This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Effect of suicide rates on life expectancy dataset

Abstract In 2015, approximately 55 million people died worldwide, of which 8 million committed suicide. In the USA, one of the main causes of death is the aforementioned suicide, therefore, this experiment is dealing with the question of how much suicide rates affects the statistics of average life expectancy. The experiment takes two datasets, one with the number of suicides and life expectancy in the second one and combine data into one dataset. Subsequently, I try to find any patterns and correlations among the variables and perform statistical test using simple regression to confirm my assumptions.

Data

The experiment uses two datasets - WHO Suicide Statistics[1] and WHO Life Expectancy[2], which were firstly appropriately preprocessed. The final merged dataset to the experiment has 13 variables, where country and year are used as index: Country, Year, Suicides number, Life expectancy, Adult Mortality, which is probability of dying between 15 and 60 years per 1000 population, Infant deaths, which is number of Infant Deaths per 1000 population, Alcohol, which is alcohol, recorded per capita (15+) consumption, Under-five deaths, which is number of under-five deaths per 1000 population, HIV/AIDS, which is deaths per 1 000 live births HIV/AIDS, GDP, which is Gross Domestic Product per capita, Population, Income composition of resources, which is Human Development Index in terms of income composition of resources, and Schooling, which is number of years of schooling.

LICENSE

THE EXPERIMENT USES TWO DATASET - WHO SUICIDE STATISTICS AND WHO LIFE EXPECTANCY, WHICH WERE COLLEECTED FROM WHO AND UNITED NATIONS WEBSITE. THEREFORE, ALL DATASETS ARE UNDER THE LICENSE ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 3.0 IGO (https://creativecommons.org/licenses/by-nc-sa/3.0/igo/).

[1] https://www.kaggle.com/szamil/who-suicide-statistics

[2] https://www.kaggle.com/kumarajarshi/life-expectancy-who

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.

The dataset shows effects of various natural disasters leading to people’s death, injury, people going missing, houses being destroyed etc. in timestamps of year 1971 to 2013. There were a total of 24,257 deaths in that time period. Where epidemic, landslide, and flood were the top three causes of human deaths. And flood, earthquake and fire were the top three causes of household damages.

This dataset identifies the total number of death certificates processed and the number of those for which pneumonia or influenza was listed as the underlying or contributing cause of death by age group in 122 Cities of United States. This dataset includes the report submitted for the year 2015.

The dataset, updated to the month of June 2015, contains the complete list of the deceased by sex, citizenship, age and major causes of death of those registered in the Register of the municipality of Matera and deceased in the same municipality. Also included are deaths occurring in other municipalities or abroad (transcribed acts) for which, not having documentation relating to the causes of death, they have been classified as: "OTHER UNKNOWN CAUSES ARISING FROM TRANSCRIPTED ACTS".

This dataset contains statistics for deaths and mortality in Australia. It includes all deaths that occurred and were registered in Australia, including deaths of persons whose place of usual residence was overseas. Deaths of Australian residents that occurred outside Australia may be registered by individual Registrars, but are not included in Australian Bureau of Statistics (ABS) death statistics. Standardised death rates in this dataset differ from those in the ABS.Stat datasets and commentary. Standardised death rates in this dataset are averaged using data for the three years ending in the reference year. They are calculated for each calendar year and then averaged. Standardised death rates in the ABS.Stat datasets and commentary are based on death registration data for the reference year only. Null values represent data not available for publication This dataset uses deaths and estimated resident population (ERP) for Statistical Area Level 4 (SA4) of Australia for 30 June 2010 to 2015, according to the 2011 edition of the Australian Statistical Geography Standard (ASGS). ERP is final for 2010 and 2011, revised for 2012 to 2014 and preliminary for 2015. For 2015, preliminary ERP used in sub-state data cubes is different from that used elsewhere in this release. Data has been sourced from the September 2016 release. For more information including which ERP was used in this dataset please visit the Australian Bureau of Statistics (ABS) Explanatory Notes. AURIN has spatially enabled the original data from the ABS 2011 SA4 boundaries.

This dataset presents the age-adjusted death rates for the 10 leading causes of death in the United States beginning in 1999. Data are based on information from all resident death certificates filed in the 50 states and the District of Columbia using demographic and medical characteristics. Age-adjusted death rates (per 100,000 population) are based on the 2000 U.S. standard population. Populations used for computing death rates after 2010 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 non-census years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Causes of death classified by the International Classification of Diseases, Tenth Revision (ICD–10) are ranked according to the number of deaths assigned to rankable causes. Cause of death statistics are based on the underlying cause of death. SOURCES 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. 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. Murphy SL, Xu JQ, Kochanek KD, Curtin SC, and Arias E. Deaths: Final data for 2015. National vital statistics reports; vol 66. no. 6. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_06.pdf.

Abstract copyright UK Data Service and data collection copyright owner.The National Survey of Bereaved People (VOICES - Views of Informal Carers - Evaluation of Services) is an annual survey designed to measure the quality of end of life care. The VOICES survey particularly focuses on the last three months of life. Results are used to inform policy decisions and enable evaluation of the quality of end of life care by age group, sex, in different settings (home, hospital, care homes and hospices) and by different causes of death. Quality of end of life care is also included as an indicator in the NHS Outcomes Framework and the VOICES survey is used to monitor progress against this. The Department of Health (DH) first commissioned the survey in 2011 to follow up on a commitment made in the End of Life Care Strategy. Previously, very little systematic information was available about the quality of care delivered to people approaching the end of life, despite reports from the Healthcare Commission and the Neuberger review highlighting deficiencies in care. The commissioning responsibility for the survey moved from DH to NHS England following the restructuring of the Health and Care systems in England in April 2013. Each year a sample of approximately 49,000 adults who died in England is selected from the deaths registration database held by the Office for National Statistics (ONS). To ensure the sample represents the deaths in England for the given period and covers the key domains of interest, the sample is stratified according to the cause of death, place of death and geography. For the 2011 and 2012 surveys, geography was based on Primary Care Trust (PCT) clusters. For the 2013 survey onwards, this is based on NHS Area Teams (NHS Area Team 2013 has also been applied to the earlier datasets). The VOICES questionnaire is sent by post to the person who registered the death of the deceased; this is usually a relative or friend of the deceased. Questionnaires are sent out between 4 and 11 months after the patient has died. As is standard in most postal surveys, if no response is received, this first questionnaire is then followed up with two reminders. Once fieldwork, data capture, cleaning and processing are complete, findings are disseminated at both the national and sub-national level. Further information about the survey and links to related publications may be found on the ONS National Bereavement Survey (VOICES) QMI webpage. End User Licence and Secure Access versions available The UK Data Service holds standard End User Licence (EUL) and Secure Access versions of the National Survey of Bereaved People data. EUL data are available to registered users but Secure Access data are only available to ONS Accredited Researchers (in addition, project approval and successful completion of a stringent training course are required before access can be granted). The Secure Access version contains finer detail variables (e.g. IMD deciles as opposed to quintiles in the EUL data, Strategic Clinical Network in addition to NHS Area Teams, and more detailed information on age, causes, dates and place of death). Users are strongly advised to check whether the EUL data are sufficient for their research needs before making an application for the Secure Access version. Main Topics:Date, cause and place of death; quality and standards of medical, nursing, social and pastoral care in the last three months of life; support for relatives/carers; demographics of deceased person and respondent.

Provisional counts of the number of deaths registered in England and Wales, by age, sex, region and Index of Multiple Deprivation (IMD), in the latest weeks for which data are available.

Context

The global and regional projections of mortality by cause for years 2015 and 2030 were carried out in 2012 based on the GHE2012 estimates of causes of death for 2011. Earlier projections from 2004 to 2030 were updated using the estimates of deaths by cause for year 2011 as a starting-point, together with revised projections of HIV deaths prepared by UNAIDS and WHO, and revised forecasts of economic growth by region published by the World Bank (baseline scenario). For further information on these estimates and on data sources and methods, refer to The global burden of disease: 2004 update and to the published paper here. It is intended to update these projections soon using the most recent GHE2015 estimates for year 2015 as a starting point.

Acknowledgements

All this data could be founded on WHO site, you can read the paper about this dataset here: http://journals.plos.org/plosmedicine/article/file?id=10.1371/journal.pmed.0030442&type=printable

Inspiration

I'm working on a research about depression and need other illness and mortality data.

Civil status statistics on deaths are based on information sent by town halls to INSEE. The civil code indeed obliges to declare any event relating to civil status (births, marriages, deaths, recognitions) to a civil status officer within the prescribed time limits. INSEE ensures the completeness and quality of the data before producing the civil status statistics files.

Filtered data on persons domiciled in Île-de-France

This dataset contains statistics on deaths in South Africa in 2015. The registration of deaths in South Africa is regulated by the Births and Deaths Registration Act, 51 of 1992. The South African Department of Home Affairs (DHA) is responsible for the registration of deaths in South Africa. The data is collected with two instruments: The death register (DHA-1663A) and the medical certificate in respect of death (DHS-1663B).The staff of the DHA Registrar of Deaths section fills in the former while the medical practitioner attending to the death completes the latter. Causes of death are coded by the Department of Home Affairs according to the tenth revision of the International Classification of Diseases (ICD-10) ICD-10, as required by the World Health Organisation for their member countries. The data is used by the Department of Home Affairs to update the Population Register. The forms are sent to Statistics South Africa (Stats SA) for their use for statistical purposes. From the two forms sent to Stats SA, the following data items of the deceased are extracted: place of residence, place of death, date of death, month and year of registration, sex, marital status, occupation, underlying cause of death, whether or not the death was certified by a medical practitioner, and whether or not the deceased died in a health institution or nursing home. From 1991 death notifications do not require data on population group, and therefore this dataset includes death data for all population groups. This dataset excludes 2014 deaths that were not registered, and late registrations which would not have been available to Stats SA in time for the production of the dataset.

Analysis of ‘💊 Drug Induced Deaths’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/drug-induced-deathse on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

This data was compiled using the CDC's WONDER database using these parameters:

• Group By: State, Year
• Measures: Deaths, Population, Crude Rate (95% Confidence Interval and Standard Error)
• Underlying Cause of Death: UCD - Drug/Alcohol Induced Causes - Drug Induced Causes
• Show Totals
• Show Zero Values
• Show Suppressed Values

Citation

Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death
1999-2015 on CDC WONDER Online Database, released December, 2016. Data are from the Multiple Cause of Death Files, 1999-2015, as
compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed
at http://wonder.cdc.gov/mcd-icd10.html on November 3, 2017.

Caveats

1. As of April 3, 2017, the underlying cause of death has been revised for 125 deaths in 2014. More information:
   http://wonder.cdc.gov/wonder/help/mcd.html#2014-Revision.
2. Circumstances in Georgia for the years 2008 and 2009 have resulted in unusually high death counts for the ICD-10 cause of
   death code R99, "Other ill-defined and unspecified causes of mortality." Caution should be used in interpreting these data.
   More information: http://wonder.cdc.gov/wonder/help/mcd.html#Georgia-Reporting-Anomalies.
3. Circumstances in New Jersey for the year 2009 have resulted in unusually high death counts for the ICD-10 cause of death code
   R99, "Other ill-defined and unspecified causes of mortality" and therefore unusually low death counts in other ICD-10 codes,
   most notably R95, "Sudden Infant Death Syndrome" and X40-X49, "Unintentional poisoning." Caution should be used in
   interpreting these data. More information: http://wonder.cdc.gov/wonder/help/mcd.html#New-Jersey-Reporting-Anomalies.
4. Circumstances in California resulted in unusually high death counts for the ICD-10 cause of death code R99, "Other
   ill-defined and unspecified causes of mortality" for deaths occurring in years 2000 and 2001. Caution should be used in
   interpreting these data. More information: http://wonder.cdc.gov/wonder/help/mcd.html#California-Reporting-Anomalies.
5. Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:
   http://wonder.cdc.gov/wonder/help/mcd.html#Unreliable.
6. The method used to calculate 95% confidence intervals is documented here: More information:
   http://wonder.cdc.gov/wonder/help/mcd.html#Confidence-Intervals.
7. The method used to calculate standard errors is documented here: More information:
   http://wonder.cdc.gov/wonder/help/mcd.html#Standard-Errors.
8. The population figures for year 2015 are bridged-race estimates of the July 1 resident population, from the Vintage 2015
   postcensal series released by NCHS on June 28, 2016. The population figures for year 2014 are bridged-race estimates of the July
   1 resident population, from the Vintage 2014 postcensal series released by NCHS on June 30, 2015. The population figures for
   year 2013 are bridged-race estimates of the July 1 resident population, from the Vintage 2013 postcensal series released by NCHS
   on June 26, 2014. The population figures for year 2012 are bridged-race estimates of the July 1 resident population, from the
   Vintage 2012 postcensal series released by NCHS on June 13, 2013. Population figures for 2011 are bridged-race estimates of the
   July 1 resident population, from the county-level postcensal Vintage 2011 series released by NCHS on July 18, 2012. Population
   figures for 2010 are April 1 Census counts. The population figures for years 2001 - 2009, are bridged-race estimates of the July
   1 resident population, from the revised intercensal county-level 2000 - 2009 series released by NCHS on October 26, 2012.
   Population figures for 2000 are April 1 Census counts. Population figures for 1999 are from the 1990-1999 intercensal series of
   July 1 estimates. Population figures for Infant Age Groups are the number of live births. Note: Rates and population
   figures for years 2001 - 2009 differ slightly from previously published reports, due to use of the population estimates which
   were available at the time of release.
9. The population figures used in the calculation of death rates for the age group 'under 1 year' are the estimates of the
   resident population that is under one year of age. More information: http://wonder.cdc.gov/wonder/help/mcd.html#Age Group.

Source: http://wonder.cdc.gov/mcd-icd10.html

This dataset was created by Health and contains around 900 samples along with Crude Rate Lower 95% Confidence Interval, Crude Rate Standard Error, technical information and other features such as: - Year - Crude Rate - and more.

How to use this dataset

• Analyze Deaths in relation to Crude Rate Upper 95% Confidence Interval
• Study the influence of State on Crude Rate Lower 95% Confidence Interval
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Health

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

This dataset as reported to the Rural Payments Agency contains cattle that died at slaughterhouses, cattle born before 1 July 1996 which were not registered until 2000, death registrations that have passed initial Cattle Tracing System validation checks, applications received for cattle born in Great Britain. Attribution statement:

Civil statistics on deaths come from the use of information transmitted by town halls to INSEE. The civil code requires that any event relating to civil status (births, marriages, deaths, recognitions) be declared to a civil registrar within prescribed deadlines. INSEE ensures the completeness and quality of the data before producing vital statistics files.

Filtered data on people domiciled in Île-de-France

This dataset, released July 2018, contains statistics for deaths of people aged 0-74 years during the years 2011-2015 based on the following causes: cancer, diabetes, circulatory system diseases, respiratory systems diseases and external causes. The data is by Local Government Area (LGA) 2016 geographic boundaries. For more information please see the data source notes on the data. Source: Data compiled by PHIDU from deaths data based on the 2011 to 2015 Cause of Death Unit Record Files supplied by the Australian Coordinating Registry and the Victorian Department of Justice, on behalf of the Registries of Births, Deaths and Marriages and the National Coronial Information System. The population at the small area level is the ABS Estimated Resident Population (ERP), 30 June 2011 to 30 June 2015, Statistical Areas Level 2; the population standard is the ABS ERP for Australia, 30 June 2011 to 30 June 2015. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

IntroductionMany national and subnational governments need to routinely measure the completeness of death registration for monitoring and statistical purposes. Existing methods, such as death distribution and capture-recapture methods, have a number of limitations such as inaccuracy and complexity that prevent widespread application. This paper presents a novel empirical method to estimate completeness of death registration at the national and subnational level.MethodsRandom-effects models to predict the logit of death registration completeness were developed from 2,451 country-years in 110 countries from 1970–2015 using the Global Burden of Disease 2015 database. Predictors include the registered crude death rate, under-five mortality rate, population age structure and under-five death registration completeness. Models were developed separately for males, females and both sexes.FindingsAll variables are highly significant and reliably predict completeness of registration across a wide range of registered crude death rates (R-squared 0.85). Mean error is highest at medium levels of observed completeness. The models show quite close agreement between predicted and observed completeness for populations outside the dataset. There is high concordance with the Hybrid death distribution method in Brazilian states. Uncertainty in the under-five mortality rate, assessed using the dataset and in Colombian departmentos, has minimal impact on national level predicted completeness, but a larger effect at the subnational level.ConclusionsThe method demonstrates sufficient flexibility to predict a wide range of completeness levels at a given registered crude death rate. The method can be applied utilising data readily available at the subnational level, and can be used to assess completeness of deaths reported from health facilities, censuses and surveys. Its utility is diminished where the adult mortality rate is unusually high for a given under-five mortality rate. The method overcomes the considerable limitations of existing methods and has considerable potential for widespread application by national and subnational governments.

This dataset contains statistics for deaths and mortality in Australia. It includes all deaths that occurred and were registered in Australia, including deaths of persons whose place of usual residence was overseas. Deaths of Australian residents that occurred outside Australia may be registered by individual Registrars, but are not included in Australian Bureau of Statistics (ABS) death statistics. Standardised death rates in this dataset differ from those in the ABS.Stat datasets and commentary. Standardised death rates in this dataset are averaged using data for the three years ending in the reference year. They are calculated for each calendar year and then averaged. Standardised death rates in the ABS.Stat datasets and commentary are based on death registration data for the reference year only. Null values represent data not available for publication This dataset uses deaths and estimated resident population (ERP) for Statistical Area Level 4 (SA4) of Australia for 30 June 2010 to 2015, according to the 2011 edition of the Australian Statistical Geography Standard (ASGS). ERP is final for 2010 and 2011, revised for 2012 to 2014 and preliminary for 2015. For 2015, preliminary ERP used in sub-state data cubes is different from that used elsewhere in this release. Data has been sourced from the September 2016 release. For more information including which ERP was used in this dataset please visit the Australian Bureau of Statistics (ABS) Explanatory Notes. AURIN has spatially enabled the original data from the ABS 2011 SA4 boundaries.

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 death certificates at Statistics Netherlands on persons listed in the Dutch population register who died in the months August to November inclusive of the survey year. The sample is raised to an annual figure.

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

Data available from: 2010, 2015 and 2021

Status of the figures: All data are definite.

Changes as of 26 May 2023: - Figures for 2021 have been added. - In 2021, there were 96 deceased persons with unknown 'medical end-of-life'. These were only added to the total. The underlying numbers therefore do not add up to the total.

When will new figures be published? The survey takes place every five years. In 2020, the survey was postponed by one year due to the high workload in the healthcare sector on account of COVID-19. As a result, there is a one-off six-year interval between 2015 and 2021.