Excel sheet with life table statistic calculations based on the data sets that are part of this project.

This Excel workbook implements a full period life table using the formulas set out in chapter 3 of:Preston S H, Heuveline P, and Guillot M (2001) Demography: Measuring and Modeling Population Processes. Blackwell.It includes example life tables for Australia.

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

Context

Data set taken from WHO: See Life Tables by Country (CUBA) & Life Expectancy at Birth (CUBA) Detailed information on year-wise deaths by age group, and population left alive by age group - this data permits calculations of Life Expectancies for Cuba. This is data for a lecture on computation of life-expectancies, which is part of a course on Real Statistics: An Islamic Approach. Lecture linked below provides further details on how to compute life expectancies from this data: Computing Life Expectancies from Mortality Tables.

Content

Rows 3 to 21 provide Age-Specific death rates for 5 year groups 0-5. 5-10, and so on up to 80-85, and 85+ Rows 22 to 40 provide probability of dying in each of these same age-categories. Rows 41 to 59 provide Number of people left alive in each of these 5- year age groups Rows 60 to 78 provide number of people who die in each of these age categories Rows 79 to 97 provide number of person-years lived by each of these 5-year age cohorts Rows 98 to 116 provide number of person-years lived ABOVE given age group Rows 117 to 135 provide life expectancy within each age category

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Notation, definition, and formula of each column of an abridged life table to calculate life expectancy.

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census ZCTA Population (2000-2010) http://factfinder.census.gov

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2013) http://factfinder.census.gov

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population that can be compared across time and populations. More information about the determinants of life expectancy that may lead to differences in life expectancy between neighborhoods can be found in the Bay Area Regional Health Inequities Initiative (BARHII) Health Inequities in the Bay Area report at http://www.barhii.org/wp-content/uploads/2015/09/barhii_hiba.pdf. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and ZIP Codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential ZIP Code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality.

For the ZIP Code-level life expectancy calculation, it is assumed that postal ZIP Codes share the same boundaries as ZIP Code Census Tabulation Areas (ZCTAs). More information on the relationship between ZIP Codes and ZCTAs can be found at http://www.census.gov/geo/reference/zctas.html. ZIP Code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 ZIP Code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for ZIP Codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest ZIP Code with population. ZIP Code population for 2000 estimates comes from the Decennial Census. ZIP Code population for 2013 estimates are from the American Community Survey (5-Year Average). ACS estimates are adjusted using Decennial Census data for more accurate population estimates. An adjustment factor was calculated using the ratio between the 2010 Decennial Census population estimates and the 2012 ACS 5-Year (with middle year 2010) population estimates. This adjustment factor is particularly important for ZCTAs with high homeless population (not living in group quarters) where the ACS may underestimate the ZCTA population and therefore underestimate the life expectancy. The ACS provides ZIP Code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to ZIP Codes based on majority land-area.

ZIP Codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, ZIP Codes with populations of less than 5,000 were aggregated with neighboring ZIP Codes until the merged areas had a population of more than 5,000. ZIP Code 94103, representing Treasure Island, was dropped from the dataset due to its small population and having no bordering ZIP Codes. In this way, the original 305 Bay Area ZIP Codes were reduced to 217 ZIP Code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

A database providing detailed mortality and population data to those interested in the history of human longevity. For each country, the database includes calculated death rates and life tables by age, time, and sex, along with all of the raw data (vital statistics, census counts, population estimates) used in computing these quantities. Data are presented in a variety of formats with regard to age groups and time periods. The main goal of the database is to document the longevity revolution of the modern era and to facilitate research into its causes and consequences. New data series is continually added to this collection. However, the database is limited by design to populations where death registration and census data are virtually complete, since this type of information is required for the uniform method used to reconstruct historical data series. As a result, the countries and areas included are relatively wealthy and for the most part highly industrialized. The database replaces an earlier NIA-funded project, known as the Berkeley Mortality Database. * Dates of Study: 1751-present * Study Features: Longitudinal, International * Sample Size: 37 countries or areas

The social and financial systems of many nations throughout the world are significantly impacted by life expectancy (LE) models. Numerous studies have pointed out the crucial effects that life expectancy projections will have on societal issues and the administration of the global healthcare system. The computation of life expectancy has primarily entailed building an ordinary life table. However, the life table is limited by its long duration, the assumption of homogeneity of cohorts and censoring. As a result, a robust and more accurate approach is inevitable. In this study, a supervised machine learning model for estimating life expectancy rates is developed. The model takes into consideration health, socioeconomic, and behavioral characteristics by using the eXtreme Gradient Boosting (XGBoost) algorithm to data from 193 UN member states. The effectiveness of the model's prediction is compared to that of the Random Forest (RF) and Artificial Neural Network (ANN) regressors utilized in earlier research. XGBoost attains an MAE and an RMSE of 1.554 and 2.402, respectively outperforming the RF and ANN models that achieved MAE and RMSE values of 7.938 and 11.304, and 3.86 and 5.002, respectively. The overall results of this study support XGBoost as a reliable and efficient model for estimating life expectancy. Methods Secondary data were used from which a sample of 2832 observations of 21 variables was sourced from the World Health Organization (WHO) and the United Nations (UN) databases. The data was on 193 UN member states from the year 2000–2015, with the LE health-related factors drawn from the Global Health Observatory data repository.

This dataset compromises all country data included in the UN Inter-agency Group for Child Mortality Estimation (IGME) database (https://childmortality.org/data, downloaded June 2019).

It includes:

Reference area: name of the country

Indicator: child mortality indicator (neonatal mortality, infant mortality, under-5 mortality and mortality rate age 5 to 14)

Sex: sex of the child (male, female and total)

Series name: name of survey/census/VR [note: UN IGME estimates, i.e. not source data, are identified as "UN IGME estimate" in this field]

Series year: year of survey/census/VR series

Observation value: value of indicator from survey/census/VR

Observation status: indicates whether the data point is included or excluded for estimation [status of "normal" indicates UN IGME estimate, i.e. not source data]

Series Category: category of survey/census/VR, and can be:

DHS [Demographic and Health Survey]

MIS [Malaria Indicator Survey]

AIS [AIDS Indicator Survey]

Interim DHS

Special DHS

NDHS [National DHS]

WFS [World Fertility Survey]

MICS [Multiple Indicator Cluster Survey]

NMICS [National MICS]

RHS [Reproductive Health Survey]

PAP [Pan Arab Project for Child or Pan Arab Project for Family Health or Gulf Famly Health Survey]

LSMS [Living Standard Measurement Survey]

Panel [Dual record, multiround/follow-up survey and longitudinal/panel survey]

Census

VR [Vital Registration]

SVR [Sample Vital Registration]

Others [e.g. Life Tables]

Series type: the type of calculation method used to derive the indicator value (direct, indirect, household deaths, life table and vital records)

Standard error: sampling standard error of the observation value

Series method: data collection method, and can be:

Survey/census with Full Birth Histories

Survey/census with Summary Birth Histories

Survey/census with Household death

Vital Registration

Other

Lower and upper bound: the lower and upper bounds of 90% uncertainty interval of UN IGME estimates (for estimates only, i.e., not source data).

The dataset is used in the following paper:

Ezbakhe, F. and Pérez-Foguet, A. (2019) Levels and trends in child mortality: a compositional approach. Demographic Research (Under Review)

Infant and Neonatal Mortality Rates

United States, 1915-2013

By Health [source]

About this dataset

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains important insight on the infant and neonatal mortality rate in the United States between 1915 to 2013. As such, it can be used to analyze trends in infant and neonatal mortality over time as well as draw comparisons between different states or regions.

Before you start diving into this data set, here are a few useful tips for exploring its contents: - Explore the columns: First, get familiar with the columns of this data set by reviewing their descriptions listed above; these will help you understand what each column offers so that you can make informed decisions when analyzing the data. - Determine which level of geography makes sense: Will your analysis focus on state-level data or looking at national trends? Make sure to select only those points relevant to your project to avoid getting overwhelmed with unnecessary information. - Choose a metric for measuring progress: Decide on your metrics for success and use those values as benchmark points when exploring information from this dataset . For example, if tracking changes over time is important in your analysis then choose “Year” from this dataset’s available fields and start sorting by that value within each geographical area of interest (for example states). Further utilize statistical measures such as averages or medians from “Mortality Rate” field to determine progress/regress in each segment over time periods of interests (again choosing year values).
- Prioritize visualization techniques appropriately: Upon gathering all needed information then focus on how best present it – using tables or graphs? Tables offer a great way to track details while visuals like charts & maps provide insights into larger trajectories that might not be clear through tables alone; ensuring both types are part of your analysis will ensure maximum clarity & accuracywith presentation yielding maximum impact & understanding among target audiences!

Research Ideas

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: NCHS_-_Infant_and_neonatal_mortality_rates_United_States_1915-2013.csv | Column name | Description | |:-------------------|:---------------------------------------------------| | Type | The type of mortality rate being measured (String) | | Year | The year the mortality rate was measured (Integer) | | Mortality Rate | The mortality rate per 1,000 live births (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.

Our study on saplings was conducted in six forested sites in three southern Michigan counties: Ingham Co. (three sites), Gratiot Co. (two sites), and Shiawassee Co. (one site), with 10 to 60 km between sites.Data set one - on the fate and density of emerald ash borer larvae and associated parasitoids on ash saplings from both biocontrol-release and non-release control plots in southern Michigan during the three-year study (2013–2015). Data set one was used for calculations and associated analyses for of the parameters presented in Figure 1, 2, 3, and 4.Data set two - on ash tree abundance (per 100 m2) and healthy conditions (or crown classes) at the six study sites in southern Michigan observed in summer 2015. Data set two was used for estimation of tree density (Figure 5) and healthy condition (or crown classes).Resources in this dataset:Resource Title: Emerald ash borer biocontrol in ash saplings: the potential for early stage recovery of North American ash trees. File Name: Sapling Data 2013-2015 FINAL.xlsx Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition)Resource Title: Emerald ash borer biocontrol in ash saplings: the potential for early stage recovery of North American ash trees. File Name: MI Ash Transect 2015 - All trees.xlsx Resource Description: Data on ash abundance and healthy conditions from transect surveyResource Title: Data Dictionary - EAB biocontrol in ash saplings. File Name: EAB_data_dictionary.csvResource Title: 2013-2014 data sorted. File Name: 2013-2014_data_sorted_EAB.csv Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition)Resource Title: 2014-2015 data sorted. File Name: 2014-2015_data_sorted_EAB.csv Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition)Resource Title: 2015-2016 data sorted. File Name: 2015-2016_data_sorted_EAB.csv Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition)Resource Title: Combined: Emerald ash borer biocontrol in ash saplings: the potential for early stage recovery of North American ash trees. File Name: Emerald ash borer biocontrol in ash saplings the potential for early stage recovery of North American ash trees.csv Resource Description: Data set one - on fate and density of emerald ash borer larvae and/or pupae and associated mortality factors (parasitoids, predators, and undetermined diseases/plant resistance /competition) All 3 sets (2013-2016) combined into a CSV for visualization purposesResource Title: Emerald ash borer biocontrol in ash saplings: the potential for early stage recovery of North American ash trees. File Name: MI Ash Transect 2015 - All trees.csv Resource Description: Data on ash abundance and healthy conditions from transect survey (CSV version for data visualization)Resource Title: Estimates of the net population growth rate of emerald ash borer on saplings from life tables constructed from Dataset One. File Name: DUAN J Data on EAB Life Tables Calculation for Saplings 2013-2015.xlsx Resource Description: This life table of emerald ash borer on saplings was constructed from Dataset One and used to estimate the next population growth rate according to method described in Duan et al. (2014, 2017)Resource Title: Estimates of the net population growth rate of emerald ash borer on saplings from life tables constructed from Dataset One. File Name: EAB_Life_Tables_Calculation_for_Saplings_2013-2015.csv Resource Description: CSV version of the data - This life table of emerald ash borer on saplings was constructed from Dataset One and used to estimate the next population growth rate according to method described in Duan et al. (2014, 2017)

Check out the PhilaStats Vital Statistics Dashboard for the City of Philadelphia, for interactive maps and charts of vital statistics and trends in natality (births), mortality (deaths), and population for Philadelphia residents. See also the technical notes for the creation and visualization of Philadelphia's Vital Statistics. View metadata for key information about this dataset.Vital statistics are annually published calculations on birth and death records that facilitate the tracking of important health and population trends in Philadelphia over time. Public officials, researchers, and citizens alike may use vital statistics to plan for population shifts and healthcare needs, to perform research, and to stay informed and up-to-date on the natality and mortality trends in our City. The vital statistics dataset consists of natality and mortality data on Philadelphia City residents for each year of finalized data available, back to 2011 for births and 2012 for deaths. Citywide metrics and metrics by Philadelphia Planning District are provided for both natality and mortality metrics. A population estimates table is also provided, which includes the population counts used to calculate some metrics.The Vital Statistics - Mortality dataset is also available in this citywide table.For questions about this dataset, contact epi@phila.gov. For technical assistance, email maps@phila.gov.

This data set contains the life tables the were computed for the study presented in: Torres, C., V. Canudas-Romo, and J. Oeppen (2019) 'The contribution of urbanization to changes in life expectancy in Scotland, 1861–1910', Population Studies, 73:3, 387-404, DOI: 10.1080/00324728.2018.1549746

The life tables are by sex and urban-rural category. For reasons explained in the paper, the tables cover periods of different lengths, from 1861 to 1910.

Example of how to load the data in R:

LT <- read.table("Urban-Rural-LifeTables-Scotland-1861-1910.txt", header = T, sep = ";")

Description of each column: Period: time-interval, including the first and excluding the last indicated years (e.g., [1861,1866) corresponds to the years from 1861 to 1865). Available periods: 1861-1865, 1866-1870, 1871-1874, 1875-1877, 1878-1880, 1881-1885, 1886-1890, 1891-1892, 1893-1896, 1897-1900, 1901-1905, 1906-1910. Population: Rural, Semi-Urban, Urban, or Total population (see definitions in Torres et al. 2019) Sex: Female or Male x : Age (from 0 to 110+, by single ages) nmx: Death rate in the age interval [x, x+n) nax: average number of person-years lived in the age interval [x, x+n) by those who die in that interval nqx: Probability of dying in the age interval [x, x+n) lx: number of survivors at exact age x, or probability of surviving until exact age x ndx: Life-table deaths in the age interval [x, x+n) nLx: Person-years lived in the age interval [x, x+n) Tx: Person-years lived above age x ex: Remaining life expectancy at age x

For more information about life tables in general, see: Preston, S., Heuveline, P., and Guillot, M. (2001). Demography: Measuring and Modeling Population Processes. Wiley-Blackwell

📘 Description:

This synthetic health dataset simulates real-world lifestyle and wellness data for individuals. It is designed to help data scientists, machine learning engineers, and students build and test health risk prediction models safely — without using sensitive medical data.

The dataset includes features such as age, weight, height, exercise habits, sleep hours, sugar intake, smoking, alcohol consumption, marital status, and profession, along with a synthetic health_risk label generated using a heuristic rule-based algorithm that mimics realistic risk behavior patterns.

🧾 Columns Description:

🧩 Use Cases:

1. Health Risk Prediction: Train classification models (Logistic Regression, RandomForest, XGBoost, CatBoost) to predict health risk (low / high).

2. Feature Importance Analysis: Identify which lifestyle factors most influence health risk.

3. Data Preprocessing & EDA Practice: Use this dataset for data cleaning, encoding, and visualization practice.

4. Model Explainability Projects: Use SHAP or LIME to explain how different lifestyle habits affect predictions.

5. Streamlit or Flask Web App Development: Build a real-time web app that predicts health risk from user input.

💡 Case Study Example:

Imagine you are a data scientist building a Health Risk Prediction App for a wellness startup. You want to analyze how exercise, sleep, and sugar intake affect overall health risk. This dataset helps you simulate those relationships without handling sensitive medical data.

You could:

• Perform EDA to find correlations between age, BMI, and health risk.
• Train a model using Random Forest to predict health_risk.
• Deploy a Streamlit app where users can input their lifestyle information and get a risk score instantly.

⚙️ Technical Information:

• Rows: 5,000 (adjustable, you can create more)
• Columns: 12
• Target variable: health_risk
• Data type: Mixed (Numeric + Categorical)
• Source: Fully synthetic, generated using Python (NumPy, Faker)

📈 License:

CC0: Public Domain You are free to use this dataset for research, learning, or commercial projects.

🌍 Author:

Created by Arif Miah Machine Learning Engineer | Kaggle Expert | Data Scientist 📧 arifmiahcse@gmail.com

Additional file 1: Supplemetary file Fig. 1. Probability density according to different θ parameters representing different distribution of the uncured function.Two figures representing Lung and Breast scenario. Supplemetary file Table 1. Performance indicators using grouped data of Model(1) in situations when there was very small or no cure at all and a persisting long-term cancer mortality (g=1). The Model(1) was used with a logistic age effect on cured (as proposed all along the manuscript), a linear age effect on cured and without cure. 1,000 estimates each composed by 10,000 cases and 15 years of follow-up. Supplementary file Table 2. Robustness analysis using Maximum likelihood on individual data. a) The times to cancer death of uncured patients do not follow a Weibull distribution; b) The extra non-cancer death risk is dependent of age at diagnosis; c) The extra non-cancer death risk randomly varies among the patients. Supplementary file Table 3. Performance indicators for a and p using individual data according to sample size and length of follow-up. 1,000 estimates each composed by different sample size and follow-up length in years.

Dataset

This dataset was created by Vallabh Kulkarni

Contents

This portion of the GapMinder data includes one year of numerous country-level indicators of health, wealth and development for 213 countries.

GapMinder collects data from a handful of sources, including the Institute for Health
Metrics and Evaluation, US Census Bureau’s International Database, United Nations Statistics Division, and the World Bank. Source: https://www.gapminder.org/

Variable Name , Description of Indicator & Sources Unique Identifier: Country

1. incomeperperson : 2010 Gross Domestic Product per capita in constant 2000 US$.The inflation but not the differences in the cost of living between countries has been taken into account. [Main Source : World Bank Work Development Indicators]

2. alcconsumption: 2008 alcohol consumption per adult (age 15+), litres Recorded and estimated average alcohol consumption, adult (15+) percapita consumption in liters pure alcohol [Main Source : WHO]

3. armedforcesrate: Armed forces personnel (% of total labor force) [Main Source : Work Development Indicators]

4. breastcancerper100TH : 2002 breast cancer new cases per 100,000 female Number of new cases of breast cancer in 100,000 female residents during the certain year. [Main Source : ARC (International Agency for Research on Cancer)]

5. co2emissions : 2006 cumulative CO2 emission (metric tons), Total amount of CO2 emission in metric tons since 1751. [*Main Source : CDIAC (Carbon Dioxide Information Analysis Center)] *

6. femaleemployrate : 2007 female employees age 15+ (% of population) Percentage of female population, age above 15, that has been employed during the given year. [ Main Source : International Labour Organization]

7. employrate : 2007 total employees age 15+ (% of population) Percentage of total population, age above 15, that has been employed during the given year. [Main Source : International Labour Organization]

8. HIVrate : 2009 estimated HIV Prevalence % - (Ages 15-49) Estimated number of people living with HIV per 100 population of age group 15-49. [Main Source : UNAIDS online database]

9. Internetuserate: 2010 Internet users (per 100 people) Internet users are people with access to the worldwide network. [Main Source : World Bank]

10. lifeexpectancy : 2011 life expectancy at birth (years) The average number of years a newborn child would live if current mortality patterns were to stay the same. [Main Source : 1) Human Mortality Database, 2) World Population Prospects: , 3) Publications and files by history prof. James C Riley , 4) Human Lifetable Database ]

11. oilperperson : 2010 oil Consumption per capita (tonnes per year and person) [Main Source : BP]

12. polityscore : 2009 Democracy score (Polity) Overall polity score from the Polity IV dataset, calculated by subtracting an autocracy score from a democracy score. The summary measure of a country's democratic and free nature. -10 is the lowest value, 10 the highest. [Main Source : Polity IV Project]

13. relectricperperson : 2008 residential electricity consumption, per person (kWh) . The amount of residential electricity consumption per person during the given year, counted in kilowatt-hours (kWh). [Main Source : International Energy Agency]

14. suicideper100TH : 2005 Suicide, age adjusted, per 100 000 Mortality due to self-inflicted injury, per 100 000 standard population, age adjusted . [Main Source : Combination of time series from WHO Violence and Injury Prevention (VIP) and data from WHO Global Burden of Disease 2002 and 2004.]

15. urbanrate : 2008 urban population (% of total) Urban population refers to people living in urban areas as defined by national statistical offices (calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects) [Main Source : World Bank]

This dataset results from DG REGIO calculations based on Eurostat data (demo_r_mwk3_t). It presents excess mortality comparisons of the number of deaths that occurred in 2020 and 2021 with the average number of deaths that occurred in the corresponding weeks of 2015 to 2019. The age structure of the population and the deaths is not taken into account. The figures shown are rolling three week averages centred around the week in question. Access the EUROSTAT data on their webpage - deaths by week and NUTS region - https://ec.europa.eu/eurostat/databrowser/view/demo_r_mwk3_t/default/table?lang=en - and see the EUROSTAT webpage on national and regional weekly death statistics - https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Weekly_death_statistics Data is not available for Ireland. For Italy no data is available for the last weeks of 2021. This dataset presents a wide view of the longitudinal timeseries data for 2020-2021. This dataset - https://cohesiondata.ec.europa.eu/dataset/2020-2021-EU-regional-excess-mortality-3-week-aver/2kk2-t5sf - provides the same values in a vertical format.

Disaster Mortality Forecasts: EM-DAT & UNFPA

This dataset provides a ready-to-use resource for global disaster mortality and population analysis at the country, region, income-group, and global levels.
It merges EM-DAT disaster data with UNFPA population figures for 2001–2023 and offers reproducible forecasting (to 2030) using Facebook Prophet to assess progress toward SDG 11.5 (significantly reduce deaths and the number of people affected by disasters).

This dataset and codebase accompany our peer-reviewed article:
Forecasting the human cost of disasters under Sustainable Development Goal: A time series analysis using Facebook Prophet model

Column Descriptions

Files Included

• 01_forecast_world.ipynb — Global mortality rate forecasting (Prophet)
• 02_forecast_income_group_high.ipynb — High-income group mortality forecasts (example; template for other groups)
• 03_forecast_region_europe_central_asia.ipynb — Region-level forecasts (Europe & Central Asia; template for other regions)
• 04_forecast_country_china.ipynb — Country-level forecast (China; template for any country)
• country_region_income_mapping.xlsx — Mapping of country to region and income group, for aggregation/analysis
• disaster_mortality_population_combined.xlsx — Main combined data file—cleaned disaster deaths, population, and calculated mortality rates (with region/income columns)

How to Use

1. Replicate country, region, group, and global analyses from our published paper.
2. Use any notebook as a template to run forecasts for your chosen country/region/group.
3. Aggregate or filter by region/income using the included mapping file.
4. Benchmark time series models or extend with your own forecasting or ML approaches.

The survey of medical end-of-life decisions, presents information on medical end-of-life decisions by attending physicians. For this survey a random sample is taken from the central death registry of Statistics Netherlands on persons in the Dutch population register who died in the period August to November in the year concerned. The sample is raised to a figure for the whole year.

This table comprises deaths by medical end-of-life decisions, cause of death and age.

Data available from: 2010, 2015

Status of the figures: All data are definite.

Changes as of August 9th 2019: The underlying coding of classifications age and cause of death used in this table has been adjusted. It is now in line with the standard encoding defined by CBS. The structure and data of the table have not been adjusted.

When will new figures be published? The survey takes place every five years.