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.

For most of the world, throughout most of human history, the average life expectancy from birth was around 24. This figure fluctuated greatly depending on the time or region, and was higher than 24 in most individual years, but factors such as pandemics, famines, and conflicts caused regular spikes in mortality and reduced life expectancy. Child mortality The most significant difference between historical mortality rates and modern figures is that child and infant mortality was so high in pre-industrial times; before the introduction of vaccination, water treatment, and other medical knowledge or technologies, women would have around seven children throughout their lifetime, but around half of these would not make it to adulthood. Accurate, historical figures for infant mortality are difficult to ascertain, as it was so prevalent, it took place in the home, and was rarely recorded in censuses; however, figures from this source suggest that the rate was around 300 deaths per 1,000 live births in some years, meaning that almost one in three infants did not make it to their first birthday in certain periods. For those who survived to adolescence, they could expect to live into their forties or fifties on average. Modern figures It was not until the eradication of plague and improvements in housing and infrastructure in recent centuries where life expectancy began to rise in some parts of Europe, before industrialization and medical advances led to the onset of the demographic transition across the world. Today, global life expectancy from birth is roughly three times higher than in pre-industrial times, at almost 73 years. It is higher still in more demographically and economically developed countries; life expectancy is over 82 years in the three European countries shown, and over 84 in Japan. For the least developed countries, mostly found in Sub-Saharan Africa, life expectancy from birth can be as low as 53 years.

Global life 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.

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.

Note: This dataset is historical only and there are not corresponding datasets for more recent time periods. For that more-recent information, please visit the Chicago Health Atlas at https://chicagohealthatlas.org.

This dataset gives the average life expectancy and corresponding confidence intervals for each Chicago community area for the years 1990, 2000 and 2010. See the full description at: https://data.cityofchicago.org/api/views/qjr3-bm53/files/AAu4x8SCRz_bnQb8SVUyAXdd913TMObSYj6V40cR6p8?download=true&filename=P:\EPI\OEPHI\MATERIALS\REFERENCES\Life Expectancy\Dataset description - LE by community area.pdf

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

Context

Although there have been lot of studies undertaken in the past on factors affecting life expectancy considering demographic variables, income composition and mortality rates. It was found that affect of immunization and human development index was not taken into account in the past. Also, some of the past research was done considering multiple linear regression based on data set of one year for all the countries. Hence, this gives motivation to resolve both the factors stated previously by formulating a regression model based on mixed effects model and multiple linear regression while considering data from a period of 2000 to 2015 for all the countries. Important immunization like Hepatitis B, Polio and Diphtheria will also be considered. In a nutshell, this study will focus on immunization factors, mortality factors, economic factors, social factors and other health related factors as well. Since the observations this dataset are based on different countries, it will be easier for a country to determine the predicting factor which is contributing to lower value of life expectancy. This will help in suggesting a country which area should be given importance in order to efficiently improve the life expectancy of its population.

Content

The project relies on accuracy of data. The Global Health Observatory (GHO) data repository under World Health Organization (WHO) keeps track of the health status as well as many other related factors for all countries The data-sets are made available to public for the purpose of health data analysis. The data-set related to life expectancy, health factors for 193 countries has been collected from the same WHO data repository website and its corresponding economic data was collected from United Nation website. Among all categories of health-related factors only those critical factors were chosen which are more representative. It has been observed that in the past 15 years , there has been a huge development in health sector resulting in improvement of human mortality rates especially in the developing nations in comparison to the past 30 years. Therefore, in this project we have considered data from year 2000-2015 for 193 countries for further analysis. The individual data files have been merged together into a single data-set. On initial visual inspection of the data showed some missing values. As the data-sets were from WHO, we found no evident errors. Missing data was handled in R software by using Missmap command. The result indicated that most of the missing data was for population, Hepatitis B and GDP. The missing data were from less known countries like Vanuatu, Tonga, Togo, Cabo Verde etc. Finding all data for these countries was difficult and hence, it was decided that we exclude these countries from the final model data-set. The final merged file(final dataset) consists of 22 Columns and 2938 rows which meant 20 predicting variables. All predicting variables was then divided into several broad categories:​Immunization related factors, Mortality factors, Economical factors and Social factors.

Acknowledgements

The data was collected from WHO and United Nations website with the help of Deeksha Russell and Duan Wang.

Inspiration

The data-set aims to answer the following key questions: 1. Does various predicting factors which has been chosen initially really affect the Life expectancy? What are the predicting variables actually affecting the life expectancy? 2. Should a country having a lower life expectancy value(<65) increase its healthcare expenditure in order to improve its average lifespan? 3. How does Infant and Adult mortality rates affect life expectancy? 4. Does Life Expectancy has positive or negative correlation with eating habits, lifestyle, exercise, smoking, drinking alcohol etc. 5. What is the impact of schooling on the lifespan of humans? 6. Does Life Expectancy have positive or negative relationship with drinking alcohol? 7. Do densely populated countries tend to have lower life expectancy? 8. What is the impact of Immunization coverage on life Expectancy?

There is no scientific consensus on the fundamental question whether the probability distribution of the human life span has a finite endpoint or not and, if so, whether this upper limit changes over time. Our study uses a unique dataset of the ages at death—in days—of all (about 285,000) Dutch residents, born in the Netherlands, who died in the years 1986–2015 at a minimum age of 92 years and is based on extreme value theory, the coherent approach to research problems of this type. Unlike some other studies, we base our analysis on the configuration of thousands of mortality data of old people, not just the few oldest old. We find compelling statistical evidence that there is indeed an upper limit to the life span of men and to that of women for all the 30 years we consider and, moreover, that there are no indications of trends in these upper limits over the last 30 years, despite the fact that the number of people reaching high age (say 95 years) was almost tripling. We also present estimates for the endpoints, for the force of mortality at very high age, and for the so-called perseverance parameter. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

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

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, region and quarter with uncertainty quantiles based on Poisson replication of death counts. Actual life tables and expected life tables (under the assumption of pre-COVID mortality trend continuation) are provided.

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

• `death_total_prop_q1`: observed proportion of deaths in first quarter of year

• `death_total_prop_q2`: observed proportion of deaths in second quarter of year

• `death_total_prop_q3`: observed proportion of deaths in third quarter of year

• `death_total_prop_q4`: observed proportion of deaths in fourth quarter of year

• `death_expected_prop_q1`: expected proportion of deaths in first quarter of year

• `death_expected_prop_q2`: expected proportion of deaths in second quarter of year

• `death_expected_prop_q3`: expected proportion of deaths in third quarter of year

• `death_expected_prop_q4`: expected proportion of deaths in fourth quarter of year

• `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

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

U.S. State Life Expectancy by Sex, 2020

  Description

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.

  Dataset Details

Publisher: Centers for Disease Control and Prevention Temporal Coverage: 2020-01-01/2020-12-31 Geographic Coverage: United States Last Modified: 2025-04-21 Contact: National Center… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/us-state-life-expectancy-by-sex-2020.

Keywords; Search terms: historical time series; historical statistics; histat / HISTAT; life expectancy; mortality rates . Abstract: In this study human life expectancy, which since the start of the 18th century has continually increased, is investigated in comparative perspective in Germany, Sweden and Norway. Topics: Regional as well as national data sets on population structure and the development of mortality. The following table overview represents a cutout from the study´s archived total stocks. The complete data stock contains not only time-series data. These complete data are available by GESIS Data Archive on request. Topics of Data-Tables with Time-Series: I (risk) population by generations II (risk) population by periods III probability of dying by generations IV probability of dying by periods V life expectancy by generations VI life expectancy by periods Systematics within the tables (Consecutively Numbering) 1. Place: Letter indicating the region: A. Germany (German Reich)/FRG B. Germany (German Reich)/GDR C. governmental district Aurich/Lower Saxony D. governmental district Kassel/Hessen E. governmental district Minden/North Rhine-Westphalia F. governmental district Trier/Saarland H. Herrenberg/South West Germany (Südwestdeuschland) N. Norway S. Sweden 2. Place: Number for the table´s subject (variable) 1. (risk) population (P´ x) 3. Probability of dying (qx) 5. Life expectancy (ex) 3. Place: Letter for the type of table (meaning of the annual details) P. period table G. generation table Stichworte: historische Zeitreihen; historische Statistik; histat / HISTAT; Lebenserwartung, Sterbewahrscheinlichkeiten . Inhalt: In dieser Untersuchung wird die seit dem Beginn des 18. Jahrhunderts stetig gestiegene menschliche Lebenserwartung in komparativer Perspektive in Deutschland, Schweden und Norwegen untersucht. Fördernde Institutionen von Mitte 1990 bis Mitte 1994: Bundesministerium für Forschung und Technologie; Bundesministerium für Familie und Senioren. Projekttitel: Die Zunahme der Lebensspanne seit 300 Jahren und die Folgen. Oder: Gewonnene Jahre - verlorene Welten: Wie erreichen wir ein neues Gleichgewicht? Das Projekt gliedert sich in drei Teile: - Die Zeitreihen zur Lebenserwartung in Deutschland vom 17. bis 19. Jahrhundert (ZA-Studie 8066) werden mit dieser Studie bis zur Gegenwart verlängert (alters- und geschlechtsspezifische Lebenserwartungen, Sterbewahrscheinlichkeiten, usw.). - Vergleich der deutschen Lebenserwartungsrechungen mit komparativen Materialien Norwegens und Schwedens, die ebenfalls gemäß dem Kohorten- und dem Periodentafelmodus angelegt sind (Lebenserwartungen in Deutschland, Norwegen und Schweden im 19. und 20. Jh.). - Analyse der Folgen einer seit 300 Jahren anhaltenden Entwicklung der Lebensspannenzunahme und den Möglichkeiten ihrer Bewältigung. Als Methodik der statistischen Untersuchung werden Sterbetafeln als Mittel der historisch-demographischen Analyse eingesetzt. Mit Hilfe der Sterbetafeln läßt sich das Mortalitätsgeschehen in einer Bevölkerung ausdrücken. Die Mortalitätsquotienten sind im Unterschied zu den rohen Sterblichkeitsziffern unabhängig von der Altersstruktur der Bevölkerung, so dass eine hohe Vergleichbarkeit gesichert ist. Themen: Regionale sowie nationale Datensätze zur Bevölkerungsstruktur und der Entwicklung der Sterblichkeit. - Gegenstand der Ergebnistabellen: Risikobevölkerung, Sterbefälle, Sterbewahrscheinlichkeiten, Überlebende, Lebenserwartungen; - Art der Tabelle: Generationen, Perioden; - Geschlecht: männlich, weiblich, insgesamt; - Regionale Unterteilung; - Todesursachenstruktur: Krankheiten der Säuglinge, Altersschwäche, Infektionskrankheiten und andere zum Tode führende Krankheiten, Unfälle, sonstige Todesursachen. Zeitreihen-Daten dieser Studie im Recherche- und Downloadsystem HISTAT: Zu folgenden Themen sind Zeitreihendaten dieser Studie über das Recherche- und Downloadsystem HISTAT zugänglich: Themenbereiche der Datentabellen in HISTAT: I. (Risiko-) Bevölkerung nach Generationen II. (Risiko-) Bevölkerung nach Perioden III. Sterbewahrscheinlichkeit nach Generationen IV. Sterbewahrscheinlichkeit nach Perioden V. Lebenserwartung nach Generationen VI. Lebenserwartung nach Perioden Systematik innerhalb der Tabellen (Durchnummerierung): 1. Stelle: Buchstabe für die regionale Unterteilung A. Deutschland (Deutsches Reich) / BRD B. Deutschland (Deutsches Reich) / DDR C. Regierungsbezirk Aurich / Niedersachsen D. Regierungsbezirk Kassel / Hessen E. Regierungsbezirk Minden / Nordrhein-Westfalen F. Regierungsbezirk Trier / Saarland H. Herrenberg / Südwestdeutschland N. Norwegen S. Schweden 2. Stelle: Ziffer für den Gegenstand der Tabelle (Variable) 1. (Risiko-) Bevölkerung (P’x) 3. Sterbewahrscheinlichkeit (qx) 5. Lebenserwartung (ex) 3. Stelle: Buchstabe für den Tabellentyp (Bedeutung der Jahresangabe) P. Periodentabelle G. Generationentabelle HINWEIS: HISTAT enthält einen Ausschnitt aus dem archivierten Gesamtbestand dieser Studie. Nicht berücksichtigt wurde die Variablen „Sterbefälle“ (Dx), „Überlebende“ (lx) und das gesamte Thema „Todesursachenstrukturen in Deutschland“. Ferner wurde die Differenzierung nach dem Geschlecht in HISTAT nicht berücksichtigt. Der komplette Datenbestand wird durch das Datenarchiv auf Anfrage bereitgestellt. Official Statistics, Census-Data, Church-Registers, Data of Civil Registry Offices. Quellen: Für Norwegen: Amtliche Statistik, Volkszählungen, zentrales norwegisches Personenregister. Für Schweden: Demographische Datenbank in Umea. Deutschland: Daten der Statistischen Ämter: Angaben in den Bänden der amtlichen Statistik, Volkszählungen; Daten auf Regierungsbezirks- und Länderebene: Angaben in den Bänden der Preußischen Statistik auf der Ebene der Preußischen Regionen, Daten der statistischen Landesämter; Daten aus Ortssippenbüchern (= Kirchenbüchern) und von Standesämtern.

Although dogs' life expectancies are six to twelve times shorter than that of humans, the demographics (e. g., living conditions) of dogs can still change considerably with aging, similarly to humans. Despite the fact that the dog is a particularly good model for human healthspan, and the number of aged dogs in the population is growing in parallel with aged humans, there has been few previous attempts to describe demographic changes statistically. We utilized an on-line questionnaire to examine the link between the age and health of the dog, and owner and dog demographics in a cross-sectional Hungarian sample. Results from univariate analyses revealed that 20 of the 27 demographic variables measured differed significantly between six dog age groups. Our results revealed that pure breed dogs suffered from health problems at a younger age, and may die at an earlier age than mixed breeds. The oldest dog group (>12 years) consisted of fewer pure breeds than mixed breeds and the mixed breeds sample was on average older than the pure breed sample. Old dogs were classified more frequently as unhealthy, less often had a “normal” body condition score, and more often received medication and supplements. They were also more often male, neutered, suffered health problems (such as sensory, joint, and/or tooth problems), received less activity/interaction/training with the owner, and were more likely to have experienced one or more traumatic events. Surprisingly, the youngest age group contained more pure breeds, were more often fed raw meat, and had owners aged under 29 years, reflecting new trends among younger owners. The high prevalence of dogs that had experienced one or more traumatic events in their lifetime (over 40% of the sample), indicates that welfare and health could be improved by informing owners of the greatest risk factors of trauma, and providing interventions to reduce their impact. Experiencing multiple life events such as spending time in a shelter, changing owners, traumatic injury/prolonged disease/surgery, getting lost, and changes in family structure increased the likelihood that owners reported that their dogs currently show behavioral signs that they attribute to the previous trauma.

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 dataset contains details about life expectancy in in King County. It has been developed for the Determinant of Equity - Health and Human Services. It includes information about Life Expectancy equity indicator. Fields describe the the average life expectancy at birth (Indicator), and the value that describes this measurement (Indicator Value).The data was compiled by the Washington State Department of Health (DOH), Center for Health Statistics.Vital RecordsFor more information about King County's equity efforts, please see:Equity, Racial & Social Justice VisionOrdinance 16948 describing the determinates of equityDeterminants of Equity and Data Tool

Basic data and life tables by educational attainment. (CSV 54 kb)

Life expectancy in India was 25.4 in the year 1800, and over the course of the next 220 years, it has increased to almost 70. Between 1800 and 1920, life expectancy in India remained in the mid to low twenties, with the largest declines coming in the 1870s and 1910s; this was because of the Great Famine of 1876-1878, and the Spanish Flu Pandemic of 1918-1919, both of which were responsible for the deaths of up to six and seventeen million Indians respectively; as well as the presence of other endemic diseases in the region, such as smallpox. From 1920 onwards, India's life expectancy has consistently increased, but it is still below the global average.

This dataset gives the average life expectancy and corresponding confidence intervals for each Chicago community area for the years 1990, 2000 and 2010. See the full description at: https://data.cityofchicago.org/api/views/qjr3-bm53/files/AAu4x8SCRz_bnQb8SVUyAXdd913TMObSYj6V40cR6p8?download=true&filename=P:\EPI\OEPHI\MATERIALS\REFERENCES\Life Expectancy\Dataset description - LE by community area.pdf