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

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

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 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.

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.

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 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 ...).

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.

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

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

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.

All predicted values are derived from the Gompertz function fit to HRS development cohort data.Predicted and Observed Median Life Expectancy, Time to 25% Mortality and Time to 75% Mortality by Risk Points.

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.

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: 2021-2070 Status of the figures: The figures in this table are calculated forecast figures. Changes as of December 16, 2022: This table has been discontinued. See section 3 for the successor to this table. Changes as of December 16, 2021: 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 2021 to 2070. When will new figures be released? The publication frequency of this table is one-off. In December 2022, a new table will be published with the projection of the period life expectancy.

This table provides projections of the population of the Netherlands on 1 January by age (three groups) and population development: the number of children born alive, the number of deaths and foreign migration. In addition, the table contains the demographic pressure, the total fertility rate per woman and the period-life expectancy at birth and on the 65th birthday by gender.

The period-life expectancy, calculated from a period-survival table, is a summary measure of the mortality rates in a calendar year. It indicates how old people would become on average if the mortality rates per age of that year applied throughout their lives. See Section 4 for an explanation of the difference between the period survival table and a cohort survival table.

The table also includes forecast intervals.

Data available from 2024 to 2070.

Status of figures: The figures in this table are calculated forecast figures.

Changes as of 17 December 2024: None, this is a new table in which the previous forecast has been adjusted on the basis of the observations that have become available. The forecast period now runs from 2024 to 2070.

When will there be new figures? The frequency of appearance of this table is one-off. A new population forecast table will be published in December 2025.

Baseline characteristics of male study cohorts.a

BackgroundThe prevalence of cardiometabolic multimorbidity (CMM), which significantly increases the risk of mortality, is increasing globally. However, the role of healthy lifestyle in the secondary prevention of CMM is unclear.MethodsIn total, 290,795 participants with CMM, which was defined as coexistence of at least two of hypertension (HTN), diabetes mellitus (DM), coronary heart disease (CHD), and stroke (ST), and those without these four diseases at baseline were derived from UK Biobank. The associations between specific CMM patterns and mortality, and that between healthy lifestyle (including physical activity, smoking, alcohol consumption, and vegetable and fruit consumption) and mortality in patients with specific CMM patterns were calculated using the flexible parametric Royston-Parmar proportion-hazard model. Hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) were calculated.ResultsDuring a median 12.3-year follow up period, 15,537 (5.3%) deaths occurred. Compared with participants without cardiometabolic diseases, the HRs for all-cause mortality were 1.54 [95% confidence interval (CI): 1.30, 1.82] in participants with HTN + DM, 1.84 (95% CI: 1.59, 2.12) in those with HTN + CHD, 1.89 (95% CI: 1.46, 2.45) in those with HTN + ST, and 2.89 (95% CI: 2.28, 3.67) in those with HTN + DM + CHD. At the age of 45 years, non-current smoking was associated with an increase in life expectancy by 3.72, 6.95, 6.75, and 4.86 years for participants with HTN + DM, HTN + CHD, HTN + ST, and HTN + DM + CHD, respectively. A corresponding increase by 2.03, 1.95, 2.99, and 1.88 years, respectively, was observed in participants with regular physical activity. Non-/moderate alcohol consumption and adequate fruit/vegetable consumption were not significantly associated with life expectancy in patients with specific CMM patterns.ConclusionCardiometabolic multimorbidity was associated with an increased risk of mortality. Regular physical activity and non-current smoking can increase life expectancy in patients with specific CMM patterns.

BackgroundLarge, international cohort studies generate high-level evidence, but are resource intense. In end-of-life care such studies are scarce. Hence, planning for future studies in terms of data on screening, recruitment, retention and survival remains a challenge.ObjectivesThe aim was to describe recruitment, follow-up and survival in a multinational study of patients’ and relatives’ expectations, concerns and preferences at the end of life.MethodsIn this 11-country cohort study with six months follow-up patients, >18 years old, were included on the basis of an adapted “surprise question” to assess patients´ end of life status. Patients were required to be aware of their limited life expectancy. We collected patient questionnaires (baseline and 1 month), and searched medical records for the date of death. One relative per patient was invited to participate.Results26735 patients were screened for inclusion, 3065 (11%) were found eligible and were invited to participate, 1509 chose to participate, i.e. 6% of those initially screened. A total of 699 patients (49%) participated in the 1-month follow-up, with proportions varying according to survival time, from 20% if the patient died at month 2, to 75% if the patient died at month 6. Survival time was not associated with patient gender or age, but with diagnosis, country of residence and healthcare setting.ConclusionApproximately 20 times the desired cohort size had to be screened for eligibility. Prognostication was difficult, we noted a wide distribution of survival after inclusion. Patients’ ability to complete follow-up questionnaires declined well before death.