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/

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. 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 https://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). 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. In this way, the original 305 Bay Area Zip codes were reduced to 218 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.

This statistic depicts the average life expectancy at the age of 60 worldwide in 1990 and 2013, by income group. In 2013, a person aged 60 from a high income household had a life expectancy of 23 more years, while a person of the same age from a low income household was expected to live 17 more years.

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.

This graph shows the life expectancy at the age of 35 for the French population from 2009 to 2013, by gender and level of education. According to the source, french women without a diploma lived on average 83 years.

Age-Adjusted Death Rates

Death Rates and Life Expectancy in the United States, 2011-2013

By Health [source]

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How to use the dataset

In order to use this dataset, start by selecting a particular set of variables to investigate. You can choose from Measure Names (e.g., Death Rates or Life Expectancy), Race (e.g., All Races), Sex (Male/Female) and Year (2011-2013). Once you have selected your desired variables, you can begin analyzing the data by looking at mortality rates and life expectancy averages amongst different populations in the United States over time.

You may also wish to perform more detailed analyses such as identifying trends or examining correlations between features, regional disparities in mortality rates or changes in average life expectancies over time. If so, you can do so by creating line graphs plotted against one or more independent variables such as Race and Sex to see how demographics impact these statistics overall and on a yearly basis using the Year variable computed from July 1st 2010 estimates

Research Ideas

• Analyzing mortality and life expectancy trends among certain races and sexes over time.
• Examining the effects of different socioeconomic factors on death rates and life expectancies.
• Making predictions about future mortality rates and average life expectancies with machine learning algorithms

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: rows.csv | Column name | Description | |:----------------------------|:----------------------------------------------------------------------| | Measure Names | The type of measure being reported. (String) | | Race | The race of the population being reported. (String) | | Sex | The gender of the population being reported. (String) | | Year | The year the data was collected. (Integer) | | Average Life Expectancy | The average life expectancy of the population being reported. (Float) | | Mortality | The mortality rate of the population being reported. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Health.

This table contains mortality indicators for Canada and provinces for the period 1980/1982 to 2013/2015. Complete mortality tables are available for men, women and both sexes combined.

Over the last two observations, the life expectancy has significantly increased in all gender groups Comparing the two different gender groups for the year 2023, the 'life expectancy of women at birth' leads the ranking with 78.73 years. Contrastingly, 'life expectancy of men at birth' is ranked last, with 68.04 years. Their difference, compared to life expectancy of women at birth, lies at 10.69 years. Life expectancy at birth refers to the number of years the average newborn is expected to live, providing that mortality patterns at the time of birth do not change thereafter.Find further similar statistics for other countries or regions like Thailand and Brunei.

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.

This dataset contains World life expectancy data from 1980 - 2015 Data from the United Nations Economic Commission for Europe. Follow datasource.kapsarc.org for timely data to advance energy economics research.

Taiwan Life Expectancy: Male: Age 80 data was reported at 8.735 Year Old in 2017. This records an increase from the previous number of 8.633 Year Old for 2016. Taiwan Life Expectancy: Male: Age 80 data is updated yearly, averaging 8.346 Year Old from Dec 1993 (Median) to 2017, with 25 observations. The data reached an all-time high of 8.892 Year Old in 2013 and a record low of 6.410 Year Old in 1995. Taiwan Life Expectancy: Male: Age 80 data remains active status in CEIC and is reported by Ministry of the Interior. The data is categorized under Global Database’s Taiwan – Table TW.G006: Vital Statistics.

Yemen: Life expectancy, in years: The latest value from 2022 is 63.72 years, a decline from 63.75 years in 2021. In comparison, the world average is 72.24 years, based on data from 192 countries. Historically, the average for Yemen from 1960 to 2022 is 54.93 years. The minimum value, 33.25 years, was reached in 1963 while the maximum of 67.54 years was recorded in 2013.

School life expectancy, primary, male (years) in Curacao was reported at 9.8514 years in 2013, according to the World Bank collection of development indicators, compiled from officially recognized sources. Curacao - School life expectancy, primary, male (years) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Yemen: Life expectancy, in years, male: The latest value from 2022 is 60.49 years, a decline from 60.63 years in 2021. In comparison, the world average is 69.65 years, based on data from 192 countries. Historically, the average for Yemen from 1960 to 2022 is 52.62 years. The minimum value, 30.35 years, was reached in 1963 while the maximum of 65.56 years was recorded in 2013.

This table contains figures on life expectancy and healthy life expectancy broken down by educational level. Healthy life expectancy is the number of years that people of a certain age can expect to live in good health, assuming that the chances of mortality and unhealth will remain the same in the future. The table provides four variants of healthy life expectancy: life expectancy in well-experienced health life expectancy without physical limitations life expectancy without chronic diseases life expectancy in good mental health

The table focuses on differences in (healthy) life expectancy by educational level and therefore provides figures across three levels of education: low, medium and high. The table also shows the mortality rates per level of education used in the calculations. The figures refer to four-year periods (2011/2014, 2013/2016, etc.). The reason for this is that the CBS Health Survey is used for the health characteristics. This survey is too small to be able to determine the figures on an annual basis.

Data available from: 2011/2014

Status of the figures: The figures in this table are final.

Changes as of 11 November 2020 None, this table has been discontinued

Changes as of 16 August 2019: The figures for 2015/2018 have been added.

When are new figures coming? No longer applicable. This table has been discontinued due to a revision of the method. New figures appear in the Healthy Life Expectancy table; gender, age and education level. See paragraph 3.

Median age, prospective median age, and remaining life expectancy at the median age: USA, 2013–2098.

School life expectancy, tertiary, male (years) in Curacao was reported at 0.6498 years in 2013, according to the World Bank collection of development indicators, compiled from officially recognized sources. Curacao - School life expectancy, tertiary, male (years) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Yemen: Life expectancy, in years, female: The latest value from 2022 is 67.22 years, an increase from 67.13 years in 2021. In comparison, the world average is 74.94 years, based on data from 192 countries. Historically, the average for Yemen from 1960 to 2022 is 57.33 years. The minimum value, 36.22 years, was reached in 1960 while the maximum of 69.53 years was recorded in 2013.

Health expectancy is a summary measure of population health that combines both non-fatal and fatal health outcomes into a single metric. Health expectancy indicators have the potential to assess how many gained years of life expectancy are spent in good health and free from functional limitations. Online tables containing detailed results are available for download:

• Active life expectancy (ALE) 1996-2013
• Independent life expectancy (ILE) 1996-2013
• Disability-free life expectancy 1996-2013

School life expectancy, primary, female (years) in Curacao was reported at 9.4604 years in 2013, according to the World Bank collection of development indicators, compiled from officially recognized sources. Curacao - School life expectancy, primary, female (years) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.