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.

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 2021 for the total, male and female populations.

This dataset includes estimates of U.S. life expectancy at birth by state and census tract for the period 2010-2015 (1). Estimates were produced for 65,662 census tracts, covering the District of Columbia (D.C.) and all states, excluding Maine and Wisconsin, representing 88.7% of all U.S. census tracts (see notes). These estimates are the result of the collaborative project, “U.S. Small-area Life Expectancy Estimates Project (USALEEP),” between the National Center for Health Statistics (NCHS), the National Association for Public Health Statistics and Information Systems (NAPHSIS), and the Robert Wood Johnson Foundation (RWJF) (2).

CT census tracts 0. Data come from CDC through 2 public sources: 0.i. Life expectancy at birth in 2017, from https://www.cdc.gov/nchs/nvss/life-expectancy.htm 0.ii. Social Vulnerability Index, 2018, by census tract, from https://www.atsdr.cdc.gov/placeandhealth/svi/index.html The CT extract combines the two UC census tract level datasets, and in addition contains spatially lagged variables for several original variables, generated after merging the CT dataset with a CT shape file, from https://magic.lib.uconn.edu/connecticut_data.html

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.

These data map life expectancy from birth at the census tract level. This data is repeated to block groups from each parent tract value. Provided by the CDC National Center for Health Statistics (NCHS). For more information, you can visit https://www.cdc.gov/nchs/data-visualization/life-expectancy/.

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 2019 for the total, male and female populations.

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 2018 for the total, male and female populations.

Published by the Centers for Disease Control (CDC) National Center for Health Statistics, The U.S. Small-area Life Expectancy Estimates Project (USALEEP) is a partnership of NCHS, the Robert Wood Johnson Foundation (RWJF), and the National Association for Public Health Statistics and Information Systems (NAPHSIS) to produce a new measure of health for where you live. The USALEEP project produced estimates of life expectancy at birth—the average number of years a person can expect to live—for most of the census tracts in the United States for the period 2010-2015.

The URL for the original data is: https://www.cdc.gov/nchs/nvss/usaleep/usaleep.html#life-expectancy

There are three files in this deposit:

1. US_A.CSV - a csv file containing the USALEEP data for the United States
2. WI_A.CSV - a csv file containing the USALEEP data for Wisconsin
3. ME_A.CSV - a csv file containing the USALEEP data for Maine The WI and MI data are not included in the US_A dataset.

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 2021 for the total, male and female populations.

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.

Includes information on the following topics: Aging, Alcohol, Arthritis,Asthma, Autism (ASD), Birth Defects, Births, Blood Disorders, Breastfeeding, Cancer, Chronic Diseases, Deaths, Diabetes, Disease Classification, Foodborne Illness, Genomics, Growth Charts, Healthy Water, Heart Disease, HIV/AIDS, Immunizations, Injuries & Violence, Life Expectancy, Lyme Disease, MRSA, Oral Health, Overweight & Obesity, Physical Inactivity, reproductive Health, Smoking & Tobacco, STDs, Vital Signs, Workplace

These data contain the Estimated Life Expectancy at Birth for residents of census tracts across the State of Colorado based on vital records data from 2010-2015. The Colorado Statewide Life Expectancy (2010-2015) is 80.5 years. The U.S. Small-area Life Expectancy Estimates Project (USALEEP) is a partnership of NCHS, the Robert Wood Johnson Foundation (RWJF), and the National Association for Public Health Statistics and Information Systems (NAPHSIS) to produce a new measure of health for where you live. The life expectancy estimates are based on data collected through Colorado's vital statistics system for deaths among residents of these census tracts between 2010-2015. Life expectancy estimates developed by the National Center for Health Statistics, Centers for Disease Control and Prevention. https://www.cdc.gov/nchs/nvss/usaleep/usaleep.html Data used in creating these estimates was provided by the Vital Statistics Program, Center for Health and Environmental Data, Colorado Department of Public Health and Environment.

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.

Wife is aged 60, he is aged 62.

Asian and Pacific Islander populations are only available in 5-year estimates due to low numbers.

Data Source: DC Vital Records, CDC WONDER single-race single-year population estimates and American Community Survey (ACS) 1-year estimates

Why This Matters

Life expectancy reflects a community’s mortality levels and overall health. In the U.S. life expectancy has been stagnant since 2010 and declined during the COVID-19 Pandemic, primarily due to heart disease, cancer, COVID-19, and fatal drug overdoses.

Changes and disparities in life expectancy at birth reflect trends and inequities in living standards, access to quality health care, and other social and economic factors.

Nationally, life expectancy at birth is lower among Black and Native Americans compared to other racial and ethnic groups. These racial disparities are rooted in a long history of racial segregation, economic and employment discrimination, and environmental racism, among other racist practices, as noted by the National Health Atlas.

The District Response

Ensuring District residents access to various healthcare programs, such as Medicaid, DC Healthcare Alliance Program, and DC Healthy Families. For more information on these programs, click here.

Initiatives and programs to reduce disparities in housing, employment, and food insecurity through programs and services, such as Supplemental Nutrition Assistance Program (SNAP), DC Child Care Subsidy Program, and DC Infrastructure Academy.

Promoting health through free DPR fitness centers, wellness classes, the MoveDC Plan for active transportation, and health and PE classes in public schools to encourage lifelong exercise habits.

By education, wife is aged 60.

Life table data for "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 life tables over the years 2015 through 2021 across 29 countries analyzed in the paper "Bounce backs amid continued losses: Life expectancy changes since COVID-19".

40-lifetables.csv

Life table statistics 2015 through 2021 by sex and region with uncertainty quantiles based on Poisson replication of death counts.

30-lt_input.csv

Life table input data.

• `id`: unique row identifier
• `region_iso`: iso3166-2 region codes
• `sex`: Male, Female, Total
• `year`: iso year
• `age_start`: start of age group
• `age_width`: width of age group, Inf for age_start 100, otherwise 1
• `nweeks_year`: number of weeks in that year, 52 or 53
• `death_total`: number of deaths by any cause
• `population_py`: person-years of exposure (adjusted for leap-weeks and missing weeks in input data on all cause deaths)
• `death_total_nweeksmiss`: number of weeks in the raw input data with at least one missing death count for this region-sex-year stratum. missings are counted when the week is implicitly missing from the input data or if any NAs are encounted in this week or if age groups are implicitly missing for this week in the input data (e.g. 40-45, 50-55)
• `death_total_minnageraw`: the minimum number of age-groups in the raw input data within this region-sex-year stratum
• `death_total_maxnageraw`: the maximum number of age-groups in the raw input data within this region-sex-year stratum
• `death_total_minopenageraw`: the minimum age at the start of the open age group in the raw input data within this region-sex-year stratum
• `death_total_maxopenageraw`: the maximum age at the start of the open age group in the raw input data within this region-sex-year stratum
• `death_total_source`: source of the all-cause death data
• `population_midyear`: midyear population (July 1st)
• `population_source`: source of the population count/exposure data
• `death_covid`: number of deaths due to covid
• `death_covid_date`: number of deaths due to covid as of
• `death_covid_nageraw`: the number of age groups in the covid input data
• `ex_wpp_estimate`: life expectancy estimates from the World Population prospects for a five year period, merged at the midpoint year
• `ex_hmd_estimate`: life expectancy estimates from the Human Mortality Database
• `nmx_hmd_estimate`: death rate estimates from the Human Mortality Database
• `nmx_cntfc`: Lee-Carter death rate projections based on trend in the years 2015 through 2019

Deaths

• source:
  • STMF input data series (https://www.mortality.org/Public/STMF/Outputs/stmf.csv)
  • ONS for GB-EAW pre 2020
  • CDC for US pre 2020
• STMF:
  • harmonized to single ages via pclm
  • pclm iterates over country, sex, year, and within-year age grouping pattern and converts irregular age groupings, which may vary by country, year and week into a regular age grouping of 0:110
  • smoothing parameters estimated via BIC grid search seperately for every pclm iteration
  • last age group set to [110,111)
  • ages 100:110+ are then summed into 100+ to be consistent with mid-year population information
  • deaths in unknown weeks are considered; deaths in unknown ages are not considered
• ONS:
  • data already in single ages
  • ages 100:105+ are summed into 100+ to be consistent with mid-year population information
  • PCLM smoothing applied to for consistency reasons
• CDC:
  • The CDC data comes in single ages 0:100 for the US. For 2020 we only have the STMF data in a much coarser age grouping, i.e. (0, 1, 5, 15, 25, 35, 45, 55, 65, 75, 85+). In order to calculate life-tables in a manner consistent with 2020, we summarise the pre 2020 US death counts into the 2020 age grouping and then apply the pclm ungrouping into single year ages, mirroring the approach to the 2020 data

Population

• source:
  • for years 2000 to 2019: World Population Prospects 2019 single year-age population estimates 1950-2019
  • for year 2020: World Population Prospects 2019 single year-age population projections 2020-2100
• mid-year population
  • mid-year population translated into exposures:
    • if a region reports annual deaths using the Gregorian calendar definition of a year (365 or 366 days long) set exposures equal to mid year population estimates
    • if a region reports annual deaths using the iso-week-year definition of a year (364 or 371 days long), and if there is a leap-week in that year, set exposures equal to 371/364\*mid_year_population to account for the longer reporting period. in years without leap-weeks set exposures equal to mid year population estimates. further multiply by fraction of observed weeks on all weeks in a year.

COVID deaths

• source: COVerAGE-DB (https://osf.io/mpwjq/)
• the data base reports cumulative numbers of COVID deaths over days of a year, we extract the most up to date yearly total

External life expectancy estimates

• source:
  • World Population Prospects (https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/CSV_FILES/WPP2019_Life_Table_Medium.csv), estimates for the five year period 2015-2019
  • Human Mortality Database (https://mortality.org/), single year and age tables

The counties comprising Appalachia, based on the Appalachian Regional Commission (https://www.arc.gov/appalachian-counties-served-by-arc), plus the counties that fall within a 10-mile buffer of the ARC counties, with 2010 RUCA codes joined. The original source of the counties shapefile was the U.S. Census Bureau's 2020 Cartographic Boundary Files. The original source of the data was the CDC USALEEP (https://www.cdc.gov/nchs/nvss/usaleep/usaleep.html) averaged from the tract level to the county level using the FIPS code.

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