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.

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.

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.

Global life expactancy at birth has risen significantly since the mid-1900s, from roughly 46 years in 1950 to 73.5 years in 2025. 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.

Chart and table of India life expectancy from 1950 to 2025. United Nations projections are also included through the year 2100.

In 2023, the average life expectancy of the world was 70 years for men and 75 years for women. The lowest life expectancies were found in Africa, while Oceania and Europe had the highest.

What is life expectancy?

Life expectancy is defined as a statistical measure of how long a person may live, based on demographic factors such as gender, current age, and most importantly the year of their birth. The most commonly used measure of life expectancy is life expectancy at birth or at age zero. The calculation is based on the assumption that mortality rates at each age were to remain constant in the future.

Life expectancy has changed drastically over time, especially during the past 200 years. In the early 20th century, the average life expectancy at birth in the developed world stood at 31 years. It has grown to an average of 70 and 75 years for males and females respectively, and is expected to keep on growing with advances in medical treatment and living standard continuing.

Highest and lowest life expectancy worldwide

Life expectancy still varies greatly between different regions and countries of the world. The biggest impact on life expectancy is the quality of public health, medical care, and diet. As of 2021, the countries with the highest life expectancy were Japan, Liechtenstein, Switzerland, and South Korea, all at 84 years. Most of the countries with the lowest life expectancy are mostly African countries. The ranking was led by the Chad, Nigeria, and Lesotho with 53 years.

Note: This dataset is historical only and there are not corresponding datasets for more recent time periods. For that more-recent information, please visit the Chicago Health Atlas at https://chicagohealthatlas.org. This dataset gives the average life expectancy and corresponding confidence intervals for sex and racial-ethnic groups in Chicago for the years 1990, 2000 and 2010. See the full description at: https://data.cityofchicago.org/api/views/3qdj-cqb8/files/pJ3PVVyubnsS2SpGO5P5IOPtNgCJZTE3LNOeLagC3mw?download=true&filename=P:\EPI\OEPHI\MATERIALS\REFERENCES\Life Expectancy\Dataset description_LE_ Sex_Race_Ethnicity.pdf

Chart and table of Canada life expectancy from 1950 to 2025. United Nations projections are also included through the year 2100.

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)

Chart and table of Taiwan life expectancy from 1950 to 2025. United Nations projections are also included through the year 2100.

Over the past 75 years, women have generally had a higher life expectancy than men by around 4-6 years. Reasons for this difference include higher susceptibility to childhood diseases among males; higher rates of accidental deaths, conflict-related deaths, and suicide among adult men; and higher prevalence of unhealthy lifestyle habits and chronic illnesses, as well as higher susceptibility to chronic diseases among men. Therefore, men not only have lower life expectancy than women overall, but also throughout each stage of life. Throughout the given period, there were notable dips in life expectancy for both sexes, including a roughly four year drop in 1960 due to China's so-called Great Leap Forward, and a 1.8 year drop due to the Covid-19 pandemic in 2021. Across the world, differences in life expectancy can vary between the sexes by large margins. In countries such as the Nordics, for example, the difference is low due to high-quality healthcare systems and access, as well as high quality diets and lifestyles. In Eastern Europe, however, the difference is over 10 years in Russia and Ukraine due to the war, although the differences were already very pronounced in this region before 2022, in large part driven by unhealthier lifestyles among men.

Chart and table of Hong Kong life expectancy from 1950 to 2025. United Nations projections are also included through the year 2100.

The dataset contains the life expectancy of US population across all ages from 2000 to 2015. Data is based on official estimates of life expectancy. The age pattern of mortality is based on life tables from the Human Mortality Database.

Chart and table of Kenya life expectancy from 1950 to 2025. United Nations projections are also included through the year 2100.

Life expectancy at birth and at age 65, by sex, on a three-year average basis.

Chart and table of Ireland life expectancy from 1950 to 2025. United Nations projections are also included through the year 2100.

Chart and table of Myanmar life expectancy from 1950 to 2025. United Nations projections are also included through the year 2100.

How the unique capacities of human cognition arose in evolution is a question of enduring interest. It is still unclear which developmental programs are responsible for the emergence of the human brain. The inability to determine corresponding ages between humans and apes has hampered progress in detecting developmental programs leading to the emergence of the human brain. I harness temporal variation in anatomical, behavioral, and transcriptional variation to determine corresponding ages from fetal to postnatal development and aging, between humans and chimpanzees. This multi-dimensional approach results in 137 corresponding time points across the lifespan, from embryonic day 44 to ~55 years of age, in humans and their equivalent ages in chimpanzees. I used these data to test whether developmental programs, such as the timeline of prefrontal cortex (PFC) maturation, previously claimed to differ between humans and chimpanzees, do so once variation in developmental schedules is controlled for. I compared the maturation of frontal cortex projections from structural magnetic resonance (MR) scans and from temporal variation in the expression of genes used to track long-range projecting neurons (i.e., supragranular-enriched genes) in chimpanzees and humans. Contrary to what has been suggested, the timetable of PFC maturation is not unusually extended in humans. This dataset, which is the largest with which to determine corresponding ages across humans and chimpanzees, provides a rigorous approach to control for variation in developmental schedules and to identify developmental programs responsible for unique features of the human brain.

A database providing detailed mortality and population data to those interested in the history of human longevity. For each country, the database includes calculated death rates and life tables by age, time, and sex, along with all of the raw data (vital statistics, census counts, population estimates) used in computing these quantities. Data are presented in a variety of formats with regard to age groups and time periods. The main goal of the database is to document the longevity revolution of the modern era and to facilitate research into its causes and consequences. New data series is continually added to this collection. However, the database is limited by design to populations where death registration and census data are virtually complete, since this type of information is required for the uniform method used to reconstruct historical data series. As a result, the countries and areas included are relatively wealthy and for the most part highly industrialized. The database replaces an earlier NIA-funded project, known as the Berkeley Mortality Database. * Dates of Study: 1751-present * Study Features: Longitudinal, International * Sample Size: 37 countries or areas

BACKGROUND: Differential coexpression is a change in coexpression between genes that may reflect 'rewiring' of transcriptional networks. It has previously been hypothesized that such changes might be occurring over time in the lifespan of an organism. While both coexpression and differential expression of genes have been previously studied in life stage change or aging, differential coexpression has not. Generalizing differential coexpression analysis to many time points presents a methodological challenge. Here we introduce a method for analyzing changes in coexpression across multiple ordered groups (e.g., over time) and extensively test its validity and usefulness. RESULTS: Our method is based on the use of the Haar basis set to efficiently represent changes in coexpression at multiple time scales, and thus represents a principled and generalizable extension of the idea of differential coexpression to life stage data. We used published microarray studies categorized by age to test the methodology. We validated the methodology by testing our ability to reconstruct Gene Ontology (GO) categories using our measure of differential coexpression and compared this result to using coexpression alone. Our method allows significant improvement in characterizing these groups of genes. Further, we examine the statistical properties of our measure of differential coexpression and establish that the results are significant both statistically and by an improvement in semantic similarity. In addition, we found that our method finds more significant changes in gene relationships compared to several other methods of expressing temporal relationships between genes, such as coexpression over time. CONCLUSION: Differential coexpression over age generates significant and biologically relevant information about the genes producing it. Our Haar basis methodology for determining age-related differential coexpression performs better than other tested methods. The Haar basis set also lends itself to ready interpretation in terms of both evolutionary and physiological mechanisms of aging and can be seen as a natural generalization of two-category differential coexpression. CONTACT: paul@bioinformatics.ubc.ca.