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.

Presents life expectancies on a period and cohort basis. Data is provided by age and sex for the UK and its constituent countries.

Source agency: Office for National Statistics

Designation: National Statistics

Language: English

Alternative title: Projected Life Expectancy

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.

Presents historic and projected data from the period and cohort life tables including the expectation of life (ex) the probability of dying (qx) and the numbers surviving (lx). Data is provided by age and sex for the UK and its constituent countries.

Source agency: Office for National Statistics

Designation: National Statistics

Language: English

Alternative title: Period and Cohort Life Tables

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 ...).

This table contains the cohort survival tables (per 1-year birth cohort) by sex and age for the population of the Netherlands. The table shows how many boys or girls out of a group of 100,000 newborns have reached the year in which they become 1, 2, 3, etc. years old. It is also possible to see how old these children will be on average. The table can be broken down into mortality probability, the number of people alive (table population), the number of deaths (table population) and (cohort) life expectancy per generation by gender and age. The (cohort) life expectancy, calculated from a cohort survival table, indicates what the actual lifespan is (or is expected to be, when the observed mortality probabilities are supplemented with mortality probabilities from the forecast period). See section 4 for an explanation of the difference between the period survival table and a cohort survival table. A choice can be made from figures in which only observed numbers have been calculated, or a series in which the observed numbers have been supplemented with future expectations of the number of deaths for the birth generations that are still alive. Data available: from birth generation 1850 Status of the figures: The figures based on the numbers of deaths observed up to and including the year 2021 are final. Figures supplemented with future expectations of the number of deaths come from the CBS Core Forecast 2022-2070. This forecast is reviewed once a year. Changes as of 16 December 2022: - The figures relating to mortality observations for 2021 have been incorporated in the table; - The figures relating to the forecasts have been replaced by those from the Core Forecast 2022-2070. When will new numbers come out? In December 2023, the mortality observations for 2022 will be processed in this table and the future expectations will be replaced by those from the Population Forecast 2023-2070.

There are two types of life tables –cohort/generational and current/period life tables. Cohort life tables are constructed using the mortality experience of the cohort and may not be useful for the cohort itself because every member of the cohort has to die before such a table can be constructed. A current or period life table uses current mortality experience applied to a cohort of births to compute the life table. On the basis of age intervals, life tables are classified as complete or abridged. A complete life table uses exact single years and an abridged life table uses age intervals. This report presents five-year age interval abridged current life tables. Computation of an abridged life table from which life expectancy is derived requires mainly population and death data by age and sex. In this report, population data consist of the 1990, 2000, and 2010 census counts of residents of each Illinois County and the city of Chicago. These data were aggregated into five-year age groups and by sex and used as denominators in computing mortality rates. The death data were received from the Illinois Center for Health Statistics (ICHS) of the Office of Health Informatics (OHI). ICHS receives these data from the Illinois Vital Records System (IVRS). Number of deaths by sex and specific age for each county were obtained from 1989 to 2011 and aggregated at county level by five-year age groups for each sex. Three-year averages were then computed for the periods 1989-1991, 1999-2001, and 2009-2011 and were used as numerators in computing mortality rates. The overall life tables were constructed using Chiang’s (1984) Method II. This method assumes a homogeneous population in which all individuals are subjected to the same force of mortality, and in which survival of an individual is independent of the survival of any other individual in the group. The method does not remove fluctuations in observed data; therefore, the 2 produced life tables exhibit more the factual mortality pattern in the actual data and less the underlying mortality picture of the populations. Margin of errors were computed to provide basis for evaluating the accuracy of the estimated life expectancies.

Work life expectancy for a 50-year-old Tables Work Life Expectancy For A 50 Year OldTSV The indicator gives the percentages of employed people and one-year survival probabilities in the population aged 50. The average life expectancy of people aged 50 is divided into two parts: lifetime in employment and the remaining lifetime. The figures describe the average life expectancy and remaining lifetime in employment of an imaginary cohort at the time it reaches age 50, assuming that the cohort will experience the age-specific employment rates and mortality conditions of the year concerned throughout its total lifetime.

BackgroundWhile combination antiretroviral therapy (cART) has significantly improved survival times for persons diagnosed with HIV, estimation of life expectancy (LE) for this cohort remains a challenge, as mortality rates are a function of both time since diagnosis and age, and mortality rates for the oldest age groups may not be available.MethodsA validated case-finding algorithm for HIV was used to update the cohort of HIV-positive adults who had entered care in Ontario, Canada as of 2012. The Chiang II abridged life table algorithm was modified to use mortality rates stratified by time since entering the cohort and to include various methods for extrapolation of the excess HIV mortality rates to older age groups.ResultsAs of 2012, there were approximately 15,000 adults in care for HIV in Ontario. The crude all-cause mortality rate declined from 2.6% (95%CI 2.3, 2.9) per year in 2000 to 1.3% (1.2, 1.5) in 2012. Mortality rates were elevated for the first year of care compared to subsequent years (rate ratio of 2.6 (95% CI 2.3, 3.1)). LE for a 20-year old living in Ontario was 62 years (expected age at death is 82), while LE for a 20-year old with HIV was estimated to be reduced to 47 years, for a loss of 15 years of life. Ignoring the higher mortality rates among new cases introduced a modest bias of 1.5 additional years of life lost. In comparison, using 55+ as the open-ended age group was a major source of bias, adding 11 years to the calculated LE.ConclusionsUse of age limits less than the expected age at death for the open-ended age group significantly overstates the estimated LE and is not recommended. The Chiang II method easily accommodated input of stratified mortality rates and extrapolation of excess mortality rates.

IntroductionAdult male and female mortality declines in Japan have been slower than in most high-income countries since the early 1990s. This study compares Japan’s recent life expectancy trends with the more favourable trends in Australia, measures the contribution of age groups and causes of death to differences in these trends, and places the findings in the context of the countries’ risk factor transitions.MethodsThe study utilises data on deaths by age, sex and cause in Australia and Japan from 1950–2016 from the Global Burden of Disease Study. A decomposition method measures the contributions of various ages and causes to the male and female life expectancy gap and changes over four distinct phases during this period. Mortality differences by cohort are also assessed.FindingsJapan’s two-year male life expectancy advantage over Australia in the 1980s closed in the following 20 years. The trend was driven by ages 45–64 and then 65–79 years, and the cohort born in the late 1940s. Over half of Australia’s gains were from declines in ischaemic heart disease (IHD) mortality, with lung cancer, chronic respiratory disease and self-harm also contributing substantially. Since 2011 the trend has reversed again, and in 2016 Japan had a slightly higher male life expectancy. The advantage in Japanese female life expectancy widened over the period to 2.3 years in 2016. The 2016 gap was mostly from differential mortality at ages 65 years and over from IHD, chronic respiratory disease and cancers.ConclusionsThe considerable gains in Australian male life expectancy from declining non-communicable disease mortality are attributable to a range of risk factors, including declining smoking prevalence due to strong public health interventions. A recent reversal in life expectancy trends could continue because Japan has greater scope for further falls in smoking and far lower levels of obesity. Japan’s substantial female life expectancy advantage however could diminish in future because it is primarily due to lower mortality at old ages.

Lists the life expectancy at year of birth, by gender, year, and , municipality and municipal district. Life expectancy is the average number of years a hypothetical birth cohort of 10 years ending with the specified year would live if they were subjected to the current mortality conditions throughout the rest of their lives.

In 2022, life expectancy at birth in Belgium reached 81.69 years. However, life expectancy was subject to gender disparity. In 2022, Belgian women had a life expectancy at birth of 83.78 years, whereas men had a life expectancy of 79.55 years. Life expectancy also differed from one country to another. For instance, in 2020, a Frenchman could at birth expect to live up to 79.2 years and a Dutch person up to 82 years. With that being said, the Belgian life expectancy at birth correlates with western European figures in 2020.

An unclear measure

In 2020, worldwide human life expectancy at birth was of 70 years for men and 75 years for women. However, comparing life expectancy between several countries can be tricky. Indeed, one country could have a rather high life expectancy at birth when another could have a rather low expectancy. Considering the median and mode are therefore crucial.

Life expectancy is commonly confused with the average age an adult could expect to live. This confusion may create the expectation that an adult would be unlikely to exceed an average life expectancy. Yet this statistical measure is based on probability. At every age, life expectancy compares the number of survivors who share the same age. In this sense, life expectancy increases with age as the individual survives the higher mortality rates. In order to compare an age cohort with its corresponding mortality rates, deaths and births need to be acutely registered.

The evolution of life expectancy

Such lists of deaths and births only started to appear in the 19th century. In old times, it can be assumed that life expectancy was fairly low. However, these times were also characterized by very high childhood mortality. It is, therefore, crucial to consider this when comparing life expectancy throughout history. When looking at life expectancy at age 10 in these times, figures are not as low as birth numbers can make you believe. Nowadays, paleodemographist can perform skeletal analysis and genetic analysis to better understand the evolution of life expectancy.

Nonetheless, an adult who has already avoided many statistical causes of mortality should expect to outlive the average life expectancy calculated from birth. Furthermore, life expectancy has undoubtedly risen hand in hand with the evolution of hygiene, technology, medicine, and living standards in general.

Period and cohort mortality rates (qx) for England and Wales using the low life expectancy variant, by single year of age 0 to 100.

A rapidly aging population, such as the United States today, is characterized by the increased prevalence of chronic impairment. Robust estimation of disability-free life expectancy (DFLE) is essential for examining whether additional years of life are spent in good health and whether life expectancy is increasing faster than the decline of disability rates. Over thirty years since its publication, Sullivan's method remains the most widely used method to estimate DFLE. Therefore, it is surprising to note that Sullivan did not provide any formal justification of his method. Debates in the literature have centered around the properties of Sullivan's method and have yielded conflicting results regarding the assumptions required for Sullivan's method. In this paper, we establish a statistical foundation of Sullivan's method. We prove that under stationarity assumptions, Sullivan's estimator is unbiased and consistent. This resolves the debate in the literature which has generally concluded that additional assumptions are necessary. We also show that the standard variance estimator is consistent and approximately unbiased. Finally, we demonstrate that Sullivan's method can be extended to estimate DFLE without stationarity assumptions. Such an extension is possible whenever a cohort life table and either consecutive cross-sectional disability surveys or a longitudinal survey are available. Our empirical analysis of the 1907 and 1912 U.S. birth cohorts suggests that while mortality rates remain approximately stationary, disability rates decline during this time period.

Following the publication of the period and cohort life expectancy tables ONS prepares databases for the UK and each of the constituent countries containing mortality data used in the calculation of historic and projected life tables. Published for the first time in this release are tables of historic and projected qx (probability of dying at each age) and lx values (numbers of people surviving at each age) for the UK, on a period and cohort basis for each year 1951 to 2060.

Source agency: Office for National Statistics

Designation: Official Statistics not designated as National Statistics

Language: English

Alternative title: qx and lx tables

Life expectancy in Sweden was 36 in the year 1765, and over the course of the next 255 years, it is expected to have increased to 82.6 by 2020. Although life expectancy has generally increased throughout Sweden's history, there was a lot of fluctuation around the turn of the nineteenth century due to The Napoleonic Wars and First Cholera Epidemic, and again in the 1910s due to the Spanish Flu Epidemic.

In 2022, the life expectancy at birth for women born in the UK was 82.57 years, compared with 78.57 years for men. By age 65 men had a life expectancy of 18.25 years, compared with 20.76 years for women.

Total expected life years and expected life years living with and without activity limitation overall and by each of two initial disability states for U.S. older adults.

This table contains forecast figures from the period survival tables (per period of 1 year) by gender and age (on 31 December) for the population of the Netherlands. The table shows how many boys or girls from a group of 100,000 newborns will reach the age of 0, 1, 2, etc. on December 31 of the year of observation. It can also be determined how old these children will be on average if the mortality probabilities of the prognosis year apply throughout their lives. This period life expectancy can therefore best be interpreted as a summary measure of the mortality probabilities in a calendar year. See section 4 for an explanation of the difference between the period survival table and a cohort survival table. The table can be broken down into the mortality probability, the number of people alive (table population), the number of deaths (table population) and the period life expectancy by gender and age. Data available: 2022-2070 Status of the figures: The figures in this table are calculated forecast figures. Changes as of December 16, 2022: None, this is a new table in which the previous forecast has been adjusted on the basis of the observations that have now become available. The forecast period now runs from 2022 to 2070. When will new figures be released? The publication frequency of this table is one-off. In December 2023, a new table will be published with the forecast period life expectancy.