In 2022, people who identified as Asian had a projected life expectancy of 84.5 years, the highest in the United States, whereas an American Indian or Alaska native had the lowest with 67.9 years. From 2019 to 2021, life expectancy at birth declined in the U.S., regardless of race and ethnicity. One of the main drivers of this decline was the COVID-19 pandemic.

In 2020, a newborn Hispanic child in the United States had a projected life expectancy of 77.9 years, the highest life expectancy among the ethnic groups studied. In comparison, the life expectancy at birth for a Black, non-Hispanic child in 2020 was 71.5 years.

We used individual-level death data to estimate county-level life expectancy at 25 (e25) for Whites, Black, AIAN and Asian in the contiguous US for 2000-2005. Race-sex-stratified models were used to examine the associations among e25, rurality and specific race proportion, adjusted for socioeconomic variables. Individual death data from the National Center for Health Statistics were aggregated as death counts into five-year age groups by county and race-sex groups for the contiguous US for years 2000-2005 (National Center for Health Statistics 2000-2005). We used bridged-race population estimates to calculate five-year mortality rates. The bridged population data mapped 31 race categories, as specified in the 1997 Office of Management and Budget standards for the collection of data on race and ethnicity, to the four race categories specified under the 1977 standards (the same as race categories in mortality registration) (Ingram et al. 2003). The urban-rural gradient was represented by the 2003 Rural Urban Continuum Codes (RUCC), which distinguished metropolitan counties by population size, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area (United States Department of Agriculture 2016). We obtained county-level sociodemographic data for 2000-2005 from the US Census Bureau. These included median household income, percent of population attaining greater than high school education (high school%), and percent of county occupied rental units (rent%). We obtained county violent crime from Uniform Crime Reports and used it to calculate mean number of violent crimes per capita (Federal Bureau of Investigation 2010). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Request to author. Format: Data are stored as csv files. This dataset is associated with the following publication: Jian, Y., L. Neas, L. Messer, C. Gray, J. Jagai, K. Rappazzo, and D. Lobdell. Divergent trends in life expectancy across the rural-urban gradient among races in the contiguous United States. International Journal of Public Health. Springer Basel AG, Basel, SWITZERLAND, 64(9): 1367-1374, (2019).

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

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

1. 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.

2. 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.

3. 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.

4. 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.

5. National Center for Health Statistics. Historical Data, 1900-1998. 2009. Available from: https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm.

Abstract

The Health Inequality Project uses big data to measure differences in life expectancy by income across areas and identify strategies to improve health outcomes for low-income Americans.

Section 7

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution. Both race-adjusted and unadjusted estimates are reported.

Source

Section 13

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution separately by year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 6

This dataset was created on 2020-01-10 18:53:00.508 by merging multiple datasets together. The source datasets for this version were:

Commuting Zone Life Expectancy Estimates by year: CZ-level by-year life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy: Commuting zone (CZ)-level life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy Trends: CZ-level estimates of trends in life expectancy for men and women, by income quartile

Commuting Zone Characteristics: CZ-level characteristics

Commuting Zone Life Expectancy for larger populations: CZ-level life expectancy estimates for men and women, by income ventile

Section 15

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by state of residence and year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 11

This table reports US mortality rates by gender, age, year and household income percentile. Household incomes are measured two years prior to the mortality rate for mortality rates at ages 40-63, and at age 61 for mortality rates at ages 64-76. The “lag” variable indicates the number of years between measurement of income and mortality.

Observations with 1 or 2 deaths have been masked: all mortality rates that reflect only 1 or 2 deaths have been recoded to reflect 3 deaths

Source

Section 3

This table reports coefficients and standard errors from regressions of life expectancy estimates for men and women at age 40 for each quartile of the national income distribution on calendar year by commuting zone of residence. Only the slope coefficient, representing the average increase or decrease in life expectancy per year, is reported. Trend estimates for both race-adjusted and unadjusted life expectancies are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 9

This table reports life expectancy estimates at age 40 for Males and Females for all countries. Source: World Health Organization, accessed at: http://apps.who.int/gho/athena/

Source

Section 10

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by county of residence. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for counties with populations larger than 25,000 only

Source

Section 2

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by commuting zone of residence and year. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 8

This table reports US population and death counts by age, year, and sex from various sources. Counts labelled “dm1” are derived from the Social Security Administration Data Master 1 file. Counts labelled “irs” are derived from tax data. Counts labelled “cdc” are derived from NCHS life tables.

Source

Section 12

This table reports numerous county characteristics, compiled from various sources. These characteristics are described in the county life expectancy table.

Two variables constructed by the Cen

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).

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 17 more years on average. Women aged 65 years can expect to live around 19.7 more years on average.

Life expectancy in the U.S.

As of 2021, the average life expectancy at birth in the United States was 76.33 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 2019, a woman in the U.S. could be expected to live up to 79.3 years.

Leading causes of death

The leading causes of death in the United States include heart disease, cancer, unintentional injuries, chronic lower respiratory diseases and cerebrovascular diseases. However, heart disease and cancer account for around 38 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.

Age-Adjusted Death Rates

Death Rates and Life Expectancy in the United States, 2011-2013

By Health [source]

About this dataset

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How to use the dataset

In order to use this dataset, start by selecting a particular set of variables to investigate. You can choose from Measure Names (e.g., Death Rates or Life Expectancy), Race (e.g., All Races), Sex (Male/Female) and Year (2011-2013). Once you have selected your desired variables, you can begin analyzing the data by looking at mortality rates and life expectancy averages amongst different populations in the United States over time.

You may also wish to perform more detailed analyses such as identifying trends or examining correlations between features, regional disparities in mortality rates or changes in average life expectancies over time. If so, you can do so by creating line graphs plotted against one or more independent variables such as Race and Sex to see how demographics impact these statistics overall and on a yearly basis using the Year variable computed from July 1st 2010 estimates

Research Ideas

• Analyzing mortality and life expectancy trends among certain races and sexes over time.
• Examining the effects of different socioeconomic factors on death rates and life expectancies.
• Making predictions about future mortality rates and average life expectancies with machine learning algorithms

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: rows.csv | Column name | Description | |:----------------------------|:----------------------------------------------------------------------| | Measure Names | The type of measure being reported. (String) | | Race | The race of the population being reported. (String) | | Sex | The gender of the population being reported. (String) | | Year | The year the data was collected. (Integer) | | Average Life Expectancy | The average life expectancy of the population being reported. (Float) | | Mortality | The mortality rate of the population being reported. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Health.

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.

Introduction

U.S. Death Statistics: The death rate in the United States reflects various factors such as health issues, lifestyle changes, and other social factors that impact people's lives. Life expectancy has generally improved due to advancements in American healthcare, but several causes of death remain significant, including heart disease, cancer, and accidents. The opioid crisis, along with mental health challenges like suicide, also adds to the national death rate.

The COVID-19 pandemic further influenced the death statistics, showing the importance of public health measures. As the population is growing enormously, thus people may pass away from age-related conditions, highlighting the need for better healthcare access and preventive measures to improve overall well-being

The life expectancy of men at birth in the United States saw no significant changes in 2023 in comparison to the previous year 2022 and remained at around 75.8 years. However, 2023 marked the second consecutive increase of the life expectancy. Life expectancy at birth refers to the number of years the average newborn is expected to live, providing that mortality patterns at the time of birth do not change thereafter.Find more statistics on other topics about the United States with key insights such as total fertility rate, infant mortality rate, and total life expectancy at birth.

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

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.

Tunisia had the highest life expectancy at birth in Africa as of 2025. A newborn infant was expected to live about 77 years in the country. Algeria, Cabo Verde, Morocco, and Mauritius followed, with a life expectancy between 77 and 75 years. On the other hand, Nigeria registered the lowest average, at 54.8 years. Overall, the life expectancy in Africa was just over 64 years in the same year.

Death rate has been age-adjusted by the 2000 U.S. standard populaton. All-cause mortality is an important measure of community health. All-cause mortality is heavily driven by the social determinants of health, with significant inequities observed by race and ethnicity and socioeconomic status. Black residents have consistently experienced the highest all-cause mortality rate compared to other racial and ethnic groups. During the COVID-19 pandemic, Latino residents also experienced a sharp increase in their all-cause mortality rate compared to White residents, demonstrating a reversal in the previously observed mortality advantage, in which Latino individuals historically had higher life expectancy and lower mortality than White individuals despite having lower socioeconomic status on average. The disproportionately high all-cause mortality rates observed among Black and Latino residents, especially since the onset of the COVID-19 pandemic, are due to differences in social and economic conditions and opportunities that unfairly place these groups at higher risk of developing and dying from a wide range of health conditions, including COVID-19.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Men born in Chad have the lowest life expectancy in the world as of 2024, reaching only 53 years. The lowest life expectancy for women in the world in 2024 was for girls born in Nigeria, with only 55 years. Except for Afghanistan, all the countries with the lowest life expectancy in the world are in Africa.

Mortality Hazard Ratios and 95% Confidence Intervals for African Americans relative to Whites by Sex and Poverty Status, Healthy Aging in Neighborhoods of Diversity across the Life Span Study, Baltimore, Maryland, 2004–2013 (N = 3675).

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:

Breakdown by race/ethnicity in pop-up:

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.

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

Background: Multiple sclerosis (MS) carries high morbidity and shortens life span. While there is recent recognition of other US minority populations such as blacks and Hispanics being affected with MS, examination of MS-specific mortality trends by race/ethnicity has been lacking. Objective: To investigate MS mortality rates and trends in the United States by sex, age, and race/ethnicity. Methods: We used the Compressed Mortality data file for 1999–2015 in the Wide-ranging online Data for Epidemiological Research system developed by the Center for Disease Control and Prevention to calculate the age-adjusted (US 2000 standard population) and age-specific MS mortality rate (per 100,000) by race/ethnicity and sex over time. Five mutually exclusive racial/ethnic groups were included in the analysis: non-Hispanic (NH) white, NH black, NH Asian or Pacific Islander (API), NH American Indian or Alaska Native, and Hispanic. Results: The average annual age-adjusted MS mortality rate was highest among NH whites (0.90 for males and 1.50 for females) immediately followed by NH blacks (0.75 for males and 1.42 for females), and lowest among APIs (0.05 for males and 0.12 for females). Statistically significant, increasing trend in age-adjusted MS mortality was observed during 1999–2015 among NH whites and NH blacks regardless of sex, more substantially in the latter. Age-specific MS mortality patterns showed NH blacks had the highest rate under age 55 and NH whites had the highest rate after that age point. For these 2 groups, MS mortality increased with age in both sexes and peaked at ages 55–64 for NH blacks and 65–74 for NH whites before declining substantially, while for Hispanic and API groups the risk plateaued after age 55. Conclusion: MS-specific mortality trends demonstrate distinctive differences by race/ethnicity and age. The observations that whites and females are more likely to die from MS is in line with the overall understanding that these groups are affected more by MS. However, the findings of blacks dying at an earlier age and having more substantially increasing mortality trends than whites suggest that MS burden weighs unequally by race. Further investigation into these trends may provide additional evidence into risk or protective factors within each group.