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.

Columns explanation: • Country. • Year: from 2000 to 2020 • Continent: names of the different continents (6 continets: Europe, Asia, Africa, North America, South America, Oceania). • Life Expectancy • Population. • CO2 emissions. • Health expenditure. • Electric power consumption. • Forest area. • GDP per capita. • Individuals using the Internet. • Military expenditure. • People practicing open defecation. • People using at least basic drinking water services. • Obesity among adults. • Beer consumption per capita. Source for the data is https://data.worldbank.org/

Attendance records for the "Swim for Life" program, which provides swimming instruction to second grade public school students. Explore the Data Dictionary View Open Data for Swim for Life (2022 onwards): here Learn more about this program on the NYC Parks website: here Note: Swim for Life program was on pause due to COVID-19 pandemic. The program resumed Spring 2022.

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

The total equity of Smart For Life with headquarters in the United States amounted to ***** million U.S. dollars in 2023. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2020 this is a total increase by approximately **** million U.S. dollars. The trend from 2020 to 2023 shows, however, that this increase did not happen continuously.

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.

The liabilities of Achieve Life Sciences, Inc. with headquarters in the United States amounted to ***** million U.S. dollars in 2024. The reported fiscal year ends on December 31.Compared to the earliest depicted value from 2020 this is a total increase by approximately ***** million U.S. dollars. The trend from 2020 to 2024 shows, however, that this increase did not happen continuously.

Life expectancy (LE), healthy life expectancy (HLE), disability-free life expectancy (DFLE), Slope Index of Inequality (SII) and range by national deprivation deciles using the Index of Multiple Deprivation 2015 for data periods from 2011 to 2013 to 2015 to 2017, and the Index of Multiple Deprivation 2019 for data periods from 2016 to 2018 to 2018 to 2020: England, 2011 to 2013 to 2018 to 2020.

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

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.

Abstract

This dataset is from the Gauteng City-Region Observatory which is a partnership between the University of Johannesburg, the University of the Witwatersrand, the Gauteng Provincial Government and several Gauteng municipalities. The GCRO has conducted previous Quality of Life Surveys in 2009 (Round 1), 2011 (Round 2), 2013-2014 (Round 3) and 2015-2016 (Round 4), and 2017-2018 (Round 5). Round 6 was conducted in 2020-2021 and is the latest round of the survey.

Geographic coverage

The survey covers the Gauteng province in South Africa.

Analysis unit

Households and individuals

Universe

The survey covers all adult residence in Gauteng province, South Africa.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Life expectancy at birth measures the average years a newborn is expected to live, reflecting a country's health standards and quality of life. This vital statistic highlights disparities in healthcare and living conditions across nations, making it essential for global health assessments.

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.

Data was initially taken from Numbeo as an aggregation of user voting.

• Quality of Life Index varies from 0 (bad quality) to 190 (top good quality)

This dataset is one of the public parts of City API project data. Need more? Try our full data

Dataset Description

The life expectancy dataset contains historical data spanning from 1960 to 2020, comprising 15,206 rows and 4 columns. The dataset provides valuable insights into life expectancy trends across different countries over the years.

Column Descriptions

1. Country_code: The unique country code assigned to each country.
2. country_name: The name of the country corresponding to the country code.
3. year: The year for which life expectancy data is recorded.
4. value: The life expectancy value, representing the average number of years a person is expected to live.

Dataset Utility

This dataset serves as a crucial resource for analyzing and comparing life expectancy trends within and across countries over several decades. By leveraging this dataset, researchers can:

• Analyze Trends: Examine changes in life expectancy over time for individual countries.
• Compare Countries: Compare life expectancy across different countries to identify disparities and patterns.
• Identify Outliers: Identify instances of unusually high or low life expectancy values, which may indicate significant events such as wars, natural disasters, or societal changes.

Researchers can use this dataset to study the impact of various factors on life expectancy, such as healthcare advancements, economic development, public health policies, and external events affecting populations.

Additional Analysis

Researchers can delve deeper into the dataset to investigate sudden changes or volatility in life expectancy, aiming to uncover underlying reasons and potential correlations with historical events or socio-economic factors.

This dataset is a valuable resource for exploring life expectancy dynamics and understanding the broader context of population health and well-being across different countries over time.

ABSTRACT: Objective: To assess the impact of the COVID-19 pandemic on mortality in Argentina, considering temporal trends in life expectancy at birth and premature mortality rate during 2010-2020. Methods: Based on demographic projections, this ecological time-series study compares a “normal” versus a “COVID-19” mortality scenario for 2020 over a set of 11 Argentine provinces. Annual life expectancy at birth and age-standardized rates of premature mortality were estimated from 2010 to 2020. Joinpoint regression and multilevel models were used. Results: A potential reduction in life expectancy at birth (a gap between scenarios >1 year) was observed. A significant (negative) point of inflection in temporal trends was identified for the country and most of the provinces, under the COVID-19 mortality scenario. However, our findings reveal disparities between provinces in the estimated life expectancy reduction toward 2020 (values range from -0.63 to -1.85 year in females and up to -2.55 years in males). While men showed more accentuated declines in life expectancy at birth in 2020 (a national gap between scenarios of -1.47 year in men vs. -1.35 year in women), women experienced more unfavorable temporal trends of premature mortality. In the absence of COVID-19, an improvement in both indicators was estimated toward 2020 in both sexes, while a return to levels reported in the past was observed under the COVID-19 scenario. Conclusion: The COVID-19 pandemic might seriously affect the trends of mortality and exacerbate health disadvantages in Argentina. A temporal and contextual perspective of health inequities merits special attention in the COVID-19 research.

Data on life forms with a closer look at the gender distributions for the year 2020 in Berlin. Among other things, it is about private households and families with and without children.