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

This dataset contains counts of deaths for California counties 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 each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county 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.

One in every 100 children dies before completing one year of life. Around 68 percent of infant mortality is attributed to deaths of children before completing 1 month. 15,000 children die every day – Child mortality is an everyday tragedy of enormous scale that rarely makes the headlines Child mortality rates have declined in all world regions, but the world is not on track to reach the Sustainable Development Goal for child mortality Before the Modern Revolution child mortality was very high in all societies that we have knowledge of – a quarter of all children died in the first year of life, almost half died before reaching the end of puberty Over the last two centuries all countries in the world have made very rapid progress against child mortality. From 1800 to 1950 global mortality has halved from around 43% to 22.5%. Since 1950 the mortality rate has declined five-fold to 4.5% in 2015. All countries in the world have benefitted from this progress In the past it was very common for parents to see children die, because both, child mortality rates and fertility rates were very high. In Europe in the mid 18th century parents lost on average between 3 and 4 of their children Based on this overview we are asking where the world is today – where are children dying and what are they dying from?

5.4 million children died in 2017 – Where did these children die? Pneumonia is the most common cause of death, preterm births and neonatal disorders is second, and diarrheal diseases are third – What are children today dying from? This is the basis for answering the question what can we do to make further progress against child mortality? We will extend this entry over the course of 2020.

@article{owidchildmortality, author = {Max Roser, Hannah Ritchie and Bernadeta Dadonaite}, title = {Child and Infant Mortality}, journal = {Our World in Data}, year = {2013}, note = {https://ourworldindata.org/child-mortality} }

Abstract

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.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The data covers all deaths that occurred in 2015 which were registered at the Department of Home Affairs.

Kind of data

Administrative records data [adm]

Mode of data collection

Other [oth]

Research instrument

The data is collected with two instruments: The death register (Department of Home Affairs form DHA-1663A) and and the medical certificate in respect of death (DHA-1663B).

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.

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

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.

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.

Thailand TH: Death Rate: Crude: per 1000 People data was reported at 7.872 Ratio in 2016. This records an increase from the previous number of 7.750 Ratio for 2015. Thailand TH: Death Rate: Crude: per 1000 People data is updated yearly, averaging 7.229 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 13.180 Ratio in 1960 and a record low of 5.663 Ratio in 1989. Thailand TH: Death Rate: Crude: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Thailand – Table TH.World Bank.WDI: Population and Urbanization Statistics. Crude death rate indicates the number of deaths occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

The National Death Index (NDI) is a centralized database of death record information on file in state vital statistics offices. Working with these state offices, the National Center for Health Statistics (NCHS) established the NDI as a resource to aid epidemiologists and other health and medical investigators with their mortality ascertainment activities. Assists investigators in determining whether persons in their studies have died and, if so, provide the names of the states in which those deaths occurred, the dates of death, and the corresponding death certificate numbers. Investigators can then make arrangements with the appropriate state offices to obtain copies of death certificates or specific statistical information such as manner of death or educational level. Cause of death codes may also be obtained using the NDI Plus service. Records from 1979 through 2011 are currently available and contain a standard set of identifying information on each death. Death records are added to the NDI file annually, approximately 12 months after the end of a particular calendar year. 2012 should be available summer 2014. Early Release Program for 2013 is now available. The NDI service is available to investigators solely for statistical purposes in medical and health research. The service is not accessible to organizations or the general public for legal, administrative, or genealogy purposes.

Lithuania LT: Death Rate: Crude: per 1000 People data was reported at 14.300 Ratio in 2016. This records a decrease from the previous number of 14.400 Ratio for 2015. Lithuania LT: Death Rate: Crude: per 1000 People data is updated yearly, averaging 10.800 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 14.400 Ratio in 2015 and a record low of 7.400 Ratio in 1964. Lithuania LT: Death Rate: Crude: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Lithuania – Table LT.World Bank.WDI: Population and Urbanization Statistics. Crude death rate indicates the number of deaths occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

Number and percentage of deaths, by month and place of residence, 1991 to most recent year.

The Rufiji Health and Demographic Surveillance System (HDSS) was established in October 1998 to evaluate the impact on burden of disease of health system reforms based on locally generated data, prioritization, resource allocation and planning for essential health interventions. The Rufiji HDSS collects detailed information on health and survival and provides a framework for population-based health research of relevance to local and national health priorities. Monitoring of households and members within households is undertaken in regular 6-month cycles known as 'rounds'. Self-reported information is collected on demographic, household, socioeconomic and geographical characteristics. Verbal autopsies were done by trained Field interviewers to collect detailed data through structured and standardized INDEPTH Network verbal autopsy forms on symptoms and signs during the terminal illness, allowing assignment of cause of death following physician's review to a list of causes of death, based on the 10th Revision of the International Classification of Diseases. From 2008 to 2015 Rufiji HDSS recorded about 5500 deaths. About 90% of them were interviewed and assigned the underlying cause of death. The Ifakara Health Institute VA data portal will be periodically updated depending on the availability of new data from the field. Face-to-face interview At the initial census (October 1998-anuary 1999), all individuals who were intending to be resident in the DSA for at least 4 months were eligible for inclusion. Verbal consent to participate in the census was sought from the head of every household. Definitions of several characteristics such as household, membership, migration and head of household are set in order to correctly assign individuals or households to events or attributes. A household in Rufiji HDSS is defined as a group of individuals sharing, or who eat from, the same cooking pot. A member of the HDSS is defined as someone who has been resident in the DSA for the preceding 4 months. New members qualify to be an in-migrant if s/he moves into the Rufiji HDSS and spends at least 4 months there. Women married to men living in the Rufiji HDSS and children born to these women qualify to be members of the Rufiji HDSS. In the case of multiple wives, the husband will be registered as a permanent resident in only one household. He will be linked to other wives by his husband identification number given to his wives. After the census, the study population is visited three times a year in cycles or updated rounds over February-May, June-September and October-January to update indicators. From July 2013 onwards, Rufiji HDSS switched to two data collection rounds per year, which happen in July-December and January-June. Mapping of households and key structures such as schools, health facilities, markets, churches and mosques was done by field interviewers using handheld global positioning systems (GPS). Updating of GPS coordinates has been an ongoing exercise especially for new structures and for demolished structures. In 2012 the population size of the DSA was about 103 503 people, residing in 19 315 households. There are several ethnic groups in the DSA. The largest is the Ndengereko; other groups include the Matumbi, Nyagatwa, Ngindo, Pogoro and Makonde. The population comprises mainly Muslims with few Christians and followers of traditional religions. The main language spoken is Kiswahili. English is not commonly used in the area. Around 75% of the population aged 7-15 years have attended primary education, 14% of those in age group 15-65 years have secondary education and only 1% of the population has tertiary education. Almost 50% of the adult population aged 15-65 are self-employed in agriculture, 28% engage in other small economic activities, 16% are selfemployed in small-scale business and 6% are unemployed. Fuel wood is the main source of energy for cooking and shallow wells are the main source of water for domestic use. The household heads in Rufiji HDSS are considered as breadwinners and most (67.3%) are male. Active community engagement programmes are in place which include key informants (KIs) days, where the HDSS team convenes meetings with KIs for presentations on recent findings to feed back to community and for distribution of newsletters to households. Community sensitization events are held at the time of introducing new studies. These initiatives have cemented good relationships with the community and eventually maintained high participation. In Health and Demographic Surveillance System (HDSS), the follow-up of individuals aged 1559years was categorized into three periods: before ART (19982003), during ART scale-up (20042007), and after widespread availability of ART (20082011). Residents were those who never migrated within and beyond HDSS, internal migrants were those who moved within the HDSS, and external migrants were those who moved into the HDSS from outside. Mortality rates were estimated from deaths and person-years of observations calculated in each time period. Hazard ratios were estimated to compare mortality between migrants and residents. AIDS deaths were identified from verbal autopsy, and the odds ratio of dying from AIDS between migrants and residents was estimated using the multivariate logistic regression model.

THIS DATASET WAS LAST UPDATED AT 7:11 AM EASTERN ON DEC. 1

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

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.

United Kingdom UK: Death Rate: Crude: per 1000 People data was reported at 9.100 Ratio in 2016. This records a decrease from the previous number of 9.200 Ratio for 2015. United Kingdom UK: Death Rate: Crude: per 1000 People data is updated yearly, averaging 11.300 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 12.200 Ratio in 1963 and a record low of 8.700 Ratio in 2011. United Kingdom UK: Death Rate: Crude: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Population and Urbanization Statistics. Crude death rate indicates the number of deaths occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

This dataset is a more different and reliable version to KumarRajarshi's Life Expectancy (WHO) dataset - where some of his values and methods can be questioned.

Context All of the data in this dataset is compiled and downloaded from the Global Health Observatory (GHO) – which is a public health data repository established by the World Health Organisation (WHO). This makes the dataset very reliable and valid.

Challenges - Perform EDA to explore factors that affect life expectancy? - Produce a model to predict life expectancy?

Dataset Contents Life Expectancy from birth: - https://www.who.int/data/gho/data/indicators/indicator-details/GHO/life-expectancy-at-birth-(years)

Mean BMI (kg/m²) (crude estimate): - https://www.who.int/data/gho/data/indicators/indicator-details/GHO/mean-bmi-(kg-m-)-(crude-estimate)

Alcohol, total per capita (15+) consumption (in litres of pure alcohol): - https://www.who.int/data/gho/data/indicators/indicator-details/GHO/total-(recorded-unrecorded)-alcohol-per-capita-(15-)-consumption

The rest of the factors: - https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death (BY COUNTRY, Summary tables of mortality estimates by cause, age and sex, by country, 2000–2019, Number of Deaths [2000, 2010, 2015, 2019]). All of the values are crude estimates number of deaths per 1000.

I did this so you don't have to!

Data Collected: March 2023

Number of deaths and age-specific mortality rates for selected grouped causes, by age group and sex, 2000 to most recent year.