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 statistic shows the average life expectancy in North America for those born in 2022, by gender and region. In Canada, the average life expectancy was 80 years for males and 84 years for females.

Life expectancy in North America

Of those considered in this statistic, the life expectancy of female Canadian infants born in 2021 was the longest, at 84 years. Female infants born in America that year had a similarly high life expectancy of 81 years. Male infants, meanwhile, had lower life expectancies of 80 years (Canada) and 76 years (USA).

Compare this to the worldwide life expectancy for babies born in 2021: 75 years for women and 71 years for men. Of continents worldwide, North America ranks equal first in terms of life expectancy of (77 years for men and 81 years for women). Life expectancy is lowest in Africa at just 63 years and 66 years for males and females respectively. Japan is the country with the highest life expectancy worldwide for babies born in 2020.

Life expectancy is calculated according to current mortality rates of the population in question. Global variations in life expectancy are caused by differences in medical care, public health and diet, and reflect global inequalities in economic circumstances. Africa’s low life expectancy, for example, can be attributed in part to the AIDS epidemic. In 2019, around 72,000 people died of AIDS in South Africa, the largest amount worldwide. Nigeria, Tanzania and India were also high on the list of countries ranked by AIDS deaths that year. Likewise, Africa has by far the highest rate of mortality by communicable disease (i.e. AIDS, neglected tropics diseases, malaria and tuberculosis).

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.

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.

In 2024, the average life expectancy in the world was 71 years for men and 76 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 standards 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 2022, the countries with the highest life expectancy were Japan, Liechtenstein, Switzerland, and Australia, all at 84–83 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 53–54 years.

Data on life expectancy at birth for different countries and their Human Development Index rank

Air pollution globalization, as a combined effect of atmospheric transport and international trade, can lead to notable transboundary health impacts. Life expectancy reduction attribution analysis of transboundary pollution can reveal the effect of pollution globalization on the lives of individuals. This study coupled five state-of-the-art models to link the regional per capita life expectancy reduction to cross-boundary pollution transport attributed to consumption in other regions. Our results revealed that pollution due to consumption in other regions contributed to a global population-weighted PM2.5 concentration of 9 μg/m3 in 2017, thereby causing 1.03 million premature deaths and reducing the global average life expectancy by 0.23 year (≈84 days). Trade-induced transboundary pollution relocation led to a significant reduction in life expectancy worldwide (from 5 to 155 days per person), and even in the least polluted regions, such as North America, Western Europe, and Russia, a 12–61-day life expectancy reduction could be attributed to consumption in other regions. Our results reveal the individual risks originating from air pollution globalization. To protect human life, all regions and residents worldwide should jointly act together to reduce atmospheric pollution and its globalization as soon as possible.

The life expectancy of men at birth in the United States stood at 75.8 years in 2023. Between 1960 and 2023, the life expectancy rose by 9.2 years, though the increase followed an uneven trajectory rather than a consistent upward trend.

This dataset explores the child's finalization age at adoption by state. The ages are grouped as under 1, 1-5, 6-10, 11-15, 16-18, 19 and older. This dataset is from October 2005 - September 2006 (Fiscal year 2006).

The life expectancy for men aged 65 years in the U.S. has gradually increased since the 1960s. Now men in the United States aged 65 can expect to live 18.2 more years on average. Women aged 65 years can expect to live around 20.7 more years on average. Life expectancy in the U.S. As of 2023, the average life expectancy at birth in the United States was 78.39 years. Life expectancy in the U.S. had steadily increased for many years but has recently dropped slightly. Women consistently have a higher life expectancy than men but have also seen a slight decrease. As of 2023, a woman in the U.S. could be expected to live up to 81.1 years. Leading causes of death The leading causes of death in the United States include heart disease, cancer, unintentional injuries, and cerebrovascular diseases. However, heart disease and cancer account for around 42 percent of all deaths. Although heart disease and cancer are the leading causes of death for both men and women, there are slight variations in the leading causes of death. For example, unintentional injury and suicide account for a larger portion of deaths among men than they do among women.

This data explores the DHHS Children's Bureau data on Children in Public Foster Care Waiting to be Adopted. *There is no federal definition for a child waiting to be adopted. For analytical purposes, the definition used in the table above includes children who have a goal of adoption and/or whose parental rights have been terminated. It excludes children 16 years old and older, whose parental rights have been terminated and who have a goal of emancipation. The # of children waiting to be adopted reported by individual States will likely differ somewhat from those in this table because State definitions vary according to State policies and practices. Because this data are being continuously updated and cleaned, the numbers reported here may differ from data reported elsewhere. This has resulted in an increase in the reported size of the waiting population for fiscal years 2002-2006. This change and other minor changes made to the computer program provide a more accurate picture of the size and nature of the "waiting" population at the end of each fiscal year.

This dataset examines the number of unidentified persons reported to the Centers for Disease Control and Preventions (CDC) National Death Index (NDI), by State, from 1980 to 2004. This report also looks at the number of unidentified human remains reported to the Federal Bureau of Investigations (FBI) National Crime Information Center (NCIC) Unidentified Person File. It describes the characteristics by race and gender and the manner of death. Highlights include the following: Between 1980 and 2004, about 10,300 unidentified human remains were reported to the National Death Index (NDI). Almost three-quarters of unidentified persons were reported by 5 states; Arizona, California, Florida, New York, and Texas. Of the 2,900 National Crime Information Center records that contained data on the manner of death, 27% were ruled homicides; 12%, accidental deaths; 7%, natural causes; and 5%, suicides. The majority of unidentified persons were white (70%); blacks made up 15% of unidentified persons; and race could not be determined in 13% of the cases. For more information about this data go to: http://www.ojp.usdoj.gov/bjs/abstract/uhrus04.htm

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.

Every year the CDC releases the country’s most detailed report on death in the United States under the National Vital Statistics Systems. This mortality dataset is a record of every death in the country for 2005 through 2015, including detailed information about causes of death and the demographic background of the deceased.

It's been said that "statistics are human beings with the tears wiped off." This is especially true with this dataset. Each death record represents somebody's loved one, often connected with a lifetime of memories and sometimes tragically too short.

Putting the sensitive nature of the topic aside, analyzing mortality data is essential to understanding the complex circumstances of death across the country. The US Government uses this data to determine life expectancy and understand how death in the U.S. differs from the rest of the world. Whether you’re looking for macro trends or analyzing unique circumstances, we challenge you to use this dataset to find your own answers to one of life’s great mysteries.

Overview

This dataset is a collection of CSV files each containing one year's worth of data and paired JSON files containing the code mappings, plus an ICD 10 code set. The CSVs were reformatted from their original fixed-width file formats using information extracted from the CDC's PDF manuals using this script. Please note that this process may have introduced errors as the text extracted from the pdf is not a perfect match. If you have any questions or find errors in the preparation process, please leave a note in the forums. We hope to publish additional years of data using this method soon.

A more detailed overview of the data can be found here. You'll find that the fields are consistent within this time window, but some of data codes change every few years. For example, the 113_cause_recode entry 069 only covers ICD codes (I10,I12) in 2005, but by 2015 it covers (I10,I12,I15). When I post data from years prior to 2005, expect some of the fields themselves to change as well.

All data comes from the CDC’s National Vital Statistics Systems, with the exception of the Icd10Code, which are sourced from the World Health Organization.

Project ideas

• The CDC's mortality data was the basis of a widely publicized paper, by Anne Case and Nobel prize winner Angus Deaton, arguing that middle-aged whites are dying at elevated rates. One of the criticisms against the paper is that it failed to properly account for the exact ages within the broad bins available through the CDC's WONDER tool. What do these results look like with exact/not-binned age data?
• Similarly, how sensitive are the mortality trends being discussed in the news to the choice of bin-widths?
• As noted above, the data preparation process could have introduced errors. Can you find any discrepancies compared to the aggregate metrics on WONDER? If so, please let me know in the forums!
• WONDER is cited in numerous economics, sociology, and public health research papers. Can you find any papers whose conclusions would be altered if they used the exact data available here rather than binned data from Wonder?

Differences from the first version of the dataset

• This version of the dataset was prepared in a completely different many. This has allowed us to provide a much larger volume of data and ensure that codes are available for every field.
• We've replaced the batch of sql files with a single JSON per year. Kaggle's platform currently offer's better support for JSON files, and this keeps the number of files manageable.
• A tutorial kernel providing a quick introduction to the new format is available here.
• Lastly, I apologize if the transition has interrupted anyone's work! If need be, you can still download v1.

This data explores special needs in adopted children's cases. In the child welfare field, the child characteristics included in the "special needs" category are determined by the State child welfare agency. They always include having a disability and frequently include membership in a sibling or minority group, or older age. ** data from Colorado, Indiana, Iowa, Maryland and Washington are questionable due to the large percentage of missing data.

This dataset explores Foster Care FY2000 - FY2005 Entries, Exits, and Numbers of Children In Care on the Last Day of Each Federal Fiscal Year. NOTE: This table reflects State data submitted to the Children's Bureau as of March 2007. The table does not include any estimates for individual States. Jurisdictions with insufficient data ("NA") are not included in the total for that year. Pre-2003 Nevada data were generated from various sources, rather than from a statewide child welfare system. NOTE: Ideally, if the number of children in the "in care" count declines, as it did during this period, the number of exits should consistently be greater than the number of entries in that year. However, this does not occur with these data. Underreporting of foster care exits by some States is the major reason for this data quality issue.

Number of deaths and mortality rates, by age group, sex, and place of residence, 1991 to most recent year.

Note: Analysis of Variance (ANOVA) test has been applied to check the difference in mean age at menarche across categories of covariates; P

This dataset explores the time between tpr and finalization for fiscal year 2006 (from October 1, 2005 to September 30, 2006) from the Department of Health and Human Services - Administration for children and families - Children's Bureau.

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.