Over the past 160 years, life expectancy (from birth) in the United States has risen from 39.4 years in 1860, to 78.9 years in 2020. One of the major reasons for the overall increase of life expectancy in the last two centuries is the fact that the infant and child mortality rates have decreased by so much during this time. Medical advancements, fewer wars and improved living standards also mean that people are living longer than they did in previous centuries.

Despite this overall increase, the life expectancy dropped three times since 1860; from 1865 to 1870 during the American Civil War, from 1915 to 1920 during the First World War and following Spanish Flu epidemic, and it has dropped again between 2015 and now. The reason for the most recent drop in life expectancy is not a result of any specific event, but has been attributed to negative societal trends, such as unbalanced diets and sedentary lifestyles, high medical costs, and increasing rates of suicide and drug use.

A global phenomenon, known as the demographic transition, has seen life expectancy from birth increase rapidly over the past two centuries. In pre-industrial societies, the average life expectancy was around 24 years, and it is believed that this was the case throughout most of history, and in all regions. The demographic transition then began in the industrial societies of Europe, North America, and the West Pacific around the turn of the 19th century, and life expectancy rose accordingly. Latin America was the next region to follow, before Africa and most Asian populations saw their life expectancy rise throughout the 20th century.

U.S. life expectancy for 2025 is 79.40, a 0.18% increase from 2024.

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

<li>U.S. life expectancy for 2024 was <strong>79.25</strong>, a <strong>1.11% increase</strong> from 2023.</li>
<li>U.S. life expectancy for 2023 was <strong>78.39</strong>, a <strong>1.23% increase</strong> from 2022.</li>
<li>U.S. life expectancy for 2022 was <strong>77.43</strong>, a <strong>1.45% 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.

For most of the world, throughout most of human history, the average life expectancy from birth was around 24. This figure fluctuated greatly depending on the time or region, and was higher than 24 in most individual years, but factors such as pandemics, famines, and conflicts caused regular spikes in mortality and reduced life expectancy. Child mortality The most significant difference between historical mortality rates and modern figures is that child and infant mortality was so high in pre-industrial times; before the introduction of vaccination, water treatment, and other medical knowledge or technologies, women would have around seven children throughout their lifetime, but around half of these would not make it to adulthood. Accurate, historical figures for infant mortality are difficult to ascertain, as it was so prevalent, it took place in the home, and was rarely recorded in censuses; however, figures from this source suggest that the rate was around 300 deaths per 1,000 live births in some years, meaning that almost one in three infants did not make it to their first birthday in certain periods. For those who survived to adolescence, they could expect to live into their forties or fifties on average. Modern figures It was not until the eradication of plague and improvements in housing and infrastructure in recent centuries where life expectancy began to rise in some parts of Europe, before industrialization and medical advances led to the onset of the demographic transition across the world. Today, global life expectancy from birth is roughly three times higher than in pre-industrial times, at almost 73 years. It is higher still in more demographically and economically developed countries; life expectancy is over 82 years in the three European countries shown, and over 84 in Japan. For the least developed countries, mostly found in Sub-Saharan Africa, life expectancy from birth can be as low as 53 years.

World life expectancy for 2025 is 73.49, a 0.23% increase from 2024.

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

<li>World life expectancy for 2024 was <strong>73.33</strong>, a <strong>0% increase</strong> from 2023.</li>
<li>World life expectancy for 2023 was <strong>73.33</strong>, a <strong>0.49% increase</strong> from 2022.</li>
<li>World life expectancy for 2022 was <strong>72.97</strong>, a <strong>2.46% 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.

Throughout most of history, average life expectancy from birth was fairly consistent across the globe, at around 24 years. A major contributor to this was high rates of infant and child mortality; those who survived into adulthood could expect to live to their 50s or 60s, yet pandemics, food instability, and conflict did cause regular spikes in mortality across the entire population. Gradually, from the 16th to 19th centuries, there was some growth in more developed societies, due to improvements in agriculture, infrastructure, and medical knowledge. However, the most significant change came with the introduction of vaccination and other medical advances in the 1800s, which saw a sharp decline in child mortality and the onset of the demographic transition. This phenomenon began in more developed countries in the 1800s, before spreading to Latin America, Asia, and (later) Africa in the 1900s. As the majority of the world's population lives in countries considered to be "less developed", this figure is much closer to the global average. However, today, there is a considerable difference in life expectancies across these countries, ranging from 84.7 years in Japan to 53 years in the Central African Republic.

Note: This dataset is historical only and there are not corresponding datasets for more recent time periods. For that more-recent information, please visit the Chicago Health Atlas at https://chicagohealthatlas.org. This dataset gives the average life expectancy and corresponding confidence intervals for each Chicago community area for the years 1990, 2000 and 2010. See the full description at: https://data.cityofchicago.org/api/views/qjr3-bm53/files/AAu4x8SCRz_bnQb8SVUyAXdd913TMObSYj6V40cR6p8?download=true&filename=P:\EPI\OEPHI\MATERIALS\REFERENCES\Life Expectancy\Dataset description - LE by community area.pdf

U.K. life expectancy for 2025 is 82.06, a 0.18% increase from 2024.

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

<li>U.K. life expectancy for 2024 was <strong>81.92</strong>, a <strong>0.83% increase</strong> from 2023.</li>
<li>U.K. life expectancy for 2023 was <strong>81.24</strong>, a <strong>0.28% increase</strong> from 2022.</li>
<li>U.K. life expectancy for 2022 was <strong>81.01</strong>, a <strong>0.45% 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.

This dataset of U.S. mortality trends since 1900 highlights the differences in age-adjusted death rates and life expectancy at birth by race and sex. Age-adjusted death rates (deaths per 100,000) after 1998 are calculated based on the 2000 U.S. standard population. Populations used for computing death rates for 2011–2017 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years between 2000 and 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Data on age-adjusted death rates prior to 1999 are taken from historical data (see References below). Life expectancy data are available up to 2017. Due to changes in categories of race used in publications, data are not available for the black population consistently before 1968, and not at all before 1960. More information on historical data on age-adjusted death rates is available at https://www.cdc.gov/nchs/nvss/mortality/hist293.htm. SOURCES CDC/NCHS, National Vital Statistics System, historical data, 1900-1998 (see https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm); CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES National Center for Health Statistics, Data Warehouse. Comparability of cause-of-death between ICD revisions. 2008. Available from: http://www.cdc.gov/nchs/nvss/mortality/comparability_icd.htm. National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm. Kochanek KD, Murphy SL, Xu JQ, Arias E. Deaths: Final data for 2017. National Vital Statistics Reports; vol 68 no 9. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_09-508.pdf. Arias E, Xu JQ. United States life tables, 2017. National Vital Statistics Reports; vol 68 no 7. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_07-508.pdf. National Center for Health Statistics. Historical Data, 1900-1998. 2009. Available from: https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm.

Life expectancy in the United Kingdom was below 39 years in the year 1765, and over the course of the next two and a half centuries, it is expected to have increased by more than double, to 81.1 by the year 2020. Although life expectancy has generally increased throughout the UK's history, there were several times where the rate deviated from its previous trajectory. These changes were the result of smallpox epidemics in the late eighteenth and early nineteenth centuries, new sanitary and medical advancements throughout time (such as compulsory vaccination), and the First world War and Spanish Flu epidemic in the 1910s.

Life expectancy at birth, female (years) in United States was reported at 81.1 years in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Life expectancy at birth, female (years) - actual values, historical data, forecasts and projections were sourced from the World Bank on May of 2025.

Life expectancy in India was 25.4 in the year 1800, and over the course of the next 220 years, it has increased to almost 70. Between 1800 and 1920, life expectancy in India remained in the mid to low twenties, with the largest declines coming in the 1870s and 1910s; this was because of the Great Famine of 1876-1878, and the Spanish Flu Pandemic of 1918-1919, both of which were responsible for the deaths of up to six and seventeen million Indians respectively; as well as the presence of other endemic diseases in the region, such as smallpox. From 1920 onwards, India's life expectancy has consistently increased, but it is still below the global average.

Note: This dataset is historical only and there are not corresponding datasets for more recent time periods. For that more-recent information, please visit the Chicago Health Atlas at https://chicagohealthatlas.org.

This dataset gives the average life expectancy and corresponding confidence intervals for sex and racial-ethnic groups in Chicago for the years 1990, 2000 and 2010. See the full description at: https://data.cityofchicago.org/api/views/3qdj-cqb8/files/pJ3PVVyubnsS2SpGO5P5IOPtNgCJZTE3LNOeLagC3mw?download=true&filename=P:\EPI\OEPHI\MATERIALS\REFERENCES\Life Expectancy\Dataset description_LE_ Sex_Race_Ethnicity.pdf

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census ZCTA Population (2000-2010) http://factfinder.census.gov

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2013) http://factfinder.census.gov

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population that can be compared across time and populations. More information about the determinants of life expectancy that may lead to differences in life expectancy between neighborhoods can be found in the Bay Area Regional Health Inequities Initiative (BARHII) Health Inequities in the Bay Area report at http://www.barhii.org/wp-content/uploads/2015/09/barhii_hiba.pdf. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and ZIP Codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential ZIP Code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality.

For the ZIP Code-level life expectancy calculation, it is assumed that postal ZIP Codes share the same boundaries as ZIP Code Census Tabulation Areas (ZCTAs). More information on the relationship between ZIP Codes and ZCTAs can be found at http://www.census.gov/geo/reference/zctas.html. ZIP Code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 ZIP Code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for ZIP Codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest ZIP Code with population. ZIP Code population for 2000 estimates comes from the Decennial Census. ZIP Code population for 2013 estimates are from the American Community Survey (5-Year Average). ACS estimates are adjusted using Decennial Census data for more accurate population estimates. An adjustment factor was calculated using the ratio between the 2010 Decennial Census population estimates and the 2012 ACS 5-Year (with middle year 2010) population estimates. This adjustment factor is particularly important for ZCTAs with high homeless population (not living in group quarters) where the ACS may underestimate the ZCTA population and therefore underestimate the life expectancy. The ACS provides ZIP Code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to ZIP Codes based on majority land-area.

ZIP Codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, ZIP Codes with populations of less than 5,000 were aggregated with neighboring ZIP Codes until the merged areas had a population of more than 5,000. ZIP Code 94103, representing Treasure Island, was dropped from the dataset due to its small population and having no bordering ZIP Codes. In this way, the original 305 Bay Area ZIP Codes were reduced to 217 ZIP Code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

The USA: Life expectancy, in years, male: The latest value from 2022 is 74.8 years, an increase from 73.5 years in 2021. In comparison, the world average is 69.65 years, based on data from 192 countries. Historically, the average for the USA from 1960 to 2022 is 71.82 years. The minimum value, 66 years, was reached in 1968 while the maximum of 76.5 years was recorded in 2014.

PLEASE if you use or like this dataset UPVOTE 👁️

This dataset offers a detailed historical record of global life expectancy, covering data from 1960 to the present. It is meticulously curated to enable deep analysis of trends and gender disparities in life expectancy worldwide.

Dataset Structure & Key Columns:

Country Code (🔤): Unique identifier for each country.

Country Name (🌍): Official name of the country.

Region (🌐): Broad geographical area (e.g., Asia, Europe, Africa).

Sub-Region (🗺️): More specific regional classification within the broader region.

Intermediate Region (🔍): Additional granular geographical grouping when applicable.

Year (📅): The specific year to which the data pertains.

Life Expectancy for Women (👩‍⚕️): Average years a woman is expected to live in that country and year.

Life Expectancy for Men (👨‍⚕️): Average years a man is expected to live in that country and year.

Context & Use Cases:

This dataset is a rich resource for exploring long-term trends in global health and demography. By comparing life expectancy data over decades, researchers can:

Analyze Time Series Trends: Forecast future changes in life expectancy and evaluate the impact of health interventions over time.

Study Gender Disparities: Investigate the differences between life expectancy for women and men, providing insights into social, economic, and healthcare factors influencing these trends.

Regional & Sub-Regional Analysis: Compare and contrast life expectancy across various regions and sub-regions to understand geographical disparities and their underlying causes.

Support Public Policy Research: Inform policymakers by linking life expectancy trends with public health policies, socioeconomic developments, and other key indicators.

Educational & Data Science Applications: Serve as a comprehensive teaching tool for courses on public health, global development, and data analysis, as well as for Kaggle competitions and projects.

With its detailed, structured format and broad temporal coverage, this dataset is ideal for anyone looking to gain a nuanced understanding of global health trends and to drive impactful analyses in public health, social sciences, and beyond.

Feel free to ask for further customizations or additional details as needed!

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.

Context

I am developing my data science skills in areas outside of my previous work. An interesting problem for me was to identify which factors influence life expectancy on a national level. There is an existing Kaggle data set that explored this, but that information was corrupted. Part of the problem solving process is to step back periodically and ask "does this make sense?" Without reasonable data, it is harder to notice mistakes in my analysis code (as opposed to unusual behavior due to the data itself). I wanted to make a similar data set, but with reliable information.

This is my first time exploring life expectancy, so I had to guess which features might be of interest when making the data set. Some were included for comparison with the other Kaggle data set. A number of potentially interesting features (like air pollution) were left off due to limited year or country coverage. Since the data was collected from more than one server, some features are present more than once, to explore the differences.

Content

A goal of the World Health Organization (WHO) is to ensure that a billion more people are protected from health emergencies, and provided better health and well-being. They provide public data collected from many sources to identify and monitor factors that are important to reach this goal. This set was primarily made using GHO (Global Health Observatory) and UNESCO (United Nations Educational Scientific and Culture Organization) information. The set covers the years 2000-2016 for 183 countries, in a single CSV file. Missing data is left in place, for the user to decide how to deal with it.

Three notebooks are provided for my cursory analysis, a comparison with the other Kaggle set, and a template for creating this data set.

Inspiration

There is a lot to explore, if the user is interested. The GHO server alone has over 2000 "indicators". - How are the GHO and UNESCO life expectancies calculated, and what is causing the difference? That could also be asked for Gross National Income (GNI) and mortality features. - How does the life expectancy after age 60 compare to the life expectancy at birth? Is the relationship with the features in this data set different for those two targets? - What other indicators on the servers might be interesting to use? Some of the GHO indicators are different studies with different coverage. Can they be combined to make a more useful and robust data feature? - Unraveling the correlations between the features would take significant work.

This line chart displays life expectancy at birth (year) by date using the aggregation average, weighted by population and is filtered where the country is Georgia. The data is about countries per year.

India life expectancy for 2025 is 70.82, a 0.29% increase from 2024.

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

<li>India life expectancy for 2024 was <strong>70.62</strong>, a <strong>1.92% decline</strong> from 2023.</li>
<li>India life expectancy for 2023 was <strong>72.00</strong>, a <strong>0.43% increase</strong> from 2022.</li>
<li>India life expectancy for 2022 was <strong>71.70</strong>, a <strong>6.56% 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.