Historical chart and dataset showing World life expectancy by year from 1950 to 2025.

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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!

This table contains 2394 series, with data for years 1991 - 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Population group (19 items: Entire cohort; Income adequacy quintile 1 (lowest);Income adequacy quintile 2;Income adequacy quintile 3 ...), Age (14 items: At 25 years; At 30 years; At 40 years; At 35 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Life expectancy; High 95% confidence interval; life expectancy; Low 95% confidence interval; life expectancy ...).

Objective Gains in life expectancy have faltered in several high-income countries in recent years. We aim to compare life expectancy trends in Scotland to those seen internationally, and to assess the timing of any recent changes in mortality trends for Scotland. Setting Austria, Croatia, Czech Republic, Denmark, England & Wales, Estonia, France, Germany, Hungary, Iceland, Israel, Japan, Korea, Latvia, Lithuania, Netherlands, Northern Ireland, Poland, Scotland, Slovakia, Spain, Sweden, Switzerland, USA. Methods We used life expectancy data from the Human Mortality Database (HMD) to calculate the mean annual life expectancy change for 24 high-income countries over five-year periods from 1992 to 2016, and the change for Scotland for five-year periods from 1857 to 2016. One- and two-break segmented regression models were applied to mortality data from National Records of Scotland (NRS) to identify turning points in age-standardised mortality trends between 1990 and 2018. Results In 2012-2016 life expectancies in Scotland increased by 2.5 weeks/year for females and 4.5 weeks/year for males, the smallest gains of any period since the early 1970s. The improvements in life expectancy in 2012-2016 were smallest among females (<2.0 weeks/year) in Northern Ireland, Iceland, England & Wales and the USA and among males (<5.0 weeks/year) in Iceland, USA, England & Wales and Scotland. Japan, Korea, and countries of Eastern Europe have seen substantial gains in the same period. The best estimate of when mortality rates changed to a slower rate of improvement in Scotland was the year to 2012 Q4 for males and the year to 2014 Q2 for females. Conclusion Life expectancy improvement has stalled across many, but not all, high income countries. The recent change in the mortality trend in Scotland occurred within the period 2012-2014. Further research is required to understand these trends, but governments must also take timely action on plausible contributors. Description of methods used for collection/generation of data: The HMD has a detailed methods protocol available here: https://www.mortality.org/Public/Docs/MethodsProtocol.pdf The ONS and NRS also have similar methods for ensuring data consistency and quality assurance. Methods for processing the data: The segmented regression was conducted using the 'segmented' package in R. The recommended references to this package and its approach are here: Vito M. R. Muggeo (2003). Estimating regression models with unknown break-points. Statistics in Medicine, 22, 3055-3071. Vito M. R. Muggeo (2008). segmented: an R Package to Fit Regression Models with Broken-Line Relationships. R News, 8/1, 20-25. URL https://cran.r-project.org/doc/Rnews/. Vito M. R. Muggeo (2016). Testing with a nuisance parameter present only under the alternative: a score-based approach with application to segmented modelling. J of Statistical Computation and Simulation, 86, 3059-3067. Vito M. R. Muggeo (2017). Interval estimation for the breakpoint in segmented regression: a smoothed score-based approach. Australian & New Zealand Journal of Statistics, 59, 311-322. Software- or Instrument-specific information needed to interpret the data, including software and hardware version numbers: The analyses were conducted in R version 3.6.1 and Microsoft Excel 2013. Please see README.txt for further information HMD international_updated Jan 2019.xlsx Comprises 20 worksheets, of which 14 contain data. These data are arranged by country and by year. Missing data codes: "" The tab 'contents and sources' provides descriptions of the data source and contents of each sheet. HMD Scotland time trend analysis.xlsx Comprises 5 worksheets, including a combination of data and charts. The sheet 'contents' describes the data source and contents of other sheets. The variables include year, life expectancy, and various measures of change in life expectancy Missing data codes: "" Segmented regression chart.xlsx Comprises 2 worksheets, 'Data' and 'Chart'. Variables within the 'data' worksheet include: Year 4 quarter rolling period ending Female observed mortality rate Female predicted by one-break model Female predicted by two-break model Male observed mortality rate Male predicted by one-break model Male predicted by two-break model Chart breakpoint indicator Missing data codes: (blank space) Summary findings from segmented regression.xlsx Excel workbook containing table 1 of paper 'summary of results of segmented regression by population group and model/test'

The life expectancy figure used is for males aged under 1 year. Figures are based on the number of deaths registered and mid-year population estimates, aggregated over three consecutive years.

Expectation of life at a given age for an area is the average number of years a person would live if he or she experienced that area's age-specific mortality rates for that time period throughout his or her life. It is therefore not the number of years someone of that age in the area could actually expect to live, both because the death rates of the area are likely to change in the future and because people may live in other areas for at least part of their lives.

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

Life expectancy at birth and at age 65, by sex, on a three-year average basis.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken out by age group. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the daily COVID-19 update. Data are reported daily, with timestamps indicated in the daily briefings posted at: portal.ct.gov/coronavirus. Data are subject to future revision as reporting changes. Starting in July 2020, this dataset will be updated every weekday. Additional notes: A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020. A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differ from the timestamp in DPH's daily PDF reports. Starting 5/10/2021, the date field will represent the date this data was updated on data.ct.gov. Previously the date the data was pulled by DPH was listed, which typically coincided with the date before the data was published on data.ct.gov. This change was made to standardize the COVID-19 data sets on data.ct.gov.

This table shows three variants of healthy life expectancy: -Life expectancy in perceived good health. -Life expectancy without reported physical limitations. -Life expectancy without reported chronic diseases. -Life expectancy in good mental health In addition, data on mortality probabilities and total life expectancy are presented. Total life expectancy indicates the number of years that a person of a given age is expected to live. In the table, the data on (healthy) life expectancy can be broken down into the following characteristics: -Gender -Age -Income The standardized disposable household income allocated to individuals is used as an indicator of socio-economic status. The figures in the publication relate to the average over the years 2004 up to and including 2007, the average over the years 2007 up to and including 2010, the average over the years 2011 up to and including 2014 and the average over the years 2014 up to and including 2017. Data available from 2004/2007 up to and including 2017. Status of the figures: The figures in this table are final Changes as of 21 December 2022: None, this table has been discontinued. When will new numbers come out? Not applicable anymore. This table is followed by the Healthy life expectancy table; income and wealth. See section 3.

This multi-scale map shows life expectancy - a widely-used measure of health and mortality. From the County Health Rankings page about Life Expectancy:"Life Expectancy is an AverageLife Expectancy measures the average number of years from birth a person can expect to live, according to the current mortality experience (age-specific death rates) of the population. Life Expectancy takes into account the number of deaths in a given time period and the average number of people at risk of dying during that period, allowing us to compare data across counties with different population sizes.Life Expectancy is Age-AdjustedAge is a non-modifiable risk factor, and as age increases, poor health outcomes are more likely. Life Expectancy is age-adjusted in order to fairly compare counties with differing age structures.What Deaths Count Toward Life Expectancy?Deaths are counted in the county where the individual lived. So, even if an individual dies in a car crash on the other side of the state, that death is attributed to his/her home county.Some Data are SuppressedA missing value is reported for counties with fewer than 5,000 population-years-at-risk in the time frame.Measure LimitationsLife Expectancy includes mortality of all age groups in a population instead of focusing just on premature deaths and thus can be dominated by deaths of the elderly.[1] This could draw attention to areas with higher mortality rates among the oldest segment of the population, where there may be little that can be done to change chronic health problems that have developed over many years. However, this captures the burden of chronic disease in a population better than premature death measures.[2]Furthermore, the calculation of life expectancy is complex and not easy to communicate. Methodologically, it can produce misleading results caused by hidden differences in age structure, is sensitive to infant and child mortality, and tends to be overestimated in small populations."Breakdown by race/ethnicity in pop-up: (This map has been updated with new data, so figures may vary from those in this image.)There are many factors that play into life expectancy: rates of noncommunicable diseases such as cancer, diabetes, and obesity, prevalence of tobacco use, prevalence of domestic violence, and many more.Proven strategies to improve life expectancy and health in general A database of dozens of strategies can be found at County Health Rankings' What Works for Health site, sorted by Health Behaviors, Clinical Care, Social & Economic Factors, and Physical Environment. Policies and Programs listed here have been evaluated as to their effectiveness. For example, consumer-directed health plans received an evidence rating of "mixed evidence" whereas cultural competence training for health care professionals received a rating of "scientifically supported." Data from County Health Rankings (layer referenced below), available for nation, state, and county, and available in ArcGIS Living Atlas of the World.

This multi-scale map shows life expectancy - a widely-used measure of health and mortality. From the 2020 County Health Rankings page about Life Expectancy:

Breakdown by race/ethnicity in pop-up:

There are many factors that play into life expectancy: rates of noncommunicable diseases such as cancer, diabetes, and obesity, prevalence of tobacco use, prevalence of domestic violence, and many more.

Data from County Health Rankings 2020 (in this layer and referenced below), available for nation, state, and county, and available in ArcGIS Living Atlas of the World

The here presented perceived age data span birth cohorts from the years 1877 to 2014. Since 2012 the database has grown to now contain around 200,000 perceived age guesses. More than 4000 citizen scientists from over 120 countries of origin have uploaded ~5000 facial photographs. Beyond ageing research, the data present a wealth of possibilities to study how humans guess ages and to use this knowledge for instance in advancing and testing emerging applications of artificial intelligence and deep learning algorithms. In many developed countries, human life expectancy has doubled over the last 180 years from ~40 to ~80 years. Underlying this great advance is a change in how we age, yet our understanding of this change remains limited. Here we present a unique database rich with possibilities to study the human ageing process: the AgeGuess.org database on people’s perceived and chronological ages. Perceived age (i.e. how old one looks to others) correlates with biological age, a measure of a person’s health condition in comparison to the average of same-aged peers. Determining biological age usually involves elaborate molecular and cellular biomarkers. Using instead perceived age as a biomarker of biological age enables us to collect large amounts of data on biological age through a citizen science project, where people upload pictures of themselves and guess the ages of other people. It furthermore allows to collect data retrospectively, because people can upload photographs of themselves when they were younger or of their parents and grandparents. We can thus study the temporal variation in the gap between perceived age and chronological age to address questions such as whether we now age slower or delay ageing until older ages.

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

• Country: Name of the country.
• Density (P/Km2): Population density measured in persons per square kilometer.
• Abbreviation: Abbreviation or code representing the country.
• Agricultural Land (%): Percentage of land area used for agricultural purposes.
• Land Area (Km2): Total land area of the country in square kilometers.
• Armed Forces Size: Size of the armed forces in the country.
• Birth Rate: Number of births per 1,000 population per year.
• Calling Code: International calling code for the country.
• Capital/Major City: Name of the capital or major city.
• CO2 Emissions: Carbon dioxide emissions in tons.
• CPI: Consumer Price Index, a measure of inflation and purchasing power.
• CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.
• Currency_Code: Currency code used in the country.
• Fertility Rate: Average number of children born to a woman during her lifetime.
• Forested Area (%): Percentage of land area covered by forests.
• Gasoline_Price: Price of gasoline per liter in local currency.
• GDP: Gross Domestic Product, the total value of goods and services produced in the country.
• Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.
• Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.
• Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.
• Largest City: Name of the country's largest city.
• Life Expectancy: Average number of years a newborn is expected to live.
• Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.
• Minimum Wage: Minimum wage level in local currency.
• Official Language: Official language(s) spoken in the country.
• Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.
• Physicians per Thousand: Number of physicians per thousand people.
• Population: Total population of the country.
• Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.
• Tax Revenue (%): Tax revenue as a percentage of GDP.
• Total Tax Rate: Overall tax burden as a percentage of commercial profits.
• Unemployment Rate: Percentage of the labor force that is unemployed.
• Urban Population: Percentage of the population living in urban areas.
• Latitude: Latitude coordinate of the country's location.
• Longitude: Longitude coordinate of the country's location.

Potential Use Cases

• Analyze population density and land area to study spatial distribution patterns.
• Investigate the relationship between agricultural land and food security.
• Examine carbon dioxide emissions and their impact on climate change.
• Explore correlations between economic indicators such as GDP and various socio-economic factors.
• Investigate educational enrollment rates and their implications for human capital development.
• Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.
• Study labor market dynamics through indicators such as labor force participation and unemployment rates.
• Investigate the role of taxation and its impact on economic development.
• Explore urbanization trends and their social and environmental consequences.

Number of deaths and mortality rates, by age group, sex, and place of residence, 1991 to most recent year.

Malaysia life expectancy for 2025 is 76.93, a 0.18% increase from 2024.

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

<li>Malaysia life expectancy for 2024 was <strong>76.79</strong>, a <strong>0.18% increase</strong> from 2023.</li>
<li>Malaysia life expectancy for 2023 was <strong>76.66</strong>, a <strong>1.61% increase</strong> from 2022.</li>
<li>Malaysia life expectancy for 2022 was <strong>75.44</strong>, a <strong>2.07% 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 data set contains the life tables the were computed for the study presented in: Torres, C., V. Canudas-Romo, and J. Oeppen (2019) 'The contribution of urbanization to changes in life expectancy in Scotland, 1861–1910', Population Studies, 73:3, 387-404, DOI: 10.1080/00324728.2018.1549746

The life tables are by sex and urban-rural category. For reasons explained in the paper, the tables cover periods of different lengths, from 1861 to 1910.

Example of how to load the data in R:

LT <- read.table("Urban-Rural-LifeTables-Scotland-1861-1910.txt", header = T, sep = ";")

Description of each column: Period: time-interval, including the first and excluding the last indicated years (e.g., [1861,1866) corresponds to the years from 1861 to 1865). Available periods: 1861-1865, 1866-1870, 1871-1874, 1875-1877, 1878-1880, 1881-1885, 1886-1890, 1891-1892, 1893-1896, 1897-1900, 1901-1905, 1906-1910. Population: Rural, Semi-Urban, Urban, or Total population (see definitions in Torres et al. 2019) Sex: Female or Male x : Age (from 0 to 110+, by single ages) nmx: Death rate in the age interval [x, x+n) nax: average number of person-years lived in the age interval [x, x+n) by those who die in that interval nqx: Probability of dying in the age interval [x, x+n) lx: number of survivors at exact age x, or probability of surviving until exact age x ndx: Life-table deaths in the age interval [x, x+n) nLx: Person-years lived in the age interval [x, x+n) Tx: Person-years lived above age x ex: Remaining life expectancy at age x

For more information about life tables in general, see: Preston, S., Heuveline, P., and Guillot, M. (2001). Demography: Measuring and Modeling Population Processes. Wiley-Blackwell