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.

The dataset presents life expectancy at birth estimates based on annual complete period life tables for each of the 50 states and the District of Columbia (D.C.) in 2020 for the total, male and female populations.

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.

This dataset of U.S. mortality trends since 1900 highlights the differences in age-adjusted death rates and life expectancy at birth by race and sex. Age-adjusted death rates (deaths per 100,000) after 1998 are calculated based on the 2000 U.S. standard population. Populations used for computing death rates for 2011–2017 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years between 2000 and 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Data on age-adjusted death rates prior to 1999 are taken from historical data (see References below). Life expectancy data are available up to 2017. Due to changes in categories of race used in publications, data are not available for the black population consistently before 1968, and not at all before 1960. More information on historical data on age-adjusted death rates is available at https://www.cdc.gov/nchs/nvss/mortality/hist293.htm. SOURCES CDC/NCHS, National Vital Statistics System, historical data, 1900-1998 (see https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm); CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES National Center for Health Statistics, Data Warehouse. Comparability of cause-of-death between ICD revisions. 2008. Available from: http://www.cdc.gov/nchs/nvss/mortality/comparability_icd.htm. National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm. Kochanek KD, Murphy SL, Xu JQ, Arias E. Deaths: Final data for 2017. National Vital Statistics Reports; vol 68 no 9. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_09-508.pdf. Arias E, Xu JQ. United States life tables, 2017. National Vital Statistics Reports; vol 68 no 7. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_07-508.pdf. National Center for Health Statistics. Historical Data, 1900-1998. 2009. Available from: https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm.

Life expectancy at birth for males and females for Middle Layer Super Output Areas (MSOAs), Leicester: 2016 to 2020The average number of years a person would expect to live based on contemporary mortality rates.For a particular area and time period, it is an estimate of the average number of years a newborn baby would survive if he or she experienced the age-specific mortality rates for that area and time period throughout his or her life.Life expectancy figures have been calculated based on death registrations between 2016 to 2020, which includes the first wave and part of the second wave of the coronavirus (COVID-19) pandemic.

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.

Data for Figures and Tables in "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 data in the figures and tables published in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19".

50-e0diffT.csv

• Figure 1: Life expectancy changes 2019/20 and 2020/21 across countries. The countries areordered by increasing cumulative life expectancy losses since 2019. Grey dots indicate theaverage annual LE changes over the years 2015 through 2019.

51-arriagaT.csv

• Figure 2: Age contributions to life expectancy changes since 2019 separated for 2020 and 2021.

52-sexdiff.csv

• Figure 3: Change in the female life expectancy advantage from 2019 through 2021. Muted colorsindicate non-significant changes.

53-e0diffcodT.csv

• Figure 4:Life expectancy deficit in 2021 decomposed into contributions by age and cause of death. LE deficit is defined as observed minus expected life expectancy had pre-pandemic mortality trends continued.

54-tab_arriaga.csv

• Table 1: Months of life expectancy (LE) changes and deficits (labelled ES) since the start of thepandemic attributed to age-specific mortality changes (labelled AT). LE deficit is defined as observed minus expected life expectancy had pre-pandemic mortality trends continued.

In 1875, the average person born in Chile could expect to live to the age of 32 years, a figure that would remain largely stagnante throughout the late 19th and early 20th century, as the country’s Parliamentary era would see relatively little change in the day to day lives of the country’s citizens. Outside of two dips in 1910 and 1920, the latter primarily driven by the 1918 Spanish Flu epidemic. Life expectancy would see two sharp increases following the end of the First World War; the first in the 1920s, and the most dramatic in the early 1950s.

The first of these spikes, under President Ibáñez del Campo, can be attributed primarily to large increases in spending on public healthcare and improvements in public sanitation by the Campo administration. The second and larger spike, under President González Videla, can be attributed to a combination of mass immunization and vaccination, and the implementation of a national health care system, drastically cutting child mortality in the country. As a result of these reforms, life expectancy in Chile would more than double in just thirty years, rising from just over 33 years in 1925 to 69 years by 1955. Following the end of the Videla administration in 1952, life expectancy would continue to rise in Chile, as increasing urbanization, and the successful eradication of many childhood diseases would see both child and overall mortality decline. This rise has continued even into the 21st century, and as a result, life expectancy in Chile rose to over 78 years by the end of the century, and in 2020, it is estimated that the average person born in Chile will live to over 82 years old, the highest in South America.

This multi-scale map shows life expectancy - a widely-used measure of health and mortality. From the 2020 County Health Rankings page about Life Expectancy:"Life Expectancy is an AverageLife Expectancy measures the average number of years from birth a person can expect to live, according to the current mortality experience (age-specific death rates) of the population. Life Expectancy takes into account the number of deaths in a given time period and the average number of people at risk of dying during that period, allowing us to compare data across counties with different population sizes.Life Expectancy is Age-AdjustedAge is a non-modifiable risk factor, and as age increases, poor health outcomes are more likely. Life Expectancy is age-adjusted in order to fairly compare counties with differing age structures.What Deaths Count Toward Life Expectancy?Deaths are counted in the county where the individual lived. So, even if an individual dies in a car crash on the other side of the state, that death is attributed to his/her home county.Some Data are SuppressedA missing value is reported for counties with fewer than 5,000 population-years-at-risk in the time frame.Measure LimitationsLife Expectancy includes mortality of all age groups in a population instead of focusing just on premature deaths and thus can be dominated by deaths of the elderly.[1] This could draw attention to areas with higher mortality rates among the oldest segment of the population, where there may be little that can be done to change chronic health problems that have developed over many years. However, this captures the burden of chronic disease in a population better than premature death measures.[2]Furthermore, the calculation of life expectancy is complex and not easy to communicate. Methodologically, it can produce misleading results caused by hidden differences in age structure, is sensitive to infant and child mortality, and tends to be overestimated in small populations."Click on the map to see a breakdown by race/ethnicity in the pop-up: Full details about this measureThere are many factors that play into life expectancy: rates of noncommunicable diseases such as cancer, diabetes, and obesity, prevalence of tobacco use, prevalence of domestic violence, and many more.Data from County Health Rankings 2020 (in this layer and referenced below), available for nation, state, and county, and available in ArcGIS Living Atlas of the World

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

San Marino Life Expectancy at Birth data was reported at 80.880 Year in 2021. This records an increase from the previous number of 79.590 Year for 2020. San Marino Life Expectancy at Birth data is updated yearly, averaging 80.825 Year from Dec 1990 (Median) to 2021, with 32 observations. The data reached an all-time high of 82.990 Year in 2019 and a record low of 78.670 Year in 1990. San Marino Life Expectancy at Birth data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s San Marino – Table SM.OECD.GGI: Social: Demography: Non OECD Member: Annual.

This dataset tracks the updates made on the dataset "U.S. State Life Expectancy by Sex, 2020" as a repository for previous versions of the data and metadata.

Period life expectancy by age and sex. Each life table is based on population estimates, births and deaths for a single year.

China Life Expectancy: Male data was reported at 75.370 Year Old in 2020. This records an increase from the previous number of 73.640 Year Old for 2015. China Life Expectancy: Male data is updated yearly, averaging 70.830 Year Old from Dec 1981 (Median) to 2020, with 7 observations. The data reached an all-time high of 75.370 Year Old in 2020 and a record low of 66.280 Year Old in 1981. China Life Expectancy: Male data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Life Expectancy: By Region.

In 1870, Brazil's life expectancy from birth was just 32 years; there was a slight increase over the next 65 years, with the only decrease coming in the 1910s during the Spanish Flu epidemic. The largest increases in life expectancy came in the 1940s, when it jumped by almost twelve years in this decade alone. Brazil's life expectancy from birth has increased steadily over the past fifty years, and life expectancy in 2020 is almost 76; more than double what it was in 1940.

This table contains the cohort survival tables (by birth cohort of 1 year) 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 have reached the year in which they are 1, 2, 3, etc. years old. It can also be seen how old these children will be on average.

The table can be broken down by mortality, the number of people living (table population), the number of deceased (table population) and the (cohort) life expectancy per generation by gender and age.

The (cohort) life expectancy, calculated from a cohort survival table, indicates the actual lifespan (or is expected to be, when the observed mortality rates are complemented by mortality from the forecast period). See section 4 for an explanation of the difference between the period survival table and a cohort survival table.

It is possible to choose from figures in which only observed numbers are calculated or a series in which the observed numbers are supplemented by future expectations of numbers of deceased for the birth generations that are still alive.

Data available: from birth generation 1850

Status of the figures: The figures based on the number of deaths observed up to and including the year 2020 are definitive. Figures supplemented by future expectations of the number of deceased are from the CBS Core Forecast 2021-2070. This forecast shall be reviewed once a year.

Changes as of 16 December 2021: — The figures relating to mortality observations for 2020 are included in the table; — The figures relating to the projections have been replaced by those from the Core Forecast 2021-2070.

When are new figures coming? In December 2022, mortality observations for 2021 will be reflected in this table and future expectations will be replaced by those from the Core Forecast 2022-2070.

This multi-scale map shows life expectancy - a widely-used measure of health and mortality. From the County Health Rankings page about Life Expectancy:"Life Expectancy is an AverageLife Expectancy measures the average number of years from birth a person can expect to live, according to the current mortality experience (age-specific death rates) of the population. Life Expectancy takes into account the number of deaths in a given time period and the average number of people at risk of dying during that period, allowing us to compare data across counties with different population sizes.Life Expectancy is Age-AdjustedAge is a non-modifiable risk factor, and as age increases, poor health outcomes are more likely. Life Expectancy is age-adjusted in order to fairly compare counties with differing age structures.What Deaths Count Toward Life Expectancy?Deaths are counted in the county where the individual lived. So, even if an individual dies in a car crash on the other side of the state, that death is attributed to his/her home county.Some Data are SuppressedA missing value is reported for counties with fewer than 5,000 population-years-at-risk in the time frame.Measure LimitationsLife Expectancy includes mortality of all age groups in a population instead of focusing just on premature deaths and thus can be dominated by deaths of the elderly.[1] This could draw attention to areas with higher mortality rates among the oldest segment of the population, where there may be little that can be done to change chronic health problems that have developed over many years. However, this captures the burden of chronic disease in a population better than premature death measures.[2]Furthermore, the calculation of life expectancy is complex and not easy to communicate. Methodologically, it can produce misleading results caused by hidden differences in age structure, is sensitive to infant and child mortality, and tends to be overestimated in small populations."Breakdown by race/ethnicity in pop-up: (This map has been updated with new data, so figures may vary from those in this image.)There are many factors that play into life expectancy: rates of noncommunicable diseases such as cancer, diabetes, and obesity, prevalence of tobacco use, prevalence of domestic violence, and many more.Proven strategies to improve life expectancy and health in general A database of dozens of strategies can be found at County Health Rankings' What Works for Health site, sorted by Health Behaviors, Clinical Care, Social & Economic Factors, and Physical Environment. Policies and Programs listed here have been evaluated as to their effectiveness. For example, consumer-directed health plans received an evidence rating of "mixed evidence" whereas cultural competence training for health care professionals received a rating of "scientifically supported." Data from County Health Rankings (layer referenced below), available for nation, state, and county, and available in ArcGIS Living Atlas of the World.

This table contains forecast figures from the period survival tables (per period of 1 year) by gender and age (on 31 December) for the population of the Netherlands. The table shows how many boys or girls from a group of 100,000 newborns will reach the age of 0, 1, 2, etc. on December 31 of the year of observation. It can also be determined how old these children will be on average if the mortality probabilities of the prognosis year apply throughout their lives. This period life expectancy can therefore best be interpreted as a summary measure of the mortality probabilities in a calendar year. See section 4 for an explanation of the difference between the period survival table and a cohort survival table. The table can be broken down into the mortality probability, the number of people alive (table population), the number of deaths (table population) and the period life expectancy by gender and age. Data available: 2020-2070 Status of the figures: The figures in this table are calculated forecast figures. Changes as of December 16, 2020: None, this is a new table in which the previous forecast has been adjusted on the basis of the observations that have now become available. The forecast period now runs from 2020 to 2070. When will new figures be released? The publication frequency of this table is one-off. In December 2021, a new table will be published with the forecast period life expectancy.

This table contains mortality indicators by sex for Canada and all provinces except Prince Edward Island. These indicators are derived from three-year complete life tables. Mortality indicators derived from single-year life tables are also available (table 13-10-0837). For Prince Edward Island, Yukon, the Northwest Territories and Nunavut, mortality indicators derived from three-year abridged life tables are available (table 13-10-0140).