As of 2022, the countries with the highest death rates worldwide were Ukraine, Bulgaria, and Moldova. In these countries, there were 17 to 21 deaths per 1,000 people. The country with the lowest death rate is Qatar, where there is just one death per 1,000 people. Leading causes of death The leading causes of death worldwide are by far, ischaemic heart disease and stroke, accounting for a combined 27 percent of all deaths in 2019. In that year, there were 8.89 million deaths worldwide from ischaemic heart disease and 6.19 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 2022, there were around 3.27 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 40 percent of all deaths in 2022. 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 65,790 deaths among men alone in the year 2024. Prostate cancer is the second-deadliest cancer for men in the U.S. while breast cancer is the second deadliest for women. In 2022, the fourth 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.

This statistic shows the 20 countries with the highest maternal mortality rate per 100,000 live births in 2017. In that year, with a maternal mortality rate of 1,150 per 100,000 live births, South Sudan was the leading country worldwide in maternal mortality. It was followed by Chad and Sierra Leone with rates of 1,140 and 1,120, respectively.

Maternal mortality rates can vary significantly around the world. For example, in 2022, Estonia had a maternal mortality rate of zero per 100,000 live births, while Mexico reported a rate of 38 deaths per 100,000 live births. However, the regions with the highest number of maternal deaths are Sub-Saharan Africa and Southern Asia, with differences between countries and regions often reflecting inequalities in health care services and access. Most causes of maternal mortality are preventable and treatable with the most common causes including severe bleeding, infections, complications during delivery, high blood pressure during pregnancy, and unsafe abortion. Maternal mortality in the United States In 2022, there were a total of 817 maternal deaths in the United States. Women aged 25 to 39 years accounted for 578 of these deaths, however, rates of maternal mortality are much higher among women aged 40 years and older. In 2022, the rate of maternal mortality among women aged 40 years and older in the U.S. was 87 per 100,000 live births, compared to a rate of 21 among women aged 25 to 39 years. The rate of maternal mortality in the U.S. has risen in recent years among all age groups. Differences in maternal mortality in the U.S. by race/ethnicity Sadly, there are great disparities in maternal mortality in the United States among different races and ethnicities. In 2022, the rate of maternal mortality among non-Hispanic white women was about 19 per 100,000 live births, while non-Hispanic Black women died from maternal causes at a rate of almost 50 per 100,000 live births. Rates of maternal mortality have risen for white and Hispanic women in recent years, but Black women have by far seen the largest increase in maternal mortality. In 2022, around 253 Black women died from maternal causes in the United States.

In 2022, Germany had an overall crude death rate of 13 per 1,000 people. In comparison, the death rate in South Korea was seven per 1,000 people. This statistic represents a ranking of select developed and developing countries based on death rates as of 2022.

This map shows the infant mortality rate and total number of infant deaths. This is shown by county, state, and country from the 2022 County Health Rankings. The national average is 5.7 deaths per 1,000.The data comes from the County Health Rankings 2022 layer. The County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. "By ranking the health of nearly every county in the nation, County Health Rankings & Roadmaps (CHR&R) illustrates how where we live affects how well and how long we live. CHR&R also shows what each of us can do to create healthier places to live, learn, work, and play – for everyone."Counties are ranked within their state on both health outcomes and health factors. Counties with a lower (better) health outcomes ranking than health factors ranking may see the health of their county decline in the future, as factors today can result in outcomes later. Conversely, counties with a lower (better) factors ranking than outcomes ranking may see the health of their county improve in the future.

This statistic shows the 20 countries* with the highest infant mortality rate in 2024. An estimated 101.3 infants per 1,000 live births died in the first year of life in Afghanistan in 2024. Infant and child mortality Infant mortality usually refers to the death of children younger than one year. Child mortality, which is often used synonymously with infant mortality, is the death of children younger than five. Among the main causes are pneumonia, diarrhea – which causes dehydration – and infections in newborns, with malnutrition also posing a severe problem. As can be seen above, most countries with a high infant mortality rate are developing countries or emerging countries, most of which are located in Africa. Good health care and hygiene are crucial in reducing child mortality; among the countries with the lowest infant mortality rate are exclusively developed countries, whose inhabitants usually have access to clean water and comprehensive health care. Access to vaccinations, antibiotics and a balanced nutrition also help reducing child mortality in these regions. In some countries, infants are killed if they turn out to be of a certain gender. India, for example, is known as a country where a lot of girls are aborted or killed right after birth, as they are considered to be too expensive for poorer families, who traditionally have to pay a costly dowry on the girl’s wedding day. Interestingly, the global mortality rate among boys is higher than that for girls, which could be due to the fact that more male infants are actually born than female ones. Other theories include a stronger immune system in girls, or more premature births among boys.

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

In 2022, with just one death per one thousand people, Qatar was the country with the lowest death rate worldwide. This statistic shows a ranking of the 20 countries with the lowest death rates worldwide, as of 2022. Health in high-income countries Countries with the highest life expectancies are also often high-income countries with well-developed economic, social and health care systems, providing adequate resources and access to treatment for health concerns. Health care expenditure as a share of GDP varies per country; for example, spending in the United States is higher than in other OECD countries due to higher costs and prices for care services and products. In developed countries, the main burden of disease is often due to non-communicable diseases occurring in old age such as cardiovascular diseases and cancer. High burden in low-income countries The countries with the lowest life expectancy worldwide are all in Africa- including Chad, Lesotho, and Nigeria- with life expectancies reaching up to 20 years shorter than the average global life expectancy. Leading causes of death in low-income countries include respiratory infections and diarrheal diseases, as these countries are often hit with the double burden of infectious diseases plus non-communicable diseases, such as those related to cardiovascular pathologies. Additionally, these countries often lack the resources and infrastructure to sustain effective healthcare systems and fail to provide appropriate access and treatment for their populations.

The average for 2020 based on 11 countries was 93 deaths per 100,000 births. The highest value was in Cambodia: 218 deaths per 100,000 births and the lowest value was in Singapore: 7 deaths per 100,000 births. The indicator is available from 2000 to 2020. Below is a chart for all countries where data are available.

Chart and table of the U.S. infant mortality rate from 1950 to 2025. United Nations projections are also included through the year 2100.

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.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

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Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

This statistic shows the 20 countries * with the lowest infant mortality rate in 2024. An estimated 1.5 out of 1,000 live births died in the first year of life in Slovenia in 2024. Infant mortality Infant mortality rates are often used as an indicator of the health and well-being of a nation. Monaco, Iceland, and Japan are among the top three countries with the lowest infant mortality rates with around 2 infant deaths per 1,000 infants within their first year of life. Generally, the countries with the lowest infant mortality also have some of the highest average life expectancy figures. Additionally, the countries with the highest density of physicians and doctors also generally report low infant mortality. Yet, many different factors contribute to differing rates, including the overall income of a country, health spending per capita, a mother’s level of education, environmental conditions, and medical infrastructure, to name a few. This creates a lot of variation concerning the level of childbirth and infant care around the world. The countries with the highest rates of infant mortality include Afghanistan, Mali, and Somalia. These countries experience around 100 infant deaths per 1,000 infants in their first year of life. While the reasons for high rates of infant mortality are numerous, the leading causes of death for children under the year five around the world are Pneumonia, Diarrhea, and Prematurity.

The average for 2021 based on 12 countries was 14 deaths per 1000 live births. The highest value was in Guyana: 23 deaths per 1000 live births and the lowest value was in Chile: 6 deaths per 1000 live births. The indicator is available from 1960 to 2022. Below is a chart for all countries where data are available.

Explore The Human Capital Report dataset for insights into Human Capital Index, Development, and World Rankings. Find data on Probability of Survival to Age 5, Expected Years of School, Harmonized Test Scores, and more.

Low income, Upper middle income, Lower middle income, High income, Human Capital Index (Lower Bound), Human Capital Index, Human Capital Index (Upper Bound), Probability of Survival to Age 5, Expected Years of School, Harmonized Test Scores, Learning-Adjusted Years of School, Fraction of Children Under 5 Not Stunted, Adult Survival Rate, Development, Human Capital, World Rankings

Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahrain, Bangladesh, Belarus, Belgium, Benin, Bhutan, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo, Costa Rica, Croatia, Cyprus, Denmark, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Kuwait, Latvia, Lebanon, Lesotho, Liberia, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovenia, Solomon Islands, South Africa, South Sudan, Spain, Sri Lanka, Sudan, Sweden, Switzerland, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Tuvalu, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Vietnam, Yemen, Zambia, Zimbabwe, WORLD

Follow data.kapsarc.org for timely data to advance energy economics research.

Last year edition of the World Economic Forum Human Capital Report explored the factors contributing to the development of an educated, productive and healthy workforce. This year edition deepens the analysis by focusing on a number of key issues that can support better design of education policy and future workforce planning.

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.

Chart and table of the India infant mortality rate from 1950 to 2025. United Nations projections are also included through the year 2100.

Chart and table of the Pakistan infant mortality rate from 1950 to 2025. United Nations projections are also included through the year 2100.

Distribution of images found in the IQ-OTHNCCD dataset into Benign, Malignant, and Normal classes.

Lung cancer is one of the leading causes of cancer-related deaths worldwide. To reduce the mortality rate, early detection and proper treatment should be ensured. Computer-aided diagnosis methods analyze different modalities of medical images to increase diagnostic precision. In this paper, we propose an ensemble model, called the Mitscherlich function-based Ensemble Network (MENet), which combines the prediction probabilities obtained from three deep learning models, namely Xception, InceptionResNetV2, and MobileNetV2, to improve the accuracy of a lung cancer prediction model. The ensemble approach is based on the Mitscherlich function, which produces a fuzzy rank to combine the outputs of the said base classifiers. The proposed method is trained and tested on the two publicly available lung cancer datasets, namely Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) and LIDC-IDRI, both of these are computed tomography (CT) scan datasets. The obtained results in terms of some standard metrics show that the proposed method performs better than state-of-the-art methods. The codes for the proposed work are available at https://github.com/SuryaMajumder/MENet.