This dataset is a synthetic, globally representative collection of mortality records designed for data analysis, visualization, and machine learning practice. It simulates real-world death statistics across multiple countries, age groups, genders, and causes of death while maintaining privacy and ethical safety.

Each row represents an individual death record with attributes such as:

Country & Region Year of Death Age Group Gender Primary Cause of Death (e.g., cardiovascular disease, cancer, accidents, infectious diseases) Number of deaths Mortality rate per 1000

This dataset is ideal for:

Exploratory Data Analysis (EDA) Trend analysis of causes of death Public health and epidemiology simulations Data visualization projects Classification & clustering models Kaggle notebooks and portfolio projects

⚠️ Disclaimer

This is a fully synthetic dataset generated for educational and research purposes only. It does not represent real individuals or official statistics.

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.

56 million people died in 2017. What did they die from?

The Global Burden of Disease is a major global study on the causes of death and disease published in the medical journal The Lancet. These estimates of the annual number of deaths dataset are shown here.

Downloaded https://ourworldindata.org/causes-of-death dataset from first chart as CSV. Loaded the raw file in tableau prep for exploratory data distribution and applying some pivoting and cleaning. The output were uploaded in this dataset as well the original raw file.

Please notice the raw file have some country agrupations by region, but there is no data indicating it's an aggregation, so be careful analyzing the whole dataset guessing there are just countries as level of detail data. In order to be more accurate, I begin to analyze countries using the ISO Country code ("Code" named column). If you have no clue as me what country ZAF is, Google is your best friend (South Africa) 😉.

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.

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.

The Detailed Mortality - Underlying Cause of Death data on CDC WONDER are county-level national mortality and population data spanning the years 1999-2009. Data are based on death certificates for U.S. residents. Each death certificate contains a single underlying cause of death, and demographic data. The number of deaths, crude death rates, age-adjusted death rates, standard errors and 95% confidence intervals for death rates can be obtained by place of residence (total U.S., region, state, and county), age group (including infants and single-year-of-age cohorts), race (4 groups), Hispanic ethnicity, sex, year of death, and cause-of-death (4-digit ICD-10 code or group of codes, injury intent and mechanism categories, or drug and alcohol related causes), year, month and week day of death, place of death and whether an autopsy was performed. The data are produced by the National Center for Health Statistics.

During the months December 2020, January 2021, and February 2021, COVID-19 was the leading cause of death in the United States based on the average number of daily deaths. Heart disease and cancer are usually the number one and number two leading causes of death, respectively. This statistic shows the average number of daily deaths in the United States among the leading causes of death from March 2020 to September 2022.

In 2023, there were approximately 750.5 deaths by all causes per 100,000 inhabitants in the United States. This statistic shows the death rate for all causes in the United States between 1950 and 2023. Causes of death in the U.S. Over the past decades, chronic conditions and non-communicable diseases have come to the forefront of health concerns and have contributed to major causes of death all over the globe. In 2022, the leading cause of death in the U.S. was heart disease, followed by cancer. However, the death rates for both heart disease and cancer have decreased in the U.S. over the past two decades. On the other hand, the number of deaths due to Alzheimer’s disease – which is strongly linked to cardiovascular disease- has increased by almost 141 percent between 2000 and 2021. Risk and lifestyle factors Lifestyle factors play a major role in cardiovascular health and the development of various diseases and conditions. Modifiable lifestyle factors that are known to reduce risk of both cancer and cardiovascular disease among people of all ages include smoking cessation, maintaining a healthy diet, and exercising regularly. An estimated two million new cases of cancer in the U.S. are expected in 2025.

The Mortality - Multiple Cause of Death data on CDC WONDER are county-level national mortality and population data spanning the yehttps://healthdata.gov/d/2sz9-6c59ars 1999-2006. These data are available in two separate data sets: one data set for years 1999-2004 with 3 race groups, and another data set for years 2005-2006 with 4 race groups and 3 Hispanic origin categories. Data are based on death certificates for U.S. residents. Each death certificate contains a single underlying cause of death, up to twenty additional multiple causes, and demographic data. The number of deaths, crude death rates, age-adjusted death rates, standard errors and 95% confidence intervals for death rates can be obtained by place of residence (total U.S., state, and county), age group (including infants), race, Hispanic ethnicity (years 2005-2006 only), sex, year of death, and cause-of-death (4-digit ICD-10 code or group of codes). The data are produced by the National Center for Health Statistics.

This dataset presents the principal causes of death in the State of Qatar, classified according to ICD-10 chapters. It includes annual death counts for various disease categories over a ten-year period. The dataset is structured by cause of death and provides a time series that enables trend analysis and comparison across years.This information is valuable for health policymakers, researchers, and public health professionals to monitor disease burdens, design interventions, and evaluate national health outcomes. It supports health planning, epidemic tracking, and resource allocation in line with international classification standards.

US Death Rates by Age and Cause

Study why are people dying

About this dataset

Data on death rates in the United States in by age and cause of death. At the bottom of the table, some of the columns are a little out of whack but if you download the file, you should be able to make out all the numbers and information

How to use the dataset

Looking at death rates in the United States can be a sobering experience, but it can also be a helpful way to see where our country needs to focus its efforts in terms of public health. This dataset contains information on death rates in the United States in 2014, by age and cause of death. This can be used to help identify which age groups are most at risk for certain causes of death, and what factors may contribute to those risks

Research Ideas

• Find out what age group is dying the most and why.
• Compare death rates from different causes of death.
• Find out which states have the highest death rates

Acknowledgements

License

Unknown License - Please check the dataset description for more information.

Columns

File: 2014 Death Rates by Age & Cause.csv | Column name | Description | |:-------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------| | Cause of death (based on ICD–10) | The cause of death that the row represents. This is given as a code based on the International Classification of Diseases (ICD). (String) | | All ages1 | The number of deaths due to the given cause in the given age group.(Integer) | | Under 1 year2 | The number of deaths due to the given cause in the given age group.(Integer) | | 1–4 | The number of deaths due to the given cause in the given age group.(Integer) | | 5–14 | The number of deaths due to the given cause in the given age group.(Integer) | | 15–24 | The number of deaths due to the given cause in the given age group.(Integer) | | 25–34 | The number of deaths due to the given cause in the given age group.(Integer) | | 35–44 | The number of deaths due to the given cause in the given age group.(Integer) | | 45–54 | The number of deaths due to the given cause in the given age group.(Integer) | | 55–64 | The number of deaths due to the given cause in the given age group.(Integer) | | 65–74 | The number of deaths due to the given cause in the given age group.(Integer) | | 75–84 | The number of deaths due to the given cause in the given age group.(Integer) | | 85 and over | The number of deaths due to the given cause in the given age group.(Integer) |

This dataset of U.S. mortality trends since 1900 highlights trends in age-adjusted death rates for five selected major causes of death.

Age-adjusted death rates (deaths per 100,000) after 1998 are calculated based on the 2000 U.S. standard population. Populations used for computing death rates for 2011–2017 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 noncensus years between 2000 and 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Data on age-adjusted death rates prior to 1999 are taken from historical data (see References below).

Revisions to the International Classification of Diseases (ICD) over time may result in discontinuities in cause-of-death trends.

SOURCES

CDC/NCHS, National Vital Statistics System, historical data, 1900-1998 (see https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm); 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

1. National Center for Health Statistics, Data Warehouse. Comparability of cause-of-death between ICD revisions. 2008. Available from: http://www.cdc.gov/nchs/nvss/mortality/comparability_icd.htm.

2. 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.

3. Kochanek KD, Murphy SL, Xu JQ, Arias E. Deaths: Final data for 2017. National Vital Statistics Reports; vol 68 no 9. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_09-508.pdf.

4. Arias E, Xu JQ. United States life tables, 2017. National Vital Statistics Reports; vol 68 no 7. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_07-508.pdf.

5. National Center for Health Statistics. Historical Data, 1900-1998. 2009. Available from: https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm.

Mortality Statistics in US Cities

Deaths by Age and Cause of Death in 2016

By Health [source]

About this dataset

This dataset contains mortality statistics for 122 U.S. cities in 2016, providing detailed information about all deaths that occurred due to any cause, including pneumonia and influenza. The data is voluntarily reported from cities with populations of 100,000 or more, and it includes the place of death and the week during which the death certificate was filed. Data is provided broken down by age group and includes a flag indicating the reliability of each data set to help inform analysis. Each row also provides longitude and latitude information for each reporting area in order to make further analysis easier. These comprehensive mortality statistics are invaluable resources for tracking disease trends as well as making comparisons between different areas across the country in order to identify public health risks quickly and effectively

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How to use the dataset

This dataset contains mortality rates for 122 U.S. cities in 2016, including deaths by age group and cause of death. The data can be used to study various trends in mortality and contribute to the understanding of how different diseases impact different age groups across the country.

In order to use the data, firstly one has to identify which variables they would like to use from this dataset. These include: reporting area; MMWR week; All causes by age greater than 65 years; All causes by age 45-64 years; All causes by age 25-44 years; All causes by age 1-24 years; All causes less than 1 year old; Pneumonia and Influenza total fatalities; Location (1 & 2); flag indicating reliability of data.

Once you have identified the variables that you are interested in,you will need to filter the dataset so that it only includes relevant information for your analysis or research purposes. For example, if you are looking at trends between different ages, then all you would need is information on those 3 specific cause groups (greater than 65, 45-64 and 25-44). You can do this using a selection tool that allows you to pick only certain columns from your data set or an excel filter tool if your data is stored as a csv file type .

Next step is preparing your data - it’s important for efficient analysis also helpful when there are too many variables/columns which can confuse our analysis process – eliminate unnecessary columns, rename column labels where needed etc ... In addition , make sure we clean up any missing values / outliers / incorrect entries before further investigation .Remember , outliers or corrupt entries may lead us into incorrect conclusions upon analyzing our set ! Once we complete the cleaning steps , now its safe enough transit into drawing insights !

The last step involves using statistical methods such as linear regression with multiple predictors or descriptive statistical measures such as mean/median etc ..to draw key insights based on analysis done so far and generate some actionable points !

With these steps taken care off , now its easier for anyone who decides dive into another project involving this particular dataset with added advantage formulated out of existing work done over our previous investigations!

Research Ideas

• Creating population health profiles for cities in the U.S.
• Tracking public health trends across different age groups
• Analyzing correlations between mortality and geographical locations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - 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.

Columns

File: rows.csv | Column name | Description | |:--------------------------------------------|:-----------------------------------...

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

A ranking of the 30 most common causes of death each year in Alberta, by ranking and total number of deaths. Vital Statistics cause of death data from 2023 onward is available on the Interactive Health Data Application under the Mortality category - Interactive Health Data Application - Mortality category

Provisional counts of deaths by the month the deaths occurred, by age group and race/ethnicity, for select underlying causes of death for 2020-2021. Final data is provided for 2019. The dataset also includes monthly provisional counts of death for COVID-19, coded to ICD-10 code U07.1 as an underlying or multiple cause of death.

MMWR Surveillance Summary 66 (No. SS-1):1-8 found that nonmetropolitan areas have significant numbers of potentially excess deaths from the five leading causes of death. These figures accompany this report by presenting information on potentially excess deaths in nonmetropolitan and metropolitan areas at the state level. They also add additional years of data and options for selecting different age ranges and benchmarks. Potentially excess deaths are defined in MMWR Surveillance Summary 66(No. SS-1):1-8 as deaths that exceed the numbers that would be expected if the death rates of states with the lowest rates (benchmarks) occurred across all states. They are calculated by subtracting expected deaths for specific benchmarks from observed deaths. Not all potentially excess deaths can be prevented; some areas might have characteristics that predispose them to higher rates of death. However, many potentially excess deaths might represent deaths that could be prevented through improved public health programs that support healthier behaviors and neighborhoods or better access to health care services. Mortality data for U.S. residents come from the National Vital Statistics System. Estimates based on fewer than 10 observed deaths are not shown and shaded yellow on the map. Underlying cause of death is based on the International Classification of Diseases, 10th Revision (ICD-10) Heart disease (I00-I09, I11, I13, and I20–I51) Cancer (C00–C97) Unintentional injury (V01–X59 and Y85–Y86) Chronic lower respiratory disease (J40–J47) Stroke (I60–I69) Locality (nonmetropolitan vs. metropolitan) is based on the Office of Management and Budget’s 2013 county-based classification scheme. Benchmarks are based on the three states with the lowest age and cause-specific mortality rates. Potentially excess deaths for each state are calculated by subtracting deaths at the benchmark rates (expected deaths) from observed deaths. Users can explore three benchmarks: “2010 Fixed” is a fixed benchmark based on the best performing States in 2010. “2005 Fixed” is a fixed benchmark based on the best performing States in 2005. “Floating” is based on the best performing States in each year so change from year to year. 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 Moy E, Garcia MC, Bastian B, Rossen LM, Ingram DD, Faul M, Massetti GM, Thomas CC, Hong Y, Yoon PW, Iademarco MF. Leading Causes of Death in Nonmetropolitan and Metropolitan Areas – United States, 1999-2014. MMWR Surveillance Summary 2017; 66(No. SS-1):1-8. Garcia MC, Faul M, Massetti G, Thomas CC, Hong Y, Bauer UE, Iademarco MF. Reducing Potentially Excess Deaths from the Five Leading Causes of Death in the Rural United States. MMWR Surveillance Summary 2017; 66(No. SS-2):1–7.

Death statistics (i) Number of Deaths for Different Sexes and Crude Death Rate for the Period from 1981 to 2023 (ii) Age-standardised Death Rate (Overall and by Sex) for the Period from 1981 to 2023 (iii) Age-specific Death Rate for Year 2013 and 2023 (iv) Death Rates by Leading Causes of Death for the Period from 2001 to 2023 (v) Number of Deaths by Leading Causes of Death for the Period from 2001 to 2023 (vi) Age-standardised Death Rates by Leading Causes of Death for the Period from 2001 to 2023 (vii) Late Foetal Mortality Rate for the Period from 1981 to 2023 (viii) Perinatal Mortality Rate for the Period from 1981 to 2023 (ix) Neonatal Mortality Rate for the Period from 1981 to 2023 (x) Infant Mortality Rate for the Period from 1981 to 2023 (xi) Number of Maternal Deaths for the Period from 1981 to 2023 (xii) Maternal Mortality Ratio for the Period from 1981 to 2023

According to the NCHS classification, the leading causes of death are provided for the total Santa Clara County population and by race/ethnicity and sex. Data are for Santa Clara County residents.Data trends are from year 2007 to 2016. Source: Santa Clara County Public Health Department, VRBIS, 2007-2016. Data as of 05/26/2017.METADATA:Notes (String): Lists table title, sourceYear (Numeric): Year of death Category (String): Lists the category representing the data: Santa Clara County is for total population, sex: Male and Female, and race/ethnicity: African American, Asian/Pacific Islander, Latino and White (non-Hispanic White only).Causes of death (String): Cause-of-death were coded using the Tenth Revision of the International Classification of Diseases codes (ICD-10). Causes are classified according to the Centers for Disease Control and Prevention, National Center for Health Statistics, Leading causes of death methodology.Count (Numeric): Number of deaths per cause of deathPercentage (Numeric): Percentage of deaths per cause of death out of total deaths in that year. Percentage value less than 1 is replaced by '<1'.

Data for deaths by leading cause of death categories are now available in the death profiles dataset for each geographic granularity.

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.

Cause of death categories for years 1999 and later are based on tenth revision of International Classification of Diseases (ICD-10) codes. Comparable categories are provided for years 1979 through 1998 based on ninth revision (ICD-9) codes. For more information on the comparability of cause of death classification between ICD revisions see Comparability of Cause-of-death Between ICD Revisions.