The average for 2022 based on 196 countries was 8.24 deaths per 1000 people. The highest value was in the Central African Republic: 55.13 deaths per 1000 people and the lowest value was in Qatar: 0.93 deaths per 1000 people. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

In 2023, Germany had an overall crude death rate of 12 per 1,000 people. In comparison, the death rate in South Korea was six per 1,000 people. This statistic represents a ranking of select developed and developing countries based on death rates as of 2023.

As of 2023, the countries with the highest death rates worldwide were Monaco, Bulgaria, and Latvia. In these countries, there were ** to ** deaths per 1,000 people. The country with the lowest death rate is Qatar, where there is just *** death per 1,000 people. Leading causes of death The leading causes of death worldwide are, by far, cardiovascular diseases, accounting for ** percent of all deaths in 2021. That year, there were **** million deaths worldwide from ischaemic heart disease and **** million from stroke. Interestingly, a worldwide survey from that year found that people greatly underestimate the proportion of deaths caused by cardiovascular disease, but overestimate the proportion of deaths caused by suicide, interpersonal violence, and substance use disorders. Death in the United States In 2023, there were around **** million deaths in the United States. The leading causes of death in the United States are currently heart disease and cancer, accounting for a combined ** percent of all deaths in 2023. Lung and bronchus cancer is the deadliest form of cancer worldwide, as well as in the United States. In the U.S. this form of cancer is predicted to cause around ****** deaths among men alone in the year 2025. Prostate cancer is the second-deadliest cancer for men in the U.S. while breast cancer is the second deadliest for women. In 2023, the tenth leading cause of death in the United States was COVID-19. Deaths due to COVID-19 resulted in a significant rise in the total number of deaths in the U.S. in 2020 and 2021 compared to 2019, and it was the third leading cause of death in the U.S. during those years.

Dear Kaggler! This dataset consists of a main CSV file: Adult mortality rate (2019-2021).csv. This file has been processed, cleaned and prepared for your use. The dataset contains information on mortality rates in different countries of the world and some factors that may affect this rate for 2019-2023.

The data contains the following columns:

Countries: Country of study.

Continent: Continent location of the country.

Average_Pop(thousands people): Average population of the country under study for 2019-2021 in thousands.

Average_GDP(M$): Average GDP of the country under study for 2019-2021 in millions of dollars.

Average_GDP_per_capita: Average GDP per capita of the country under study for 2019-2021 in dollars.

Average_HEXP($): Health Expenditure Per Capita in the country under study in dollars.

Development_level: Level of development of the state under study (calculated by GDP per capita of the country). Please note that in this dataset we calculate this indicator only by calculating GDP per capita! Despite the fact that the United Nations (UN) does not have an unambiguous classification of countries into developed, developing and backward based on only one indicator, such as the amount of GDP per capita. It uses a wider range of economic, social and quality indicators to determine the level of development of countries.

AMR_female(per_1000_female_adults): Average mortality of adult women in the country under study (per 1000 adult women per year) for 2019-2023.

AMR_male(per_1000_male_adults): Average mortality of adult men in the country under study (per 1000 adult men per year) for 2019-2023.

Average_CDR: Average crude mortality rate for 2019–2021 in the country under study.

The dataset also contains additional files: Draft_AMR.csv, Draft_CDR.csv, Draft_Expenses.csv, Draft_GDP.csv, Draft_Population.csv. In fact, the main dataset consists of parts of these files. If you are interested in working more deeply on data cleaning and preparation, you can of course use these files. You can also use these files to create your own dataset. And be careful! Additional files may contain a different number of rows and columns with different names and data types. And of course these files are not cleaned. You will see not only the NaN values, but also other symbols in their place.

Enjoy your training, my dear Kaggler!

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The average for 2022 based on 187 countries was 27 deaths per 1000 births. The highest value was in Niger: 121 deaths per 1000 births and the lowest value was in Estonia: 2 deaths per 1000 births. The indicator is available from 1960 to 2022. Below is a chart for all countries where data are available.

The COVID-19 pandemic increased the global death rate, reaching *** in 2021, but had little to no significant impact on birth rates, causing population growth to dip slightly. On a global level, population growth is determined by the difference between the birth and death rates, known as the rate of natural change. On a national or regional level, migration also affects population change. Ongoing trends Since the middle of the 20th century, the global birth rate has been well above the global death rate; however, the gap between these figures has grown closer in recent years. The death rate is projected to overtake the birth rate in the 2080s, which means that the world's population will then go into decline. In the future, death rates will increase due to ageing populations across the world and a plateau in life expectancy. Why does this change? There are many reasons for the decline in death and birth rates in recent decades. Falling death rates have been driven by a reduction in infant and child mortality, as well as increased life expectancy. Falling birth rates were also driven by the reduction in child mortality, whereby mothers would have fewer children as survival rates rose - other factors include the drop in child marriage, improved contraception access and efficacy, and women choosing to have children later in life.

This dataset offers a detailed compilation of mortality data reported annually by WHO Member States, spanning from 1950 to the present. The data is derived from national civil registration and vital statistics systems, providing an invaluable resource for comparative epidemiological studies.

Key Features: - Detailed cause-of-death information categorized by ICD-7, ICD-8, ICD-9, and ICD-10 revisions. - Mortality data from over 190 countries, updated to reflect the latest available year. - Population and live birth reference data included to facilitate demographic analyses. - Comprehensive coverage estimates and completeness data for vital registration systems across Member States. - Provided in CSV format for ease of import into database management systems, ensuring accessibility for large-scale data analyses.

This dataset is intended for research purposes and requires adequate IT resources for use. It includes the necessary documentation, file structures, and code reference tables to facilitate detailed analyses. Users are advised to consult the "documentation.zip" file for further instructions on data handling and system requirements.

Important Considerations: - Data use is restricted to non-commercial purposes. - Users must acknowledge WHO as the source and attribute any analyses, interpretations, or conclusions to the author of the published data, not WHO. - Adherence to WHO guidelines for data use and dissemination is required.

Unlock the potential of this rich dataset for your research on global health trends, mortality rates, and cause-of-death analyses.

The Global Subnational Infant Mortality Rates, Version 2.01 consist of Infant Mortality Rate (IMR) estimates for 234 countries and territories, 143 of which include subnational Units. The data are benchmarked to the year 2015 (Version 1 was benchmarked to the year 2000), and are drawn from national offices, Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other sources from 2006 to 2014. In addition to Infant Mortality Rates, Version 2.01 includes crude estimates of births and infant deaths, which could be aggregated or disaggregated to different geographies to calculate infant mortality rates at different scales or resolutions, where births are the rate denominator and infant deaths are the rate numerator. Boundary inputs are derived primarily from the Gridded Population of the World, Version 4 (GPWv4) data collection. National and subnational data are mapped to grid cells at a spatial resolution of 30 arc-seconds (~1 km) (Version 1 has a spatial resolution of 1/4 degree, ~28 km at the equator), allowing for easy integration with demographic, environmental, and other spatial data.

Excess mortality rates for South Africa and other countries, including regional breakdowns and annual statistics as reported by the World Bank.

Global Health Outcomes Data

Impact on Mortality Rates and Malnutrition in Countries Around the World

By Humanitarian Data Exchange [source]

About this dataset

This dataset provides comprehensive insights into critical health conditions around the world, such as mortality rate, malnutrition levels, and frequency of preventable diseases. It documents the prevalence of life-threatening diseases like malaria and tuberculosis, and are tracked alongside key health indicators like adult mortality rates, HIV prevalence, physicians per 10,000 people ratio and public health expenditures. Such metrics provide us with an accurate picture of how developed healthcare systems are in certain countries which ultimately leads to improvements in public policy formation and awareness amongst decision-makers. With this data it is possible to observe disparities between different regions of the world which can help inform global strategies for providing equitable care globally. This dataset is a valuable source for researchers interested in understanding global health trends over time or seeking to evaluate regional differences within countries

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How to use the dataset

This dataset provides comprehensive global health outcome data for countries around the world. It includes vital information such as infant mortality rates, child malnutrition rates, adult mortality rates, deaths due to malaria and tuberculosis, HIV prevalence rates, life expectancy at age 60 and public health expenditure. This dataset can be used to gain valuable insight into the challenges faced by different countries in providing a good quality of life for their citizens.

To use this dataset, first identify what questions you need answered and what outcomes you are looking to measure. You may want to look at specific disease-based indicators (e.g. malaria or tuberculosis), health-related indicators (e.g., nutrition), or overall population markers (e.g., life expectancy).

Then decide which data points from the provided fields will help answer your questions and provide the results needed - e.g,. infant mortality rate or HIV prevalence rate - extracting these values from relevant columns like “Infants lacking immunization (% of one-year-olds) Measles 2013” or “HIV prevalence, adult (% ages 15Ð49) 2013” respectively

Next extract other columnwise relevant information - e.g., country name — that could also aid your analysis using tools like Excel or Python's Pandas library; sorting through them based on any metric desired — e..g,, physicians per 10k people — while being mindful that some data points are missing in some cases (denoted by NA).

Finally perform basic analyses with either your own scripting language, like R/Python libraries' numerical functions with accompanying visuals/graphs etc if elucidating trends is desired; drawing meaningful conclusions about overall state of global health outcomes accordingly before making informed decisions thereafter if needed too!

Research Ideas

• Create a world health map to visualize the differences in health outcomes across different countries and regions.
• Develop an AI-based decision support tool that identifies optimal public health policies or interventions based on these metrics for different countries.
• Design a dashboard or web app that displays and updates this data in real-time, to allow users to compare the current state of global health indicators and benchmark them against historical figures

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: health-outcomes-csv-1.csv | Column name | Description | |:-------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------| | Country | The name of the country. (String) ...

The Global Subnational Infant Mortality Rates, Version 2.01 consist of Infant Mortality Rate (IMR) estimates for 234 countries and territories, 143 of which include subnational Units. The data are benchmarked to the year 2015 (Version 1 was benchmarked to the year 2000), and are drawn from national offices, Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other sources from 2006 to 2014. In addition to Infant Mortality Rates, Version 2.01 includes crude estimates of births and infant deaths, which could be aggregated or disaggregated to different geographies to calculate infant mortality rates at different scales or resolutions, where births are the rate denominator and infant deaths are the rate numerator. Boundary inputs are derived primarily from the Gridded Population of the World, Version 4 (GPWv4) data collection. National and subnational data are mapped to grid cells at a spatial resolution of 30 arc-seconds (~1 km) (Version 1 has a spatial resolution of 1/4 degree, ~28 km at the equator), allowing for easy integration with demographic, environmental, and other spatial data.

This dataset provides values for CORONAVIRUS DEATHS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

Trends in Covid total deaths per million. The latest data for over 100 countries around the world.

Canada CA: Mortality Rate: Under-5: Female: per 1000 Live Births data was reported at 4.700 Ratio in 2023. This stayed constant from the previous number of 4.700 Ratio for 2022. Canada CA: Mortality Rate: Under-5: Female: per 1000 Live Births data is updated yearly, averaging 7.000 Ratio from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 28.600 Ratio in 1960 and a record low of 4.700 Ratio in 2023. Canada CA: Mortality Rate: Under-5: Female: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Social: Health Statistics. Under-five mortality rate, female is the probability per 1,000 that a newborn female baby will die before reaching age five, if subject to female age-specific mortality rates of the specified year.;Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.;Weighted average;Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys. Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation. This is a sex-disaggregated indicator for Sustainable Development Goal 3.2.1 [https://unstats.un.org/sdgs/metadata/].

The Poverty Mapping Project: Global Subnational Infant Mortality Rates data set consists of estimates of infant mortality rates for the year 2000. The infant mortality rate for a region or country is defined as the number of children who die before their first birthday for every 1,000 live births. The data products include a shapefile (vector data) of rates, grids (raster data) of rates (per 10,000 live births in order to preserve precision in integer format), births (the rate denominator) and deaths (the rate numerator), and a tabular data set of the same and associated data. Over 10,000 national and subnational Units are represented in the tabular and grid data sets, while the shapefile uses approximately 1,000 Units in order to protect the intellectual property of source data sets for Brazil, China, and Mexico. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

This dataset provides global suicide mortality rates (per 100,000 population) by country from 2000 to 2021, sourced from the World Bank. It includes 200+ countries with annual data, cleaned to remove missing values. Columns: Country Name, Country Code, Year, Suicide Rate. Ideal for analyzing trends in mental health, public health policy, and socio-economic correlations. Please use this sensitive data ethically and responsibly.

The average for 2022 based on 47 countries was 5.85 deaths per 1000 people. The highest value was in Japan: 12.9 deaths per 1000 people and the lowest value was in Qatar: 0.93 deaths per 1000 people. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

Graph and download economic data for Infant Mortality Rate for Low and Middle Income Countries (SPDYNIMRTINLMY) from 1990 to 2023 about mortality, infant, income, and rate.

As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had spread to almost every country in the world, and more than 6.86 million people had died after contracting the respiratory virus. Over 1.16 million of these deaths occurred in the United States.

Waves of infections Almost every country and territory worldwide have been affected by the COVID-19 disease. At the end of 2021 the virus was once again circulating at very high rates, even in countries with relatively high vaccination rates such as the United States and Germany. As rates of new infections increased, some countries in Europe, like Germany and Austria, tightened restrictions once again, specifically targeting those who were not yet vaccinated. However, by spring 2022, rates of new infections had decreased in many countries and restrictions were once again lifted.

What are the symptoms of the virus? It can take up to 14 days for symptoms of the illness to start being noticed. The most commonly reported symptoms are a fever and a dry cough, leading to shortness of breath. The early symptoms are similar to other common viruses such as the common cold and flu. These illnesses spread more during cold months, but there is no conclusive evidence to suggest that temperature impacts the spread of the SARS-CoV-2 virus. Medical advice should be sought if you are experiencing any of these symptoms.