key: cord-0907922-of1p79eu authors: Sharrow, David; Hug, Lucia; You, Danzhen; Alkema, Leontine; Black, Robert; Cousens, Simon; Croft, Trevor; Gaigbe-Togbe, Victor; Gerland, Patrick; Guillot, Michel; Hill, Kenneth; Masquelier, Bruno; Mathers, Colin; Pedersen, Jon; Strong, Kathleen L; Suzuki, Emi; Wakefield, Jon; Walker, Neff title: Global, regional, and national trends in under-5 mortality between 1990 and 2019 with scenario-based projections until 2030: a systematic analysis by the UN Inter-agency Group for Child Mortality Estimation date: 2022-01-18 journal: Lancet Glob Health DOI: 10.1016/s2214-109x(21)00515-5 sha: f9fb88502fedd9d097178441ff21c0f811c43265 doc_id: 907922 cord_uid: of1p79eu BACKGROUND: The Sustainable Development Goals (SDGs), set in 2015 by the UN General Assembly, call for all countries to reach an under-5 mortality rate (U5MR) of at least as low as 25 deaths per 1000 livebirths and a neonatal mortality rate (NMR) of at least as low as 12 deaths per 1000 livebirths by 2030. We estimated levels and trends in under-5 mortality for 195 countries from 1990 to 2019, and conducted scenario-based projections of the U5MR and NMR from 2020 to 2030 to assess country progress in, and potential for, reaching SDG targets on child survival and the potential under-5 and neonatal deaths over the next decade. METHODS: Levels and trends in under-5 mortality are based on the UN Inter-agency Group for Child Mortality Estimation (UN IGME) database on under-5 mortality, which contains around 18 000 country-year datapoints for 195 countries—nearly 10 000 of those datapoints since 1990. The database includes nationally representative mortality data from vital registration systems, sample registration systems, population censuses, and household surveys. As with previous sets of national UN IGME estimates, a Bayesian B-spline bias-reduction model (B3) that considers the systematic biases associated with the different data source types was fitted to these data to generate estimates of under-5 (age 0–4 years) mortality with uncertainty intervals for 1990–2019 for all countries. Levels and trends in the neonatal mortality rate (0–27 days) are modelled separately as the log ratio of the neonatal mortality rate to the under-5 mortality rate using a Bayesian model. Estimated mortality rates are combined with livebirths data to calculate the number of under-5 and neonatal deaths. To assess the regional and global burden of under-5 deaths in the present decade and progress towards SDG targets, we constructed several scenario-based projections of under-5 mortality from 2020 to 2030 and estimated national, regional, and global under-5 mortality trends up to 2030 for each scenario. FINDINGS: The global U5MR decreased by 59% (90% uncertainty interval [UI] 56–61) from 93·0 (91·7–94·5) deaths per 1000 livebirths in 1990 to 37·7 (36·1–40·8) in 2019, while the annual number of global under-5 deaths declined from 12·5 (12·3–12·7) million in 1990 to 5·2 (5·0–5·6) million in 2019—a 58% (55–60) reduction. The global NMR decreased by 52% (90% UI 48–55) from 36·6 (35·6–37·8) deaths per 1000 livebirths in 1990, to 17·5 (16·6–19·0) in 2019, and the annual number of global neonatal deaths declined from 5·0 (4·9–5·2) million in 1990, to 2·4 (2·3–2·7) million in 2019, a 51% (47–54) reduction. As of 2019, 122 of 195 countries have achieved the SDG U5MR target, and 20 countries are on track to achieve the target by 2030, while 53 will need to accelerate progress to meet the target by 2030. 116 countries have reached the SDG NMR target with 16 on track, leaving 63 at risk of missing the target. If current trends continue, 48·1 million under-5 deaths are projected to occur between 2020 and 2030, almost half of them projected to occur during the neonatal period. If all countries met the SDG target on under-5 mortality, 11 million under-5 deaths could be averted between 2020 and 2030. INTERPRETATION: As a result of effective global health initiatives, millions of child deaths have been prevented since 1990. However, the task of ending all preventable child deaths is not done and millions more deaths could be averted by meeting international targets. Geographical and economic variation demonstrate the possibility of even lower mortality rates for children under age 5 years and point to the regions and countries with highest mortality rates and in greatest need of resources and action. FUNDING: Bill & Melinda Gates Foundation, US Agency for International Development. 1 Supplementary appendix to Global, regional and national trends in under-5 mortality 1990-2019 with scenario-based projections to 2030: a systematic analysis by the United Nations Inter-agency Group for Child Mortality Estimation UN IGME follows the following broad strategy to arrive at annual estimates of child mortality: 1. Compile and assess the quality of all available nationally representative data relevant to the estimation of child mortality, including data from vital registration systems, population censuses, household surveys and sample registration systems; 2. Assess data quality, recalculate data inputs and make adjustments as needed by applying standard methods; 3. Fit a statistical model to these data to generate a smooth trend curve that averages possibly disparate estimates from the different data sources for a country; 4. Extrapolate the model to a target year (in this case, mid-2019). To increase the transparency of the estimation process, UN IGME has developed a child mortality web portal, Child Mortality Estimation (CME) Info, available at . It includes all available data and shows estimates for each country as well as which data are currently officially used by UN IGME. Once new estimates are finalized, CME Info is updated accordingly. UN IGME applies a common methodology across countries and uses original empirical data from each country. However, it does not report figures produced by individual countries using other methods, which would not be comparable to other country estimates. Applying a consistent methodology allows for comparisons between countries, despite the varied number and types of data sources. UN IGME estimates are based on nationally available data from censuses, surveys, vital registration systems or sample registration systems. UN IGME does not use covariates to derive its estimates but rather, applies a curve-fitting method to empirical data after data quality assessment. Countries often use a single source for their official estimates or apply methods different to those used by UN IGME. The differences between UN IGME and national official estimates are usually not large if the empirical data are of good quality. UN IGME aims to minimize errors for each estimate, harmonize trends over time, and produce up to date and properly evaluated estimates of child mortality. Because errors are inevitable in data, there will always be uncertainty around data and estimates, both nationally and internationally. To allow for added comparability, UN IGME generates all child mortality estimates with uncertainty bounds. The first step in the process of arriving at estimates of levels and recent trends of child mortality is to compile all newly available data and add the data to the UN IGME database (newly available may include newer, recently released data and occasionally, results from older censuses or surveys not previously available). Nationally representative estimates of under-five mortality can be derived from several different sources, including civil registration and sample surveys. Demographic surveillance sites and hospital data are excluded as they are rarely nationally representative. The preferred source of data is a civil registration system that records births and deaths on a continuous basis. If registration is complete and this system functions efficiently, the resulting estimates will be accurate and timely. However, many low-and middle-income countries do not have well-functioning vital registration (VR) systems. Therefore, household surveys such as the UNICEF-supported Multiple Indicator Cluster Surveys, the USAID-supported Demographic and Health Surveys, and periodic population censuses have become the primary sources of data on mortality among children under age 5. These surveys ask women about the survival of their children and it is these reports (or microdata upon availability) that provide the basis of child mortality estimates for a majority of low-and middle-income countries. Whatever the method used to derive the estimates, data quality is critical. UN IGME assesses data quality and does not include data sources with substantial non-sampling errors or omissions as underlying empirical data in its statistical model. The full set of empirical data used in this analysis is publicly available from the UN IGME web portal, CME Info . The web portal also includes data that have been excluded and indicates the fact. Data from civil registration systems are the preferred data source for child mortality estimation. For data from civil registration, the calculation of U5MR and IMR is derived from a standard period abridged life table. The inputs are number of deaths for age group <1 year (noted D 0 ) and for the age group 1-4 years (D 1-4 ), as well as the mid-year population for the same age groups (P 0 and P 1-4 ). The formulae are as follows: Given that: n q x is the probability of dying between age x and age x+n, 1 M 0 = D 0 /P 0 , death rate for age <1, 4 M 1 = D 1-4 /P 1-4 , death rate for age group 1-4, Then: where 1 a 0 is the fraction of year lived by an infant who died 1 a 0 = 0.1 for low mortality country and 1 a 0 = 0.3 for high mortality country 5 q 0 = 1-(1-1 q 0 )*(1-4 q 1 ) where 4 q 1 = 4* 4 M 1 /[1+ (4-4 a 1 ) * 4 M 1 ] where 4 a 1 is the fraction of years lived by a child aged 1-4 years who died 4 a 1 = 1.6 Finally: IMR = 1 q 0 *1000 and U5MR= 5 q 0 *1000 For NMR, the number of deaths under 28 days of age and live births are used to calculate the neonatal mortality rate. For civil registration data (with available data on the number of deaths and mid-year populations), annual observations were initially constructed for all observation years in a country. For country-years in which the coefficient of variation exceeded 10 per cent for children under 5 years, deaths and mid-year populations were pooled over longer periods. Starting from more recent years, deaths and population were combined with one or more previous years to reduce spurious fluctuations in countries where small numbers of births and deaths were observed. The coefficient of variation is defined to be the stochastic standard error of the 5q0 (5q0=U5MR/1,000) observation divided by the value of the 5q0 observation. The stochastic standard error of the observation is calculated using a Poisson approximation using live birth numbers, given by sqrt(5q0/lb), where lb is the number of live births in the year of the observation. After this recalculation of the civil registration data, the standard errors are set to a minimum of 2.5 per cent for input into the model following the original model specification 1 . A similar approach was used for neonatal mortality. Figure 1 illustrates the recalculation of civil registration data for Iceland. Figure 1 Illustration of the recalculation of civil registration data for Iceland. Black dots refer to annual observations while red dots refer to the recalculated observations with longer observation periods, with 95% observation-specific uncertainty intervals The majority of survey data on under-five mortality is collected in one of two ways: the full birth history (FBH), whereby women are asked for the date of birth of each of their children, whether the child is still alive, and if not, the age at death; and the summary birth history (SBH), whereby women are asked only about the number of their children ever born and the number that have died (or equivalently the number still alive). FBH data, collected by all Demographic and Health Surveys (DHS) and increasingly also by Multiple Indicator Cluster Surveys (MICS), allow the calculation of child mortality indicators for specific time periods in the past. DHS and MICS usually publish under-five child mortality estimates for three 5-year periods before the survey, that is, 0 to 4, 5 to 9, and 10 to 14 years before the survey. 2, 3 ,4 UN IGME has re-calculated estimates for calendar year periods, using single calendar years for periods shortly before the survey, and then gradually increasing the number of years for periods further in the past, whenever microdata from the survey are available. The cut-off points for a given survey for shifting from estimates for single calendar years to two years, or two years to three, etc., are based on the coefficients of variation (a measure of sampling uncertainty) of the estimates 5 . In general, SBH data, collected by censuses and many household surveys, use the age of the woman as an indicator of the exposure time of her children to the risk of death and use models to estimate underfive mortality indicators for periods in the past for women aged 25 to 29 through 45 to 49. This method is well known but has several shortcomings. Starting with the 2014 round of estimation, the UN IGME changed the method of estimation for summary birth histories to one based on classification of women by the time that has passed since their first birth. This newer method has several benefits over the previous one: First, it generally has lower sampling errors. Second, it avoids the problematic assumption that the estimates derived for each age group adequately represent the mortality of the whole population. As a result, it has less susceptibility to the selection effect of young women who give birth early, since all women who give birth necessarily must have a first birth and therefore are not selected for. Third, the method tends to show less fluctuation across time, in particular in countries with relatively low fertility and mortality. The IGME considers the improvements in the estimates based on time since first birth worthwhile when compared to the estimates derived from the classification by age of mother, hence in cases where the microdata is available, the UN IGME has reanalysed the data using the new method. Child mortality estimates from SBH data were not included if estimates from FBH data in the same survey were available 6 . SBH data are not used to derive neonatal mortality. In populations severely affected by HIV/AIDS, HIV-positive (HIV+) children will be more likely to die than other children and will also be less likely to be reported since their mothers will have been more likely to 2 http://mics.unicef.org/tools 3 Croft, Trevor N., Aileen M. J. Marshall, Courtney K. Allen, et al. Guide to DHS Statistics. Rockville, Maryland, USA: ICF. 2018 die also. Child mortality estimates will thus be biased downwards. The magnitude of the bias will depend on the extent to which the elevated under-five mortality of HIV+ children is not reported because of the deaths of their mothers. A method was developed to adjust HIV/AIDS related mortality for each survey data observation from FBH during HIV/AIDS epidemics (1980-present) , by adopting a set of simplified but reasonable assumptions about the distribution of births to HIV+ women, primarily relating to the duration of their infection, vertical transmission rates, and survival times of both mothers and children from the time of the birth 7 . This method was applied to all DHS and MICS surveys with FBH. The model was improved to incorporate the impact of antiretroviral therapies (ART) and prevention of mother to child transmission (PMTCT) 8 . Data from these different sources require different calculation methods and may suffer from different errors, for example random errors in sample surveys or systematic errors due to misreporting. As a result, different surveys often yield widely different estimates of U5MR or other mortality indicators for a given time period. In order to reconcile these differences and take better account of the systematic biases associated with the various types of data inputs, Alkema and New (2014) developed an estimation method to fit a smoothed trend curve to a set of observations and to extrapolate that trend to a defined time point, in this case 2019. This method is described in the following section. Estimation and extrapolation of under-5 mortality rates (U5MR) was undertaken using the Bayesian Bsplines bias-adjusted model, referred to as the B3 model. This model was developed, validated, and used to produce the previous round of UN IGME child mortality estimates. In the B3 model, log(U5MR) is estimated with a flexible spline regression model. The spline regression model is fitted to all U5MR observations in the country. An observed value for U5MR is considered to be the true value for U5MR multiplied by an error factor, i.e. observed U5MR = true U5MR * error, or on the log-scale, log(observed u5mr) = log(true U5MR) + log(error), where error refers to the relative difference between an observation and the truth. While estimating the true U5MR, properties of the errors that provide information about the quality of the observation, or in other words, the extent of error that we expect, are taken into account. These properties include the standard error of the observation, its source type (e.g. DHS versus census) and if the observation is part of a data series from a specific survey (and how far the data series is from other series with overlapping observation periods). These properties are summarized in the so-called data model. When estimating the U5MR, the data model adjusts for the errors in the observations, including the average systematic biases associated with different types of data sources, using information on data quality for different source types from all countries in the world. Figure 2 displays plots of the U5MR over time for Senegal, used here for illustrative purposes. The B3 estimates are in red. Ninety per cent uncertainty intervals for the U5MR are given by the pink bands. All data available for the country are shown as coloured points, with observations from the same data series joined by lines. Solid points and lines represent data series/observations that were included for curve-fitting. Grey bands represent the standard errors of the observations where available. The B3 method was developed and implemented for the UN IGME by Leontine Alkema and Jin Rou New from the National University of Singapore with guidance and review by the TAG of the UN IGME. A more complete technical description of the B3 model is available elsewhere 9 . The splines regression fitting method for Norway is illustrated in Figure 3 . Splines are smooth curves, placed 2.5 years apart, that add up to 1 at any point in time. For any year, the estimated log(U5MR) is the sum of the non-zero splines in that year multiplied by the corresponding spline coefficients (displayed by dots). For example, log(U5MR) in 1980 in Norway is given by the sum of the yellow and grey splines to the left of black line (at the year 1980) and the black and red splines to the right, multiplied by their respective spline coefficients in the same colour. The spline coefficients determine what the resulting fitted curve looks like. When estimating the spline coefficients, we obtain a flexible yet reasonably smooth U5MR curve by assuming that the difference between two adjacent coefficients (for example for years 1981 and 1983.5 ) is given by the difference between the previous two coefficients (for years 1978.5 and 1981) with an estimated data-driven "distortion term" added to it. For example, in Norway during the early 1980s, these distortion terms are estimated to be around zero when U5MR did not change much, but they are negative in the late 1980s when the U5MR started to decline again. The resulting fit in Norway illustrates that the spline fit is able to follow the observed changes in the data closely. The variance of the distortion terms determines the smoothness of the fit during the observation period; large fluctuations in these distortion terms imply that the trend can vary greatly from one period to the next. The amount of smoothing is country-specific for the majority of countries. An average global level of smoothing is used for countries with a small number of live births, countries with both vital registration (VR) and non-VR data included in the fitting and countries with a gap of more than five years in their VR data. Due to the nature of the data in such countries, a large variance for the distortion terms tends to be estimated, so a global level of smoothing helps to reduce fluctuations in the trend. After the most recent observation period ends, country-specific U5MR is extrapolated to the target year through the estimation of "future spline coefficients", or equivalently, by 'projecting' the differences between adjacent spline coefficients for the extrapolation period. The mean projected difference in spline coefficients is given by the estimated difference in the two most recent adjacent spline coefficients, and the uncertainty therein is based on the variability in the observed distortions in the country's past. Based on out-of-sample validation exercises, this approach is shown to work well for the majority of countries but leads to unnecessarily wide uncertainty intervals (or extreme extrapolations) for a subset of countries where the most recent change in spline coefficients is very uncertain (or an extreme value). We avoid such uncertain and extreme U5MR extrapolations in longer extrapolation periods by combining the country-specific projected differences in spline coefficients with a global distribution of observed differences in the past. This final step results in the removal of very extreme country-specific U5MR extrapolations. Figure 4 Extrapolation period (i.e. the difference in years between the most recent included data point and the common end year, 2019.5) for the UN IGME 2020 estimation round Note: This map does not reflect a position by UN IGME agencies or those of the institutions to which the authors are affiliated on the legal status of any country or territory or the delimitation of any frontiers. To capture the extraordinarily rapid changes in child mortality driven by HIV/AIDS over the epidemic period in some countries, the regression model was fitted to data points for the U5MR from all causes other than HIV/AIDS, and then UNAIDS estimates of HIV/AIDS under-five mortality 10 were added to the estimates from the regression model. This method was used for 17 countries where the HIV prevalence rate exceeded 5 per cent at any point in time since 1980. Steps were as follows: 1. Compile and assess the quality of all newly available nationally representative data relevant to the estimation of child mortality. 2. Use UNAIDS estimates of HIV/AIDS child mortality 10 to adjust the data points from 1980 onwards to exclude HIV deaths (see section 1.2. 3. Fit the standard statistical model to the observations to HIV-free data points. 4. Extrapolate the model to the target year, in this case 2019. 5. Add back estimates of deaths due to HIV/AIDS (from UNAIDS) 10 UNAIDS 1990 HIV and AIDS estimates, 2019 6. For the epidemic period, a non-HIV curve of IMR is derived from U5MR using model life tables (see Section 4) and then the UNAIDS estimates of HIV/AIDS deaths for children under age 1 are added to generate the final IMR estimates. The neonatal mortality rate (NMR) is defined as the probability of dying between birth and exact age 28 days per 1000 live births. In 2015 the UN IGME method for estimating NMR was updated. The new Bayesian methodology is similar to that used to estimate U5MR and derive estimates by sex. It has the advantage that, compared to the previous model, it can capture data-driven trends in NMR within countries and over time for all countries. A more complete technical description of the model is available elsewhere 11 . We model the ratio R(c,t), which refers to the ratio of NMR to the difference of U5MR and NMR in country c and year t, i.e. R(c,t) = NMR/(U5MR -NMR). For each country-year, we assume that the ratio is given by: R(c,t) = W(c,t) * P(c,t), where W(c,t) refers to the expected ratio for that country-year, and Country multiplier P(c,t) represents country-specific trends in the ratio over time that differ from the expected level. As U5MR decreases, the proportional share of mortality in the first month of life tends to increase. The W(c,t) term accounts for this relationship; it is the expected ratio for the country-year based on the UN IGME-estimated U5MR for that country-year. It is modelled as a linear function of U5MR with a changing slope: W(c,t) = β0 if U5MR(c,t) < Ucut W(c,t) = β0 + β1*U5MR(c,t) if U5MR(c,t) ≥ Ucut Ucut is an estimated constant that represents the level of U5MR after which as U5MR increases, the ratio NMR/(U5MR -NMR) decreases. The parameters of this model are estimated based on all available data such that W(c,t) represents a 'global relation' between the ratio and U5MR. The country multiplier P(c,t) is modelled with a B-splines regression model. The P(c,t) represents a country-specific intercept, which is modelled hierarchically, and fluctuations around that intercept over time. For any particular country, the ratio can overall be higher-or lower-than-expected given the level of U5MR in that country, but the fluctuations allow this relationship to change over time within a country. A degree of smoothness is imposed on the fluctuations to ensure relatively smooth trajectories for any given country through time. We model the ratio of NMR/(U5MR -NMR); estimates of NMR are obtained by recombining the estimates of the ratio with UN IGME-estimated U5MR. For neonatal mortality in HIV-affected and crisis-affected populations, the ratio is estimated initially for non-AIDS and non-crisis mortality. After estimation, crisis neonatal deaths are added back on to the neonatal deaths to compute the total estimated neonatal mortality rate. No AIDS deaths are added back to the NMR, thereby assuming that HIV/AIDS-related deaths only affect child mortality after the first month of life. A birth-week cohort method is used to calculate the absolute number of deaths among neonates, infants, and children under age 5. First, each annual birth cohort is divided into 52 equal birth-week cohorts. Then, each birth-week cohort is exposed throughout the first five years of life to the appropriate calendar year-and age-specific mortality rates depending on cohort age, assuming that mortality rates are constant over the age intervals. For example, the 20th birth week cohort of the year 2000 will be exposed to the infant mortality rates in both 2000 and 2001. All deaths from birth-week cohorts occurring as a result of exposure to the mortality rate for a given calendar year are allocated to that year and are summed by age group at death to get the total number of deaths for a given year and age group. Continuing with the above example, deaths from the 20th birth- 1.6. Child mortality due to conflict and environmental disasters Estimated deaths for major crises including conflicts, environmental disasters (e.g. floods, earthquakes, cyclones), and epidemics were derived from various data sources from 1990 to present. Data on all-age deaths from environmental disasters were obtained from the CRED International Disaster Database 13 and the proportions of deaths occurring under age 5 and under age 1 were estimated using disastertype specific age patterns described elsewhere 14 . Conflict deaths were obtained from the Uppsala Conflict Data Program/Peace Research Institute Oslo datasets as well as reports prepared by the UN and other organizations on specific conflicts, again using age patterns to estimate the proportions of underfive and under-one deaths. We calculate a crisis-U5MR and crisis-IMR from these age-specific crisis deaths. Estimated child deaths due to major crises were used to adjust mortality rates in specific country-years if the crisis met the following criteria: 1. The crisis was isolated to a few years 2. Under-five crisis deaths were more than 10% of under-five non-crisis deaths in the given year 3. Crisis U5MR was more than 0.2 per 1,000 in the given year 4. Number of under-five crisis deaths was greater than 10. These criteria resulted in crisis adjustments being incorporated into the UN IGME under-five mortality estimates for 22 countries. Crisis deaths were incorporated into the U5MR estimates by first excluding any data points from crisis years, fitting the B3 model to the remaining data, and then adding the crisisspecific death rate to the fitted crisis-free B3 curve. For countries where IMR is derived from U5MR, the crisis-free U5MR trajectories are used to derive a crisis-free IMR to which infant crisis adjustments are applied. The procedure produces a smooth crisis-free fit in all non-crisis years, while also including acute spikes related to specific crises. Crisis death estimates are uncertain but presently no uncertainty around crisis deaths is included in the U5MR uncertainty intervals. We assume the relative uncertainty in the adjusted U5MR is equal to the relative uncertainty in the non-adjusted U5MR; this assumption will be revisited in the near future. The UN IGME has assessed recent, ongoing crises, and based on the lack of currently available data and the difficulties of estimating the broader impact of these crises on health systems, UN IGME holds the mortality rate estimates constant from the start of the crisis while increasing the uncertainty over the crisis time for three countries: South Sudan, Venezuela (Bolivarian Republic of) and Yemen. Where applicable, direct crisis deaths have been added to the constant trend estimate. UN IGME will review new data, if available, in the next estimation round and revise estimates accordingly. 3.1. Note: The series name refers to a specific data collection source and estimation method. There are several sources -including VR, censuses and sample surveys -where:  VR (direct) series are based on birth and death registration data  Survey (direct) series are based on data on births and deaths from full birth histories  Census (direct) series are based on household deaths in the previous 12 months  Census or survey (indirect) series are based on summary birth histories (i.e., mean number of children ever born and children surviving by age of mother or time since first birth) Life Tables (Life Table) (Life Table) 1 Tables (Life Table) 0 Tables (Life Table) 0 TABLAS DE VIDA NACIONALES (Life Table) 0 Table) 0 UNPD Demographic Yearbook Data 2020 version 2020 (VR) 1 VR Submitted to WHO/UNIGME 2020 version 2020 (VR) 1 Italy VR Submitted to WHO/UNIGME 2020 version 2020 (VR) 1 Jamaica National Life Tables (Life Table) Table (Life Table) Table (Life Table) 0 National Life Table 1970 -2015 (Life Table) 1 Recalculated VR Submitted to WHO/UNIGME 2020 version 2020 (VR) 1 VR Submitted to WHO/UNIGME 2020 version 2020 (VR) 0 Saint Lucia Recalculated VR Submitted to WHO/UNIGME 2020 version 2020 (VR) 1 UNPD Demographic Yearbook Data 2020 version 2020 (VR) 1 VR Submitted to WHO/UNIGME 2020 version 2020 (VR) 0 Saint Vincent and the Grenadines Recalculated VR Submitted to WHO/UNIGME 2020 version 2020 (VR) 1 UNPD Demographic Yearbook Data 2020 version 2020 (VR) 1 VR Submitted to WHO/UNIGME 2020 version 2020 (VR) 0 Samoa Census 1956 Table) 1 Life Table in 2002 by Hill (Life Table) 1 National Life Tables (Life Table) 1 Year Year UN IGME 2020 Year Year UN IGME 2020 Year Year Year Year UN IGME 2020 Year Year Year Year UN IGME 2020 Year U5MR VR WHO 1950 1970 Year Year UN IGME 2020 Year U5MR Year Year Year (Others Life Table) Fertility Year Year Year UN IGME 2020 Year Year Year UN IGME 2020 Year Year UN IGME 2020 Year Year Year Year UN IGME 2020 Year Year UN IGME 2020 Year IGME 2020 1990 1995 2000 2005 2010 2015 2020 50 100 150 Year UN IGME 2020 Year Year UN IGME 2020 Year U5MR Year Year UN IGME 2020 Year Year UN IGME 2020 Year U5MR Year Year U5MR UN IGME 2020 Year Year UN IGME 2020 Year Year UN IGME 2020 Year Year UN IGME 2020 Year U5MR Year Year Year Year Year UN IGME 2020 Year U5MR VR WHO 1950 1970 Year Year Year U5MR Year UN IGME 2020 Year Year Year Year Year Year UN IGME 2020 Year Year UN IGME 2020 Year UN IGME 2020 Year Year Year Year UN IGME 2020 Year Year Year Year Year Year Year U5MR Year Year Year UN IGME 2020 Year Year Year Year Year Year 1950 1960 1970 1980 1990 2000 Year Year Year U5MR VR WHO 1950 1970 Year Year Year U5MR • • UN IGME 2020 Year • 1950 1960 1970 1980 1990 2000 Year U5MR 1950 1960 1970 1980 1990 2000 Year U5MR UN IGME 2020 Year U5MR VR WHO 1950 1970 1950 1960 1970 1980 1990 2000 Year NMR VR WHO 1950 1970 Others Direct) Population Change Survey 1970−1972 (Others Household Deaths) Population Change Survey 1970−1972 (Others Indirect) Multiple Indicator Cluster Survey 1999 (Others Indirect) Multiple Indicator Cluster Survey 2006 (MICS Direct) Multiple Indicator Cluster Survey Multiple Indicator Cluster Survey NN adjusted) 2010 (DHS) Demographic and Health Survey 2015 (DHS) Demographic and Health Survey 2018 (Others) (Others) Demographic and Health Survey PAPCHILD Maternal and Child Health Survey 1992 (Others) Multiple Indicator Cluster Survey Indicator Survey 2011 (Other DHS) Demographic and Health Survey Gulf Family Health Survey 1995 (Others) VR WHO VR WHO Multiple Indicator Cluster Survey Other DHS) Demographic and Health Survey 1996 (DHS) Demographic and Health Survey 2001 (DHS) Demographic and Health Survey 2006 (DHS) Demographic and Health Survey Multiple Indicator Cluster Survey 2014 (MICS) Demographic and Health Survey Others) National Health Survey 2012 (Others) (DHS) Demographic and Health Survey 1994 (DHS) Demographic and Health Survey 1998 (DHS) Demographic and Health Survey 2003 (DHS) Demographic and Health Survey 2008 (DHS) Demographic and Health Survey VR Vital Statistics Report from Statistics Health Survey 1986 (DHS) Demographic and Health Survey VR Data from Information System of Ministry of Health (Busca Ativa) VR WHO Health Survey 1993 (DHS) Demographic and Health Survey 1998−1999 (DHS) Demographic and Health Survey 2003 (DHS) Demographic and Health Survey (DHS) Demographic and Health Survey 2011 (DHS) Demographic and Health Survey Other DHS) Demographic and Health Survey 2000 (DHS) Demographic and Health Survey 2005 (DHS) Demographic and Health Survey 2010 (DHS) Demographic and Health Survey 2014 (DHS) Other DHS) Demographic and Health Survey 1991 (DHS) Demographic and Health Survey 1998 (DHS) Demographic and Health Survey 2004 (DHS) Demographic and Health Survey Multiple Indicator Cluster Surveys 2014 (MICS) Demographic and Health Survey • • Demographic and Reproductive Health Survey 1998 (Others) Demographic and Health Survey UN IGME 2020 • Demographic and Health Survey Health Survey 1996−1997 (DHS) Demographic and Health Survey 2004 (DHS) Demographic and Health Survey Death Registration Data Health Survey 1996 (DHS) Demographic and Health Survey (DHS) Demographic and Health Survey (DHS) Demographic and Health Survey Multiple Indicator Cluster Survey UN IGME 2020 VR Vital Statistics Report Other DHS) Demographic and Health Survey 1994 (DHS) Demographic and Health Survey Other DHS) Demographic and Health Survey PAPFAM Family Health Survey 2012 (Others) UN IGME 2020 • Demographic and Health Survey 2011 (DHS) Health Survey 1996 (DHS) Demographic and Health Survey 2002 (DHS) Population and Health Survey 2010 (Other DHS) DHS) Demographic and Health Survey 2005 (DHS) Demographic and Health Survey 2011 (DHS) Demographic and Health Survey 2016 (DHS) Demographic and Health Survey 2019 (DHS Federated States of) Year NMR DHS) Demographic and Health Survey 2012 (DHS Multiple Indicator Cluster Survey 2018 (MICS) Demographic and Health Survey (Preliminary) 2019−2020 (DHS) VR Vital Statistics of the National Center for Disease Control and Public Health VR WHO (DHS) Demographic and Health Survey 1995 (DHS) Demographic and Health Survey Encuesta Nacional de Salud Materno Infantil (ENSMI) 2002 (Others) Encuesta Nacional de Salud Materno Infantil (ENSMI) 2009 (Others) Demographic and Health Survey Health Survey 1992 (DHS) Demographic and Health Survey 1999 (DHS) Demographic and Health Survey 2005 (DHS) Demographic and Health Survey Multiple Indicator Cluster Survey 2016 (MICS) Demographic and Health Survey 2018 (DHS) Mortality, morbidity and service utilization survey 1987 (Others) Demographic and Health Survey 1994−1995 (DHS) Demographic and Health Survey 2000 (DHS) Demographic and Health Survey Other DHS) National Multiple−Indicator Demographic and Health Survey 2010 (Other DHS) VR WHO and Maternal Mortality Survey Living Conditions Survey 2004 (Others) Demographic and Health Survey 1995 (DHS) Demographic and Health Survey 1999 (DHS) VR WHO Other DHS) Demographic and Health Survey 1989 (DHS) Demographic and Health Survey 1993 (DHS) Demographic and Health Survey 1998 (DHS) Demographic and Health Survey 2003 (DHS) Demographic and Health Survey Demographic and Health Survey MICS) VR WHO UN IGME 2020 • Multiple Indicator Cluster Survey 2017 (MICS) Multiple Indicator Cluster Survey 2017 (MICS) Maternal and Child Health Survey 1996 (Others) PAPFAM Family Health Survey 2004 (Others) VR WHO Other DHS) Demographic and Health Survey 2004 (DHS) Demographic and Health Survey 2009 (DHS) Demographic and Health Survey Health Survey 1986 (DHS) Demographic and Health Survey Other DHS) Demographic and Health Survey 2013 (DHS) Demographic and Health Survey (Preliminary) 2019−2020 (DHS) Maternal and Child Health Survey 1995 (Others) UN IGME 2020 • Multiple Indicator Cluster Survey 2018−2019 (MICS) VR WHO (DHS) Demographic and Health Survey 1997 (DHS) Demographic and Health Survey (DHS) Demographic and Health Survey 2000 (DHS) Demographic and Health Survey Multiple Indicator Cluster Survey 2006 (MICS) Demographic and Health Survey MICS) Demographic and Health Survey UN IGME 2020 • World Fertility Survey VR Vital Registration Data from Department of Statistics VR WHO (DHS) Demographic and Health Survey (DHS) Demographic and Health Survey 1995−1996 (DHS) Demographic and Health Survey 2001 (DHS) Demographic and Health Survey 2006 (DHS) Demographic and Health Survey Multiple Indicator Cluster Survey 2015 (MICS) Demographic and Health Survey UN IGME 2020 • Demographic and Health Survey PAPCHILD Maternal and Child Health Survey 1990 (Others) Demographic and Health Survey 2001 (DHS) EMIP survey Encuesta Nacional de la Dinamica Demografica (ENADID) 1992 (Others) Encuesta Nacional de la Dinamica Demografica (ENADID) 1997 (Others) Encuesta Nacional de la Dinamica Demografica (ENADID) 2009 (Others) Encuesta Nacional de la Dinamica Demografica (ENADID) 2014 (Others) Encuesta Nacional de la Dinamica Demografica (ENADID) 2018 (Others) VR Vital Registration data from Mexico Ministry of Health VR WHO Multiple Indicator Cluster Survey 2012 (MICS) VR WHO MICS) VR WHO (DHS) Demographic and Health Survey Multiple Indicator Cluster Survey 2008 (MICS) Demographic and Health Survey 2011 (DHS) and Reproductive Health Survey 2001 (Others) Fertility and Reproductive Health Survey 2007 (Others) Demographic and Health Survey Health Survey 1992 (DHS) Demographic and Health Survey 2000 (DHS) Demographic and Health Survey UN IGME 2020 • Demographic and Health Survey Other DHS) Demographic and Health Survey 1996 (DHS) Demographic and Health Survey 2001 (DHS) Demographic and Health Survey 2006 (DHS) Demographic and Health Survey Multiple Indicator Cluster Survey 2014 (MICS) Demographic and Health Survey 2016 (DHS) (Others) Demographic and Health Survey 1998 (DHS) Demographic and Health Survey Encuesta Nicaraguense de Demografia y Salud Encuesta Nicaraguense de Demografia y Salud (DHS) Demographic and Health Survey 1998 (DHS) Demographic and Health Survey Child Survival and Mortality Survey New 2010 (Others) Demographic and Health Survey Etude Nationale d'Evaluation des Indicateurs Socioeconomiques et Demographiques (Preliminary Other DHS) Demographic and Health Survey 1990 (DHS) Demographic and Health Survey 1999 (Other DHS) Demographic and Health Survey 2003 (DHS) Demographic and Health Survey Other DHS) Demographic and Health Survey Multiple Indicator Cluster Survey Niue Year NMR Other DHS) Demographic and Health Survey 2006 (Other DHS) Demographic and Health Survey Health Survey 1990 (DHS) National Survey of Demography and Reproductive Health Encuesta Nacional de Demografia y Salud Sexual y Reproductiva 2008 (Others) Salud Pulbica y Bienestar Social VR WHO UN IGME 2020 • World Fertility Survey UN IGME 2020 • Gulf Family Health Survey 1998 (Others) Other DHS) Demographic and Health Survey 1992 (DHS) Demographic and Health Survey 2000 (DHS) Demographic and Health Survey Other DHS) Demographic and Health Survey 2010 (DHS) Demographic and Health Survey DHS) Demographic and Health Survey 2014 (Other DHS • • Family Health Survey 1996 (Others) Demographic Survey 2016 (Others) Multiple Indicator Cluster Survey 2017 (MICS) Demographic and Health Survey UN IGME 2020 • Multiple Indicator Cluster Survey (DHS) Demographic and Health Survey 2004 (DHS) Demographic and Health Survey 2016 (DHS) Data from District Health Information System 2016 (Others) Data from District Health Information System 2017 (Others) VR WHO Sudan Household Health Survey 2006 (Others) Multiple Indicator Cluster Survey 2010 (MICS) Other DHS) Demographic and Health Survey 1987 (DHS) Demographic and Health Survey 1993 (Other DHS) Demographic and Health Survey 2000 (Other DHS) Demographic and Health Survey 2006 (Other DHS) Demographic and Health Survey Saint Vincent and the Grenadines Year NMR UN IGME UN IGME 2020 VR WHO VR WHO (Recalculated) Health Survey 2000 (MICS) Demographic and Health Survey Multiple Indicator Cluster Survey−Family Health Survey 2010 (MICS) Multiple Indicator Cluster Survey 2014 (MICS) VR WHO PAPCHILD Maternal and Child Health Survey 1992 (Others) Sudan Household Health Survey 2006 (Others) Multiple Indicator Cluster Survey 2014 (MICS) PAPCHILD Maternal and Child Health Survey 1993 (Others) PAPFAM Family Health Survey 2001 (Others) Health survey for causes of child deaths 2008 (Others) PAPFAM Family Health Survey 2009 (Others) Causes of Mortality Among Children Under Five Years Tajikistan Living Standards Survey 2007 (Others) Demographic and Health Survey 2012 (DHS) Demographic and Health Survey 2017 (DHS) Health Survey 1991−1992 (DHS) Demographic and Health Survey 1996 (DHS) Demographic and Health Survey 1999 (DHS) Demographic and Health Survey Other DHS) Demographic and Health Survey 2010 (DHS) Demographic and Health Survey Other DHS) Demographic and Health Survey VR Vital Registration from Ministry of Public Health VR WHO Other DHS) Demographic and Health Survey 2010 (DHS) Demographic and Health Survey (DHS) Demographic and Health Survey 1998 (DHS) Demographic and Health Survey Multiple Indicator Cluster Survey 2017 (MICS) • • National Demographic and Health Survey Multiple Indicator Cluster Survey (Preliminary) 2019 (MICS) Recalculated) VR WHO Other DHS) Demographic and Health Survey VR Preliminary MoH Vital Registration Data VR WHO Other DHS) Demographic and Health Survey PAPCHILD Maternal and Child Health Survey 1994 (Others) Multiple Indicator Cluster Survey Multiple Indicator Cluster Survey Multiple Indicator Cluster Survey 2019 (MICS) VR WHO UN IGME 2020 • Demographic and Health Survey Health Survey 1988−1989 (DHS) Demographic and Health Survey 1995 (DHS) Demographic and Health Survey Other DHS) Demographic and Health Survey 2011 (DHS) Demographic and Health Survey UN IGME 2020 • Family Health Survey 1995 (Others) VR Vital Registration by Federal Competitiveness and Statistics Authority VR WHO • • Demographic and Health Survey 1996 (DHS) Demographic and Health Survey VR Vital registration from National Statistical Office VR WHO UN IGME 2020 • Demographic and Health Survey UN IGME 2020 • National Population and Family Survey 1998 (Others) VR Provisional adjusted data from Ministerio del Poder Popular para la Salud and Instituto Nacion VR WHO Health Survey 1997 (DHS) Demographic and Health Survey Multiple Indicator Cluster Survey Other DHS) Demographic and Health Survey 1991−1992 (DHS) Demographic and Health Survey Multiple Indicator Cluster Survey 2006 (MICS) Demographic and Health Survey 2013 (DHS) DHS) Demographic and Health Survey 1996−1997 (DHS) Demographic and Health Survey Health Survey 1988−1989 (DHS) Demographic and Health Survey 1994 (DHS) Demographic and Health Survey 1999 (DHS) Demographic and Health Survey Multiple Indicator Cluster Survey 2009 (MICS) Demographic and Health Survey Multiple Indicator Cluster Survey 2014 (MICS) Demographic and Health Survey