Actual value and historical data chart for World Death Rate Crude Per 1 000 People

Around 56 million people die each year.

This Dataset contains the causes of death and how the causes of death changed over time between different countries and world regions.

This dataset contains counts of deaths for California counties based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

The Global Population Count Grid Time Series Estimates provide a back-cast time series of population grids based on the year 2000 population grid from SEDAC's Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) data set. The grids were created by using rates of population change between decades from the coarser resolution History Database of the Global Environment (HYDE) database to back-cast the GRUMPv1 population count grids. Mismatches between the spatial extent of the HYDE calculated rates and GRUMPv1 population data were resolved via infilling rate cells based on a focal mean of values. Finally, the grids were adjusted so that the population totals for each country equaled the UN World Population Prospects (2008 Revision) estimates for that country for the respective year (1970, 1980, 1990, and 2000). These data do not represent census observations for the years prior to 2000, and therefore can at best be thought of as estimations of the populations in given locations. The population grids are consistent internally within the time series, but are not recommended for use in creating longer time series with any other population grids, including GRUMPv1, Gridded Population of the World, Version 4 (GPWv4), or non-SEDAC developed population grids. These population grids served as an input to SEDAC's Global Estimated Net Migration Grids by Decade: 1970-2000 data set.

COVID-19 rate of death, or the known deaths divided by confirmed cases, was over ten percent in Yemen, the only country that has 1,000 or more cases. This according to a calculation that combines coronavirus stats on both deaths and registered cases for 221 different countries. Note that death rates are not the same as the chance of dying from an infection or the number of deaths based on an at-risk population. By April 26, 2022, the virus had infected over 510.2 million people worldwide, and led to a loss of 6.2 million. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. Note that Statista aims to also provide domestic source material for a more complete picture, and not to just look at one particular source. Examples are these statistics on the confirmed coronavirus cases in Russia or the COVID-19 cases in Italy, both of which are from domestic sources. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

A word on the flaws of numbers like this

People are right to ask whether these numbers are at all representative or not for several reasons. First, countries worldwide decide differently on who gets tested for the virus, meaning that comparing case numbers or death rates could to some extent be misleading. Germany, for example, started testing relatively early once the country’s first case was confirmed in Bavaria in January 2020, whereas Italy tests for the coronavirus postmortem. Second, not all people go to see (or can see, due to testing capacity) a doctor when they have mild symptoms. Countries like Norway and the Netherlands, for example, recommend people with non-severe symptoms to just stay at home. This means not all cases are known all the time, which could significantly alter the death rate as it is presented here. Third and finally, numbers like this change very frequently depending on how the pandemic spreads or the national healthcare capacity. It is therefore recommended to look at other (freely accessible) content that dives more into specifics, such as the coronavirus testing capacity in India or the number of hospital beds in the UK. Only with additional pieces of information can you get the full picture, something that this statistic in its current state simply cannot provide.

COVID-19 Global Case and Death Data

Global COVID-19 Cases and Deaths Over Time

By Coronavirus (COVID-19) Data Hub [source]

About this dataset

The COVID-19 Global Time Series Case and Death Data is a comprehensive collection of global COVID-19 case and death information recorded over time. This dataset includes data from various sources such as JHU CSSE COVID-19 Data and The New York Times.

The dataset consists of several columns providing detailed information on different aspects of the COVID-19 situation. The COUNTRY_SHORT_NAME column represents the short name of the country where the data is recorded, while the Data_Source column indicates the source from which the data was obtained.

Other important columns include Cases, which denotes the number of COVID-19 cases reported, and Difference, which indicates the difference in case numbers compared to the previous day. Additionally, there are columns such as CONTINENT_NAME, DATA_SOURCE_NAME, COUNTRY_ALPHA_3_CODE, COUNTRY_ALPHA_2_CODE that provide additional details about countries and continents.

Furthermore, this dataset also includes information on deaths related to COVID-19. The column PEOPLE_DEATH_NEW_COUNT shows the number of new deaths reported on a specific date.

To provide more context to the data, certain columns offer demographic details about locations. For instance, Population_Count provides population counts for different areas. Moreover,**FIPS** code is available for provincial/state regions for identification purposes.

It is important to note that this dataset covers both confirmed cases (Case_Type: confirmed) as well as probable cases (Case_Type: probable). These classifications help differentiate between various types of COVID-19 infections.

Overall, this dataset offers a comprehensive picture of global COVID-19 situations by providing accurate and up-to-date information on cases, deaths, demographic details like population count or FIPS code), source references (such as JHU CSSE or NY Times), geographical information (country names coded with ALPHA codes) , etcetera making it useful for researchers studying patterns and trends associated with this pandemic

How to use the dataset

• Understanding the Dataset Structure:

  • The dataset is available in two files: COVID-19 Activity.csv and COVID-19 Cases.csv.
  • Both files contain different columns that provide information about the COVID-19 cases and deaths.
  • Some important columns to look out for are: a. PEOPLE_POSITIVE_CASES_COUNT: The total number of confirmed positive COVID-19 cases. b. COUNTY_NAME: The name of the county where the data is recorded. c. PROVINCE_STATE_NAME: The name of the province or state where the data is recorded. d. REPORT_DATE: The date when the data was reported. e. CONTINENT_NAME: The name of the continent where the data is recorded. f. DATA_SOURCE_NAME: The name of the data source. g. PEOPLE_DEATH_NEW_COUNT: The number of new deaths reported on a specific date. h.COUNTRY_ALPHA_3_CODE :The three-letter alpha code represents country f.Lat,Long :latitude and longitude coordinates represent location i.Country_Region or COUNTRY_SHORT_NAME:The country or region where cases were reported.

• Choosing Relevant Columns: It's important to determine which columns are relevant to your analysis or research question before proceeding with further analysis.

• Exploring Data Patterns: Use various statistical techniques like summarizing statistics, creating visualizations (e.g., bar charts, line graphs), etc., to explore patterns in different variables over time or across regions/countries.

• Filtering Data: You can filter your dataset based on specific criteria using column(s) such as COUNTRY_SHORT_NAME, CONTINENT_NAME, or PROVINCE_STATE_NAME to focus on specific countries, continents, or regions of interest.

• Combining Data: You can combine data from different sources (e.g., COVID-19 cases and deaths) to perform advanced analysis or create insightful visualizations.

• Analyzing Trends: Use the dataset to analyze and identify trends in COVID-19 cases and deaths over time. You can examine factors such as population count, testing count, hospitalization count, etc., to gain deeper insights into the impact of the virus.

• Comparing Countries/Regions: Compare COVID-19

Research Ideas

• Trend Analysis: This dataset can be used to analyze and track the trends of COVID-19 cases and deaths over time. It provides comprehensive global data, allowing researchers and po...

This dataset provides comprehensive global population dynamics data, spanning from 1950 to 2100. It includes historical estimates and medium-scenario projections from the United Nations World Population Prospects 2024 edition. Covering 237 countries or areas, this dataset offers researchers, policymakers, and data enthusiasts a valuable resource for analyzing long-term demographic trends and their potential impacts across a 150-year period.

Key features of this dataset include:

• Annual birth and death figures for each country/area
• Historical estimates from 1950 to 2024
• Medium-scenario projections from 2024 to 2100
• Data for both sexes combined and all age groups
• Consistent methodology across countries for comparability

This dataset is ideal for:

• Long-term demographic trend analysis and forecasting
• Historical population studies and future projections
• Policy planning for healthcare, education, and social services
• Economic growth and labor force projections over extended periods
• Environmental impact studies related to population changes
• Academic research in social sciences, public health, and historical demography

Whether you're a data scientist, historian, policymaker, or social researcher, this dataset offers a wealth of information to explore and analyze global population dynamics across a century and a half.

Estimates for the total death count of the Second World War generally range somewhere between 70 and 85 million people. The Soviet Union suffered the highest number of fatalities of any single nation, with estimates mostly falling between 22 and 27 million deaths. China then suffered the second greatest, at around 20 million, although these figures are less certain and often overlap with the Chinese Civil War. Over 80 percent of all deaths were of those from Allied countries, and the majority of these were civilians. In contrast, 15 to 20 percent were among the Axis powers, and the majority of these were military deaths, as shown in the death ratios of Germany and Japan. Civilian deaths and atrocities It is believed that 60 to 67 percent of all deaths were civilian fatalities, largely resulting from war-related famine or disease, and war crimes or atrocities. Systematic genocide, extermination campaigns, and forced labor, particularly by the Germans, Japanese, and Soviets, led to the deaths of millions. In this regard, Nazi activities alone resulted in 17 million deaths, including six million Jews in what is now known as The Holocaust. Not only was the scale of the conflict larger than any that had come before, but the nature of and reasoning behind this loss make the Second World War stand out as one of the most devastating and cruelest conflicts in history. Problems with these statistics Although the war is considered by many to be the defining event of the 20th century, exact figures for death tolls have proven impossible to determine, for a variety of reasons. Countries such as the U.S. have fairly consistent estimates due to preserved military records and comparatively few civilian casualties, although figures still vary by source. For most of Europe, records are less accurate. Border fluctuations and the upheaval of the interwar period mean that pre-war records were already poor or non-existent for many regions. The rapid and chaotic nature of the war then meant that deaths could not be accurately recorded at the time, and mass displacement or forced relocation resulted in the deaths of many civilians outside of their homeland, which makes country-specific figures more difficult to find. Early estimates of the war’s fatalities were also taken at face value and formed the basis of many historical works; these were often very inaccurate, but the validity of the source means that the figures continue to be cited today, despite contrary evidence.

In comparison to Europe, estimate ranges are often greater across Asia, where populations were larger but pre-war data was in short supply. Many of the Asian countries with high death tolls were European colonies, and the actions of authorities in the metropoles, such as the diversion of resources from Asia to Europe, led to millions of deaths through famine and disease. Additionally, over one million African soldiers were drafted into Europe’s armies during the war, yet individual statistics are unavailable for most of these colonies or successor states (notably Algeria and Libya). Thousands of Asian and African military deaths went unrecorded or are included with European or Japanese figures, and there are no reliable figures for deaths of millions from countries across North Africa or East Asia. Additionally, many concentration camp records were destroyed, and such records in Africa and Asia were even sparser than in Europe. While the Second World War is one of the most studied academic topics of the past century, it is unlikely that we will ever have a clear number for the lives lost in the conflict.

This file contains COVID-19 death counts, death rates, and percent of total deaths by jurisdiction of residence. The data is grouped by different time periods including 3-month period, weekly, and total (cumulative since January 1, 2020). United States death counts and rates include the 50 states, plus the District of Columbia and New York City. New York state estimates exclude New York City. Puerto Rico is included in HHS Region 2 estimates. Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1. Number of deaths reported in this file are the total number of COVID-19 deaths received and coded as of the date of analysis and may not represent all deaths that occurred in that period. Counts of deaths occurring before or after the reporting period are not included in the file. Data during recent periods are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more, depending on the jurisdiction and cause of death. Death counts should not be compared across states. Data timeliness varies by state. Some states report deaths on a daily basis, while other states report deaths weekly or monthly. The ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York, New York City, Puerto Rico; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington. Rates were calculated using the population estimates for 2021, which are estimated as of July 1, 2021 based on the Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Blended Base consists of the blend of Vintage 2020 postcensal population estimates, 2020 Demographic Analysis Estimates, and 2020 Census PL 94-171 Redistricting File (see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020-2021/methods-statement-v2021.pdf). Rates are based on deaths occurring in the specified week/month and are age-adjusted to the 2000 standard population using the direct method (see https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-08-508.pdf). These rates differ from annual age-adjusted rates, typically presented in NCHS publications based on a full year of data and annualized weekly/monthly age-adjusted rates which have been adjusted to allow comparison with annual rates. Annualization rates presents deaths per year per 100,000 population that would be expected in a year if the observed period specific (weekly/monthly) rate prevailed for a full year. Sub-national death counts between 1-9 are suppressed in accordance with NCHS data confidentiality standards. Rates based on death counts less than 20 are suppressed in accordance with NCHS standards of reliability as specified in NCHS Data Presentation Standards for Proportions (available from: https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf.).

The Global Population Density Grid Time Series Estimates provide a back-cast time series of population density grids based on the year 2000 population grid from SEDAC's Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) data set. The grids were created by using rates of population change between decades from the coarser resolution History Database of the Global Environment (HYDE) database to back-cast the GRUMPv1 population density grids. Mismatches between the spatial extent of the HYDE calculated rates and GRUMPv1 population data were resolved via infilling rate cells based on a focal mean of values. Finally, the grids were adjusted so that the population totals for each country equaled the UN World Population Prospects (2008 Revision) estimates for that country for the respective year (1970, 1980, 1990, and 2000). These data do not represent census observations for the years prior to 2000, and therefore can at best be thought of as estimations of the populations in given locations. The population grids are consistent internally within the time series, but are not recommended for use in creating longer time series with any other population grids, including GRUMPv1, Gridded Population of the World, Version 4 (GPWv4), or non-SEDAC developed population grids. These population grids served as an input to SEDAC's Global Estimated Net Migration Grids by Decade: 1970-2000 data set.

Chad TD: Lifetime Risk Of Maternal Death data was reported at 4.225 % in 2023. This records a decrease from the previous number of 4.529 % for 2022. Chad TD: Lifetime Risk Of Maternal Death data is updated yearly, averaging 7.943 % from Dec 1985 (Median) to 2023, with 39 observations. The data reached an all-time high of 8.559 % in 1993 and a record low of 4.225 % in 2023. Chad TD: Lifetime Risk Of Maternal Death data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chad – Table TD.World Bank.WDI: Social: Health Statistics. Life time risk of maternal death is the probability that a 15-year-old female will die eventually from a maternal cause assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future, taking into account competing causes of death.;WHO, UNICEF, UNFPA, World Bank Group, and UNDESA/Population Division. Trends in maternal mortality estimates 2000 to 2023. Geneva, World Health Organization, 2025;Weighted average;

Abstract Objective To analyze the temporal trend of mortality in the old people population in selected municipalities in the five regional development areas in the state of Acre, Brazil. Method Descriptive, ecological study of time series, which used data from the Mortality Information System (SIM), in which the universe of deaths occurred in old people, recorded in the cities of Acre from 1996 to 2015 was selected. Crude and age-adjusted mortality rates were calculated using the direct method and the world population as a reference. For the analysis of trends, the annual percentage change in mortality was estimated with a 95% confidence level using the Joinpoint Regression Program software. Results The general mortality trend was decreasing in Rio Branco and with fluctuations in the other municipalities investigated. The main causes of death were diseases of the circulatory, respiratory and neoplasms. In Rio Branco, mortality rates standardized by age in diseases of the circulatory system decreased by 2.26% (p

This dataset about World Death Rate by Country is extracted from Flourish visualisation. If you want to know more about Flourish click here.

Real-time world population counter with births, deaths, and demographic breakdowns

Real-time world population data including births, deaths, and growth rates

In 2019, the leading causes of death globally included ischemic heart disease, stroke and chronic obstructive pulmonary disease (COPD). There were **** million deaths from ischemic heart disease at that time and about **** million deaths caused by stroke. In recent history, increases in life expectancy, increases in population and better standards of living have changed the leading causes of death over time. Non-Communicable Disease Deaths The number of deaths due to non-communicable diseases has remained relatively stable in recent years. A large majority of non-communicable or chronic disease deaths globally are caused by cardiovascular diseases, followed by cancer. Various lifestyle choices cause or exacerbate many of these chronic diseases. Drinking, smoking and lack of exercise can contribute to higher rates of non-communicable diseases and early death. It is estimated that the relative risk of death before the age of 65 was ** times greater among those that smoked and never quit. Infectious Disease Deaths Trends indicate that the number of deaths due to infectious diseases have decreased in recent years. However, infectious diseases still disproportionately impact low- and middle-income countries. In 2021, tuberculosis, malaria and HIV/AIDS were still among the leading causes of death in low-income countries. However, the leading causes of death in upper income countries are almost exclusively non-communicable, chronic conditions.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretabilty. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datsets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of aquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis:

• Analyze missing data: Project Tycho datasets do not inlcude time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported.
• Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exxclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

The United Nations Department of Economics and Social Affairs, as part of their World Population Prospects initiative, have collated 75 years worth of annual death statistics. They have setup a variety of age groups to monitor, and for given population group they have estimated the fraction of the deaths that fall in each of the age groups on an annual basis.

The dataset breaks out the population along a variety of factors - by sex - by country - by region or sub-region - by income group - and so on

Coronavirus caused many deaths in Italy, leading to *** of the highest death rates worldwide. When comparing the number of deaths occurred between ******* and *******, 2020 with the average number of deaths during the same period of the years 2015 to 2019, it can be seen that the former was significantly higher. In fact, a total of approximately ** thousand individuals died between ******* and ******* 2020, while it amounted to roughly ** thousand during the same period of the years 2015 to 2019. For a global overview visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

BackgroundDespite the sharp decline in global under-5 deaths since 1990, uneven progress has been achieved across and within countries. In sub-Saharan Africa (SSA), the Millennium Development Goals (MDGs) for child mortality were met only by a few countries. Valid concerns exist as to whether the region would meet new Sustainable Development Goals (SDGs) for under-5 mortality. We therefore examine further sources of variation by assessing age patterns, trends, and forecasts of mortality rates.Methods and findingsData came from 106 nationally representative Demographic and Health Surveys (DHSs) with full birth histories from 31 SSA countries from 1990 to 2017 (a total of 524 country-years of data). We assessed the distribution of age at death through the following new demographic analyses. First, we used a direct method and full birth histories to estimate under-5 mortality rates (U5MRs) on a monthly basis. Second, we smoothed raw estimates of death rates by age and time by using a two-dimensional P-Spline approach. Third, a variant of the Lee–Carter (LC) model, designed for populations with limited data, was used to fit and forecast age profiles of mortality. We used mortality estimates from the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) to adjust, validate, and minimize the risk of bias in survival, truncation, and recall in mortality estimation. Our mortality model revealed substantive declines of death rates at every age in most countries but with notable differences in the age patterns over time. U5MRs declined from 3.3% (annual rate of reduction [ARR] 0.1%) in Lesotho to 76.4% (ARR 5.2%) in Malawi, and the pace of decline was faster on average (ARR 3.2%) than that observed for infant (IMRs) (ARR 2.7%) and neonatal (NMRs) (ARR 2.0%) mortality rates. We predict that 5 countries (Kenya, Rwanda, Senegal, Tanzania, and Uganda) are on track to achieve the under-5 sustainable development target by 2030 (25 deaths per 1,000 live births), but only Rwanda and Tanzania would meet both the neonatal (12 deaths per 1,000 live births) and under-5 targets simultaneously. Our predicted NMRs and U5MRs were in line with those estimated by the UN IGME by 2030 and 2050 (they overlapped in 27/31 countries for NMRs and 22 for U5MRs) and by the Institute for Health Metrics and Evaluation (IHME) by 2030 (26/31 and 23/31, respectively). This study has a number of limitations, including poor data quality issues that reflected bias in the report of births and deaths, preventing reliable estimates and predictions from a few countries.ConclusionsTo our knowledge, this study is the first to combine full birth histories and mortality estimates from external reliable sources to model age patterns of under-5 mortality across time in SSA. We demonstrate that countries with a rapid pace of mortality reduction (ARR ≥ 3.2%) across ages would be more likely to achieve the SDG mortality targets. However, the lower pace of neonatal mortality reduction would prevent most countries from achieving those targets: 2 countries would reach them by 2030, 13 between 2030 and 2050, and 13 after 2050.