As of 2023, the countries with the highest life expectancy included Liechtenstein, Switzerland, and Japan. In Japan, a person could expect to live up to around ** years. In general, the life expectancy for females is higher than that of males, with lifestyle choices and genetics the two major determining factors of life expectancy. Life expectancy worldwide The overall life expectancy worldwide has increased since the development of modern medicine and technology. In 2011, the global life expectancy was **** years. By 2023, it had increased to **** years. However, the years 2020 and 2021 saw a decline in global life expectancy due to the COVID-19 pandemic. Furthermore, not every country has seen a substantial increase in life expectancy. In Nigeria, for example, the life expectancy is only ** years, almost ***years shorter than the global average. In addition to Nigeria, the countries with the shortest life expectancy include Chad, Lesotho, and the Central African Republic. Life expectancy in the U.S. In the United States, life expectancy at birth is currently ***** years. Life expectancy in the U.S. generally increases every year, however, over the past decade, life expectancy has seen some surprising decreases. The major contributing factors to this drop have been the ongoing opioid epidemic, which claimed around ****** lives in 2022 alone, and the COVID-19 pandemic.

This statistic shows the top ten countries worldwide with longest average female life expectancy at birth in 2010 and 2030. South Korea had the longest average life expectancy for females in 2030 at 90.82 years.

Monaco had the highest life expectancy among both men and women worldwide as of 2024. That year, life expectancy for men and women was ** and ** years, respectively. The East Asian countries and regions, Hong Kong, Japan, South Korea, and Macao, followed. Many of the countries on the list are struggling with aging populations and a declining workforce as more people enter retirement age compared to people entering employment.

The average for 2022 based on 47 countries was 71.89 years. The highest value was in Macao: 82.75 years and the lowest value was in Afghanistan: 59.77 years. The indicator is available from 1960 to 2022. Below is a chart for all countries where data are available.

As of 2023, the countries with the highest life expectancy included Switzerland, Japan, and Spain. As of that time, a new-born child in Switzerland could expect to live an average of **** years. Around the world, females consistently have a higher average life expectancy than males, with females in Europe expected to live an average of *** years longer than males on this continent. Increases in life expectancy The overall average life expectancy in OECD countries increased by **** years from 1970 to 2019. The countries that saw the largest increases included Turkey, India, and South Korea. The life expectancy at birth in Turkey increased an astonishing 24.4 years over this period. The countries with the lowest life expectancy worldwide as of 2022 were Chad, Lesotho, and Nigeria, where a newborn could be expected to live an average of ** years. Life expectancy in the U.S. The life expectancy in the United States was ***** years as of 2023. Shockingly, the life expectancy in the United States has decreased in recent years, while it continues to increase in other similarly developed countries. The COVID-19 pandemic and increasing rates of suicide and drug overdose deaths from the opioid epidemic have been cited as reasons for this decrease.

In 2024, the average life expectancy in the world was 71 years for men and 76 years for women. The lowest life expectancies were found in Africa, while Oceania and Europe had the highest. What is life expectancy?Life expectancy is defined as a statistical measure of how long a person may live, based on demographic factors such as gender, current age, and most importantly the year of their birth. The most commonly used measure of life expectancy is life expectancy at birth or at age zero. The calculation is based on the assumption that mortality rates at each age were to remain constant in the future. Life expectancy has changed drastically over time, especially during the past 200 years. In the early 20th century, the average life expectancy at birth in the developed world stood at 31 years. It has grown to an average of 70 and 75 years for males and females respectively, and is expected to keep on growing with advances in medical treatment and living standards continuing. Highest and lowest life expectancy worldwide Life expectancy still varies greatly between different regions and countries of the world. The biggest impact on life expectancy is the quality of public health, medical care, and diet. As of 2022, the countries with the highest life expectancy were Japan, Liechtenstein, Switzerland, and Australia, all at 84–83 years. Most of the countries with the lowest life expectancy are mostly African countries. The ranking was led by the Chad, Nigeria, and Lesotho with 53–54 years.

The average for 2022 based on 12 countries was 72.9 years. The highest value was in Chile: 79.52 years and the lowest value was in Bolivia: 64.93 years. The indicator is available from 1960 to 2022. Below is a chart for all countries where data are available.

The countries with the lowest life expectancy worldwide include the Nigeria, Chad, and Lesotho. As of 2023, people born in Nigeria could be expected to live only up to ** years. This is almost ** years shorter than the global life expectancy. Life expectancy The global life expectancy has gradually increased over the past couple decades, rising from **** years in 2011 to **** years in 2023. However, the years 2020 and 2021 saw a decrease in global life expectancy due to the COVID-19 pandemic. Furthermore, life expectancy can vary greatly depending on the country and region. For example, all the top 20 countries with the lowest life expectancy worldwide are in Africa. The countries with the highest life expectancy include Liechtenstein, Switzerland, and Japan. Causes of death The countries with the lowest life expectancy worldwide are all low-income or developing countries that lack health care access and treatment that more developed countries can provide. The leading causes of death in these countries therefore differ from those of middle-income and upper-income countries. The leading causes of death in low-income countries include diseases such as HIV/AIDS and malaria, as well as preterm birth complications, which do not cause substantial death in higher income countries.

Graph and download economic data for Life Expectancy at Birth, Total for the United States (SPDYNLE00INUSA) from 1960 to 2023 about life expectancy, life, birth, and USA.

China life expectancy for 2025 is 77.81, a 0.22% increase from 2024.

<ul style='margin-top:20px;'>

<li>China life expectancy for 2024 was <strong>77.64</strong>, a <strong>0.22% increase</strong> from 2023.</li>
<li>China life expectancy for 2023 was <strong>77.47</strong>, a <strong>0.22% increase</strong> from 2022.</li>
<li>China life expectancy for 2022 was <strong>77.30</strong>, a <strong>0.22% increase</strong> from 2021.</li>
</ul>Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.

The average for 2022 based on 11 countries was 72.94 years. The highest value was in Singapore: 82.9 years and the lowest value was in Burma (Myanmar): 67.26 years. The indicator is available from 1960 to 2022. Below is a chart for all countries where data are available.

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

In 2024, Trentino-South Tyrol was the Italian region where both women and men were predicted to live the longest lives in the whole peninsula. In the German-speaking region, the life expectancy at birth of men was almost 83 years, whereas women were expected to live almost 87 years. The second best ranked region was different for males and females. In Veneto, this figure stood at about 82 years for males and in Veneto at 86 years for females. When compared to the country’s average, women in Trentino-South Tyrol were expected to live roughly three years longer. Long life span and low birth rate Around 20 percent of the Italian population in 2023 was above 65 years. Together with a long life expectancy, Italy also has very low birth and fertility rates. In 2024, the country resulted among the 20 states with the lowest fertility rate in the world. One of the longest-living nations From a global perspective, Italy was the ninth country in the world with the highest life expectancy. The inhabitants of Japan and Liechtenstein were expected to live about 84 years, while Italians' life expectancy was of 83 years.

Global life expectancy at birth has risen significantly since the mid-1900s, from roughly 46 years in 1950 to 73.2 years in 2023. Post-COVID-19 projections There was a drop of 1.7 years during the COVID-19 pandemic, between 2019 and 2021, however, figures resumed upon their previous trajectory the following year due to the implementation of vaccination campaigns and the lower severity of later strains of the virus. By the end of the century it is believed that global life expectancy from birth will reach 82 years, although growth will slow in the coming decades as many of the more-populous Asian countries reach demographic maturity. However, there is still expected to be a wide gap between various regions at the end of the 2100s, with the Europe and North America expected to have life expectancies around 90 years, whereas Sub-Saharan Africa is predicted to be in the low-70s. The Great Leap Forward While a decrease of one year during the COVID-19 pandemic may appear insignificant, this is the largest decline in life expectancy since the "Great Leap Forward" in China in 1958, which caused global life expectancy to fall by almost four years between by 1960. The "Great Leap Forward" was a series of modernizing reforms, which sought to rapidly transition China's agrarian economy into an industrial economy, but mismanagement led to tens of millions of deaths through famine and disease.

In the World Health Organization (WHO)-coordinated Cardiovascular Disease and Alimentary Comparison Study, isoflavones (I; biomarker for dietary soy) and taurine (T; biomarker for dietary fish) in 24-hour—urine (24U) were inversely related to coronary heart disease (CHD) mortality. High levels of these biomarkers are found in Japanese people, whose CHD mortality is lowest among developed countries. We analyzed the association of these biomarkers with cardiovascular disease risk in the Japanese to know their health effects within one ethnic population. First, to compare the Japanese intake of I and T with international intakes, the ratios of 24UI and 24UT to creatinine from the WHO Study were divided into quintiles for analysis. The ratio for the Japanese was the highest in the highest quintiles for both I and T, reaching 88.1%, far higher than the average ratio for the Japanese (26.3%) in the total study population. Second, 553 inhabitants of Hyogo Prefecture, Japan, aged 30 to 79 years underwent 24-U collection and blood analyses. The 24UT and 24UI were divided into tertiles and adjusted for age and sex. The highest T tertile, compared with the lowest tertile, showed significantly higher levels of high-density lipoprotein-cholesterol (HDL-C), total cholesterol, 24U sodium (Na) and potassium (K). The highest I tertile showed significantly higher folate, 24UNa and 24UK compared with the lowest tertile. The highest tertile of both T and I showed significantly higher HDL-C, folate, and 24UNa and 24UK compared with the lowest tertile. Thus, greater consumption of fish and soy were significantly associated with higher HDL-C and folate levels, possibly a contributor to Japan having the lowest CHD mortality and longest life expectancy among developed countries. As these intakes were also associated with a high intake of salt, a low-salt intake of fish and soy should be recommended for healthy life expectancy.

Supercentenarians (110 years or older) are the world’s oldest people. Seventy four are alive worldwide, with twenty two in the United States. We performed whole-genome sequencing on 17 supercentenarians to explore the genetic basis underlying extreme human longevity. We found no significant evidence of enrichment for a single rare protein-altering variant or for a gene harboring different rare protein altering variants in supercentenarian compared to control genomes. We followed up on the gene most enriched for rare protein-altering variants in our cohort of supercentenarians, TSHZ3, by sequencing it in a second cohort of 99 long-lived individuals but did not find a significant enrichment. The genome of one supercentenarian had a pathogenic mutation in DSC2, known to predispose to arrhythmogenic right ventricular cardiomyopathy, which is recommended to be reported to this individual as an incidental finding according to a recent position statement by the American College of Medical Genetics and Genomics. Even with this pathogenic mutation, the proband lived to over 110 years. The entire list of rare protein-altering variants and DNA sequence of all 17 supercentenarian genomes is available as a resource to assist the discovery of the genetic basis of extreme longevity in future studies.

Men born in Chad have the lowest life expectancy in the world as of 2024, reaching only 53 years. The lowest life expectancy for women in the world in 2024 was for girls born in Nigeria, with only 55 years. Except for Afghanistan, all the countries with the lowest life expectancy in the world are in Africa.

Longevity and Anti-Aging Drugs Market Outlook

The global longevity and anti-aging drugs market size was valued at USD 8.2 billion in 2023 and is expected to grow significantly to USD 18.5 billion by 2032, exhibiting a robust CAGR of 9.3% during the forecast period. This market is primarily driven by the increasing aging population, rising awareness about anti-aging treatments, and advancements in biotechnology. With the growing emphasis on health and wellness, consumers are now more inclined towards preventive healthcare, further propelling the market growth.

One of the key growth factors contributing to the expansion of the longevity and anti-aging drugs market is the escalating global aging population. According to the World Health Organization (WHO), the number of people aged 60 years and older will triple by 2050, reaching roughly 2 billion. This demographic shift is creating a significant demand for anti-aging solutions as older individuals seek to maintain their health and vitality. Additionally, the rising disposable incomes and improved standard of living among the global population have increased the affordability and accessibility of these treatments, further stimulating market growth.

Another pivotal factor is the continuous advancements in biotechnology and pharmaceutical research. Innovations such as senolytics, mTOR inhibitors, and NAD+ boosters are at the forefront of the anti-aging industry. These novel therapeutic agents target the biological pathways associated with aging, offering promising results in extending healthspan and lifespan. The surge in investment from both public and private sectors into the R&D of anti-aging drugs is expected to yield new, more effective products, thereby driving market expansion.

The growing awareness and acceptance of anti-aging treatments among the general population are also contributing significantly to market growth. Social media and the internet have played a crucial role in disseminating information about the benefits and availability of longevity and anti-aging drugs. As consumers become more knowledgeable about these treatments, the demand for scientifically backed, effective anti-aging solutions is rapidly increasing. This trend is further supported by the endorsements and usage of these products by celebrities and influencers, thereby boosting consumer confidence and adoption rates.

Sarcopenia Therapeutic is emerging as a significant focus within the anti-aging and longevity market. As the global population continues to age, sarcopenia, characterized by the progressive loss of muscle mass and strength, poses a major health challenge. Addressing this condition is crucial for maintaining mobility and quality of life in older adults. Recent advancements in therapeutic approaches are targeting the underlying mechanisms of sarcopenia, offering promising solutions to mitigate its effects. The development of drugs and interventions aimed at enhancing muscle regeneration and function is gaining traction, supported by a growing body of research. This focus not only aligns with the broader goals of anti-aging treatments but also highlights the importance of comprehensive healthcare strategies in promoting longevity.

Regionally, North America currently holds the largest share of the longevity and anti-aging drugs market, driven by a high prevalence of age-related conditions, advanced healthcare infrastructure, and significant investment in research and development. Europe follows closely, benefitting from similar factors along with strong regulatory support for innovative treatments. The Asia Pacific region is expected to witness the highest growth rate, attributed to its large aging population, rising healthcare expenditures, and increasing awareness about anti-aging products. Latin America and the Middle East & Africa, while smaller in market size, are also projected to experience substantial growth due to improving economic conditions and healthcare access.

Drug Type Analysis

The longevity and anti-aging drugs market is segmented by drug type, with key categories including senolytics, mTOR inhibitors, NAD+ boosters, antioxidants, and others. Senolytics are emerging as a groundbreaking class of drugs that specifically target senescent cells, which are associated with aging and age-related diseases. By promoting the selective elimination of these cells, senolytics have shown promise in extending healthspan and potentially lifespan. The ongoing clinical trials and positive prelim

Abstract

The Health Inequality Project uses big data to measure differences in life expectancy by income across areas and identify strategies to improve health outcomes for low-income Americans.

Section 7

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution. Both race-adjusted and unadjusted estimates are reported.

Source

Section 13

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution separately by year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 6

This dataset was created on 2020-01-10 18:53:00.508 by merging multiple datasets together. The source datasets for this version were:

Commuting Zone Life Expectancy Estimates by year: CZ-level by-year life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy: Commuting zone (CZ)-level life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy Trends: CZ-level estimates of trends in life expectancy for men and women, by income quartile

Commuting Zone Characteristics: CZ-level characteristics

Commuting Zone Life Expectancy for larger populations: CZ-level life expectancy estimates for men and women, by income ventile

Section 15

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by state of residence and year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 11

This table reports US mortality rates by gender, age, year and household income percentile. Household incomes are measured two years prior to the mortality rate for mortality rates at ages 40-63, and at age 61 for mortality rates at ages 64-76. The “lag” variable indicates the number of years between measurement of income and mortality.

Observations with 1 or 2 deaths have been masked: all mortality rates that reflect only 1 or 2 deaths have been recoded to reflect 3 deaths

Source

Section 3

This table reports coefficients and standard errors from regressions of life expectancy estimates for men and women at age 40 for each quartile of the national income distribution on calendar year by commuting zone of residence. Only the slope coefficient, representing the average increase or decrease in life expectancy per year, is reported. Trend estimates for both race-adjusted and unadjusted life expectancies are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 9

This table reports life expectancy estimates at age 40 for Males and Females for all countries. Source: World Health Organization, accessed at: http://apps.who.int/gho/athena/

Source

Section 10

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by county of residence. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for counties with populations larger than 25,000 only

Source

Section 2

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by commuting zone of residence and year. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 8

This table reports US population and death counts by age, year, and sex from various sources. Counts labelled “dm1” are derived from the Social Security Administration Data Master 1 file. Counts labelled “irs” are derived from tax data. Counts labelled “cdc” are derived from NCHS life tables.

Source

Section 12

This table reports numerous county characteristics, compiled from various sources. These characteristics are described in the county life expectancy table.

Two variables constructed by the Cen

Life table data for "Bounce backs amid continued losses: Life expectancy changes since COVID-19"

cc-by Jonas Schöley, José Manuel Aburto, Ilya Kashnitsky, Maxi S. Kniffka, Luyin Zhang, Hannaliis Jaadla, Jennifer B. Dowd, and Ridhi Kashyap. "Bounce backs amid continued losses: Life expectancy changes since COVID-19".

These are CSV files of life tables over the years 2015 through 2021 across 29 countries analyzed in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19".

40-lifetables.csv

Life table statistics 2015 through 2021 by sex, region and quarter with uncertainty quantiles based on Poisson replication of death counts. Actual life tables and expected life tables (under the assumption of pre-COVID mortality trend continuation) are provided.

30-lt_input.csv

Life table input data.

id: unique row identifier

region_iso: iso3166-2 region codes

sex: Male, Female, Total

year: iso year

age_start: start of age group

age_width: width of age group, Inf for age_start 100, otherwise 1

nweeks_year: number of weeks in that year, 52 or 53

death_total: number of deaths by any cause

population_py: person-years of exposure (adjusted for leap-weeks and missing weeks in input data on all cause deaths)

death_total_nweeksmiss: number of weeks in the raw input data with at least one missing death count for this region-sex-year stratum. missings are counted when the week is implicitly missing from the input data or if any NAs are encounted in this week or if age groups are implicitly missing for this week in the input data (e.g. 40-45, 50-55)

death_total_minnageraw: the minimum number of age-groups in the raw input data within this region-sex-year stratum

death_total_maxnageraw: the maximum number of age-groups in the raw input data within this region-sex-year stratum

death_total_minopenageraw: the minimum age at the start of the open age group in the raw input data within this region-sex-year stratum

death_total_maxopenageraw: the maximum age at the start of the open age group in the raw input data within this region-sex-year stratum

death_total_source: source of the all-cause death data

death_total_prop_q1: observed proportion of deaths in first quarter of year

death_total_prop_q2: observed proportion of deaths in second quarter of year

death_total_prop_q3: observed proportion of deaths in third quarter of year

death_total_prop_q4: observed proportion of deaths in fourth quarter of year

death_expected_prop_q1: expected proportion of deaths in first quarter of year

death_expected_prop_q2: expected proportion of deaths in second quarter of year

death_expected_prop_q3: expected proportion of deaths in third quarter of year

death_expected_prop_q4: expected proportion of deaths in fourth quarter of year

population_midyear: midyear population (July 1st)

population_source: source of the population count/exposure data

death_covid: number of deaths due to covid

death_covid_date: number of deaths due to covid as of

death_covid_nageraw: the number of age groups in the covid input data

ex_wpp_estimate: life expectancy estimates from the World Population prospects for a five year period, merged at the midpoint year

ex_hmd_estimate: life expectancy estimates from the Human Mortality Database

nmx_hmd_estimate: death rate estimates from the Human Mortality Database

nmx_cntfc: Lee-Carter death rate projections based on trend in the years 2015 through 2019

Deaths

source:

STMF input data series (https://www.mortality.org/Public/STMF/Outputs/stmf.csv)

ONS for GB-EAW pre 2020

CDC for US pre 2020

STMF:

harmonized to single ages via pclm

pclm iterates over country, sex, year, and within-year age grouping pattern and converts irregular age groupings, which may vary by country, year and week into a regular age grouping of 0:110

smoothing parameters estimated via BIC grid search seperately for every pclm iteration

last age group set to [110,111)

ages 100:110+ are then summed into 100+ to be consistent with mid-year population information

deaths in unknown weeks are considered; deaths in unknown ages are not considered

ONS:

data already in single ages

ages 100:105+ are summed into 100+ to be consistent with mid-year population information

PCLM smoothing applied to for consistency reasons

CDC:

The CDC data comes in single ages 0:100 for the US. For 2020 we only have the STMF data in a much coarser age grouping, i.e. (0, 1, 5, 15, 25, 35, 45, 55, 65, 75, 85+). In order to calculate life-tables in a manner consistent with 2020, we summarise the pre 2020 US death counts into the 2020 age grouping and then apply the pclm ungrouping into single year ages, mirroring the approach to the 2020 data

Population

source:

for years 2000 to 2019: World Population Prospects 2019 single year-age population estimates 1950-2019

for year 2020: World Population Prospects 2019 single year-age population projections 2020-2100

mid-year population

mid-year population translated into exposures:

if a region reports annual deaths using the Gregorian calendar definition of a year (365 or 366 days long) set exposures equal to mid year population estimates

if a region reports annual deaths using the iso-week-year definition of a year (364 or 371 days long), and if there is a leap-week in that year, set exposures equal to 371/364*mid_year_population to account for the longer reporting period. in years without leap-weeks set exposures equal to mid year population estimates. further multiply by fraction of observed weeks on all weeks in a year.

COVID deaths

source: COVerAGE-DB (https://osf.io/mpwjq/)

the data base reports cumulative numbers of COVID deaths over days of a year, we extract the most up to date yearly total

External life expectancy estimates

source:

World Population Prospects (https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/CSV_FILES/WPP2019_Life_Table_Medium.csv), estimates for the five year period 2015-2019

Human Mortality Database (https://mortality.org/), single year and age tables