·6) 84·3) 133·9) 179·9) 149·9) 67·4) 6·5) 0·11) 1 073 343) Bahrain 0·2 15·1 95·6 127·7 87·0 59·9 20·6 3·2 0·06 2·0 0·55 0·85 19 881 0·99 (0·09– (12·7– (83·7– (118·9– (78·0– (50·6– (16·2– (2·3– (0·06– (1·9–2·2) (0·49–0·62) (0·76–0·95) (18 264– (0·9–1·07) 0·4) 18·4) 108·6) 136·8) 96·6) 70·2) 25·7) 4·4) 0·06) 21 611) Egypt 0·3 61·8 171·9 136·6 102·0 46·7 11·0 2·0 0·04 2·7 1·2 0·81 2 127 960 1·26 (0·13– (52·7– (147·3– (124·0– (88·2– (37·0– (8·3– (1·4– (0·04– (2·4–2·9) (1·0–1·4) (0·71–0·93) (1 940 392– (1·15–1·37) 0·62) 72·3) 198·8) 151·6) 118·9) 57·8) 14·8) 2·8) 0·04) 2 330 506) Iran 0·52 26·4 77·0 100·2 79·9 46·2 14·3 1·2 0·02 1·7 0·52 0·71 1 274 094 0·82 (0·22– (22·1– (62·4– (89·8– (68·1– (36·0– (10·7– (0·8– (0·02– (1·5–2·0) (0·44–0·61) (0·58–0·86) (1 085 203– (0·7–0·96) 1·06) 32·0) 93·6) 111·3) 92·8) 58·1) 18·7) 1·7) 0·02) 1 494 227) (Table 1 continues on next page)

www.thelancet.com Vol 392 November 10, 2018 2015 Global Health Metrics

Age-specific fertility rate (livebirths per 1000 women annually) Total Total Total fertility Number of Net fertility fertility rate rate from ages livebirths reproductive rate under age 30 to 54 years rate 25 years 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 years years years years years years years years years (Continued from previous page) Iraq 0·29 59·7 173·8 186·2 175·3 113·3 37·4 5·5 0·1 3·8 1·2 1·7 1 255 056 1·75 (0·13– (50·8– (149·3– (172·4– (159·5– (97·7– (30·6– (4·0– (0·1– (3·4–4·1) (1·0–1·4) (1·5–1·8) (1 135 149– (1·59–1·93) 0·61) 71·0) 203·1) 202·2) 193·5) 129·5) 44·9) 7·2) 0·11) 1 393 958) Jordan 0·09 26·1 126·5 180·3 157·5 96·7 22·1 1·5 0·03 3·1 0·76 1·4 243 217 1·46 (0·04– (21·9– (106·5– (165·9– (140·7– (81·0– (16·7– (1·0– (0·03– (2·8–3·4) (0·66–0·89) (1·2–1·6) (223 920– (1·34–1·6) 0·18) 31·2) 151·4) 197·0) 174·8) 113·4) 28·6) 2·1) 0·03) 266 880) Kuwait 0·03 8·3 62·3 78·4 68·9 44·8 18·8 2·8 0·05 1·4 0·35 0·68 60 885 0·68 (0·01– (6·9– (52·6– (71·8– (60·6– (36·4– (14·3– (1·8– (0·05– (1·3–1·6) (0·3–0·41) (0·59–0·77) (55 064– (0·62–0·75) 0·06) 10·2) 73·2) 85·5) 78·0) 54·3) 24·0) 4·1) 0·06) 67 021) Lebanon 0·29 57·6 117·0 138·8 106·1 51·1 7·2 1·3 0·02 2·4 0·87 0·83 186 159 1·15 (0·13– (48·9– (96·9– (125·9– (91·9– (40·5– (5·6– (0·9– (0·02– (2·1–2·8) (0·73–1·06) (0·7–0·98) (160 797– (0·99–1·34) 0·6) 68·8) 142·3) 154·1) 123·3) 64·9) 9·2) 1·7) 0·03) 217 399) Libya 0·13 13·3 50·8 114·5 122·3 77·7 36·8 8·0 0·15 2·1 0·32 1·2 122 256 0·99 (0·06– (11·0– (40·2– (102·8– (106·1– (61·4– (28·1– (5·4– (0·15– (1·8–2·6) (0·26–0·41) (1·0–1·5) (102 820– (0·84–1·19) 0·27) 16·3) 65·0) 128·6) 141·8) 98·4) 48·1) 11·7) 0·16) 146 859) Morocco 0·2 20·0 73·3 97·3 106·8 80·8 43·3 6·2 0·12 2·1 0·47 1·2 601 214 1·01 (0·09– (16·6– (58·9– (86·9– (91·8– (64·1– (33·6– (4·1– (0·12– (1·9–2·4) (0·38–0·57) (1·0–1·5) (528 391– (0·89–1·15) 0·42) 24·0) 90·4) 108·8) 125·1) 101·8) 55·7) 9·5) 0·12) 683 943) Oman 0·12 12·4 83·3 142·8 133·3 92·6 39·1 6·1 0·12 2·5 0·48 1·4 80 314 1·23 (0·05– (10·7– (69·6– (131·2– (118·8– (78·5– (31·7– (4·8– (0·11– (2·3–2·8) (0·41–0·56) (1·2–1·5) (72 628– (1·11–1·35) 0·25) 14·3) 98·7) 154·8) 148·4) 107·8) 47·2) 7·5) 0·12) 88 378) Palestine 0·05 77·9 201·0 184·9 131·4 75·8 25·6 2·0 0·04 3·5 1·4 1·2 138 165 1·66 (0·02– (67·5– (176·8– (171·1– (116·6– (63·4– (21·2– (1·5– (0·04– (3·2–3·9) (1·3–1·5) (1·0–1·3) (125 084– (1·51–1·83) 0·11) 91·0) 226·0) 199·0) 146·9) 89·4) 30·6) 2·5) 0·04) 152 033) Qatar 0·18 11·2 75·5 118·4 108·6 66·8 24·9 2·4 0·05 2·0 0·43 1·0 30 253 0·99 (0·08– (9·5– (64·1– (109·1– (97·6– (57·6– (20·3– (1·8– (0·04– (1·9–2·2) (0·38–0·5) (0·9–1·1) (27 787– (0·9–1·07) 0·37) 13·1) 88·1) 128·1) 120·1) 76·8) 30·0) 3·2) 0·05) 32 803) Saudi Arabia 0·11 9·7 59·6 82·9 85·4 63·0 29·6 3·5 0·07 1·7 0·35 0·91 502 343 0·8 (0·05– (8·1– (48·8– (75·5– (74·4– (50·5– (23·3– (2·4– (0·06– (1·5–1·9) (0·29–0·42) (0·78–1·05) (444 354– (0·7–0·9) 0·23) 11·4) 72·1) 90·8) 97·2) 77·0) 36·9) 4·9) 0·07) 565 465) Sudan 0·35 85·5 185·2 204·6 186·7 116·6 52·1 11·7 0·23 4·2 1·4 1·8 1 336 735 1·93 (0·15– (74·2– (162·3– (190·7– (170·1– (99·2– (42·9– (8·6– (0·22– (3·9–4·6) (1·2–1·6) (1·7–2·0) (1 216 996– (1·78–2·1) 0·74) 99·7) 212·3) 220·6) 205·5) 134·6) 62·0) 15·4) 0·23) 1 472 638) Syria 0·22 35·2 98·7 113·6 103·5 60·3 19·1 3·8 0·07 2·2 0·67 0·93 276 298 0·99 (0·1– (29·9– (82·1– (103·1– (90·2– (47·9– (14·3– (2·5– (0·07– (1·9–2·5) (0·58–0·77) (0·78–1·11) (239 261– (0·85–1·14) 0·46) 42·0) 117·2) 124·7) 117·7) 74·5) 24·9) 5·9) 0·08) 317 552) Tunisia 0·44 5·9 47·2 97·8 106·5 72·0 22·8 1·6 0·03 1·8 0·27 1·0 167 745 0·84 (0·19– (4·9– (37·8– (87·8– (93·5– (59·7– (18·2– (1·2– (0·03– (1·5–2·1) (0·22–0·34) (0·9–1·2) (144 002– (0·72–0·99) 0·91) 7·3) 59·8) 109·8) 122·3) 87·5) 28·7) 2·3) 0·03) 197 624) Turkey 0·19 25·9 90·4 106·3 81·1 42·0 11·0 1·1 0·02 1·8 0·58 0·68 1 116 714 0·85 (0·08– (21·9– (76·2– (96·4– (71·3– (35·1– (8·9– (0·8– (0·02– (1·6–2·0) (0·51–0·67) (0·61–0·75) (1 004 994– (0·77–0·95) 0·38) 30·6) 108·5) 118·2) 93·1) 49·7) 13·5) 1·4) 0·02) 1 245 930) United Arab 0·44 12·1 58·5 70·2 66·5 36·8 16·3 1·6 0·03 1·3 0·36 0·61 71 039 0·63 Emirates (0·19– (10·2– (46·9– (62·1– (56·6– (29·6– (12·9– (1·1– (0·03– (1·2–1·5) (0·29–0·45) (0·52–0·7) (62 614– (0·56–0·71) 0·89) 14·7) 73·9) 80·2) 77·3) 45·1) 20·3) 2·1) 0·03) 80 613) Yemen 0·35 82·1 205·3 221·3 177·9 124·5 64·9 29·0 0·56 4·5 1·4 2·0 1 046 417 2·05 (0·15– (71·0– (180·0– (206·8– (160·4– (105·8– (54·4– (24·1– (0·54– (4·2–5·0) (1·3–1·7) (1·8–2·2) (953 260– (1·89–2·23) 0·73) 96·0) 234·7) 237·8) 197·8) 143·5) 75·7) 33·7) 0·58) 1 151 663) South Asia 0·43 32·6 159·3 138·0 78·0 32·9 9·8 3·4 0·06 2·3 0·96 0·62 33 968 926 1·01 (0·19– (28·4– (137·3– (126·3– (69·5– (27·6– (7·4– (2·5– (0·06– (2·0–2·5) (0·83–1·12) (0·54–0·72) (30 525 169– (0·92–1·13) 0·88) 37·8) 185·9) 151·8) 87·4) 39·4) 12·8) 4·6) 0·07) 38 074 532) Bangladesh 0·97 70·9 133·1 99·5 56·2 28·2 6·9 3·4 0·06 2·0 1·0 0·47 2 858 475 0·93 (0·42– (60·8– (115·0– (90·3– (48·6– (22·2– (5·2– (2·3– (0·06– (1·8–2·2) (0·9–1·1) (0·39–0·57) (2 598 018– (0·84–1·04) 2·02) 82·0) 155·3) 110·5) 64·6) 35·3) 9·0) 4·7) 0·07) 3 180 667) (Table 1 continues on next page)

2016 www.thelancet.com Vol 392 November 10, 2018 Global Health Metrics

Age-specific fertility rate (livebirths per 1000 women annually) Total Total Total fertility Number of Net fertility fertility rate rate from ages livebirths reproductive rate under age 30 to 54 years rate 25 years 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 years years years years years years years years years (Continued from previous page) Bhutan 0·44 35·4 125·3 113·5 71·1 38·4 9·6 3·1 0·06 2·0 0·81 0·61 17 338 0·93 (0·2– (29·6– (103·6– (101·8– (59·9– (29·1– (6·8– (2·0– (0·06– (1·8–2·3) (0·67–0·98) (0·49–0·75) (15 394– (0·82–1·06) 0·92) 42·9) 152·4) 127·6) 83·5) 49·6) 13·1) 4·6) 0·06) 19 757) India 0·37 25·4 162·3 133·7 70·0 26·3 8·0 2·6 0·05 2·1 0·94 0·53 24 568 864 0·96 (0·16– (21·2– (140·1– (122·1– (60·5– (20·7– (5·9– (1·9– (0·05– (1·9–2·4) (0·81–1·1) (0·45–0·63) (22 072 577– (0·87–1·06) 0·76) 30·8) 189·1) 147·4) 80·3) 32·8) 10·4) 3·6) 0·05) 27 481 958) Nepal 0·58 59·0 156·9 114·0 67·7 30·8 9·9 3·3 0·06 2·2 1·1 0·56 632 646 1·03 (0·25– (50·7– (134·6– (102·9– (57·7– (24·1– (7·5– (2·3– (0·06– (2·0–2·5) (1·0–1·2) (0·48–0·67) (560 875– (0·91–1·17) 1·21) 68·5) 183·9) 127·4) 80·3) 39·9) 12·8) 4·6) 0·07) 717 437) Pakistan 0·34 43·6 161·5 200·4 152·9 85·5 26·2 9·7 0·19 3·4 1·0 1·4 5 891 600 1·48 (0·15– (36·8– (137·5– (185·8– (135·6– (69·1– (19·7– (6·7– (0·18– (3·0–3·9) (0·9–1·2) (1·2–1·6) (5 173 076– (1·3–1·69) 0·71) 52·4) 190·5) 217·2) 173·1) 105·8) 33·8) 14·0) 0·19) 6 733 145) Southeast Asia, 0·15 18·9 93·6 117·7 71·9 32·1 9·0 1·1 0·02 1·7 0·56 0·57 28 562 870 0·79 east Asia, and (0·07– (17·3– (86·4– (113·6– (68·4– (29·5– (8·1– (0·9– (0·02– (1·6–1·8) (0·52–0·61) (0·54–0·61) (27 037 176– (0·75–0·84) Oceania 0·31) 20·8) 101·6) 121·9) 75·9) 35·3) 10·1) 1·3) 0·02) 30 297 827) East Asia 0·1 8·7 89·4 116·8 61·8 20·5 5·5 0·8 0·02 1·5 0·49 0·44 17 180 872 0·69 (0·04– (8·0– (82·8– (112·3– (57·9– (18·4– (4·8– (0·61– (0·01– (1·4–1·6) (0·45–0·53) (0·41–0·48) (16 167 753– (0·65–0·73) 0·21) 9·4) 96·2) 121·4) 65·7) 22·7) 6·2) 1·07) 0·02) 18 210 317) China 0·1 8·9 91·5 117·6 61·4 20·2 5·5 0·82 0·02 1·5 0·5 0·44 16 469 641 0·69 (0·05– (8·2– (84·5– (112·9– (57·4– (18·0– (4·8– (0·62– (0·02– (1·4–1·6) (0·46–0·54) (0·4–0·48) (15 475 992– (0·65–0·74) 0·21) 9·7) 98·6) 122·2) 65·4) 22·5) 6·3) 1·1) 0·02) 17 488 048) North Korea 0·05 1·8 49·2 112·8 71·8 23·3 5·4 0·43 0·01 1·3 0·26 0·5 258 789 0·62 (0·02– (1·5– (39·0– (101·2– (60·5– (17·2– (3·8– (0·26– (0·01– (1·2–1·5) (0·2–0·32) (0·42–0·61) (228 429– (0·55–0·71) 0·09) 2·3) 61·3) 126·9) 84·4) 30·7) 7·8) 0·7) 0·01) 295 066) Taiwan (province 0·08 4·0 23·5 63·5 78·8 34·1 4·4 0·03 0·0 1·0 0·14 0·59 175 666 0·5 of China) (0·03– (3·4– (19·4– (58·0– (70·3– (28·0– (3·2– (0·02– (0·0– (0·9–1·2) (0·11–0·17) (0·51–0·67) (154 235– (0·44–0·57) 0·17) 4·9) 28·4) 69·7) 88·2) 41·5) 6·0) 0·04) 0·0) 200 219) Oceania 0·54 56·3 194·1 194·9 171·6 123·9 49·6 12·4 0·23 4·0 1·3 1·8 398 611 1·77 (0·24– (48·0– (170·4– (182·0– (155·8– (106·5– (40·7– (9·1– (0·22– (3·7–4·4) (1·1–1·4) (1·6–2·0) (364 718– (1·62–1·93) 1·13) 65·8) 219·3) 208·4) 189·1) 143·3) 60·2) 16·7) 0·24) 433 764) American Samoa 0·72 35·3 142·8 171·4 136·3 80·4 16·1 0·91 0·02 2·9 0·89 1·2 1134 1·37 (0·32– (29·7– (122·6– (157·9– (120·1– (65·8– (12·1– (0·57– (0·02– (2·5–3·3) (0·76–1·04) (1·0–1·4) (987–1298) (1·2–1·57) 1·49) 41·9) 165·4) 185·8) 153·8) 97·0) 21·0) 1·4) 0·02) Federated States 1·8 36·0 127·9 146·1 127·2 82·6 18·0 4·3 0·08 2·7 0·83 1·2 2118 1·26 of Micronesia (0·8– (30·5– (106·5– (133·0– (110·9– (66·6– (13·1– (2·7– (0·08– (2·4–3·1) (0·72–0·96) (1·0–1·3) (1891–2392) (1·12–1·43) 3·6) 42·6) 154·5) 161·7) 146·9) 100·1) 24·9) 6·7) 0·09) Fiji 0·07 34·4 149·8 156·6 110·6 57·2 12·5 1·2 0·02 2·6 0·92 0·91 18 373 1·21 (0·03– (29·2– (129·9– (144·6– (98·2– (47·9– (9·8– (0·9– (0·02– (2·3–3·0) (0·8–1·06) (0·78–1·05) (16 204– (1·07–1·37) 0·14) 40·4) 172·0) 169·6) 124·4) 67·9) 15·9) 1·6) 0·02) 20 801) Guam 0·65 44·8 146·7 165·2 141·2 74·3 15·5 0·56 0·01 2·9 0·96 1·2 3350 1·38 (0·29– (38·1– (128·8– (153·0– (127·1– (61·0– (11·5– (0·36– (0·01– (2·7–3·2) (0·84–1·1) (1·1–1·3) (3080–3638) (1·28–1·5) 1·35) 52·7) 166·5) 179·3) 156·5) 91·1) 20·3) 0·85) 0·01) Kiribati 0·33 38·8 188·0 184·8 175·3 117·1 29·9 7·6 0·15 3·7 1·1 1·7 3544 1·66 (0·15– (32·8– (162·6– (170·7– (158·1– (98·6– (23·3– (5·5– (0·14– (3·3–4·2) (1·0–1·3) (1·4–1·9) (3124–4001) (1·46–1·86) 0·68) 45·7) 215·5) 199·9) 193·3) 136·9) 37·8) 10·4) 0·15) Marshall Islands 0·82 66·1 177·1 148·8 109·1 55·0 14·9 1·1 0·02 2·9 1·2 0·9 1298 1·32 (0·36– (57·8– (154·4– (136·4– (96·0– (44·7– (11·5– (0·7– (0·02– (2·5–3·2) (1·1–1·4) (0·76–1·05) (1159–1448) (1·17–1·47) 1·71) 76·5) 201·0) 161·7) 123·1) 66·6) 18·8) 1·6) 0·02) Northern Mariana 0·91 38·5 102·3 107·1 104·8 49·5 9·2 0·11 0·0 2·1 0·71 0·82 547 0·98 Islands (0·4– (33·6– (85·3– (96·4– (91·1– (40·3– (6·7– (0·07– (0·0– (1·8–2·3) (0·61–0·81) (0·72–0·92) (483–612) (0·86–1·09) 1·89) 44·0) 121·1) 118·4) 119·6) 60·0) 12·3) 0·17) 0·0) Papua New 0·57 59·4 200·1 198·5 178·4 133·2 56·1 14·9 0·29 4·2 1·3 1·9 309 184 1·83 Guinea (0·25– (50·4– (173·1– (183·9– (160·3– (113·1– (45·0– (10·6– (0·28– (3·8–4·6) (1·1–1·5) (1·6–2·2) (282 124– (1·67–2·0) 1·2) 70·0) 229·1) 213·9) 199·0) 156·0) 69·5) 20·5) 0·3) 336 719) (Table 1 continues on next page)

www.thelancet.com Vol 392 November 10, 2018 2017 Global Health Metrics

Age-specific fertility rate (livebirths per 1000 women annually) Total Total Total fertility Number of Net fertility fertility rate rate from ages livebirths reproductive rate under age 30 to 54 years rate 25 years 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 years years years years years years years years years (Continued from previous page) Samoa 0·17 42·9 207·9 244·8 222·1 153·5 56·0 11·0 0·21 4·7 1·3 2·2 6070 2·18 (0·07– (36·5– (182·2– (230·5– (205·2– (134·7– (46·0– (8·1– (0·2– (4·2–5·2) (1·1–1·4) (2·0–2·5) (5464–6756) (1·96–2·42) 0·35) 51·1) 237·7) 260·6) 240·2) 173·9) 68·0) 14·4) 0·22) Solomon Islands 0·59 62·2 207·0 211·7 176·0 124·1 46·9 11·1 0·21 4·2 1·3 1·8 20 410 1·91 (0·26– (52·9– (180·4– (196·9– (158·0– (104·4– (37·0– (7·7– (0·21– (3·8–4·6) (1·2–1·5) (1·6–2·0) (18 459– (1·72–2·12) 1·22) 72·9) 237·8) 228·6) 196·6) 146·7) 57·7) 15·4) 0·22) 22 554) Tonga 0·4 17·1 108·3 176·1 170·7 121·8 36·6 2·8 0·05 3·2 0·63 1·7 2184 1·49 (0·17– (14·3– (89·8– (162·2– (153·8– (103·5– (28·7– (2·0– (0·05– (2·8–3·6) (0·52–0·75) (1·4–1·9) (1908–2489) (1·3–1·68) 0·8) 20·5) 129·6) 191·1) 188·6) 141·3) 45·9) 3·8) 0·05) Vanuatu 0·53 51·2 190·1 187·2 158·8 109·5 41·3 8·0 0·15 3·7 1·2 1·6 8428 1·71 (0·23– (43·4– (164·6– (172·7– (141·5– (91·1– (32·3– (5·3– (0·15– (3·4–4·1) (1·0–1·4) (1·4–1·8) (7704–9314) (1·56–1·88) 1·1) 61·2) 220·0) 204·0) 176·7) 128·8) 51·3) 11·3) 0·16) Southeast Asia 0·21 32·0 98·6 118·2 94·0 54·1 17·1 1·8 0·03 2·1 0·65 0·84 10 983 387 0·97 (0·09– (28·5– (83·7– (111·0– (86·3– (47·9– (14·8– (1·4– (0·03– (1·9–2·3) (0·56–0·77) (0·75–0·93) (9 949 746– (0·88–1·08) 0·43) 36·3) 117·5) 127·0) 103·2) 61·5) 19·9) 2·2) 0·03) 12 233 978) Cambodia 0·34 43·6 133·8 150·7 118·5 75·4 20·4 4·1 0·08 2·7 0·89 1·1 377 406 1·28 (0·15– (37·0– (114·2– (138·2– (104·6– (61·4– (16·0– (2·9– (0·08– (2·5–3·0) (0·76–1·05) (0·9–1·3) (343 642– (1·17–1·42) 0·7) 52·2) 157·9) 165·5) 133·2) 90·7) 25·5) 5·6) 0·08) 419 172) Indonesia 0·15 27·7 92·8 113·7 89·5 51·7 16·4 1·8 0·03 2·0 0·6 0·8 4 032 914 0·92 (0·06– (23·5– (76·9– (103·0– (77·7– (41·7– (12·7– (1·2– (0·03– (1·7–2·3) (0·52–0·7) (0·67–0·96) (3 491 800– (0·8–1·08) 0·3) 32·6) 113·1) 126·6) 104·1) 64·8) 21·5) 2·5) 0·04) 4 705 431) Laos 0·41 62·9 164·6 147·0 109·1 65·9 23·1 7·7 0·15 2·9 1·1 1·0 176 836 1·32 (0·18– (54·0– (143·4– (135·4– (96·9– (54·4– (18·1– (5·4– (0·14– (2·6–3·2) (1·0–1·3) (0·9–1·2) (161 828– (1·21–1·45) 0·85) 74·2) 190·0) 159·0) 122·1) 78·6) 29·0) 10·5) 0·15) 194 522) Malaysia 0·22 11·1 51·7 122·2 125·1 72·6 20·7 1·4 0·03 2·0 0·32 1·1 508 960 0·96 (0·09– (9·4– (44·2– (113·9– (114·1– (61·9– (16·4– (0·9– (0·03– (1·8–2·3) (0·28–0·36) (1·0–1·2) (457 674– (0·86–1·07) 0·44) 13·1) 60·2) 130·8) 136·6) 84·2) 25·6) 2·2) 0·03) 566 097) Maldives 0·12 17·6 96·6 112·9 83·1 46·0 15·8 1·9 0·04 1·9 0·57 0·73 6844 0·9 (0·05– (14·7– (83·8– (104·0– (73·3– (36·9– (11·7– (1·2– (0·04– (1·7–2·0) (0·5–0·64) (0·66–0·82) (6312–7356) (0·83–0·97) 0·24) 21·0) 110·2) 121·9) 93·7) 58·1) 21·1) 2·9) 0·04) Mauritius 0·46 23·0 61·0 82·1 63·0 28·0 6·8 0·31 0·01 1·3 0·42 0·49 12 416 0·64 (0·2– (19·6– (52·3– (75·2– (55·5– (22·4– (4·9– (0·19– (0·01– (1·2–1·4) (0·36–0·49) (0·43–0·55) (11 454– (0·59–0·69) 0·93) 26·9) 70·6) 90·5) 72·2) 34·8) 9·4) 0·49) 0·01) 13 507) Myanmar 0·25 24·8 84·7 105·4 94·6 65·3 25·5 3·0 0·06 2·0 0·55 0·94 876 249 0·92 (0·11– (20·9– (70·9– (95·7– (83·1– (53·9– (20·1– (2·1– (0·06– (1·9–2·2) (0·46–0·66) (0·84–1·06) (804 806– (0·85–1·01) 0·51) 29·8) 102·3) 115·6) 106·9) 77·9) 31·8) 4·1) 0·06) 959 636) Philippines 0·22 54·2 137·3 162·4 141·5 92·2 32·2 3·5 0·07 3·1 0·96 1·3 2 526 359 1·44 (0·1– (46·1– (116·6– (149·5– (126·5– (77·4– (26·0– (2·5– (0·06– (2·8–3·4) (0·81–1·14) (1·2–1·5) (2 304 451– (1·31–1·59) 0·47) 64·6) 162·7) 177·5) 157·0) 107·9) 39·1) 4·7) 0·07) 2 800 024) Sri Lanka 0·13 18·5 67·3 109·6 97·2 51·3 14·3 0·81 0·02 1·8 0·43 0·82 292 833 0·86 (0·06– (15·4– (54·0– (98·6– (84·2– (40·9– (10·9– (0·56– (0·02– (1·5–2·1) (0·35–0·53) (0·68–0·98) (248 351– (0·73–1·01) 0·27) 22·2) 83·3) 121·7) 111·8) 63·7) 18·6) 1·17) 0·02) 344 723) Seychelles 1·1 58·0 114·4 111·8 81·8 48·8 13·6 0·5 0·01 2·1 0·87 0·72 1497 1·03 (0·5– (50·5– (98·8– (102·4– (73·8– (42·0– (11·1– (0·31– (0·01– (1·9–2·4) (0·75–1·0) (0·64–0·82) (1322–1693) (0·91–1·16) 2·4) 66·6) 132·0) 122·0) 90·7) 56·6) 16·7) 0·78) 0·01) Thailand 0·36 32·4 64·6 66·0 48·6 21·8 7·6 0·49 0·01 1·2 0·49 0·39 613 237 0·58 (0·16– (27·1– (52·1– (58·6– (41·2– (16·8– (5·8– (0·34– (0·01– (1·1–1·4) (0·4–0·6) (0·32–0·48) (539 713– (0·51–0·66) 0·73) 39·3) 81·1) 74·0) 57·0) 27·7) 9·9) 0·69) 0·01) 701 506) Timor-Leste 0·4 61·1 176·0 209·8 194·0 123·2 52·5 10·7 0·21 4·1 1·2 1·9 38 826 1·92 (0·18– (51·9– (150·4– (195·1– (176·2– (103·5– (41·9– (7·4– (0·2– (3·6–4·7) (1·0–1·4) (1·6–2·2) (34 156– (1·69–2·17) 0·84) 71·8) 204·0) 225·2) 212·3) 144·2) 64·4) 14·8) 0·21) 43 904) Vietnam 0·25 24·7 107·8 114·7 77·0 36·7 8·8 0·48 0·01 1·9 0·66 0·61 1 504 552 0·88 (0·11– (20·9– (91·8– (105·5– (67·9– (30·4– (7·0– (0·34– (0·01– (1·7–2·0) (0·56–0·79) (0·53–0·71) (1 372 351– (0·8–0·97) 0·51) 29·6) 127·8) 124·5) 86·8) 43·9) 10·8) 0·65) 0·01) 1 660 292) Sub-Saharan Africa 2·1 93·7 199·0 206·3 190·1 137·5 71·2 23·3 0·44 4·6 1·5 2·1 36 181 702 2·02 (0·9– (84·2– (184·9– (198·2– (179·9– (125·6– (64·2– (21·2– (0·43– (4·3–4·9) (1·4–1·6) (2·0–2·3) (34 016 504– (1·91–2·14) 4·3) 105·2) 215·8) 215·8) 201·1) 149·2) 78·1) 25·3) 0·46) 38 650 498) (Table 1 continues on next page)

2018 www.thelancet.com Vol 392 November 10, 2018 Global Health Metrics

Age-specific fertility rate (livebirths per 1000 women annually) Total Total Total fertility Number of Net fertility fertility rate rate from ages livebirths reproductive rate under age 30 to 54 years rate 25 years 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 years years years years years years years years years (Continued from previous page) Central sub-Saharan 1·5 94·1 195·3 209·5 214·4 159·2 81·9 19·0 0·37 4·9 1·5 2·4 4 318 103 2·14 Africa (0·6– (85·6– (178·9– (199·7– (202·8– (147·9– (75·8– (16·3– (0·35– (4·6–5·1) (1·3–1·6) (2·3–2·5) (4 060 044– (2·04–2·23) 3·1) 103·0) 212·1) 219·6) 225·9) 169·6) 87·8) 21·9) 0·38) 4 568 273) Angola 1·7 120·6 206·1 212·7 209·3 162·3 91·6 20·0 0·39 5·1 1·6 2·4 1 052 695 2·27 (0·7– (105·7– (179·5– (198·1– (192·1– (143·3– (81·2– (16·0– (0·37– (4·7–5·5) (1·4–1·9) (2·2–2·6) (962 954– (2·09–2·46) 3·5) 137·3) 234·4) 228·0) 226·9) 182·6) 102·5) 24·9) 0·4) 1 146 611) Central African 1·4 87·9 162·5 127·3 150·2 104·0 55·9 22·9 0·44 3·6 1·3 1·7 133 353 1·44 Republic (0·6– (75·6– (137·3– (114·8– (132·6– (85·0– (45·0– (17·9– (0·42– (3·2–4·0) (1·1–1·4) (1·5–1·9) (119 271– (1·31–1·58) 3·0) 101·8) 192·9) 142·3) 170·9) 126·8) 67·4) 28·1) 0·46) 149 763) Congo 1·2 69·8 130·4 137·4 157·4 111·7 41·4 11·0 0·21 3·3 1·0 1·6 131 030 1·48 (Brazzaville) (0·5– (60·5– (111·1– (125·8– (142·7– (92·5– (32·2– (7·8– (0·2– (3·0–3·7) (0·9–1·2) (1·4–1·9) (119 585– (1·35–1·63) 2·6) 81·5) 154·2) 151·2) 172·5) 131·7) 51·6) 14·9) 0·22) 145 001) Democratic 1·4 87·4 200·0 221·3 227·4 168·1 85·1 19·5 0·38 5·1 1·4 2·5 2 920 848 2·21 Republic of the (0·6– (77·7– (178·9– (208·0– (213·0– (153·2– (76·9– (15·8– (0·36– (4·7–5·4) (1·3–1·6) (2·4–2·6) (2 712 396– (2·09–2·32) Congo 2·9) 97·9) 221·6) 234·6) 241·5) 182·2) 93·1) 23·5) 0·39) 3 123 108) Equatorial Guinea 1·6 107·7 167·1 157·1 161·5 108·8 58·0 13·7 0·26 3·9 1·4 1·7 39 049 1·74 (0·7– (93·4– (141·7– (143·3– (143·6– (89·5– (46·8– (9·7– (0·25– (3·4–4·4) (1·2–1·6) (1·5–2·0) (33 922– (1·51–1·98) 3·3) 123·7) 195·1) 172·0) 180·4) 129·8) 70·2) 19·0) 0·27) 44 596) Gabon 1·2 62·9 112·7 123·9 131·1 91·4 30·0 4·8 0·09 2·8 0·88 1·3 41 125 1·3 (0·5– (53·4– (92·5– (111·6– (114·4– (73·7– (22·6– (3·2– (0·09– (2·5–3·2) (0·73–1·07) (1·1–1·5) (36 192– (1·15–1·48) 2·5) 75·0) 138·4) 138·7) 151·2) 110·5) 38·6) 7·0) 0·1) 47 105) Eastern 1·9 92·8 209·7 203·1 186·7 138·2 73·1 23·9 0·46 4·6 1·5 2·1 13 995 648 2·07 sub-Saharan Africa (0·8– (82·2– (192·1– (193·6– (176·0– (125·4– (65·6– (21·4– (0·44– (4·4–5·0) (1·4–1·7) (2·0–2·3) (13 041 912– (1·95–2·2) 4·0) 105·6) 229·6) 213·9) 198·8) 151·0) 80·2) 26·2) 0·47) 15 084 874) Burundi 1·4 52·6 206·7 238·1 243·2 198·9 96·7 23·0 0·44 5·3 1·3 2·8 406 276 2·32 (0·6– (45·0– (184·8– (224·9– (228·8– (185·5– (88·1– (18·9– (0·43– (5·0–5·6) (1·2–1·5) (2·7–2·9) (379 690– (2·2–2·46) 2·9) 62·2) 232·0) 253·1) 258·6) 211·0) 104·7) 27·1) 0·46) 436 338) Comoros 1·3 43·7 123·3 144·1 169·2 120·1 49·5 25·5 0·49 3·4 0·84 1·8 18 191 1·54 (0·6– (37·0– (102·5– (131·0– (151·5– (100·7– (39·4– (20·4– (0·47– (2·9–3·9) (0·7–1·0) (1·6–2·1) (15 712– (1·34–1·77) 2·7) 51·6) 147·0) 158·4) 187·9) 140·8) 60·9) 30·8) 0·51) 20 908) Djibouti 1·4 49·0 135·0 147·7 200·5 128·1 61·0 39·8 0·77 3·8 0·93 2·2 34 700 1·72 (0·6– (41·3– (112·3– (134·3– (182·8– (108·0– (49·6– (36·6– (0·74– (3·3–4·3) (0·77–1·1) (1·9–2·4) (30 383– (1·5–1·95) 2·8) 58·0) 160·8) 162·3) 218·7) 149·3) 73·4) 42·5) 0·79) 39 346) Eritrea 1·3 48·2 146·7 128·6 185·8 167·4 82·5 43·8 0·84 4·0 0·98 2·4 177 412 1·82 (0·6– (40·6– (122·7– (116·0– (167·6– (148·5– (70·8– (41·8– (0·81– (3·6–4·6) (0·82–1·18) (2·1–2·7) (155 541– (1·61–2·04) 2·8) 58·1) 176·3) 143·7) 206·4) 187·4) 95·1) 45·5) 0·88) 203 011) Ethiopia 1·9 89·0 202·7 207·2 200·2 151·3 75·3 29·4 0·57 4·8 1·5 2·3 3 714 299 2·15 (0·8– (77·4– (178·6– (193·0– (183·4– (133·3– (65·7– (25·1– (0·54– (4·4–5·2) (1·3–1·7) (2·1–2·5) (3 402 189– (2·0–2·32) 3·9) 103·5) 230·7) 223·4) 218·9) 168·9) 84·9) 33·4) 0·59) 4 069 663) Kenya 1·5 70·9 184·0 153·7 139·0 85·0 35·2 6·4 0·12 3·4 1·3 1·3 1 365 160 1·54 (0·6– (60·6– (158·3– (140·2– (122·1– (68·2– (27·0– (4·3– (0·12– (3·0–3·8) (1·1–1·4) (1·1–1·6) (1 208 543– (1·34–1·75) 3·1) 83·9) 211·0) 167·8) 156·5) 103·3) 44·6) 8·9) 0·13) 1 535 478) Madagascar 2·4 128·0 230·2 215·6 175·1 134·5 75·9 16·8 0·32 4·9 1·8 2·0 975 570 2·16 (1·0– (113·1– (204·3– (201·2– (157·7– (115·5– (65·5– (13·2– (0·31– (4·4–5·5) (1·6–2·0) (1·8–2·3) (871 322– (1·93–2·4) 5·0) 144·4) 257·3) 230·7) 193·5) 154·3) 86·6) 21·0) 0·34) 1 083 828) Malawi 2·7 110·3 220·2 191·9 154·1 107·7 69·9 33·9 0·65 4·5 1·7 1·8 612 862 1·99 (1·2– (98·7– (199·4– (179·5– (140·3– (93·9– (60·9– (30·3– (0·63– (4·2–4·8) (1·5–1·9) (1·6–2·0) (571 079– (1·86–2·12) 5·6) 124·4) 244·0) 206·2) 168·2) 122·2) 78·9) 37·2) 0·68) 660 504) Mozambique 2·0 98·2 187·4 160·7 157·7 114·7 74·4 36·5 0·7 4·2 1·4 1·9 988 056 1·78 (0·8– (87·6– (167·5– (149·2– (143·6– (99·8– (65·4– (33·0– (0·68– (3·8–4·5) (1·3–1·6) (1·8–2·1) (912 263– (1·66–1·91) 4·1) 111·0) 208·0) 172·6) 172·0) 130·2) 83·3) 39·6) 0·73) 1 068 141) Rwanda 1·1 30·1 167·6 205·2 219·1 158·4 83·4 20·9 0·4 4·4 0·99 2·4 423 424 2·03 (0·5– (25·5– (143·9– (190·6– (202·2– (139·9– (72·8– (16·1– (0·39– (4·0–4·9) (0·85–1·17) (2·2–2·6) (381 994– (1·85–2·24) 2·2) 36·2) 196·1) 222·0) 237·6) 178·5) 93·5) 26·7) 0·42) 470 006) Somalia 1·9 96·4 248·7 273·3 251·7 202·6 101·6 42·3 0·81 6·1 1·7 3·0 685 515 2·56 (0·8– (83·1– (221·2– (259·9– (236·9– (188·7– (92·0– (39·8– (0·78– (5·7–6·5) (1·5–2·0) (2·8–3·1) (638 214– (2·44–2·69) 4·1) 112·9) 279·3) 288·1) 267·4) 214·9) 110·1) 44·3) 0·85) 737 921) (Table 1 continues on next page)

www.thelancet.com Vol 392 November 10, 2018 2019 Global Health Metrics

Age-specific fertility rate (livebirths per 1000 women annually) Total Total Total fertility Number of Net fertility fertility rate rate from ages livebirths reproductive rate under age 30 to 54 years rate 25 years 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 years years years years years years years years years (Continued from previous page) South Sudan 2·3 129·6 271·7 262·4 251·7 163·5 78·4 25·7 0·49 5·9 2·0 2·6 413 783 2·5 (1·0– (113·5– (245·0– (248·5– (237·0– (144·6– (66·6– (20·4– (0·48– (5·6–6·3) (1·8–2·3) (2·3–2·8) (387 551– (2·36–2·66) 4·8) 149·1) 300·8) 277·7) 265·6) 181·5) 90·0) 31·2) 0·51) 444 396) Tanzania 2·0 99·2 220·7 211·7 183·2 138·7 79·4 21·9 0·42 4·8 1·6 2·1 1 986 281 2·16 (0·9– (87·4– (197·7– (198·2– (167·3– (122·5– (71·0– (17·9– (0·41– (4·4–5·2) (1·4–1·8) (2·0–2·3) (1 828 505– (2·02–2·32) 4·1) 113·8) 247·1) 227·1) 201·4) 155·0) 87·8) 26·0) 0·44) 2 163 767) Uganda 2·1 108·5 246·8 246·1 198·9 146·0 80·2 19·2 0·37 5·2 1·8 2·2 1 550 366 2·37 (0·9– (97·4– (226·3– (234·2– (185·1– (131·7– (73·0– (15·7– (0·36– (5·0–5·5) (1·6–2·0) (2·1–2·4) (1 471 798– (2·26–2·48) 4·4) 121·9) 269·9) 257·9) 212·7) 160·0) 87·4) 23·0) 0·38) 1 638 382) Zambia 2·0 104·8 206·2 199·3 183·5 136·6 81·0 23·1 0·44 4·7 1·6 2·1 634 965 2·1 (0·9– (91·1– (180·0– (184·8– (165·8– (116·9– (69·8– (18·0– (0·43– (4·2–5·2) (1·4–1·8) (1·9–2·3) (568 251– (1·9–2·31) 4·3) 121·6) 236·7) 216·1) 203·6) 158·8) 91·8) 28·3) 0·46) 710 865) Southern 0·77 69·2 124·3 137·5 100·3 66·1 22·8 2·9 0·05 2·6 0·97 0·96 1 748 266 1·19 sub-Saharan Africa (0·34– (59·8– (109·2– (127·6– (88·0– (54·1– (18·7– (2·1– (0·05– (2·4–2·9) (0·85–1·12) (0·82–1·12) (1 595 640– (1·09–1·32) 1·61) 81·1) 143·1) 149·0) 113·7) 79·6) 27·5) 3·8) 0·06) 1 938 810) Botswana 0·5 45·8 115·1 119·0 95·3 66·5 24·9 4·3 0·08 2·4 0·81 0·96 48 644 1·1 (0·22– (39·3– (102·5– (111·2– (87·3– (58·7– (21·5– (3·3– (0·08– (2·2–2·5) (0·71–0·92) (0·87–1·04) (45 386– (1·03–1·19) 1·03) 54·0) 130·6) 127·2) 103·8) 74·8) 28·7) 5·5) 0·09) 52 258) Lesotho 1·1 70·2 150·9 123·4 108·8 73·9 38·4 6·5 0·12 2·9 1·1 1·1 48 751 1·23 (0·5– (61·2– (132·5– (113·2– (97·0– (61·8– (32·1– (4·5– (0·12– (2·6–3·2) (1·0–1·3) (1·0–1·3) (44 699– (1·12–1·36) 2·2) 81·3) 173·1) 135·7) 121·3) 87·2) 45·3) 8·9) 0·13) 53 717) Namibia 3·1 54·5 129·4 146·9 128·9 95·5 37·3 7·0 0·14 3·0 0·93 1·3 59 520 1·4 (1·4– (47·6– (114·1– (136·8– (116·4– (82·0– (30·6– (4·9– (0·13– (2·8–3·3) (0·82–1·07) (1·2–1·5) (54 862– (1·28–1·52) 6·4) 63·1) 147·9) 157·4) 142·0) 109·8) 44·8) 9·8) 0·14) 64 829) South Africa 0·59 59·3 102·9 129·6 89·0 56·7 18·1 1·9 0·04 2·3 0·81 0·83 1 091 574 1·05 (0·26– (50·2– (84·1– (117·0– (76·0– (44·2– (13·4– (1·3– (0·04– (2·0–2·6) (0·67–0·99) (0·68–1·0) (976 081– (0·94–1·19) 1·24) 70·9) 126·9) 144·8) 103·3) 71·1) 23·7) 2·8) 0·04) 1 238 233) Swaziland 1·2 73·3 153·0 129·3 125·5 83·8 35·8 6·5 0·12 3·0 1·1 1·3 30 680 1·36 (eSwatini) (0·5– (63·7– (133·0– (117·9– (111·1– (69·3– (28·4– (4·4– (0·12– (2·7–3·5) (1·0–1·3) (1·1–1·5) (26 863– (1·18–1·56) 2·4) 85·2) 177·1) 143·0) 142·7) 101·8) 45·3) 9·1) 0·13) 35 270) Zimbabwe 0·86 100·7 192·2 176·1 143·4 98·3 39·0 6·1 0·12 3·8 1·5 1·4 469 094 1·7 (0·38– (89·6– (176·1– (166·7– (132·6– (87·1– (32·7– (4·8– (0·11– (3·5–4·0) (1·4–1·6) (1·3–1·6) (438 268– (1·6–1·81) 1·82) 114·2) 208·8) 185·8) 154·6) 109·9) 45·8) 7·8) 0·12) 501 128) Western 2·5 98·0 203·0 222·8 207·2 147·2 78·0 29·1 0·56 4·9 1·5 2·3 16 119 684 2·12 sub-Saharan Africa (1·1– (86·9– (187·9– (214·1– (196·6– (133·7– (69·4– (26·2– (0·54– (4·6–5·3) (1·4–1·7) (2·1–2·5) (15 142 476– (2·01–2·25) 5·3) 111·6) 219·9) 232·4) 218·2) 160·6) 86·6) 31·8) 0·58) 17 204 806) Benin 6·2 79·3 204·3 224·2 203·1 134·7 74·0 28·9 0·56 4·8 1·4 2·2 420 926 2·09 (2·7– (69·3– (182·9– (210·5– (186·6– (116·5– (63·7– (23·9– (0·54– (4·4–5·2) (1·3–1·6) (2·0–2·4) (387 743– (1·96–2·24) 12·9) 91·6) 229·0) 239·8) 221·6) 153·0) 84·3) 33·7) 0·58) 457 500) Burkina Faso 2·4 98·0 235·6 247·3 217·2 164·4 85·9 29·0 0·56 5·4 1·7 2·5 850 128 2·31 (1·0– (86·9– (214·1– (234·4– (201·6– (148·0– (76·6– (25·1– (0·54– (5·1–5·8) (1·5–1·9) (2·3–2·7) (792 781– (2·2–2·44) 5·0) 111·5) 260·0) 261·7) 234·4) 180·2) 94·9) 32·7) 0·58) 913 615) Cameroon 2·3 91·9 172·9 164·5 166·3 115·4 55·3 18·0 0·35 3·9 1·3 1·8 860 875 1·73 (1·0– (80·0– (150·4– (151·4– (149·8– (97·5– (45·8– (14·4– (0·33– (3·5–4·4) (1·2–1·5) (1·6–2·0) (767 156– (1·54–1·94) 4·9) 106·7) 199·7) 179·8) 185·4) 136·4) 65·5) 21·9) 0·36) 970 019) Cape Verde 1·4 35·3 88·8 113·3 89·1 63·4 26·2 19·4 0·37 2·2 0·63 0·99 9895 1·04 (0·6– (29·6– (71·9– (101·8– (75·8– (49·3– (19·5– (14·4– (0·36– (1·8–2·6) (0·51–0·76) (0·8–1·22) (8296–11 738) (0·86–1·24) 2·9) 42·1) 108·7) 126·1) 104·2) 80·1) 34·6) 25·0) 0·39) Chad 3·2 172·7 294·7 306·6 270·3 188·0 84·4 24·2 0·47 6·7 2·4 2·8 716 150 2·81 (1·4– (155·9– (271·9– (295·9– (258·4– (173·9– (75·2– (20·2– (0·45– (6·4–7·0) (2·2–2·6) (2·7–3·0) (684 354– (2·71–2·92) 6·9) 192·3) 319·2) 317·0) 281·4) 201·0) 93·3) 28·2) 0·48) 753 893) Côte d’Ivoire 3·1 99·1 187·7 184·2 183·6 136·2 77·9 28·1 0·54 4·5 1·4 2·1 863 669 1·97 (1·4– (87·1– (163·1– (169·9– (166·2– (117·3– (67·8– (23·4– (0·52– (4·1–4·9) (1·3–1·7) (1·9–2·3) (785 916– (1·8–2·16) 6·6) 113·9) 216·8) 200·9) 203·5) 155·2) 87·9) 32·6) 0·56) 951 705) (Table 1 continues on next page)

2020 www.thelancet.com Vol 392 November 10, 2018 Global Health Metrics

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 single calendar years and single-year age groups country variation, with 35 countries showing decreasing compared with previous assessments that reported populations in 2017 whereas 57 countries had population results for 5-year age groups.4 The global population growth at a rate higher than 2·0%. Country variation in increased nearly three-fold between 1950 and 2017, from population growth rates is driven to a large extent by 2·6 billion (2·5–2·6) people in 1950 to 7·6 billion wide variations in fertility rates and to a lesser extent by (7·4–7·9) people in 2017. Although global population migration rates. growth rates have declined from a peak of 2·0% in 1964 Of the 59 countries with a TFR of more than to 1·1% in 2017, the size of the global population has three livebirths per woman in 2017 (figure 9), 41 are in steadily been increasing by more than 80 million people sub-Saharan Africa. Of the remainder, six countries are annually since 1985. These global estimates mask huge in north Africa and the Middle East. These continuous www.thelancet.com Vol 392 November 10, 2018 2021 Global Health Metrics

iebirths er oa ≤0·24 1·25–1·49 0·25–0·49 1·50–1·74 0·50–0·74 1·75–1·99 0·75–0·99 ≥2·00 1·00–1·24

ATG VCT Barbados Comoros Marshall Isl Kiribati West Africa Eastern Mediterranean Solomon Isl FSM