In 2021, COVID-19 caused about *** deaths per 100,000 population in high-income countries. This statistic displays the leading causes of death in high-income countries in 2021 by deaths per 100,000 population. Mortality from chronic diseases such as cancer and heart diseases are increasing around the world. Chronic deaths are especially prominent in Western countries, but have also recently began to increase in the developing world. Non-communicable disease burden This increase in chronic and degenerative non-communicable diseases globally stems from aging populations, modernization, and rapid urbanization. Though these are all signs of socioeconomic progress, the resulting shift in disease carries a heavy burden for societies. Health expenditure makes up around ** percent or more of the GDP in most high-income countries, and the global spending on medicines is expected to more than double from 2010 to 2027. Non-communicable disease risk factors and prevention In most OECD countries, over 30 percent of adults are overweight. Lack of exercise, poor nutrition, and generally unhealthy lifestyles can often lead to a cluster of symptoms including abnormal blood levels, high blood pressure, and excess body fat, which in turn pose an increased risk of heart disease, stroke, and diabetes. However, most non-communicable diseases are preventable, and their modifiable risk factors can be lowered through lifestyle and behavioral changes.

The leading cause of death in low-income countries worldwide in 2021 was lower respiratory infections, followed by stroke and ischemic heart disease. The death rate from lower respiratory infections that year was 59.4 deaths per 100,000 people. While the death rate from stroke was around 51.6 per 100,000 people. Many low-income countries suffer from health issues not seen in high-income countries, including infectious diseases, malnutrition and neonatal deaths, to name a few. Low-income countries worldwide Low-income countries are defined as those with per gross national incomes (GNI) per capita of 1,045 U.S. dollars or less. A majority of the world’s low-income countries are located in sub-Saharan Africa and South East Asia. Some of the lowest-income countries as of 2023 include Burundi, Sierra Leone, and South Sudan. Low-income countries have different health problems that lead to worse health outcomes. For example, Chad, Lesotho, and Nigeria have some of the lowest life expectancies on the planet. Health issues in low-income countries Low-income countries also tend to have higher rates of HIV/AIDS and other infectious diseases as a consequence of poor health infrastructure and a lack of qualified health workers. Eswatini, Lesotho, and South Africa have some of the highest rates of new HIV infections worldwide. Likewise, tuberculosis, a treatable condition that affects the respiratory system, has high incident rates in lower income countries. Other health issues can be affected by the income of a country as well, including maternal and infant mortality. In 2023, Afghanistan had one of the highest rates of infant mortality rates in the world.

In 2021, COVID-19 caused around **** million deaths in high-income countries, making it the second leading cause of death. Ischemic heart disease was the number one cause of death in high-income countries that year, causing around **** million deaths. This statistic displays the leading causes of death in high-income countries in 2021 by deaths per 100,000 population.

BackgroundGlobal and regional projections of mortality and burden of disease by cause for the years 2000, 2010, and 2030 were published by Murray and Lopez in 1996 as part of the Global Burden of Disease project. These projections, which are based on 1990 data, continue to be widely quoted, although they are substantially outdated; in particular, they substantially underestimated the spread of HIV/AIDS. To address the widespread demand for information on likely future trends in global health, and thereby to support international health policy and priority setting, we have prepared new projections of mortality and burden of disease to 2030 starting from World Health Organization estimates of mortality and burden of disease for 2002. This paper describes the methods, assumptions, input data, and results. Methods and FindingsRelatively simple models were used to project future health trends under three scenarios—baseline, optimistic, and pessimistic—based largely on projections of economic and social development, and using the historically observed relationships of these with cause-specific mortality rates. Data inputs have been updated to take account of the greater availability of death registration data and the latest available projections for HIV/AIDS, income, human capital, tobacco smoking, body mass index, and other inputs. In all three scenarios there is a dramatic shift in the distribution of deaths from younger to older ages and from communicable, maternal, perinatal, and nutritional causes to noncommunicable disease causes. The risk of death for children younger than 5 y is projected to fall by nearly 50% in the baseline scenario between 2002 and 2030. The proportion of deaths due to noncommunicable disease is projected to rise from 59% in 2002 to 69% in 2030. Global HIV/AIDS deaths are projected to rise from 2.8 million in 2002 to 6.5 million in 2030 under the baseline scenario, which assumes coverage with antiretroviral drugs reaches 80% by 2012. Under the optimistic scenario, which also assumes increased prevention activity, HIV/AIDS deaths are projected to drop to 3.7 million in 2030. Total tobacco-attributable deaths are projected to rise from 5.4 million in 2005 to 6.4 million in 2015 and 8.3 million in 2030 under our baseline scenario. Tobacco is projected to kill 50% more people in 2015 than HIV/AIDS, and to be responsible for 10% of all deaths globally. The three leading causes of burden of disease in 2030 are projected to include HIV/AIDS, unipolar depressive disorders, and ischaemic heart disease in the baseline and pessimistic scenarios. Road traffic accidents are the fourth leading cause in the baseline scenario, and the third leading cause ahead of ischaemic heart disease in the optimistic scenario. Under the baseline scenario, HIV/AIDS becomes the leading cause of burden of disease in middle- and low-income countries by 2015. ConclusionsThese projections represent a set of three visions of the future for population health, based on certain explicit assumptions. Despite the wide uncertainty ranges around future projections, they enable us to appreciate better the implications for health and health policy of currently observed trends, and the likely impact of fairly certain future trends, such as the ageing of the population, the continued spread of HIV/AIDS in many regions, and the continuation of the epidemiological transition in developing countries. The results depend strongly on the assumption that future mortality trends in poor countries will have a relationship to economic and social development similar to those that have occurred in the higher-income countries.

The leading cause of death in low-income countries worldwide in 2021 was lower respiratory infections, followed by stroke and ischemic heart disease. That year, lower respiratory infections caused an estimated 415,000 deaths in low-income countries worldwide. This statistic shows the number of deaths for the leading causes of death in low-income countries worldwide in 2021.

In 2021, across OECD countries, diseases of the circulatory system and cancer were the two leading causes of death, accounting for approximately ** and ** percent of all deaths, respectively. COVID-19 also ranked among the leading causes, contributing to approximately ***** percent of total deaths. This statistic depicts the distribution of the main causes of mortality across OECD countries in 2021 or from the nearest year.

BackgroundMortality in children under five years has been widely studied, whereas mortality at 5–9 years has received little attention. Using unique data from national registers in three Nordic countries, we aimed to characterize mortality directionality in children aged 0 to 9 years.Methods and FindingsThe cohort study included all children born in Denmark from 1973 to 2008 (n = 2,433,758), Sweden from 1973 to 2006 (n = 3,400,212), and a random sample of 89.3% of children born in Finland from 1987 to 2007 (n = 1,272,083). Children were followed from 0 to 9 years, and cumulative mortality and mortality rates were compared by age, gender, cause of death, and calendar periods. Among the 7,105,962 children, there were 48,299 deaths during study period. From 1981–1985 to 2001–2005, all-cause mortality rates were reduced by between 34% and 62% at different ages. Overall mortality rate ratio between boys and girls decreased from 1.25 to 1.21 with the most prominent reduction in children aged 5–9 years (from 1.59 to 1.19). Neoplasms, diseases of the nervous system and transport accidents were the most frequent cause of death after the first year of life. These three leading causes of death declined by 42% (from 6.2 to 3.6 per 100,000 person years), 43% (from 3.7 to 2.1) and 62% (from 3.9 to 1.5) in boys, and 25% (from 4.1 to 3.1 per 100000 person years), 42% (from 3.4 to 1.9) and 63% (from 3.0 to 1.1) in girls, respectively. Mortality from neoplasms was the highest in each age except infants when comparing cause-specific mortality, and half of deaths from diseases of the nervous system occurred in infancy. Mortality rate due to transport accidents increased with age and was highest in boys aged 5–9 years.ConclusionsMortality rate in children aged 0–9 years has been decreasing with diminished difference between genders over the past decades. Our results suggest the importance of further research on mortality by causes of neoplasms, and causes of transport accidents—especially in children aged 5–9 years.

BR: Cause of Death: by Non-Communicable Diseases: % of Total data was reported at 74.738 % in 2019. This records an increase from the previous number of 73.370 % for 2015. BR: Cause of Death: by Non-Communicable Diseases: % of Total data is updated yearly, averaging 73.194 % from Dec 2000 (Median) to 2019, with 4 observations. The data reached an all-time high of 74.738 % in 2019 and a record low of 69.140 % in 2000. BR: Cause of Death: by Non-Communicable Diseases: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Social: Health Statistics. Cause of death refers to the share of all deaths for all ages by underlying causes. Non-communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.;Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death;Weighted average;

Top-10 leading causes of death by country and sex (high impact garbage codes marked in red; low impact ones in yellow).

Germany DE: Cause of Death: by Non-Communicable Diseases: % of Total data was reported at 90.598 % in 2019. This records a decrease from the previous number of 91.046 % for 2015. Germany DE: Cause of Death: by Non-Communicable Diseases: % of Total data is updated yearly, averaging 91.273 % from Dec 2000 (Median) to 2019, with 4 observations. The data reached an all-time high of 91.869 % in 2000 and a record low of 90.598 % in 2019. Germany DE: Cause of Death: by Non-Communicable Diseases: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Social: Health Statistics. Cause of death refers to the share of all deaths for all ages by underlying causes. Non-communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.;Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death;Weighted average;

In 2021, the leading causes of death in lower-middle income countries worldwide were COVID-19, ischemic heart disease, and stroke. That year, COVID-19 resulted in around 3.94 million deaths in lower-middle income countries, over a million more deaths than ischemic heart disease, the second leading cause of death for countries in this income group. This statistic displays the number of deaths for the leading causes of death in lower-middle income countries in 2021.

Colombia CO: Cause of Death: by Injury: % of Total data was reported at 14.015 % in 2019. This records a decrease from the previous number of 16.221 % for 2015. Colombia CO: Cause of Death: by Injury: % of Total data is updated yearly, averaging 18.432 % from Dec 2000 (Median) to 2019, with 4 observations. The data reached an all-time high of 27.795 % in 2000 and a record low of 14.015 % in 2019. Colombia CO: Cause of Death: by Injury: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Colombia – Table CO.World Bank.WDI: Social: Health Statistics. Cause of death refers to the share of all deaths for all ages by underlying causes. Injuries include unintentional and intentional injuries.;Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death;Weighted average;

Bolivia BO: Cause of Death: by Non-Communicable Diseases: % of Total data was reported at 72.676 % in 2019. This records an increase from the previous number of 70.254 % for 2015. Bolivia BO: Cause of Death: by Non-Communicable Diseases: % of Total data is updated yearly, averaging 67.435 % from Dec 2000 (Median) to 2019, with 4 observations. The data reached an all-time high of 72.676 % in 2019 and a record low of 53.198 % in 2000. Bolivia BO: Cause of Death: by Non-Communicable Diseases: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bolivia – Table BO.World Bank.WDI: Social: Health Statistics. Cause of death refers to the share of all deaths for all ages by underlying causes. Non-communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.;Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death;Weighted average;

Cuba CU: Cause of Death: by Non-Communicable Diseases: % of Total data was reported at 83.307 % in 2019. This records a decrease from the previous number of 83.667 % for 2015. Cuba CU: Cause of Death: by Non-Communicable Diseases: % of Total data is updated yearly, averaging 83.487 % from Dec 2000 (Median) to 2019, with 4 observations. The data reached an all-time high of 85.024 % in 2010 and a record low of 81.252 % in 2000. Cuba CU: Cause of Death: by Non-Communicable Diseases: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Cuba – Table CU.World Bank.WDI: Social: Health Statistics. Cause of death refers to the share of all deaths for all ages by underlying causes. Non-communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.;Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death;Weighted average;

Registered leading causes of death by age, sex and country, UK, 2001 to 2018

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.

This horizontal bar chart displays deaths (people) by country using the aggregation sum. The data is filtered where the disease is COVID-19. The data is about diseases per day.

In 2021, COVID-19 caused around **** million deaths in upper-middle-income countries, making it the third leading cause of death. The leading causes of death in upper-middle-income countries that year were stroke and ischemic heart disease. This statistic displays the number of deaths from the leading causes of death in upper-middle-income countries in 2021.

Belarus BY: Cause of Death: by Injury: % of Total data was reported at 5.715 % in 2019. This records a decrease from the previous number of 6.403 % for 2015. Belarus BY: Cause of Death: by Injury: % of Total data is updated yearly, averaging 7.563 % from Dec 2000 (Median) to 2019, with 4 observations. The data reached an all-time high of 10.286 % in 2000 and a record low of 5.715 % in 2019. Belarus BY: Cause of Death: by Injury: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Belarus – Table BY.World Bank.WDI: Social: Health Statistics. Cause of death refers to the share of all deaths for all ages by underlying causes. Injuries include unintentional and intentional injuries.;Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death;Weighted average;

BackgroundOver 75% of the annual estimated 9.5 million deaths in India occur in the home, and the large majority of these do not have a certified cause. India and other developing countries urgently need reliable quantification of the causes of death. They also need better epidemiological evidence about the relevance of physical (such as blood pressure and obesity), behavioral (such as smoking, alcohol, HIV-1 risk taking, and immunization history), and biological (such as blood lipids and gene polymorphisms) measurements to the development of disease in individuals or disease rates in populations. We report here on the rationale, design, and implementation of the world's largest prospective study of the causes and correlates of mortality. Methods and FindingsWe will monitor nearly 14 million people in 2.4 million nationally representative Indian households (6.3 million people in 1.1 million households in the 1998–2003 sample frame and 7.6 million people in 1.3 million households in the 2004–2014 sample frame) for vital status and, if dead, the causes of death through a well-validated verbal autopsy (VA) instrument. About 300,000 deaths from 1998–2003 and some 700,000 deaths from 2004–2014 are expected; of these about 850,000 will be coded by two physicians to provide causes of death by gender, age, socioeconomic status, and geographical region. Pilot studies will evaluate the addition of physical and biological measurements, specifically dried blood spots. Preliminary results from over 35,000 deaths suggest that VA can ascertain the leading causes of death, reduce the misclassification of causes, and derive the probable underlying cause of death when it has not been reported. VA yields broad classification of the underlying causes in about 90% of deaths before age 70. In old age, however, the proportion of classifiable deaths is lower. By tracking underlying demographic denominators, the study permits quantification of absolute mortality rates. Household case-control, proportional mortality, and nested case-control methods permit quantification of risk factors. ConclusionsThis study will reliably document not only the underlying cause of child and adult deaths but also key risk factors (behavioral, physical, environmental, and eventually, genetic). It offers a globally replicable model for reliably estimating cause-specific mortality using VA and strengthens India's flagship mortality monitoring system. Despite the misclassification that is still expected, the new cause-of-death data will be substantially better than that available previously.