From the mid-19th century until today, life expectancy at birth in the United States has roughly doubled, from 39.4 years in 1850 to 79.6 years in 2025. It is estimated that life expectancy in the U.S. began its upward trajectory in the 1880s, largely driven by the decline in infant and child mortality through factors such as vaccination programs, antibiotics, and other healthcare advancements. Improved food security and access to clean water, as well as general increases in living standards (such as better housing, education, and increased safety) also contributed to a rise in life expectancy across all age brackets. There were notable dips in life expectancy; with an eight year drop during the American Civil War in the 1860s, a seven year drop during the Spanish Flu empidemic in 1918, and a 2.5 year drop during the Covid-19 pandemic. There were also notable plateaus (and minor decreases) not due to major historical events, such as that of the 2010s, which has been attributed to a combination of factors such as unhealthy lifestyles, poor access to healthcare, poverty, and increased suicide rates, among others. However, despite the rate of progress slowing since the 1950s, most decades do see a general increase in the long term, and current UN projections predict that life expectancy at birth in the U.S. will increase by another nine years before the end of the century.

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 ...).

A global phenomenon, known as the demographic transition, has seen life expectancy from birth increase rapidly over the past two centuries. In pre-industrial societies, the average life expectancy was around 24 years, and it is believed that this was the case throughout most of history, and in all regions. The demographic transition then began in the industrial societies of Europe, North America, and the West Pacific around the turn of the 19th century, and life expectancy rose accordingly. Latin America was the next region to follow, before Africa and most Asian populations saw their life expectancy rise throughout the 20th century.

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.

64.5 (Years) in 2016. Number of years that the would have lived person from generation born in a given year, provided that throughout the life of this generation mortality at each age remains the same as in the year for which life expectancy is calculated

76,3 (Years) in 2016. Number of years that the would have lived person from generation born in a given year, provided that throughout the life of this generation mortality at each age remains the same as in the year for which life expectancy is calculated

64.2 (Years) in 2016. Number of years that the would have lived person from generation born in a given year, provided that throughout the life of this generation mortality at each age remains the same as in the year for which life expectancy is calculated

77.4 (Years) in 2016. Number of years that the would have lived person from generation born in a given year, provided that throughout the life of this generation mortality at each age remains the same as in the year for which life expectancy is calculated

76,6 (Years) in 2016. Number of years that the would have lived person from generation born in a given year, provided that throughout the life of this generation mortality at each age remains the same as in the year for which life expectancy is calculated

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.

Keywords; Search terms: historical time series; historical statistics; histat / HISTAT; life expectancy; mortality rates . Abstract: In this study human life expectancy, which since the start of the 18th century has continually increased, is investigated in comparative perspective in Germany, Sweden and Norway. Topics: Regional as well as national data sets on population structure and the development of mortality. The following table overview represents a cutout from the study´s archived total stocks. The complete data stock contains not only time-series data. These complete data are available by GESIS Data Archive on request. Topics of Data-Tables with Time-Series: I (risk) population by generations II (risk) population by periods III probability of dying by generations IV probability of dying by periods V life expectancy by generations VI life expectancy by periods Systematics within the tables (Consecutively Numbering) 1. Place: Letter indicating the region: A. Germany (German Reich)/FRG B. Germany (German Reich)/GDR C. governmental district Aurich/Lower Saxony D. governmental district Kassel/Hessen E. governmental district Minden/North Rhine-Westphalia F. governmental district Trier/Saarland H. Herrenberg/South West Germany (Südwestdeuschland) N. Norway S. Sweden 2. Place: Number for the table´s subject (variable) 1. (risk) population (P´ x) 3. Probability of dying (qx) 5. Life expectancy (ex) 3. Place: Letter for the type of table (meaning of the annual details) P. period table G. generation table Stichworte: historische Zeitreihen; historische Statistik; histat / HISTAT; Lebenserwartung, Sterbewahrscheinlichkeiten . Inhalt: In dieser Untersuchung wird die seit dem Beginn des 18. Jahrhunderts stetig gestiegene menschliche Lebenserwartung in komparativer Perspektive in Deutschland, Schweden und Norwegen untersucht. Fördernde Institutionen von Mitte 1990 bis Mitte 1994: Bundesministerium für Forschung und Technologie; Bundesministerium für Familie und Senioren. Projekttitel: Die Zunahme der Lebensspanne seit 300 Jahren und die Folgen. Oder: Gewonnene Jahre - verlorene Welten: Wie erreichen wir ein neues Gleichgewicht? Das Projekt gliedert sich in drei Teile: - Die Zeitreihen zur Lebenserwartung in Deutschland vom 17. bis 19. Jahrhundert (ZA-Studie 8066) werden mit dieser Studie bis zur Gegenwart verlängert (alters- und geschlechtsspezifische Lebenserwartungen, Sterbewahrscheinlichkeiten, usw.). - Vergleich der deutschen Lebenserwartungsrechungen mit komparativen Materialien Norwegens und Schwedens, die ebenfalls gemäß dem Kohorten- und dem Periodentafelmodus angelegt sind (Lebenserwartungen in Deutschland, Norwegen und Schweden im 19. und 20. Jh.). - Analyse der Folgen einer seit 300 Jahren anhaltenden Entwicklung der Lebensspannenzunahme und den Möglichkeiten ihrer Bewältigung. Als Methodik der statistischen Untersuchung werden Sterbetafeln als Mittel der historisch-demographischen Analyse eingesetzt. Mit Hilfe der Sterbetafeln läßt sich das Mortalitätsgeschehen in einer Bevölkerung ausdrücken. Die Mortalitätsquotienten sind im Unterschied zu den rohen Sterblichkeitsziffern unabhängig von der Altersstruktur der Bevölkerung, so dass eine hohe Vergleichbarkeit gesichert ist. Themen: Regionale sowie nationale Datensätze zur Bevölkerungsstruktur und der Entwicklung der Sterblichkeit. - Gegenstand der Ergebnistabellen: Risikobevölkerung, Sterbefälle, Sterbewahrscheinlichkeiten, Überlebende, Lebenserwartungen; - Art der Tabelle: Generationen, Perioden; - Geschlecht: männlich, weiblich, insgesamt; - Regionale Unterteilung; - Todesursachenstruktur: Krankheiten der Säuglinge, Altersschwäche, Infektionskrankheiten und andere zum Tode führende Krankheiten, Unfälle, sonstige Todesursachen. Zeitreihen-Daten dieser Studie im Recherche- und Downloadsystem HISTAT: Zu folgenden Themen sind Zeitreihendaten dieser Studie über das Recherche- und Downloadsystem HISTAT zugänglich: Themenbereiche der Datentabellen in HISTAT: I. (Risiko-) Bevölkerung nach Generationen II. (Risiko-) Bevölkerung nach Perioden III. Sterbewahrscheinlichkeit nach Generationen IV. Sterbewahrscheinlichkeit nach Perioden V. Lebenserwartung nach Generationen VI. Lebenserwartung nach Perioden Systematik innerhalb der Tabellen (Durchnummerierung): 1. Stelle: Buchstabe für die regionale Unterteilung A. Deutschland (Deutsches Reich) / BRD B. Deutschland (Deutsches Reich) / DDR C. Regierungsbezirk Aurich / Niedersachsen D. Regierungsbezirk Kassel / Hessen E. Regierungsbezirk Minden / Nordrhein-Westfalen F. Regierungsbezirk Trier / Saarland H. Herrenberg / Südwestdeutschland N. Norwegen S. Schweden 2. Stelle: Ziffer für den Gegenstand der Tabelle (Variable) 1. (Risiko-) Bevölkerung (P’x) 3. Sterbewahrscheinlichkeit (qx) 5. Lebenserwartung (ex) 3. Stelle: Buchstabe für den Tabellentyp (Bedeutung der Jahresangabe) P. Periodentabelle G. Generationentabelle HINWEIS: HISTAT enthält einen Ausschnitt aus dem archivierten Gesamtbestand dieser Studie. Nicht berücksichtigt wurde die Variablen „Sterbefälle“ (Dx), „Überlebende“ (lx) und das gesamte Thema „Todesursachenstrukturen in Deutschland“. Ferner wurde die Differenzierung nach dem Geschlecht in HISTAT nicht berücksichtigt. Der komplette Datenbestand wird durch das Datenarchiv auf Anfrage bereitgestellt. Official Statistics, Census-Data, Church-Registers, Data of Civil Registry Offices. Quellen: Für Norwegen: Amtliche Statistik, Volkszählungen, zentrales norwegisches Personenregister. Für Schweden: Demographische Datenbank in Umea. Deutschland: Daten der Statistischen Ämter: Angaben in den Bänden der amtlichen Statistik, Volkszählungen; Daten auf Regierungsbezirks- und Länderebene: Angaben in den Bänden der Preußischen Statistik auf der Ebene der Preußischen Regionen, Daten der statistischen Landesämter; Daten aus Ortssippenbüchern (= Kirchenbüchern) und von Standesämtern.

77,7 (Years) in 2016. Number of years that the would have lived person from generation born in a given year, provided that throughout the life of this generation mortality at each age remains the same as in the year for which life expectancy is calculated

75,0 (Years) in 2016. Number of years that the would have lived person from generation born in a given year, provided that throughout the life of this generation mortality at each age remains the same as in the year for which life expectancy is calculated

According to the INSEE, healthy life expectancy is the average life in good health - that is to say without irreversible limitation of activity in daily life or incapacities - of a fictitious generation subject to the conditions of mortality and morbidity prevailing that year. It characterizes mortality and morbidity regardless of the age structure. In 2022, the healthy life expectancy of French women was almost ** years, while that of men was **** years.

As defined by the INSEE, healthy life expectancy is the average life in good health - that is, without irreversible limitation of activity in daily life or incapacities - of a fictitious generation subject to the conditions of mortality and morbidity prevailing that year. It characterizes mortality and morbidity regardless of the age structure. In 2021, the healthy life expectancy of French men was **** years, while that of men in Europe was *****years.

63,7 (Years) in 2016. Number of years that the would have lived person from generation born in a given year, provided that throughout the life of this generation mortality at each age remains the same as in the year for which life expectancy is calculated

75.1 (Years) in 2016. Number of years that the would have lived person from generation born in a given year, provided that throughout the life of this generation mortality at each age remains the same as in the year for which life expectancy is calculated

Objective Gains in life expectancy have faltered in several high-income countries in recent years. We aim to compare life expectancy trends in Scotland to those seen internationally, and to assess the timing of any recent changes in mortality trends for Scotland. Setting Austria, Croatia, Czech Republic, Denmark, England & Wales, Estonia, France, Germany, Hungary, Iceland, Israel, Japan, Korea, Latvia, Lithuania, Netherlands, Northern Ireland, Poland, Scotland, Slovakia, Spain, Sweden, Switzerland, USA. Methods We used life expectancy data from the Human Mortality Database (HMD) to calculate the mean annual life expectancy change for 24 high-income countries over five-year periods from 1992 to 2016, and the change for Scotland for five-year periods from 1857 to 2016. One- and two-break segmented regression models were applied to mortality data from National Records of Scotland (NRS) to identify turning points in age-standardised mortality trends between 1990 and 2018. Results In 2012-2016 life expectancies in Scotland increased by 2.5 weeks/year for females and 4.5 weeks/year for males, the smallest gains of any period since the early 1970s. The improvements in life expectancy in 2012-2016 were smallest among females (<2.0 weeks/year) in Northern Ireland, Iceland, England & Wales and the USA and among males (<5.0 weeks/year) in Iceland, USA, England & Wales and Scotland. Japan, Korea, and countries of Eastern Europe have seen substantial gains in the same period. The best estimate of when mortality rates changed to a slower rate of improvement in Scotland was the year to 2012 Q4 for males and the year to 2014 Q2 for females. Conclusion Life expectancy improvement has stalled across many, but not all, high income countries. The recent change in the mortality trend in Scotland occurred within the period 2012-2014. Further research is required to understand these trends, but governments must also take timely action on plausible contributors. Methods Description of methods used for collection/generation of data: The HMD has a detailed methods protocol available here: https://www.mortality.org/Public/Docs/MethodsProtocol.pdf The ONS and NRS also have similar methods for ensuring data consistency and quality assurance.

Methods for processing the data: The segmented regression was conducted using the 'segmented' package in R. The recommended references to this package and its approach are here: Vito M. R. Muggeo (2003). Estimating regression models with unknown break-points. Statistics in Medicine, 22, 3055-3071.

Vito M. R. Muggeo (2008). segmented: an R Package to Fit Regression Models with Broken-Line Relationships. R News, 8/1, 20-25. URL https://cran.r-project.org/doc/Rnews/.

Vito M. R. Muggeo (2016). Testing with a nuisance parameter present only under the alternative: a score-based approach with application to segmented modelling. J of Statistical Computation and Simulation, 86, 3059-3067.

Vito M. R. Muggeo (2017). Interval estimation for the breakpoint in segmented regression: a smoothed score-based approach. Australian & New Zealand Journal of Statistics, 59, 311-322.

Software- or Instrument-specific information needed to interpret the data, including software and hardware version numbers: The analyses were conducted in R version 3.6.1 and Microsoft Excel 2013.

Please see README.txt for further information

Individuals within populations vary enormously in mortality risk and longevity, but the causes of this variation remain poorly understood. A potentially important and phylogenetically widespread source of such variation is maternal age at breeding, which typically has negative effects on offspring longevity. Here, we show that paternal age can affect offspring longevity as strongly as maternal age does and that breeding age effects can interact over 2 generations in both matrilines and patrilines. We manipulated maternal and paternal ages at breeding over 2 generations in the neriid fly Telostylinus angusticollis. To determine whether breeding age effects can be modulated by the environment, we also manipulated larval diet and male competitive environment in the first generation. We found separate and interactive effects of parental and grand-parental ages at breeding on descendants’ mortality rate and life span in both matrilines and patrilines. These breeding age effects were not modulated by grand-parental larval diet quality or competitive environment. Our findings suggest that variation in maternal and paternal ages at breeding could contribute substantially to intrapopulation variation in mortality and longevity.

Utredare och systemutvecklare vid CEDAR, avdelningen för Demografiska databasen, har arbetat med att sammanställa data från databasen POPUM för att möjliggöra forskning om intergenerationell överföring av hälsa och förväntad livslängd under 1800-talet.