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.

Over the past 160 years, life expectancy (from birth) in the United States has risen from 39.4 years in 1860, to 78.9 years in 2020. One of the major reasons for the overall increase of life expectancy in the last two centuries is the fact that the infant and child mortality rates have decreased by so much during this time. Medical advancements, fewer wars and improved living standards also mean that people are living longer than they did in previous centuries.

Despite this overall increase, the life expectancy dropped three times since 1860; from 1865 to 1870 during the American Civil War, from 1915 to 1920 during the First World War and following Spanish Flu epidemic, and it has dropped again between 2015 and now. The reason for the most recent drop in life expectancy is not a result of any specific event, but has been attributed to negative societal trends, such as unbalanced diets and sedentary lifestyles, high medical costs, and increasing rates of suicide and drug use.

Keywords; Search terms: historical time series; historical statistics; histat / HISTAT . Abstract: In this study the constantly rising human life expectancy since the beginning of the 18th century is analysed in some regions of Germany in comparative point of view. On the basis of worldwide singular sources in terms of clan registers of villages and localities as well as flow sheets the researcher Arthur E. Imhof and his research group of the ‘Freie Universität Berlin’ analysed more than 130.000 individual biografies from the 17th till the 19th century in six regions of northern, southern and central Germany. Aim of this research project was to compile area life-tables and to compute the life-expectancy. To enable comparisons with life-expectancy-calculations of today, all data originally prepared by generations are transformed into period-tables according to modern demografic methods. Topics Regional and national datafiles on populationstructure, development of mortality, historical demography, family structure, date of birth, marriages, number of birth, date of death, cause of death, locality of death, occupation, occupation of the parents. This study is available as SPSS-Data file as well as a downloadable EXCEL-Data-File, offered via the online-downloadsystem HISTAT (Historical Statistics). In HISTAT timeseries data are available. Categorisation in HISTAT:In HISTAT an excerpt of the archived total data stock is offered. The total data stock can be ordered as individual personal data at GESIS, Data Archive and Data Analysis. A. Datatables about mortality (14 tables, timeseries)B. Synoptical mortality tables (14 tables, timeseries)C. Datatables about life expectancy (14 tables, timeseries)D. Synoptical tables: all regions (without Hamburg) by sex in periodical presentation. (14 tables, timeseries)

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 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.

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)

3. (risk) population (P´ x)

4. Probability of dying (qx)

5. Life expectancy (ex)

6. Place: Letter for the type of table (meaning of the annual details) P. period table G. generation table

See also ZA-Study 8066: Expectancy of life in Germany, 1700 to 1890.

This file provides the necessary input data (crude vital rates) and shows the calculations for the indirect estimation of life expectancy at birth (e0) for males and females combined, using the method developed in McCann, J. 1976. 'A Technique for Estimating Life Expectancy with Crude Vital Rates', Demography, 13(2): pp. 259-272.

Coverage: Sweden (1736-1750), Norway (1735-1845), Denmark (1800-1834), Iceland (1735-1837), and Finland (1751-1877).

The annual estimates end in the year before estimates in the Human Mortality Database become available.

For a detailed description see Torres, C. and Oeppen, J. 2019. The Health Transition in the Nordic Countries (Working paper, available upon request: ctorres@sdu.dk).

Life expectancy in France was below thirty in the late 1700s, but over the course of the next two and a half centuries it is expected to reach 82.5 by the year 2020. Although life expectancy has generally increased throughout France's history, there were several times where the rate deviated from its previous trajectory. The most noticeable changes were because of smallpox and influenza epidemics in the 1700s, medical advancements (such as vaccination and pasteurization) saw life expectancy increase in the 1800s, and then both World Wars and the epidemics that followed caused brief drops in the first half of the twentieth century.

Global life expectancy at birth has risen significantly since the mid-1900s, from roughly 46 years in 1950 to 73.2 years in 2023. Post-COVID-19 projections There was a drop of 1.7 years during the COVID-19 pandemic, between 2019 and 2021, however, figures resumed upon their previous trajectory the following year due to the implementation of vaccination campaigns and the lower severity of later strains of the virus. By the end of the century it is believed that global life expectancy from birth will reach 82 years, although growth will slow in the coming decades as many of the more-populous Asian countries reach demographic maturity. However, there is still expected to be a wide gap between various regions at the end of the 2100s, with the Europe and North America expected to have life expectancies around 90 years, whereas Sub-Saharan Africa is predicted to be in the low-70s. The Great Leap Forward While a decrease of one year during the COVID-19 pandemic may appear insignificant, this is the largest decline in life expectancy since the "Great Leap Forward" in China in 1958, which caused global life expectancy to fall by almost four years between by 1960. The "Great Leap Forward" was a series of modernizing reforms, which sought to rapidly transition China's agrarian economy into an industrial economy, but mismanagement led to tens of millions of deaths through famine and disease.

It is only in the past two centuries where demographics and the development of human populations has emerged as a subject in its own right, as industrialization and improvements in medicine gave way to exponential growth of the world's population. There are very few known demographic studies conducted before the 1800s, which means that modern scholars have had to use a variety of documents from centuries gone by, along with archeological and anthropological studies, to try and gain a better understanding of the world's demographic development. Genealogical records One such method is the study of genealogical records from the past; luckily, there are many genealogies relating to European families that date back as far as medieval times. Unfortunately, however, all of these studies relate to families in the upper and elite classes; this is not entirely representative of the overall population as these families had a much higher standard of living and were less susceptible to famine or malnutrition than the average person (although elites were more likely to die during times of war). Nonetheless, there is much to be learned from this data. Impact of the Black Death In the centuries between 1200 and 1745, English male aristocrats who made it to their 21st birthday were generally expected to live to an age between 62 and 72 years old. The only century where life expectancy among this group was much lower was in the 1300s, where the Black Death caused life expectancy among adult English noblemen to drop to just 45 years. Experts assume that the pre-plague population of England was somewhere between four and seven million people in the thirteenth century, and just two million in the fourteenth century, meaning that Britain lost at least half of its population due to the plague. Although the plague only peaked in England for approximately eighteen months, between 1348 and 1350, it devastated the entire population, and further outbreaks in the following decades caused life expectancy in the decade to drop further. The bubonic plague did return to England sporadically until the mid-seventeenth century, although life expectancy among English male aristocrats rose again in the centuries following the worst outbreak, and even peaked at more than 71 years in the first half of the sixteenth century.

Study question: Do the mothers of twins and singletons differ regarding post-partum and old-age mortality?

Summary answer: Mothers of twins had twice as high post-partum mortality as mothers of singletons; survival of twinners was higher than survival of the mothers of singletons after the 67^th lifespan percentile.

What is known already: Twinning is typically associated with higher post-partum maternal mortality. The evidence about whether twinning incurs long-term survival costs of reproduction or is a trait pertinent to long-lived women is scarce and contradictory.

Study design, size, duration: The study is based on the data of the Estonian Family Register (operating from 1926-43) and involves 5 565 mothers of twins and 119 613 mothers of singletons born between 1850-99. The subset for comparing maternal lifespans included 1 703 – 1 884 mothers of twins and 19 747 – 36 690 mothers of singletons.

Participants/materials, setting, methods: Post-partum maternal mortality was analysed in the whole sample (including mothers of a single child) by logistic regression. Most of the analyses were performed in samples where each mother of twins was matched against mothers of singletons based on parity, urban versus rural origin, whether their lifespan was known, date of birth and age at first birth. Quantile regression was used to analyse age-dependent variations in maternal mortality rates. Lifespans were compared in linear mixed models. All models were adjusted for relevant biodemographic covariates.

Main results and the role of chance: The twinning rate in the whole sample was 4.4%. During the year after giving birth, maternal mortality for multiple gestations was 0.75% (17/2 273) and 0.37% (449/122 750) for single gestations (OR = 2.05, 95% CI = 1.21 – 3.23). The association between twinning and post-natal maternal mortality remained significant in a model controlling for parity and age of first and last birth. The life spans of the mothers of twins and singletons did not differ in matched samples. Past the 67^th lifespan percentile, the odds of survival were significantly higher for mothers of twins than mothers of singletons, as indicated by non-overlapping 95% confidence intervals.

Limitations, reasons for caution: Relatively low number of individuals (22 802) with known age at death due to discontinuation of the register after 1943.

Throughout most of history, average life expectancy from birth was fairly consistent across the globe, at around 24 years. A major contributor to this was high rates of infant and child mortality; those who survived into adulthood could expect to live to their 50s or 60s, yet pandemics, food instability, and conflict did cause regular spikes in mortality across the entire population. Gradually, from the 16th to 19th centuries, there was some growth in more developed societies, due to improvements in agriculture, infrastructure, and medical knowledge. However, the most significant change came with the introduction of vaccination and other medical advances in the 1800s, which saw a sharp decline in child mortality and the onset of the demographic transition. This phenomenon began in more developed countries in the 1800s, before spreading to Latin America, Asia, and (later) Africa in the 1900s. As the majority of the world's population lives in countries considered to be "less developed", this figure is much closer to the global average. However, today, there is a considerable difference in life expectancies across these countries, ranging from 84.7 years in Japan to 53 years in the Central African Republic.

Sources: Conscription lists of the birth cohorst 1813-1842, Department II, - of the capital and royal seat Munich, - of the royal county court Toelz and - of the public records office of Munich. Reichenhall: conscription lists are available only for the birth cohort 1840. Districts Miesbach, Toelz, Wasserburg and Reichenhall: all available conscription lists of the public records office are evaluated and all inductees of the birth cohorts 1813 to 1842 are collected.

A dataset to advance the study of life-cycle interactions of biomedical and socioeconomic factors in the aging process. The EI project has assembled a variety of large datasets covering the life histories of approximately 39,616 white male volunteers (drawn from a random sample of 331 companies) who served in the Union Army (UA), and of about 6,000 African-American veterans from 51 randomly selected United States Colored Troops companies (USCT). Their military records were linked to pension and medical records that detailed the soldiers������?? health status and socioeconomic and family characteristics. Each soldier was searched for in the US decennial census for the years in which they were most likely to be found alive (1850, 1860, 1880, 1900, 1910). In addition, a sample consisting of 70,000 men examined for service in the Union Army between September 1864 and April 1865 has been assembled and linked only to census records. These records will be useful for life-cycle comparisons of those accepted and rejected for service. Military Data: The military service and wartime medical histories of the UA and USCT men were collected from the Union Army and United States Colored Troops military service records, carded medical records, and other wartime documents. Pension Data: Wherever possible, the UA and USCT samples have been linked to pension records, including surgeon''''s certificates. About 70% of men in the Union Army sample have a pension. These records provide the bulk of the socioeconomic and demographic information on these men from the late 1800s through the early 1900s, including family structure and employment information. In addition, the surgeon''''s certificates provide rich medical histories, with an average of 5 examinations per linked recruit for the UA, and about 2.5 exams per USCT recruit. Census Data: Both early and late-age familial and socioeconomic information is collected from the manuscript schedules of the federal censuses of 1850, 1860, 1870 (incomplete), 1880, 1900, and 1910. Data Availability: All of the datasets (Military Union Army; linked Census; Surgeon''''s Certificates; Examination Records, and supporting ecological and environmental variables) are publicly available from ICPSR. In addition, copies on CD-ROM may be obtained from the CPE, which also maintains an interactive Internet Data Archive and Documentation Library, which can be accessed on the Project Website. * Dates of Study: 1850-1910 * Study Features: Longitudinal, Minority Oversamples * Sample Size: ** Union Army: 35,747 ** Colored Troops: 6,187 ** Examination Sample: 70,800 ICPSR Link: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06836

Study of disease in the past can help illuminate patterns of human health, disease, and aging in the present. As average human life expectancy and incidence of chronic disease have increased in the last century, efforts to understand this epidemiologic shift have led to more investigation of healthy aging. Using osteological and radiological methods of analysis, this study examined 212 mostly nineteenth century adult skeletons from the crypt of St. Bride’s in London, in order to investigate the relationship between age-at-death, sex, and number of lesions observed in bone. Lesions were classified into macro-level categories according to the Rapid Method for Recording Human Skeletal Data, and the correlation between age group and number of lesions in each category, as well as the total number of lesions, were analyzed. Correlations between age-at-death and the number and type of lesions were compared across both methods of analysis. A greater total number of lesions and a greater number of types of lesions was observed for the osteologically analyzed data, compared to the radiologically analyzed data. Correlations between age-at-death and specific pathology groups were in general weak, though stronger for the osteologically analyzed data. For each method of analysis, there were statistically significant differences between the total number of lesions and age group, with total number of lesions increasing with age, regardless of method of analysis. Joint and metabolic lesions were the most significant predictors of age-at-death. The correlations between total lesions observed and age-at-death were similar for radiologically and osteologically analyzed data, for the same set of bones. This suggests that, for the bones analyzed, while the number of lesions recorded differed according to method of analysis, the relationship between overall observed lesion burden and age-at-death was similar for both osteological and radiological analysis.

Between 1953 and 2021, the death rate of the United Kingdom fluctuated between a high of 12.2 deaths per 1,000 people in 1962 and a low of 8.7 in 2011. From 2011 onwards, the death rate creeped up slightly and, in 2020, reached 10.3 deaths per 1,000 people. In 2021, the most recent year provided here, the death rate was ten, a decline from 2020 but still higher than in almost every year in the twenty-first century. The recent spike in the death rate corresponds to the emergence of the COVID-19 pandemic in the UK, with the first cases recorded in early 2020. Most deaths since 1918 in 2020 In 2020, there were 689,629 deaths in the United Kingdom, the highest in more than a century. Although there were fewer deaths in 2021, at 667,479, this was still far higher than in recent years. When looking at the weekly deaths in England and Wales for this time period, two periods stand out for reporting far more deaths than usual. The first period was between weeks 13 and 22 of 2020, which saw two weeks in late April report more than 20,000 deaths. Excess deaths for the week ending April 17, 2020, were 11,854, and 11,539 for the following week. Another wave of deaths occurred in January 2021, when there were more than 18,000 deaths per week between weeks three and five of that year. Improvements to life expectancy slowing Between 2020 and 2022, life expectancy in the United Kingdom was approximately 82.57 years for women and 78.57 years for men. Compared with life expectancy in 1980/82 this marked an increase of around six years for women and almost eight years for men. Despite these long-term developments, improvements to life expectancy have been slowing in recent years, and have declined since 2017/19. As of 2022, the country with the highest life expectancy in the World was Japan, which was 84.5 years, followed by South Korea, at 83.6 years.

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.

For most of the world, throughout most of human history, the average life expectancy from birth was around 24. This figure fluctuated greatly depending on the time or region, and was higher than 24 in most individual years, but factors such as pandemics, famines, and conflicts caused regular spikes in mortality and reduced life expectancy. Child mortality The most significant difference between historical mortality rates and modern figures is that child and infant mortality was so high in pre-industrial times; before the introduction of vaccination, water treatment, and other medical knowledge or technologies, women would have around seven children throughout their lifetime, but around half of these would not make it to adulthood. Accurate, historical figures for infant mortality are difficult to ascertain, as it was so prevalent, it took place in the home, and was rarely recorded in censuses; however, figures from this source suggest that the rate was around 300 deaths per 1,000 live births in some years, meaning that almost one in three infants did not make it to their first birthday in certain periods. For those who survived to adolescence, they could expect to live into their forties or fifties on average. Modern figures It was not until the eradication of plague and improvements in housing and infrastructure in recent centuries where life expectancy began to rise in some parts of Europe, before industrialization and medical advances led to the onset of the demographic transition across the world. Today, global life expectancy from birth is roughly three times higher than in pre-industrial times, at almost 73 years. It is higher still in more demographically and economically developed countries; life expectancy is over 82 years in the three European countries shown, and over 84 in Japan. For the least developed countries, mostly found in Sub-Saharan Africa, life expectancy from birth can be as low as 53 years.

Recent research has explored the distributive consequences of major historical epidemics, and the current crisis triggered by Covid-19 prompts us to look at the past for insights about how pandemics can affect inequalities in income, wealth, and health. The fourteenth-century Black Death, which is usually believed to have led to a significant reduction in economic inequality, has attracted the greatest attention. However, the picture becomes much more complex if other epidemics are considered. This article covers the worst epidemics of preindustrial times, from Justinian’s Plague of 540-41 to the last great European plagues of the seventeenth century, as well as the cholera waves of the nineteenth. It shows how the distributive outcomes of lethal epidemics do not only depend upon mortality rates, but are mediated by a range of factors, chief among them the institutional framework in place at the onset of each crisis. It then explores how past epidemics affected poverty, arguing that highly lethal epidemics could reduce its prevalence through two deeply different mechanisms: redistribution towards the poor, or extermination of the poor. It concludes by recalling the historical connection between the progressive weakening and spacing in time of lethal epidemics and improvements in life expectancy, and by discussing how epidemics affected inequality in health and living standards.