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

Life expectancy 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.

Life expectancy in China was just 32 in the year 1850, and over the course of the next 170 years, it is expected to more than double to 76.6 years in 2020. Between 1850 and 1950, finding reliable data proved difficult for anthropologists, however some events, such as the Taiping Rebellion and Dungan Revolt in the nineteenth century did reduce life expectancy by a few years, and also the Chinese Civil War and Second World War in the first half of the twentieth century. In the second half of the 1900s, Chinese life expectancy increased greatly, as the country became more industrialized and the standard of living increased.

Life expectancy in Russia was 29.6 in the year 1845, and over the course of the next 175 years, it is expected to have increased to 72.3 years by 2020. Generally speaking, Russian life expectancy has increased over this 175 year period, however events such as the World Wars, Russian Revolution and a series of famines caused fluctuations before the mid-twentieth century, where the rate fluctuated sporadically. Between 1945 and 1950, Russian life expectancy more than doubled in this five year period, and it then proceeded to increase until the 1970s, when it then began to fall again. Between 1970 and 2005, the number fell from 68.5 to 65, before it then grew again in more recent years.

Life table data for "Bounce backs amid continued losses: Life expectancy changes since COVID-19"

cc-by Jonas Schöley, José Manuel Aburto, Ilya Kashnitsky, Maxi S. Kniffka, Luyin Zhang, Hannaliis Jaadla, Jennifer B. Dowd, and Ridhi Kashyap. "Bounce backs amid continued losses: Life expectancy changes since COVID-19".

These are CSV files of life tables over the years 2015 through 2021 across 29 countries analyzed in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19".

40-lifetables.csv

Life table statistics 2015 through 2021 by sex, region and quarter with uncertainty quantiles based on Poisson replication of death counts. Actual life tables and expected life tables (under the assumption of pre-COVID mortality trend continuation) are provided.

30-lt_input.csv

Life table input data.

• `id`: unique row identifier
• `region_iso`: iso3166-2 region codes
• `sex`: Male, Female, Total
• `year`: iso year
• `age_start`: start of age group
• `age_width`: width of age group, Inf for age_start 100, otherwise 1
• `nweeks_year`: number of weeks in that year, 52 or 53
• `death_total`: number of deaths by any cause
• `population_py`: person-years of exposure (adjusted for leap-weeks and missing weeks in input data on all cause deaths)
• `death_total_nweeksmiss`: number of weeks in the raw input data with at least one missing death count for this region-sex-year stratum. missings are counted when the week is implicitly missing from the input data or if any NAs are encounted in this week or if age groups are implicitly missing for this week in the input data (e.g. 40-45, 50-55)
• `death_total_minnageraw`: the minimum number of age-groups in the raw input data within this region-sex-year stratum
• `death_total_maxnageraw`: the maximum number of age-groups in the raw input data within this region-sex-year stratum
• `death_total_minopenageraw`: the minimum age at the start of the open age group in the raw input data within this region-sex-year stratum
• `death_total_maxopenageraw`: the maximum age at the start of the open age group in the raw input data within this region-sex-year stratum
• `death_total_source`: source of the all-cause death data

• `death_total_prop_q1`: observed proportion of deaths in first quarter of year

• `death_total_prop_q2`: observed proportion of deaths in second quarter of year

• `death_total_prop_q3`: observed proportion of deaths in third quarter of year

• `death_total_prop_q4`: observed proportion of deaths in fourth quarter of year

• `death_expected_prop_q1`: expected proportion of deaths in first quarter of year

• `death_expected_prop_q2`: expected proportion of deaths in second quarter of year

• `death_expected_prop_q3`: expected proportion of deaths in third quarter of year

• `death_expected_prop_q4`: expected proportion of deaths in fourth quarter of year

• `population_midyear`: midyear population (July 1st)
• `population_source`: source of the population count/exposure data
• `death_covid`: number of deaths due to covid
• `death_covid_date`: number of deaths due to covid as of
• `death_covid_nageraw`: the number of age groups in the covid input data
• `ex_wpp_estimate`: life expectancy estimates from the World Population prospects for a five year period, merged at the midpoint year
• `ex_hmd_estimate`: life expectancy estimates from the Human Mortality Database
• `nmx_hmd_estimate`: death rate estimates from the Human Mortality Database
• `nmx_cntfc`: Lee-Carter death rate projections based on trend in the years 2015 through 2019

Deaths

• source:
  • STMF input data series (https://www.mortality.org/Public/STMF/Outputs/stmf.csv)
  • ONS for GB-EAW pre 2020
  • CDC for US pre 2020
• STMF:
  • harmonized to single ages via pclm
  • pclm iterates over country, sex, year, and within-year age grouping pattern and converts irregular age groupings, which may vary by country, year and week into a regular age grouping of 0:110
  • smoothing parameters estimated via BIC grid search seperately for every pclm iteration
  • last age group set to [110,111)
  • ages 100:110+ are then summed into 100+ to be consistent with mid-year population information
  • deaths in unknown weeks are considered; deaths in unknown ages are not considered
• ONS:
  • data already in single ages
  • ages 100:105+ are summed into 100+ to be consistent with mid-year population information
  • PCLM smoothing applied to for consistency reasons
• CDC:
  • The CDC data comes in single ages 0:100 for the US. For 2020 we only have the STMF data in a much coarser age grouping, i.e. (0, 1, 5, 15, 25, 35, 45, 55, 65, 75, 85+). In order to calculate life-tables in a manner consistent with 2020, we summarise the pre 2020 US death counts into the 2020 age grouping and then apply the pclm ungrouping into single year ages, mirroring the approach to the 2020 data

Population

• source:
  • for years 2000 to 2019: World Population Prospects 2019 single year-age population estimates 1950-2019
  • for year 2020: World Population Prospects 2019 single year-age population projections 2020-2100
• mid-year population
  • mid-year population translated into exposures:
    • if a region reports annual deaths using the Gregorian calendar definition of a year (365 or 366 days long) set exposures equal to mid year population estimates
    • if a region reports annual deaths using the iso-week-year definition of a year (364 or 371 days long), and if there is a leap-week in that year, set exposures equal to 371/364\*mid_year_population to account for the longer reporting period. in years without leap-weeks set exposures equal to mid year population estimates. further multiply by fraction of observed weeks on all weeks in a year.

COVID deaths

• source: COVerAGE-DB (https://osf.io/mpwjq/)
• the data base reports cumulative numbers of COVID deaths over days of a year, we extract the most up to date yearly total

External life expectancy estimates

• source:
  • World Population Prospects (https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/CSV_FILES/WPP2019_Life_Table_Medium.csv), estimates for the five year period 2015-2019
  • Human Mortality Database (https://mortality.org/), single year and age tables

In 2022 life expectancy for both males and females at birth fell when compared to 2021. Male life expectancy fell from 78.71 years to 78.57 years, and from 82.68 years to 82.57 years for women. Throughout most of this period, there is a steady rise in life expectancy for both males and females, with improvements in life expectancy beginning to slow in the 2010s and then starting to decline in the 2020s. Life expectancy since the 18th Century Although there has been a recent dip in life expectancy in the UK, long-term improvements to life expectancy stretch back several centuries. In 1765, life expectancy was below 39 years, and only surpassed 40 years in the 1810s, 50 years by the 1910s, 60 years by the 1930s and 70 by the 1960s. While life expectancy has broadly improved since the 1700s, this trajectory was interrupted at various points due to wars and diseases. In the early 1920s, for example, life expectancy suffered a noticeable setback in the aftermath of the First World War and Spanish Flu Epidemic. Impact of COVID-19 While improvements to UK life expectancy stalled during the 2010s, it wasn't until the 2020s that it began to decline. The impact of COVID-19 was one of the primary factors in this respect, with 2020 seeing the most deaths in the UK since 1918. The first wave of the pandemic in Spring of that year was a particularly deadly time, with weekly death figures far higher than usual. A second wave that winter saw a peak of almost 5,700 excess deaths a week in late January 2021, with excess deaths remaining elevated for several years afterward.

Life expectancy in Italy was just under thirty in the year 1870, and over the course of the next 150 years, it is expected to have increased to 83.3 by the year 2020. Although life expectancy has generally increased throughout Italy's history, there were several times where the rate deviated from its previous trajectory. The most noticeable changes were a result of the First World War and Spanish Flu epidemic, and also the Second World War and Italian Civil War.

Period survival tables (per period of 5 years) by gender and age for the population of the Netherlands.

The table shows how many boys or girls from a group of 100 thousand newborns will reach the age of ½, 1½, 2½ etc. years. It can also be seen how old these children will be on average.

The following breakdowns are possible: — Mortality rate by sex and age; — Living (table population) by gender and age; — Deceased (table population) by gender and age; — Life expectancy by gender and age.

Data available from period 1861 to 1866 to period 2006 to 2011.

Status of the figures: All figures in the table are final.

Changes as at 31 March 2016: None, this table has been discontinued.

When will there be new figures? No longer applicable. This table is followed by the Life Expectancy Table; gender, age (per year and per period of 5 years). See paragraph 3.

Period and Cohort numbers surviving at exact age (lx) in England using the moderately low life expectancy variant by single year of age 0 to 100.

Plant lifespan has important evolutionary, physiological, and ecological implications related to population persistence, community stability, and resilience to ongoing environmental change impacts. Although biologists have long been puzzled over the extraordinary variation in plant lifespan and its causes, our understanding of interspecific variability in plant lifespan and the key internal and external factors influencing longevity remains limited. Here, we demonstrate the concurrent impacts of environmental, morphological, physiological, and anatomical constraints on interspecific variation in longevity among >300 vascular dicot plant species naturally occurring at an elevation gradient (2800-6150 m) in the western Himalayas. First, we show that plant longevity (ranging from 1 to 100 years) is largely related to species' habitat preferences. Ecologically stressful habitats such as alpine and subnival host long-lived species, while productive ruderal and wetland habitats contain a higher proportion of shorter-lived species. Second, longevity is influenced by growth form with monocarpic forbs having the shortest lifespan and woody shrubs having the highest. Small-statured cushion plants with compact canopies and deep roots, most found on cold and infertile alpine and subnival soils, had a higher chance of achieving longevity. Third, plant traits reflecting plant adaptations to stress and disturbance affect interspecific differences in plant longevity. We show that longevity and growth are negatively correlated. Slow-growing species are those that have a higher chance of reaching a high age. Finally, higher longevity was associated with high leaf carbon and phosphorus, low root phosphorus and nitrogen, and with large bark-xylem ratio. Our findings suggest that plant longevity in high elevation is intricately determined by a combination of habitat preferences and growth form, as well as the plant growth rate and physiological processes.

Life expectancy (from birth) in Mexico was below thirty until the 1920s, and over the course of the next hundred years, it is expected to have increased to roughly 75 in the year 2020. Although life expectancy has generally increased throughout Mexico's history, there were several times where the rate deviated from its previous trajectory. The main change coincided with the Mexican Revolution in the 1910s, and again in the early 2000s. Life expectancy has plateaued around 75 in the last fifteen years, and is now decreasing, because of unhealthy lifestyles, violent crime and an increase in the number of people with chronic illnesses (such as diabetes).

Like most complex phenotypes, exceptional longevity is thought to reflect a combined influence of environmental (e.g., lifestyle choices, where we live) and genetic factors. To explore the genetic contribution, we undertook a genome-wide association study of exceptional longevity in 801 centenarians (median age at death 104 years) and 914 genetically matched healthy controls. Using these data, we built a genetic model that includes 281 single nucleotide polymorphisms (SNPs) and discriminated between cases and controls of the discovery set with 89% sensitivity and specificity, and with 58% specificity and 60% sensitivity in an independent cohort of 341 controls and 253 genetically matched nonagenarians and centenarians (median age 100 years). Consistent with the hypothesis that the genetic contribution is largest with the oldest ages, the sensitivity of the model increased in the independent cohort with older and older ages (71% to classify subjects with an age at death>102 and 85% to classify subjects with an age at death>105). For further validation, we applied the model to an additional, unmatched 60 centenarians (median age 107 years) resulting in 78% sensitivity, and 2863 unmatched controls with 61% specificity. The 281 SNPs include the SNP rs2075650 in TOMM40/APOE that reached irrefutable genome wide significance (posterior probability of association = 1) and replicated in the independent cohort. Removal of this SNP from the model reduced the accuracy by only 1%. Further in-silico analysis suggests that 90% of centenarians can be grouped into clusters characterized by different “genetic signatures” of varying predictive values for exceptional longevity. The correlation between 3 signatures and 3 different life spans was replicated in the combined replication sets. The different signatures may help dissect this complex phenotype into sub-phenotypes of exceptional longevity.

Construction professionals must now integrate environmental concerns with life cycle assessment (LCA) early in the procurement process. Building lifespan is important to LCA, since results must be normalized on an annualized basis for comparison. However, the scientific literature shows that issues of building lifespan are inadequately addressed. The aim of this research is therefore to explore how environmental impact from building components is affected by building lifespans of 50, 80, 100 and 120 years in a Danish context. LCAs are undertaken for 792 parametric variations of typical construction solutions, covering all primary building components and based on contemporary practice. A full statistical analysis is carried out, which shows a significant statistical correlation between changes in building lifespan and environmental impact for all primary building components, except windows/rooflights. On average, a building lifespan of 80 years reduces environmental impact by 29%, 100 years by 38%, and 120 years by 44%, all in relation to a lifespan of 50 years. The results show that if construction professionals and policy-makers use short building lifespans, then resource allocation to reduce environmental impact during procurement may become disproportionately focused on the construction contra operational phases of the lifecycle.

Period and cohort expectation of life in England using the high life expectancy variant by single year of age 0 to 100.

Men’s municipal index for the theme Health is based on the indicators Average life expectancy women, years, average life expectancy men, years, Obesity population, percentage (%), long-term sick leave with mental illnesses and syndromes and behavioural disorders, percentage (%), incidence of cancer, age standardised, number/100000, incidence of heart attacks, age-standardised 20+ years, number/100000 inhabitants, inhabitants 16-84 years with good or very good self-rated health, share (%), residents with good self-rated dental health, percentage (%), population 16-84 years with impaired mental well-being, percentage (%). The KPIs are normalised so that all municipalities’ values are placed on a scale from 0 to 100 where 0 is the worst and 100 is best (for some indicators, inverted scale is used). In the next step, the standardised indicator values are weighed together into indices at aspect level (currently, the theme is based on indicators in three aspects). This is done with averages, all indicators weighed together with the same weight in each aspect. The values are also at this level in the range 0 to 100. Then the index at aspect level is weighed together to the thematic level according to the same principle and these values also fall between 0 and 100. The weighting is equal for all aspects of the theme.

The sewerage monitoring map indicates at neighborhood level (area boundaries according to Statistics Netherlands) what percentage of the sewer system is older than 50 years. The lifespan of sewers varies widely and can be up to 100 years. The average lifespan is about 50 years, but this is regularly not achieved due to subsidence, for example. The map has classified the life of the sewer. '0-30 years' indicates that there is no to very small chance that the sewer system will be replaced within 10 years. '30-50 years' shows where it is likely that the sewer will be replaced within 10 years. '>50 years' shows where the sewer probably needs to be replaced (based on lifespan). The length is determined in meters per class.

This map service, derived from World Bank data, shows various characteristics of the Health topic. The World Bank Group provides financing, state-of-the-art analysis, and policy advice to help countries expand access to quality, affordable health care; protects people from falling into poverty or worsening poverty due to illness; and promotes investments in all sectors that form the foundation of healthy societies.Age Dependency Ratio: Age dependency ratio is the ratio of dependents--people younger than 15 or older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population. Data from 1960 – 2012.Age Dependency Ratio Old: Age dependency ratio, old, is the ratio of older dependents--people older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population. Data from 1960 – 2012.Birth/Death Rate: Crude birth/death rate indicates the number of births/deaths occurring during the year, per
1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration. Data spans from 1960 – 2008.Total Fertility: Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with current age-specific fertility rates. Data shown is for 1960 - 2008.Population Growth: Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage.
Population is based on the de facto definition of population, which
counts all residents regardless of legal status or citizenship--except
for refugees not permanently settled in the country of asylum, who are
generally considered part of the population of the country of origin. Data spans from 1960 – 2009.Life Expectancy: 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. Data spans from 1960 – 2008.Population Female: Female population is the percentage of the population that is female. Population is based on the de facto definition of population. Data from 1960 – 2009.For more information, please visit: World Bank Open Data. _Other International User Community content that may interest you World Bank World Bank Age World Bank Health

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.

Russia Population: 100 Years and Older data was reported at 17,580.000 Person in 2017. This records an increase from the previous number of 15,703.000 Person for 2016. Russia Population: 100 Years and Older data is updated yearly, averaging 7,993.000 Person from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 17,580.000 Person in 2017 and a record low of 5,814.000 Person in 1997. Russia Population: 100 Years and Older data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA005: Population: by Age: 0 to 100 Years.

Thematic maps of the population. Share of population by age group in the total population in 2020, 2040, 2070. % change in population by age group in 2020 compared to 2040 and 2070. Youth and old-age dependency ratios 2020, 2040 and 2070. The youth quotient indicates the number of persons under the age of 20 per 100 persons between the ages of 20 and 65, the old-age quotient indicates the number of persons aged 65 and older per 100 persons between the ages of 20 and 65. The data come from the sixth regionalized population projection (medium variant). This projection is based on the results of the population update as at 31. December 2020. The birth rate will rise from 1.57 today to 1.6 children per woman by 2025. It will remain constant until 2070. Life expectancy will increase from 83 to 85 years for women today and from 79 to 82 years for men by 2040. By 2070, a further increase is assumed for women to 87 years and for men to 85 years. The projection assumes that the net migration will increase from 17,300 to +20,000 persons per year by 2025. It will remain at this level until 2030. The balance will then fall to +15,000 persons by 2040. This corresponds roughly to the long-term net migration recorded by Rhineland-Palatinate on average per year between 1951 and 2020. From 2040 onwards, the balance will remain constant until the end of the forecasting period. Population under 20 years 2040, % change from 2040.