23 datasets found
  1. COVID-19-related excess mortality rates in select countries in 2020, by age

    • statista.com
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    Statista, COVID-19-related excess mortality rates in select countries in 2020, by age [Dataset]. https://www.statista.com/statistics/1259019/covid-related-excess-mortality-rate-in-the-us-and-select-countries-by-age/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2020
    Area covered
    Worldwide
    Description

    In 2020, the U.S. had the highest COVID-19 pandemic-related excess mortality rate among non-elderly people compared to other peer countries. “Excess deaths” represent the number of deaths beyond what is expected in a typical year. This measure illustrates the mortality directly or indirectly associated with the COVID-19 pandemic. This statistic presents the COVID-19 pandemic-related excess mortality rate in the U.S. and select countries in 2020, by age group (per 100,000 people in age group).

  2. Rate of excess deaths due to COVID-19 pandemic in select countries worldwide...

    • statista.com
    Updated May 5, 2022
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    Statista (2022). Rate of excess deaths due to COVID-19 pandemic in select countries worldwide 2020-21 [Dataset]. https://www.statista.com/statistics/1083605/rate-excess-deaths-covid-pandemic-select-countries/
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    Dataset updated
    May 5, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    It is estimated that from 2020 to 2021, the mean rate of excess deaths associated with the COVID-19 pandemic from all-causes was highest in Peru. In 2020-2021, there were around 437 excess deaths due to the COVID-19 pandemic per 100,000 population in Peru. This statistic shows the mean number of excess deaths associated with the COVID-19 pandemic from all-causes in 2020-2021 in select countries worldwide, per 100,000 population.

  3. COVID-19-related excess mortality rate in the U.S. in 2020, by age and...

    • statista.com
    Updated Nov 29, 2025
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    Statista (2025). COVID-19-related excess mortality rate in the U.S. in 2020, by age and ethnicity [Dataset]. https://www.statista.com/statistics/1259041/covid-related-excess-mortality-rate-in-the-us-by-age-and-ethnicity/
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    Dataset updated
    Nov 29, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2020
    Area covered
    United States
    Description

    In 2020, the highest COVID-19 pandemic-related excess mortality rate was among older Hispanics. “Excess deaths” represent the number of deaths beyond what is expected in a typical year. This measure illustrates the mortality directly or indirectly associated with the COVID-19 pandemic. This statistic presents COVID-19 pandemic-related excess mortality rates in the U.S. in 2020, by age group and ethnicity.

  4. Excess mortality in England and English regions: March 2020 to December 2023...

    • gov.uk
    Updated Feb 20, 2024
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    Office for Health Improvement and Disparities (2024). Excess mortality in England and English regions: March 2020 to December 2023 [Dataset]. https://www.gov.uk/government/statistics/excess-mortality-in-england-and-english-regions
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    Dataset updated
    Feb 20, 2024
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Office for Health Improvement and Disparities
    Area covered
    England
    Description

    This analysis is no longer being updated. This is because the methodology and data for baseline measurements is no longer applicable.

    From February 2024, excess mortality reporting is available at: Excess mortality in England.

    Measuring excess mortality: a guide to the main reports details the different analysis available and how and when they should be used for the UK and England.

    The data in these reports is from 20 March 2020 to 29 December 2023. The first 2 reports on this page provide an estimate of excess mortality during and after the COVID-19 pandemic in:

    • England
    • English regions

    ‘Excess mortality’ in these analyses is defined as the number of deaths that are above the estimated number expected. The expected number of deaths is modelled using 5 years of data from preceding years to estimate the number of death registrations expected in each week.

    In both reports, excess deaths are broken down by age, sex, upper tier local authority, ethnic group, level of deprivation, cause of death and place of death. The England report also includes a breakdown by region.

    For previous reports, see:

    If you have any comments, questions or feedback, contact us at pha-ohid@dhsc.gov.uk.

    Other excess mortality analyses

    We also publish a set of bespoke analyses using the same excess mortality methodology and data but cut in ways that are not included in the England and English regions reports on this page.

  5. Cardiovascular Disease Death Rates, Trends, and Excess Death Rates Among US...

    • catalog.data.gov
    • data.virginia.gov
    • +2more
    Updated Feb 3, 2025
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    Centers for Disease Control and Prevention (2025). Cardiovascular Disease Death Rates, Trends, and Excess Death Rates Among US Adults (35+) by County and Age Group – 2010-2020 [Dataset]. https://catalog.data.gov/dataset/cardiovascular-disease-death-rates-trends-and-excess-death-rates-among-us-adults-35-b-2010
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    Dataset updated
    Feb 3, 2025
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Description

    This dataset documents cardiovascular disease (CVD) death rates, relative and absolute excess death rates, and trends. Specifically, this report presents county (or county equivalent) estimates of CVD death rates in 2000-2020, trends during 2010-2019, and relative and absolute excess death rates in 2020 by age group (ages 35–64 years, ages 65 years and older). All estimates were generated using a Bayesian spatiotemporal model and a smoothed over space, time, and 10-year age groups. Rates are age-standardized in 10-year age groups using the 2010 US population. Data source: National Vital Statistics System.

  6. Weekly all-cause mortality surveillance: 2023 to 2024

    • gov.uk
    Updated Jul 18, 2024
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    UK Health Security Agency (2024). Weekly all-cause mortality surveillance: 2023 to 2024 [Dataset]. https://www.gov.uk/government/statistics/weekly-all-cause-mortality-surveillance-2023-to-2024
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    Dataset updated
    Jul 18, 2024
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    UK Health Security Agency
    Description

    The UK Health Security Agency (UKHSA) weekly all-cause mortality surveillance helps to detect and report significant weekly excess mortality (deaths) above normal seasonal levels. This report doesn’t assess general trends in death rates or link excess death figures to particular factors.

    Excess mortality is defined as a significant number of deaths reported over that expected for a given week in the year, allowing for weekly variation in the number of deaths. UKHSA investigates any spikes seen which may inform public health actions.

    Reports are currently published weekly. In previous years, reports ran from October to September. From 2021 to 2022, reports will run from mid-July to mid-July each year. This change is to align with the reports for the national flu and COVID-19 weekly surveillance report.

    This page includes reports published from 13 July 2023 to the present.

    Reports are also available for:

    Please direct any enquiries to enquiries@ukhsa.gov.uk

    Our statistical practice is regulated by the Office for Statistics Regulation (OSR). The OSR sets the standards of trustworthiness, quality and value in the https://code.statisticsauthority.gov.uk">Code of Practice for Statistics that all producers of Official Statistics should adhere to.

  7. Weekly number of excess deaths in England and Wales 2020-2025

    • statista.com
    Updated Sep 11, 2025
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    Statista (2025). Weekly number of excess deaths in England and Wales 2020-2025 [Dataset]. https://www.statista.com/statistics/1131428/excess-deaths-in-england-and-wales/
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    Dataset updated
    Sep 11, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2020 - Aug 2025
    Area covered
    Wales, England
    Description

    For the week ending August 29, 2025, weekly deaths in England and Wales were 985 below the number expected, compared with 855 below what was expected in the previous week. In late 2022 and through early 2023, excess deaths were elevated for a number of weeks, with the excess deaths figure for the week ending January 13, 2023, the highest since February 2021. In the middle of April 2020, at the height of the COVID-19 pandemic, there were almost 12,000 excess deaths a week recorded in England and Wales. It was not until two months later, in the week ending June 19, 2020, that the number of deaths began to be lower than the five-year average for the corresponding week. Most deaths since 1918 in 2020 In 2020, there were 689,629 deaths in the United Kingdom, making that year the deadliest since 1918, at the height of the Spanish influenza pandemic. As seen in the excess death figures, April 2020 was by far the worst month in terms of deaths during the pandemic. The weekly number of deaths for weeks 16 and 17 of that year were 22,351, and 21,997 respectively. Although the number of deaths fell to more usual levels for the rest of that year, a winter wave of the disease led to a high number of deaths in January 2021, with 18,676 deaths recorded in the fourth week of that year. For the whole of 2021, there were 667,479 deaths in the UK, 22,150 fewer than in 2020. Life expectancy in the UK goes into reverse In 2022, life expectancy at birth for women in the UK was 82.6 years, while for men it was 78.6 years. This was the lowest life expectancy in the country for ten years, and came after life expectancy improvements stalled throughout the 2010s, and then declined from 2020 onwards. There is also quite a significant regional difference in life expectancy in the UK. In the London borough of Kensington and Chelsea, for example, the life expectancy for men was 81.5 years, and 86.5 years for women. By contrast, in Blackpool, in North West England, male life expectancy was just 73.1 years, while for women, life expectancy was lowest in Glasgow, at 78 years.

  8. Excess Deaths Associated with COVID-19

    • datalumos.org
    delimited
    Updated Apr 24, 2025
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    United States Department of Health and Human Services. Centers for Disease Control and Prevention. National Center for Health Statistics (2025). Excess Deaths Associated with COVID-19 [Dataset]. http://doi.org/10.3886/E227667V1
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    delimitedAvailable download formats
    Dataset updated
    Apr 24, 2025
    Authors
    United States Department of Health and Human Services. Centers for Disease Control and Prevention. National Center for Health Statistics
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Time period covered
    2017 - 2023
    Area covered
    United States
    Description

    Estimates of excess deaths can provide information about the burden of mortality potentially related to the COVID-19 pandemic, including deaths that are directly or indirectly attributed to COVID-19. Excess deaths are typically defined as the difference between the observed numbers of deaths in specific time periods and expected numbers of deaths in the same time periods. This visualization provides weekly estimates of excess deaths by the jurisdiction in which the death occurred. Weekly counts of deaths are compared with historical trends to determine whether the number of deaths is significantly higher than expected.Counts of deaths from all causes of death, including COVID-19, are presented. As some deaths due to COVID-19 may be assigned to other causes of deaths (for example, if COVID-19 was not diagnosed or not mentioned on the death certificate), tracking all-cause mortality can provide information about whether an excess number of deaths is observed, even when COVID-19 mortality may be undercounted. Additionally, deaths from all causes excluding COVID-19 were also estimated. Comparing these two sets of estimates — excess deaths with and without COVID-19 — can provide insight about how many excess deaths are identified as due to COVID-19, and how many excess deaths are reported as due to other causes of death. These deaths could represent misclassified COVID-19 deaths, or potentially could be indirectly related to the COVID-19 pandemic (e.g., deaths from other causes occurring in the context of health care shortages or overburdened health care systems).Estimates of excess deaths can be calculated in a variety of ways, and will vary depending on the methodology and assumptions about how many deaths are expected to occur. Estimates of excess deaths presented in this webpage were calculated using Farrington surveillance algorithms (1). A range of values for the number of excess deaths was calculated as the difference between the observed count and one of two thresholds (either the average expected count or the upper bound of the 95% prediction interval), by week and jurisdiction.Provisional death counts are weighted to account for incomplete data. However, data for the most recent week(s) are still likely to be incomplete. Weights are based on completeness of provisional data in prior years, but the timeliness of data may have changed in 2020 relative to prior years, so the resulting weighted estimates may be too high in some jurisdictions and too low in others. As more information about the accuracy of the weighted estimates is obtained, further refinements to the weights may be made, which will impact the estimates. Any changes to the methods or weighting algorithm will be noted in the Technical Notes when they occur. More detail about the methods, weighting, data, and limitations can be found in the Technical Notes.This visualization includes several different estimates:Number of excess deaths: A range of estimates for the number of excess deaths was calculated as the difference between the observed count and one of two thresholds (either the average expected count or the upper bound threshold), by week and jurisdiction. Negative values, where the observed count fell below the threshold, were set to zero.Percent excess: The percent excess was defined as the number of excess deaths divided by the threshold.Total number of excess deaths: The total number of excess deaths in each jurisdiction was calculated by summing the excess deaths in each week, from February 1, 2020 to present. Similarly, the total number of excess deaths for the US overall was computed as a sum of jurisdiction-specific numbers of excess deaths (with negative values set to zero), and not directly estimated using the Farrington surveillance algorithms.Select a dashboard from the menu, then click on “Update Dashboard” to navigate through the different graphics.The first dashboard shows the weekly predicted counts of deaths from all causes, and the threshold for the expected number of deaths. Select a jurisdiction from the drop-down menu to show data for that jurisdiction.The second dashboard shows the weekly predicted counts of deaths from all causes and the weekly count of deaths from all causes excluding COVID-19. Select a jurisdiction from the drop-down menu to show data for that jurisdiction.The th

  9. Data_Sheet_1_Sex disparities of the effect of the COVID-19 pandemic on...

    • frontiersin.figshare.com
    • datasetcatalog.nlm.nih.gov
    docx
    Updated Jun 18, 2024
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    Huan Deng; Yishan Liu; Fan Lv; Xiaofeng Li; Mingyan Qi; Yajing Bo; Sikai Qiu; Xinyuan He; Fanpu Ji; Qing-Lei Zeng; Ning Gao (2024). Data_Sheet_1_Sex disparities of the effect of the COVID-19 pandemic on mortality among patients living with tuberculosis in the United States.docx [Dataset]. http://doi.org/10.3389/fpubh.2024.1413604.s001
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    docxAvailable download formats
    Dataset updated
    Jun 18, 2024
    Dataset provided by
    Frontiers Mediahttp://www.frontiersin.org/
    Authors
    Huan Deng; Yishan Liu; Fan Lv; Xiaofeng Li; Mingyan Qi; Yajing Bo; Sikai Qiu; Xinyuan He; Fanpu Ji; Qing-Lei Zeng; Ning Gao
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundWe aimed to determine the trend of TB-related deaths during the COVID-19 pandemic.MethodsTB-related mortality data of decedents aged ≥25 years from 2006 to 2021 were analyzed. Excess deaths were estimated by determining the difference between observed and projected mortality rates during the pandemic.ResultsA total of 18,628 TB-related deaths were documented from 2006 to 2021. TB-related age-standardized mortality rates (ASMRs) were 0.51 in 2020 and 0.52 in 2021, corresponding to an excess mortality of 10.22 and 9.19%, respectively. Female patients with TB demonstrated a higher relative increase in mortality (26.33 vs. 2.17% in 2020; 21.48 vs. 3.23% in 2021) when compared to male. Female aged 45–64 years old showed a surge in mortality, with an annual percent change (APC) of −2.2% pre-pandemic to 22.8% (95% CI: −1.7 to 68.7%) during the pandemic, corresponding to excess mortalities of 62.165 and 99.16% in 2020 and 2021, respectively; these excess mortality rates were higher than those observed in the overall female population ages 45–64 years in 2020 (17.53%) and 2021 (33.79%).ConclusionThe steady decline in TB-related mortality in the United States has been reversed by COVID-19. Female with TB were disproportionately affected by the pandemic.

  10. COVID-19 death rates in the United States as of March 10, 2023, by state

    • statista.com
    Updated May 15, 2024
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    Statista (2024). COVID-19 death rates in the United States as of March 10, 2023, by state [Dataset]. https://www.statista.com/statistics/1109011/coronavirus-covid19-death-rates-us-by-state/
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    Dataset updated
    May 15, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    As of March 10, 2023, the death rate from COVID-19 in the state of New York was 397 per 100,000 people. New York is one of the states with the highest number of COVID-19 cases.

  11. Excess and avoidable mortality by region and time period, U.S., January 3,...

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Michael A. Stoto; Samantha Schlageter; John D. Kraemer (2023). Excess and avoidable mortality by region and time period, U.S., January 3, 2020 –September 26, 2021. [Dataset]. http://doi.org/10.1371/journal.pone.0265053.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Michael A. Stoto; Samantha Schlageter; John D. Kraemer
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    United States
    Description

    Excess and avoidable mortality by region and time period, U.S., January 3, 2020 –September 26, 2021.

  12. H

    Replication Data for: Bowling Alone or Masking Together? The Role of Social...

    • dataverse.harvard.edu
    Updated Mar 18, 2021
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    Timothy Fraser; Daniel P. Aldrich; Courtney Page-Tan (2021). Replication Data for: Bowling Alone or Masking Together? The Role of Social Capital in Excess Death Rates from COVID19 [Dataset]. http://doi.org/10.7910/DVN/JTTNKO
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 18, 2021
    Dataset provided by
    Harvard Dataverse
    Authors
    Timothy Fraser; Daniel P. Aldrich; Courtney Page-Tan
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Abstract: Much attention on the spread and impact of the ongoing pandemic has focused on institutional factors such as government capacity along with population level characteristics such as race, income and age. This paper draws on a growing body of evidence that bonding, bridging, and linking social capital - the horizontal and vertical ties that bind societies together - impact public health to explain why some US counties have seen higher (or lower) excess deaths during the COVID19 pandemic than others. Drawing on county-level reports from the Centers for Disease Control and Prevention (CDC) since February 2020, we calculated the number of excess deaths per county compared to 2018. Starting with a balanced panel dataset of county observations over time, we used coarsened exact matching to create a smaller but more similar set of communities which differ primarily in terms of social capital. Controlling for a number of factors, including mobility, politics and governance, health care quality, and demographic characteristics, we find that both bonding and linking social capital reduce the toll of COVID19 on communities. Our findings bring with them policy implications for public health officials, local government officials, and civil society organizations.

  13. Cardiovascular Disease Death Rates, Trends, and Excess Death Rates Among US...

    • healthdata.gov
    csv, xlsx, xml
    Updated Jul 16, 2025
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    (2025). Cardiovascular Disease Death Rates, Trends, and Excess Death Rates Among US Adults (35+) by County and Age Group – 2010-2020 - au45-g5w7 - Archive Repository [Dataset]. https://healthdata.gov/dataset/Cardiovascular-Disease-Death-Rates-Trends-and-Exce/wqpu-k6jb
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    xml, csv, xlsxAvailable download formats
    Dataset updated
    Jul 16, 2025
    Description

    This dataset tracks the updates made on the dataset "Cardiovascular Disease Death Rates, Trends, and Excess Death Rates Among US Adults (35+) by County and Age Group – 2010-2020" as a repository for previous versions of the data and metadata.

  14. H

    COVID-19: US federal accountability for entry, spread, and...

    • dataverse.harvard.edu
    Updated Feb 17, 2021
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    W.P. Hanage; Testa, C.,; J.T. Chen; Letitia Davis; Elise Pechter; Peg Seminario; Mauricio Santillana; Nancy Krieger (2021). COVID-19: US federal accountability for entry, spread, and inequities—lessons for the future [Dataset]. http://doi.org/10.7910/DVN/JFKNEO
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 17, 2021
    Dataset provided by
    Harvard Dataverse
    Authors
    W.P. Hanage; Testa, C.,; J.T. Chen; Letitia Davis; Elise Pechter; Peg Seminario; Mauricio Santillana; Nancy Krieger
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    United States
    Description

    The United States (US) has been among those nations most severely affected by the first—and subsequent—phases of the pandemic of COVID-19, the disease caused by SARS-CoV-2. With only 4% of the worldwide population, the US has seen about 22% of COVID-19 deaths. Despite formidable advantages in resources and expertise, presently the per capita mortality rate is over 585/million, respectively 2.4 and 5 times higher compared to Canada and Germany. As we enter Fall 2020, the US is enduring ongoing outbreaks across large regions of the country. Moreover, within the US, an early and persistent feature of the pandemic has been the disproportionate impact on populations already made vulnerable by racism and dangerous jobs, inadequate wages, and unaffordable housing, and this is true for both the headline public health threat and the additional disastrous economic impacts. In this article we assess the impact of missteps by the Federal Government in three specific areas: the introduction of the virus to the US and the establishment of community transmission; the lack of national COVID-19 workplace standards and enforcement, and lack of personal protective equipment (PPE) for workplaces as represented by complaints to the Occupational Safety and Health Administration (OSHA) which we find are correlated with deaths 16 days later (ρ = 0.83); and the total excess deaths in 2020 to date already total more than 230,000, while COVID-19 mortality rates exhibit severe—and rising—inequities in race/ethnicity, including among working age adults.

  15. Death rates for all causes in the U.S. 1950-2023

    • statista.com
    Updated Mar 15, 2025
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    Statista (2025). Death rates for all causes in the U.S. 1950-2023 [Dataset]. https://www.statista.com/statistics/189670/death-rates-for-all-causes-in-the-us-since-1950/
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    Dataset updated
    Mar 15, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    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.

  16. Mortality rates, by age group

    • www150.statcan.gc.ca
    • open.canada.ca
    Updated Dec 4, 2024
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    Government of Canada, Statistics Canada (2024). Mortality rates, by age group [Dataset]. http://doi.org/10.25318/1310071001-eng
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    Dataset updated
    Dec 4, 2024
    Dataset provided by
    Government of Canadahttp://www.gg.ca/
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    Number of deaths and mortality rates, by age group, sex, and place of residence, 1991 to most recent year.

  17. f

    Excess mortality in Sierra Leone at ages ≥30 years comparing weekly average...

    • plos.figshare.com
    xls
    Updated Sep 10, 2024
    + more versions
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    Ahmed Osman; Ashley Aimone; Rashid Ansumana; Isaac Bogoch; Hellen Gelband; Karen Colwill; Anne-Claude Gingras; Marc-André Langlois; Ronald Carshon-Marsh; Ibrahim Bob Swaray; Amara Jambai; Mohamed Vandi; Alimatu Vandi; Mohamed Massaquoi; Anteneh Assalif; H. Chaim Birnboim; Patrick E. Brown; Nico Nagelkerke; Prabhat Jha (2024). Excess mortality in Sierra Leone at ages ≥30 years comparing weekly average death rates (per 100,000 population) and number of deaths during COVID peak and non-peak periods by age group and cause of death. [Dataset]. http://doi.org/10.1371/journal.pgph.0003411.t003
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    xlsAvailable download formats
    Dataset updated
    Sep 10, 2024
    Dataset provided by
    PLOS Global Public Health
    Authors
    Ahmed Osman; Ashley Aimone; Rashid Ansumana; Isaac Bogoch; Hellen Gelband; Karen Colwill; Anne-Claude Gingras; Marc-André Langlois; Ronald Carshon-Marsh; Ibrahim Bob Swaray; Amara Jambai; Mohamed Vandi; Alimatu Vandi; Mohamed Massaquoi; Anteneh Assalif; H. Chaim Birnboim; Patrick E. Brown; Nico Nagelkerke; Prabhat Jha
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Sierra Leone
    Description

    Excess mortality in Sierra Leone at ages ≥30 years comparing weekly average death rates (per 100,000 population) and number of deaths during COVID peak and non-peak periods by age group and cause of death.

  18. Preliminary 2024-2025 U.S. COVID-19 Burden Estimates

    • data.cdc.gov
    • data.virginia.gov
    • +1more
    csv, xlsx, xml
    Updated Sep 26, 2025
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    Coronavirus and Other Respiratory Viruses Division (CORVD), National Center for Immunization and Respiratory Diseases (NCIRD). (2025). Preliminary 2024-2025 U.S. COVID-19 Burden Estimates [Dataset]. https://data.cdc.gov/Public-Health-Surveillance/Preliminary-2024-2025-U-S-COVID-19-Burden-Estimate/ahrf-yqdt
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    xlsx, csv, xmlAvailable download formats
    Dataset updated
    Sep 26, 2025
    Dataset provided by
    National Center for Immunization and Respiratory Diseases
    Authors
    Coronavirus and Other Respiratory Viruses Division (CORVD), National Center for Immunization and Respiratory Diseases (NCIRD).
    License

    https://www.usa.gov/government-workshttps://www.usa.gov/government-works

    Description

    This dataset represents preliminary estimates of cumulative U.S. COVID-19 disease burden for the 2024-2025 period, including illnesses, outpatient visits, hospitalizations, and deaths. The weekly COVID-19-associated burden estimates are preliminary and based on continuously collected surveillance data from patients hospitalized with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. The data come from the Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET), a surveillance platform that captures data from hospitals that serve about 10% of the U.S. population. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of COVID-19 -associated burden that have occurred since October 1, 2024.

    Note: Data are preliminary and subject to change as more data become available. Rates for recent COVID-19-associated hospital admissions are subject to reporting delays; as new data are received each week, previous rates are updated accordingly.

    References

    1. Reed C, Chaves SS, Daily Kirley P, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. https://doi.org/10.1371/journal.pone.0118369 
    2. Rolfes, MA, Foppa, IM, Garg, S, et al. Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness. Influenza Other Respi Viruses. 2018; 12: 132– 137. https://doi.org/10.1111/irv.12486
    3. Tokars JI, Rolfes MA, Foppa IM, Reed C. An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States. Vaccine. 2018;36(48):7331-7337. doi:10.1016/j.vaccine.2018.10.026 
    4. Collier SA, Deng L, Adam EA, Benedict KM, Beshearse EM, Blackstock AJ, Bruce BB, Derado G, Edens C, Fullerton KE, Gargano JW, Geissler AL, Hall AJ, Havelaar AH, Hill VR, Hoekstra RM, Reddy SC, Scallan E, Stokes EK, Yoder JS, Beach MJ. Estimate of Burden and Direct Healthcare Cost of Infectious Waterborne Disease in the United States. Emerg Infect Dis. 2021 Jan;27(1):140-149. doi: 10.3201/eid2701.190676. PMID: 33350905; PMCID: PMC7774540.
    5. Reed C, Kim IK, Singleton JA,  et al. Estimated influenza illnesses and hospitalizations averted by vaccination–United States, 2013-14 influenza season. MMWR Morb Mortal Wkly Rep. 2014 Dec 12;63(49):1151-4. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6349a2.htm 
    6. Reed C, Angulo FJ, Swerdlow DL, et al. Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009. Emerg Infect Dis. 2009;15(12):2004-2007. https://dx.doi.org/10.3201/eid1512.091413
    7. Devine O, Pham H, Gunnels B, et al. Extrapolating Sentinel Surveillance Information to Estimate National COVID-19 Hospital Admission Rates: A Bayesian Modeling Approach. Influenza and Other Respiratory Viruses. https://onlinelibrary.wiley.com/doi/10.1111/irv.70026. Volume18, Issue10. October 2024.
    8. https://www.cdc.gov/covid/php/covid-net/index.html">COVID-NET | COVID-19 | CDC 
    9. https://www.cdc.gov/covid/hcp/clinical-care/systematic-review-process.html 
    10. https://academic.oup.com/pnasnexus/article/1/3/pgac079/6604394?login=false">Excess natural-cause deaths in California by cause and setting: March 2020 through February 2021 | PNAS Nexus | Oxford Academic (oup.com)
    11. Kruschke, J. K. 2011. Doing Bayesian data analysis: a tutorial with R and BUGS. Elsevier, Amsterdam, Section 3.3.5.

  19. Model-based estimates for the proportion of non-COVID-19 excess deaths...

    • plos.figshare.com
    xls
    Updated Jul 9, 2024
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    Rachel Laing; Christl A. Donnelly (2024). Model-based estimates for the proportion of non-COVID-19 excess deaths attributable to opioids. [Dataset]. http://doi.org/10.1371/journal.pone.0306395.t004
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jul 9, 2024
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Rachel Laing; Christl A. Donnelly
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Model-based estimates for the proportion of non-COVID-19 excess deaths attributable to opioids.

  20. Weekly actual number of U.S. deaths from all causes vs expected maximum...

    • statista.com
    Updated Jun 15, 2021
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    Statista (2021). Weekly actual number of U.S. deaths from all causes vs expected maximum 2017-2020 [Dataset]. https://www.statista.com/statistics/1168522/us-weekly-number-deaths-all-causes-acual-vs-maximum-estimate/
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    Dataset updated
    Jun 15, 2021
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 14, 2017 - Aug 1, 2020
    Area covered
    United States
    Description

    This statistic shows the weekly number of actual deaths in the United States from all causes compared to the maximum expected number of deaths from January 14, 2017 to August 1, 2020. In March 2020, the actual number of deaths from all causes began to exceed the estimated maximum number of deaths due to the COVID-19 pandemic.

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Statista, COVID-19-related excess mortality rates in select countries in 2020, by age [Dataset]. https://www.statista.com/statistics/1259019/covid-related-excess-mortality-rate-in-the-us-and-select-countries-by-age/
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COVID-19-related excess mortality rates in select countries in 2020, by age

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Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
2020
Area covered
Worldwide
Description

In 2020, the U.S. had the highest COVID-19 pandemic-related excess mortality rate among non-elderly people compared to other peer countries. “Excess deaths” represent the number of deaths beyond what is expected in a typical year. This measure illustrates the mortality directly or indirectly associated with the COVID-19 pandemic. This statistic presents the COVID-19 pandemic-related excess mortality rate in the U.S. and select countries in 2020, by age group (per 100,000 people in age group).

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