The leading causes of death in the United States have changed significantly from the year 1900 to the present. Leading causes of death in 1900, such as tuberculosis, gastrointestinal infections, and diphtheria have seen huge decreases in death rates and are no longer among the leading causes of death in the United States. However, other diseases such as heart disease and cancer have seen increased death rates. Vaccinations One major factor contributing to the decrease in death rates for many diseases since the year 1900 is the introduction of vaccinations. The decrease seen in the rates of death due to pneumonia and influenza is a prime example of this. In 1900, pneumonia and influenza were the leading causes of death, with around *** deaths per 100,000 population. However, in 2023 pneumonia and influenza were not even among the ten leading causes of death. Cancer One disease that has seen a large increase in death rates since 1900 is cancer. Cancer currently accounts for almost ** percent of all deaths in the United States, with death rates among men higher than those for women. The deadliest form of cancer for both men and women is cancer of the lung and bronchus. Some of the most common avoidable risk factors for cancer include smoking, drinking alcohol, sun exposure, and obesity.

This statistic shows the number of famine deaths per100,000 people worldwide from 1900 to 2010. In the 1920s, about 814 people per 100,000 of the global population died as a result of famine.

This table presents a wide variety of historical data in the field of health, lifestyle and health care. Figures on births and mortality, causes of death and the occurrence of certain infectious diseases are available from 1900, other series from later dates. In addition to self-perceived health, the table contains figures on infectious diseases, hospitalisations per diagnosis, life expectancy, lifestyle factors such as smoking, alcohol consumption and obesity, and causes of death. The table also gives information on several aspects of health care, such as the number of practising professionals, the number of available hospital beds, nursing day averages and the expenditures on care. Many subjects are also covered in more detail by data in other tables, although sometimes with a shorter history. Data on notifiable infectious diseases and HIV/AIDS are not included in other tables.

Data available from: 1900

Status of the figures:

2025: The available figures are definite.

2024: Most available figures are definite. Figures are provisional for: - notifiable infectious diseases, hiv, aids; - causes of death.

2023: Most available figures are definite. Figures are provisional for: - notifiable infectious diseases, HIV/AIDS; - diagnoses at hospital admissions; - number of hospital discharges and length of stay; - number of hospital beds; - health professions; - perinatal and infant mortality. Figures are revised provisional for: - expenditures on health and welfare.

2022: Most available figures are definite. Figures are provisional for: - notifiable infectious diseases, HIV/AIDS; - diagnoses at hospital admissions; - number of hospital discharges and length of stay; - number of hospital beds; - health professions. Figures are revised provisional for: - expenditures on health and welfare.

2021: Most available figures are definite. Figures are provisional for: - notifiable infectious diseases, HIV/AIDS; Figures are revised provisional for: - expenditures on health and welfare.

2020 and earlier: Most available figures are definite. Due to 'dynamic' registrations, figures for notifiable infectious diseases, HIV/AIDS remain provisional.

Changes as of 4 July 2025: The most recent available figures have been added for: - population on January 1; - live born children, deaths; - persons in (very) good health; - notifiable infectious diseases, HIV/AIDS; - diagnoses at hospital admissions; - use of medication; - sickness absence; - lifestyle; - use of health care services; - number of hospital discharges and length of stay; - number of hospital beds; - health professions; - expenditures on health and welfare; - healthy life expectancy; - causes of death.

Changes as of 18 december 2024: - Due to a revision of the statistics Health and welfare expenditure 2021, figures for expenditure on health and welfare have been replaced from 2021 onwards. - Revised figures on the volume index of healthcare costs are not yet available, these figures have been deleted from 2021 onwards.

When will new figures be published? December 2025.

This dataset contains information on the number of deaths and age-adjusted death rates for the five leading causes of death in 1900, 1950, and 2000. 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). 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 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. 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. 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. 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. National Center for Health Statistics. Historical Data, 1900-1998. 2009. Available from: https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm.

In 2022, the age-adjusted drug overdose death rate in the United States was 32.6 per 100,000 population. Opioids are the main driver of overdose deaths in the United States. This statistic presents the age-adjusted drug overdose death rate in the U.S. from 1999 to 2022, per 100,000 population.

Natural Disasters Deaths

People killed in natural disasters by country by year

About this dataset

How much do natural disasters cost us? In lives, in dollars, in infrastructure? This dataset attempts to answer those questions, tracking the death toll and damage cost of major natural disasters since 1985. Disasters included are storms ( hurricanes, typhoons, and cyclones ), floods, earthquakes, droughts, wildfires, and extreme temperatures

How to use the dataset

This dataset contains information on natural disasters that have occurred around the world from 1900 to 2017. The data includes the date of the disaster, the location, the type of disaster, the number of people killed, and the estimated cost in US dollars

Research Ideas

• An all-in-one disaster map displaying all recorded natural disasters dating back to 1900.
• Natural disaster hotspots - where do natural disasters most commonly occur and kill the most people?
• A live map tracking current natural disasters around the world

Acknowledgements

License

See the dataset description for more information.

This dataset of U.S. mortality trends since 1900 highlights the differences in age-adjusted death rates and life expectancy at birth by race and sex.

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).

Life expectancy data are available up to 2017. Due to changes in categories of race used in publications, data are not available for the black population consistently before 1968, and not at all before 1960. More information on historical data on age-adjusted death rates is available at https://www.cdc.gov/nchs/nvss/mortality/hist293.htm.

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.

What are people dying from?

This question is essential to guide decisions in public health, and find ways to save lives.

Many leading causes of death receive little mainstream attention. If news reports reflected what children died from, they would say that around 1,400 young children die from diarrheal diseases, 1,000 die from malaria, and 1,900 from respiratory infections – every day.

This can change. Over time, death rates from these causes have declined across the world.

A better understanding of the causes of death has led to the development of technologies, preventative measures, and better healthcare, reducing the chances of dying from a wide range of different causes, across all age groups.

In the past, infectious diseases dominated. But death rates from infectious diseases have fallen quickly – faster than other causes. This has led to a shift in the leading causes of death. Now, non-communicable diseases – such as heart diseases and cancers – are the most common causes of death globally.

More progress is possible, and the impact of causes of death can fall further.

On this page, you will find global data and research on leading causes of death and how they can be prevented.

This data can also help understand the burden of disease more broadly, and offer a lens to see the impacts of healthcare and medicine, habits and behaviours, environmental factors, health infrastructure, and more.

By Saloni Dattani, Fiona Spooner, Hannah Ritchie and Max Roser

This statistic shows the ten countries with the most deaths resulting from earthquakes between 1900 and 2016. Total 876,487 people were killed due to earthquakes in China. Fatalities around the world The leading causes of death worldwide for humans in 2012 were ischaemic heart diseases, with 7.4 million deaths and strokes, with 6.7 million deaths. Apart from these diseases, there are many other dangers for humans all over the world, such as famine, drugs, epidemics or the everyday traffic.

The global famine death rate has decreased over the past decades, 814 people per 100,000 of the global population died as a result of famine, while the number of deaths due to famine was about 3 per 100,000 of the global population in 2000. Famine is a scarcity of food, which can be caused by crop failure, population unbalance or drought. Between 1900 and 2014, the number of deaths due to droughts stood at 3,000,000 in China.

Compared to other countries, the Unites States are ranked as the country with the highest amount of drug-related deaths around the world. 40,393 people passed away due to drugs in 2012, while only 944 drug-related deaths were reported in Germany.

The Ebola outbreak in West Africa is one of the largest outbreaks in history and costs the life of many people. The Ebola virus disease has a high risk of deaths, as of August 26, 2014 there have been 3,069 cases, resulting 1,552 deaths due to outbreak in West Africa.

According to the World Health Organization (WHO), 162 annual traffic fatalities per 100,000 registered vehicles were counted in South Africa, which is the country with the highest number of road-traffic fatalities from 2006 – 2008. Germany is on of the country with the lowest annual traffic fatalities, there were only 9 traffic fatalities per 100,000 registered vehicles.

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

This list contains information about crowd accidents that occurred worldwide between 1900 and 2019. The files included in this dataset are described as follows:

• accident_data_raw.csv - CSV file containing information for all accidents as given in the header. Date, country or location are provided using commonly used standard. Information such as fatalities or the number of people injured are reported using expressions found in the employed references (for example "a dozens of people" is given as "dozens"). References are given in the form of text files as reported below.
• references.zip - ZIP file containing the references used to collect information on each accident. The date of the accident is used as filename (and reported in the last column of the list above). Sources for each accident are given on each line of the relative text file.
• accident_data_numeric.csv - CSV file containing only numeric values corresponding to word expressions. The conversion scheme used here is provided below. Alternative approaches are possible.
• number_conversion.csv - CSV file providing the conversion scheme used to convert word expressions into number.

The burden of influenza in the United States can vary from year to year depending on which viruses are circulating, how many people receive an influenza vaccination, and how effective the vaccination is in that particular year. During the 2023-2024 flu season, around 28,000 people lost their lives to the disease. Although most people recover from influenza without needing medical care, the disease can be deadly among young children, the elderly, and those with weakened immune systems or chronic illnesses. Deaths due to influenza Even though most people recover from influenza without medical care, influenza and pneumonia can be deadly, especially for older people and those with certain preexisting conditions. Influenza is a common cause of pneumonia and although most cases of influenza do not develop into pneumonia, those that do are often more severe and more deadly. Deaths due to influenza are most common among the elderly, with a mortality rate of around 32 per 100,000 population during the 2023-2024 flu season. In comparison, the mortality rate for those aged 50 to 64 years was 9.1 per 100,000 population. Flu vaccinations The most effective way to prevent influenza is to receive an annual influenza vaccination. These vaccines have proven to be safe and are usually cheap and easily accessible. Nevertheless, every year a large share of the population in the United States still fails to get vaccinated against influenza. For example, in the 2022-2023 flu season, only 35 percent of those aged 18 to 49 years received a flu vaccination. Unsurprisingly, children and the elderly are the most likely to get vaccinated. It is estimated that during the 2022-2023 flu season, vaccinations prevented over 929 thousand influenza cases among children aged 6 months to 4 years.

Abstract copyright UK Data Service and data collection copyright owner. The dataset was originally created to allow the construction of age-specific mortality series and cohort mortality series for particular diseases, from the mid-nineteenth century to the present (in conjunction with the comparable mortality database created by the Office of National Statistics which covers 1901 – present). The dataset is fairly comprehensive and therefore allows both fine analysis of trends in single causes and also the construction of consistent aggregated categories of causes over time. Additionally, comparison of trends in individual causes can be used to infer transfers of deaths between categories over time, that may cause artifactual changes in mortality rates of particular causes. The data are presented by sex, allowing calculation of sex ratios. The age-specific and annual nature of the dataset allows the analysis of cause-specific mortality by birth cohort (assuming low migration at the national level). The database can be used in conjunction with the ONS database “Historic Mortality and Population Data, 1901-1992”, already in the UK Data Archive collection as SN 2902, to create continuous cause-of-death series for the period 1848-1992 (or later, if using more recent versions of the ONS database).

Since 1900, the earthquake in Tangshan in China in 1976 caused the highest number of deaths, reaching over 240,000. However, some estimate the number to be over 650,000 fatalities. The earthquake in Haiti in 2010 has the second-highest death toll, but also here numbers vary from just above 100,000 to over 300,000 fatalities. Four of the 10 deadliest earthquakes during the period were registered in China.

BackgroundSuicide underreporting undermines accurate public health assessments and resource allocation for suicide prevention. This study aims at synthesizing evidence on suicide underreporting and to estimate a global underreporting rate.MethodsWe conducted a PRISMA-compliant systematic review on suicide underreporting, following a pre-registered protocol. A meta-analytical synthesis was also conducted. Quantitative data from individual studies was extracted to provide an overall global estimate of suicide underreporting (42 studies covering 71 countries out of the initial 770 unique studies, spanning 1900–2021). Most studies used retrospective institutional datasets to estimate underreporting through reclassification of undetermined deaths or comparisons across databases. Demographic and geographic disparities were also examined.ResultsThe 42 studies selected provided some quantitative data on suicide underreporting for general or specific populations. 14 of these studies provided data to be meta-analyzed. The global suicide underreporting rate was estimated to be 17.9% (95% CI: 10.9–28.1%) with large differences between countries with high and low/very low data quality. In this scenario, the last WHO estimates of suicide deaths – corrected for underreporting – would be more than one million (1,000,638; 95% CI: 859,511–1,293,006) and not 727,000 suicides per year. Underreporting was higher in low- and middle-income countries (LMICs) with incomplete death registration systems, such as India and China (34.9%; 95% CI 20.3–53%), while high-income countries exhibited lower rates (11.5%; 95% CI 6.6–19.3%). Contributing factors included stigma, religiosity, limited forensic resources, and inconsistent use of International Classification of Diseases (ICD) codes. Gender and age disparities were notable; Female suicides and those among younger or older individuals were more likely to be misclassified.DiscussionAddressing suicide underreporting requires improving death registration systems globally, particularly in LMICs. Standardizing ICD usage, improving forensic capacity, and reducing stigma are critical steps to ensure accurate data. Heterogeneity, geographical disparities, temporal biases, and invariance of suicide underreporting for countries with low-quality data demand further corroboration of these findings.Systematic Review Registrationhttps://osf.io/9j8dg.

Crime and socioeconomic data for the German Reich and mortality statistics for Prussia at county level for 1871 to 1912. Topics:A: variables for the entire German Reich (1047 counties) 1. crime data:a) totals of all convicted for crimes and offences per 100000b) number convicted due to dangerous bodily injury per 100000c) number convicted due to simple theft per 100000 2. demographic information:a) totals of population of the age of criminal responsibilityin the counties for 1885, 1905 and 1910b) male German-speaking population in 1900c) female German-speaking population in 1900d) male, non-German-speaking population in 1900e) female, non-German-speaking population in 1900f) primary ethnic groups in 1900 3. data on urbanization:a) total population of the municipalities with more than 2000 residents per county in 1900b) population in medium-sized cities per county in 1900c) population in large cities per county in 1900d) total population per county in 1900e) typing the counties in city counties (=1) and districts (=2) in 1900 4. Geographic dataa) short designation of all counties (1881 to 1912)b) identification number of all counties listed under 4a)c) surface area of the county in square kilometers in 1900 B: variables for Prussia (583 counties) mortality data for 1885,1886, 1904, 1905 and 1906: a) totals of deaths (according to sex) for the respective yearb) number of deaths due to Tuberculosis (according to sex) for therespective yearc) number of deaths due to suicide (according to sex) for the respective yeard) number of deaths due to murder and manslaughter(according to sex) for the respective year The variables for the Prussian counties can be compared with the corresponding counties of the German Reich.

This statistic illustrates deaths due to drought worldwide from 1900 to 2016*. The dry period of April 1983 in Sudan caused around 150,000 deaths.

Deaths due to drought worldwide

The 1928 drought in the People’s Republic of China was the deadliest drought during the period between 1900 and 2016, causing the death of an estimated three million people. This drought in the Chinese provinces of Henan, Shaanxi and Gansu brought about crop failure and widespread famine. It lasted from 1928 to 1930 and the effects were exacerbated by insufficient or inefficient government relief and aide.

Though China’s drought of 1928 is listed as the deadliest drought event, India’s drought of May 1987 is reported to have affected over 300 million individuals. During this weather disaster, India’s northern grain lands were hit hardest, cutting deeply into grain and milk production. Less than a third of the country received normal rainfall that year—rainfall deficiency was at 26 percent—but in this instance the government was able to prevent much suffering by distributing stored food.

The costliest drought occurred in the United States in 2012. The U.S. suffered an economic loss of about 20 billion U.S. dollars.

Other deadly natural disasters are tropical cyclones, severe thunderstorms and wildfires. 52 people died because of a tropical cyclone in the U.S. in 2016. 32 people died of wildfire that year. The United States ranks second on the list of countries with the most natural disasters in 2015, due in great part to the 13 meteorological disasters that occurred that year.

Crime and socioeconomic data for the German Reich and mortality statistics for Prussia at county level for 1871 to 1912.

Topics: A: variables for the entire German Reich (1047 counties)

1. crime data: a) totals of all convicted for crimes and offences per 100000 b) number convicted due to dangerous bodily injury per 100000 c) number convicted due to simple theft per 100000

2. demographic information: a) totals of population of the age of criminal responsibility in the counties for 1885, 1905 and 1910 b) male German-speaking population in 1900 c) female German-speaking population in 1900 d) male, non-German-speaking population in 1900 e) female, non-German-speaking population in 1900 f) primary ethnic groups in 1900

3. data on urbanization: a) total population of the municipalities with more than 2000 residents per county in 1900 b) population in medium-sized cities per county in 1900 c) population in large cities per county in 1900 d) total population per county in 1900 e) typing the counties in city counties (=1) and districts (=2) in 1900

4. Geographic data a) short designation of all counties (1881 to 1912) b) identification number of all counties listed under 4a) c) surface area of the county in square kilometers in 1900

B: variables for Prussia (583 counties) mortality data for 1885, 1886, 1904, 1905 and 1906:

a) totals of deaths (according to sex) for the respective year b) number of deaths due to Tuberculosis (according to sex) for the respective year c) number of deaths due to suicide (according to sex) for the respective year d) number of deaths due to murder and manslaughter (according to sex) for the respective year

The variables for the Prussian counties can be compared with the corresponding counties of the German Reich.

A dataset to advance the study of life-cycle interactions of biomedical and socioeconomic factors in the aging process. The EI project has assembled a variety of large datasets covering the life histories of approximately 39,616 white male volunteers (drawn from a random sample of 331 companies) who served in the Union Army (UA), and of about 6,000 African-American veterans from 51 randomly selected United States Colored Troops companies (USCT). Their military records were linked to pension and medical records that detailed the soldiers������?? health status and socioeconomic and family characteristics. Each soldier was searched for in the US decennial census for the years in which they were most likely to be found alive (1850, 1860, 1880, 1900, 1910). In addition, a sample consisting of 70,000 men examined for service in the Union Army between September 1864 and April 1865 has been assembled and linked only to census records. These records will be useful for life-cycle comparisons of those accepted and rejected for service. Military Data: The military service and wartime medical histories of the UA and USCT men were collected from the Union Army and United States Colored Troops military service records, carded medical records, and other wartime documents. Pension Data: Wherever possible, the UA and USCT samples have been linked to pension records, including surgeon''''s certificates. About 70% of men in the Union Army sample have a pension. These records provide the bulk of the socioeconomic and demographic information on these men from the late 1800s through the early 1900s, including family structure and employment information. In addition, the surgeon''''s certificates provide rich medical histories, with an average of 5 examinations per linked recruit for the UA, and about 2.5 exams per USCT recruit. Census Data: Both early and late-age familial and socioeconomic information is collected from the manuscript schedules of the federal censuses of 1850, 1860, 1870 (incomplete), 1880, 1900, and 1910. Data Availability: All of the datasets (Military Union Army; linked Census; Surgeon''''s Certificates; Examination Records, and supporting ecological and environmental variables) are publicly available from ICPSR. In addition, copies on CD-ROM may be obtained from the CPE, which also maintains an interactive Internet Data Archive and Documentation Library, which can be accessed on the Project Website. * Dates of Study: 1850-1910 * Study Features: Longitudinal, Minority Oversamples * Sample Size: ** Union Army: 35,747 ** Colored Troops: 6,187 ** Examination Sample: 70,800 ICPSR Link: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06836

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.