100+ datasets found
  1. Share of U.S. COVID-19 cases resulting in death from Feb. 12 to Mar. 16, by...

    • statista.com
    Updated Jul 27, 2022
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    Share of U.S. COVID-19 cases resulting in death from Feb. 12 to Mar. 16, by age [Dataset]. https://www.statista.com/statistics/1105431/covid-case-fatality-rates-us-by-age-group/
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    Dataset updated
    Jul 27, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Feb 12, 2020 - Mar 16, 2020
    Area covered
    United States
    Description

    Among COVID-19 patients in the United States from February 12 to March 16, 2020, estimated case-fatality rates were highest for adults aged 85 years and older. Younger people appeared to have milder symptoms, and there were no deaths reported among persons aged 19 years and under.

    Tracking the virus in the United States The outbreak of a previously unknown viral pneumonia was first reported in China toward the end of December 2019. The first U.S. case of COVID-19 was recorded in mid-January 2020, confirmed in a patient who had returned to the United States from China. The virus quickly started to spread, and the first community-acquired case was confirmed one month later in California. Overall, there had been approximately 4.5 million coronavirus cases in the country by the start of August 2020.

    U.S. health care system stretched California, Florida, and Texas are among the states with the most coronavirus cases. Even the best-resourced hospitals in the United States have struggled to cope with the crisis, and certain areas of the country were dealt further blows by new waves of infections in July 2020. Attention is rightly focused on fighting the pandemic, but as health workers are redirected to care for COVID-19 patients, the United States must not lose sight of other important health care issues.

  2. COVID-19 deaths reported in the U.S. as of June 14, 2023, by age

    • statista.com
    Updated Jun 21, 2023
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    Statista (2023). COVID-19 deaths reported in the U.S. as of June 14, 2023, by age [Dataset]. https://www.statista.com/statistics/1191568/reported-deaths-from-covid-by-age-us/
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    Dataset updated
    Jun 21, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 1, 2020 - Jun 14, 2023
    Area covered
    United States
    Description

    Between the beginning of January 2020 and June 14, 2023, of the 1,134,641 deaths caused by COVID-19 in the United States, around 307,169 had occurred among those aged 85 years and older. This statistic shows the number of coronavirus disease 2019 (COVID-19) deaths in the U.S. from January 2020 to June 2023, by age.

  3. d

    MD COVID-19 - Confirmed Deaths by Age Distribution

    • catalog.data.gov
    • opendata.maryland.gov
    • +1more
    Updated Mar 22, 2025
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    opendata.maryland.gov (2025). MD COVID-19 - Confirmed Deaths by Age Distribution [Dataset]. https://catalog.data.gov/dataset/md-covid-19-confirmed-deaths-by-age-distribution
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    Dataset updated
    Mar 22, 2025
    Dataset provided by
    opendata.maryland.gov
    Area covered
    Maryland
    Description

    Note: Starting April 27, 2023 updates change from daily to weekly. Summary The cumulative number of confirmed COVID-19 deaths among Maryland residents by age: 0-9; 10-19; 20-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80+; Unknown. Description The MD COVID-19 - Confirmed Deaths by Age Distribution data layer is a collection of the statewide confirmed COVID-19 related deaths that have been reported each day by the Vital Statistics Administration by designated age ranges. A death is classified as confirmed if the person had a laboratory-confirmed positive COVID-19 test result. Some data on deaths may be unavailable due to the time lag between the death, typically reported by a hospital or other facility, and the submission of the complete death certificate. Probable deaths are available from the MD COVID-19 - Probable Deaths by Age Distribution data layer. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

  4. f

    Table_2_Age-Related Risk Factors and Complications of Patients With...

    • frontiersin.figshare.com
    xlsx
    Updated May 30, 2023
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    Han Zhang; Yingying Wu; Yuqing He; Xingyuan Liu; Mingqian Liu; Yuhong Tang; Xiaohua Li; Guang Yang; Gang Liang; Shabei Xu; Minghuan Wang; Wei Wang (2023). Table_2_Age-Related Risk Factors and Complications of Patients With COVID-19: A Population-Based Retrospective Study.XLSX [Dataset]. http://doi.org/10.3389/fmed.2021.757459.s003
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    xlsxAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    Frontiers
    Authors
    Han Zhang; Yingying Wu; Yuqing He; Xingyuan Liu; Mingqian Liu; Yuhong Tang; Xiaohua Li; Guang Yang; Gang Liang; Shabei Xu; Minghuan Wang; Wei Wang
    License

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

    Description

    Objective: To study the differences in clinical characteristics, risk factors, and complications across age-groups among the inpatients with the coronavirus disease 2019 (COVID-19).Methods: In this population-based retrospective study, we included all the positive hospitalized patients with COVID-19 at Wuhan City from December 29, 2019 to April 15, 2020, during the first pandemic wave. Multivariate logistic regression analyses were used to explore the risk factors for death from COVID-19. Canonical correlation analysis (CCA) was performed to study the associations between comorbidities and complications.Results: There are 36,358 patients in the final cohort, of whom 2,492 (6.85%) died. Greater age (odds ration [OR] = 1.061 [95% CI 1.057–1.065], p < 0.001), male gender (OR = 1.726 [95% CI 1.582–1.885], p < 0.001), alcohol consumption (OR = 1.558 [95% CI 1.355–1.786], p < 0.001), smoking (OR = 1.326 [95% CI 1.055–1.652], p = 0.014), hypertension (OR = 1.175 [95% CI 1.067–1.293], p = 0.001), diabetes (OR = 1.258 [95% CI 1.118–1.413], p < 0.001), cancer (OR = 1.86 [95% CI 1.507–2.279], p < 0.001), chronic kidney disease (CKD) (OR = 1.745 [95% CI 1.427–2.12], p < 0.001), and intracerebral hemorrhage (ICH) (OR = 1.96 [95% CI 1.323–2.846], p = 0.001) were independent risk factors for death from COVID-19. Patients aged 40–80 years make up the majority of the whole patients, and them had similar risk factors with the whole patients. For patients aged

  5. Distribution of total COVID-19 deaths in the U.S. as of April 26, 2023, by...

    • statista.com
    Updated Nov 29, 2023
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    Statista (2023). Distribution of total COVID-19 deaths in the U.S. as of April 26, 2023, by age [Dataset]. https://www.statista.com/statistics/1254488/us-share-of-total-covid-deaths-by-age-group/
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    Dataset updated
    Nov 29, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    As of April 26, 2023, around 27 percent of total COVID-19 deaths in the United States have been among adults 85 years and older, despite this age group only accounting for two percent of the U.S. population. This statistic depicts the distribution of total COVID-19 deaths in the United States as of April 26, 2023, by age group.

  6. Rates of COVID-19 Cases or Deaths by Age Group and Vaccination Status

    • healthdata.gov
    • data.virginia.gov
    • +1more
    application/rdfxml +5
    Updated Jun 16, 2023
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    data.cdc.gov (2023). Rates of COVID-19 Cases or Deaths by Age Group and Vaccination Status [Dataset]. https://healthdata.gov/w/894y-jyp5/default?cur=dwO3erkKZG1
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    application/rdfxml, json, csv, xml, application/rssxml, tsvAvailable download formats
    Dataset updated
    Jun 16, 2023
    Dataset provided by
    data.cdc.gov
    Description

    Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Vaccination Status. Click 'More' for important dataset description and footnotes

    Dataset and data visualization details: These data were posted on October 21, 2022, archived on November 18, 2022, and revised on February 22, 2023. These data reflect cases among persons with a positive specimen collection date through September 24, 2022, and deaths among persons with a positive specimen collection date through September 3, 2022.

    Vaccination status: A person vaccinated with a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. Additional or booster dose: A person vaccinated with a primary series and an additional or booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after receipt of an additional or booster dose of any COVID-19 vaccine on or after August 13, 2021. For people ages 18 years and older, data are graphed starting the week including September 24, 2021, when a COVID-19 booster dose was first recommended by CDC for adults 65+ years old and people in certain populations and high risk occupational and institutional settings. For people ages 12-17 years, data are graphed starting the week of December 26, 2021, 2 weeks after the first recommendation for a booster dose for adolescents ages 16-17 years. For people ages 5-11 years, data are included starting the week of June 5, 2022, 2 weeks after the first recommendation for a booster dose for children aged 5-11 years. For people ages 50 years and older, data on second booster doses are graphed starting the week including March 29, 2022, when the recommendation was made for second boosters. Vertical lines represent dates when changes occurred in U.S. policy for COVID-19 vaccination (details provided above). Reporting is by primary series vaccine type rather than additional or booster dose vaccine type. The booster dose vaccine type may be different than the primary series vaccine type. ** Because data on the immune status of cases and associated deaths are unavailable, an additional dose in an immunocompromised person cannot be distinguished from a booster dose. This is a relevant consideration because vaccines can be less effective in this group. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Rates of COVID-19 deaths by vaccination status are reported based on when the patient was tested for COVID-19, not the date they died. Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 31 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (New York), North Carolina, Philadelphia (Pennsylvania), Rhode Island, South Dakota, Tennessee, Texas, Utah, Washington, and West Virginia; 30 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 72% of the total U.S. population and all ten of the Health and Human Services Regions. Data on cases

  7. Coronavirus death rate in Italy as of May 2023, by age group

    • statista.com
    Updated May 15, 2023
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    Coronavirus death rate in Italy as of May 2023, by age group [Dataset]. https://www.statista.com/statistics/1106372/coronavirus-death-rate-by-age-group-italy/
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    Dataset updated
    May 15, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    May 3, 2023
    Area covered
    Italy
    Description

    The spread of coronavirus (COVID-19) in Italy has hit every age group uniformly and claimed over 190 thousand lives since it entered the country. As the chart shows, however, mortality rate appeared to be much higher for the elderly patient. In fact, for people between 80 and 89 years of age, the fatality rate was 6.1 percent. For patients older than 90 years, this figure increased to 12.1 percent. On the other hand, the death rate for individuals under 60 years of age was well below 0.5 percent. Overall, the mortality rate of coronavirus in Italy was 0.7 percent.

    Italy's death toll was one of the most tragic in the world. In the last months, however, the country started to see the end of this terrible situation: as of May 2023, roughly 84.7 percent of the total Italian population was fully vaccinated.

    Since the first case was detected at the end of January in Italy, coronavirus has been spreading fast. As of May, 2023, the authorities reported over 25.8 million cases in the country. The area mostly hit by the virus is the North, in particular the region of Lombardy.

    For a global overview visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

  8. Coronavirus (COVID-19) deaths in Poland 2021, by age

    • statista.com
    Updated Apr 10, 2024
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    Coronavirus (COVID-19) deaths in Poland 2021, by age [Dataset]. https://www.statista.com/statistics/1110890/poland-coronavirus-covid-19-fatalities-by-age/
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    Dataset updated
    Apr 10, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 10, 2021 - Dec 31, 2021
    Area covered
    Poland
    Description

    In 2021, 60,185 unvaccinated individuals and 7,116 vaccinated individuals died from COVID-19 in Poland. The estimated risk of death from COVID-19 in the unvaccinated versus vaccinated population (using the Mantel-Haenszel Adjusted Ratio) was 9,156, almost 10 times higher.

    The first cases of coronavirus infection in Poland were reported on 4 March 2020.

    For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

  9. Deaths by vaccination status, England

    • ons.gov.uk
    • cy.ons.gov.uk
    xlsx
    Updated Aug 25, 2023
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    Office for National Statistics (2023). Deaths by vaccination status, England [Dataset]. https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/deathsbyvaccinationstatusengland
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    xlsxAvailable download formats
    Dataset updated
    Aug 25, 2023
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    Age-standardised mortality rates for deaths involving coronavirus (COVID-19), non-COVID-19 deaths and all deaths by vaccination status, broken down by age group.

  10. Stroke risk, phenotypes, and death in COVID-19: systematic review and newly...

    • zenodo.org
    • data.niaid.nih.gov
    • +1more
    pdf
    Updated Jul 18, 2024
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    Luciano Sposato; Luciano Sposato; Sebastian Fridman; Maria Bres Bullrich; Amado Jimenez-Ruiz; Pablo Costantini; Palak Shah; Caroline Just; Daniel Vela-Duarte; Italo Linfante; Athena Sharifi-Razavi; Narges Karimi; Rodrigo Bagur; Derek Debicki; Teneille Emma Gofton; David A Steven; Sebastian Fridman; Maria Bres Bullrich; Amado Jimenez-Ruiz; Pablo Costantini; Palak Shah; Caroline Just; Daniel Vela-Duarte; Italo Linfante; Athena Sharifi-Razavi; Narges Karimi; Rodrigo Bagur; Derek Debicki; Teneille Emma Gofton; David A Steven (2024). Stroke risk, phenotypes, and death in COVID-19: systematic review and newly reported cases [Dataset]. http://doi.org/10.5061/dryad.n5tb2rbt5
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    pdfAvailable download formats
    Dataset updated
    Jul 18, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Luciano Sposato; Luciano Sposato; Sebastian Fridman; Maria Bres Bullrich; Amado Jimenez-Ruiz; Pablo Costantini; Palak Shah; Caroline Just; Daniel Vela-Duarte; Italo Linfante; Athena Sharifi-Razavi; Narges Karimi; Rodrigo Bagur; Derek Debicki; Teneille Emma Gofton; David A Steven; Sebastian Fridman; Maria Bres Bullrich; Amado Jimenez-Ruiz; Pablo Costantini; Palak Shah; Caroline Just; Daniel Vela-Duarte; Italo Linfante; Athena Sharifi-Razavi; Narges Karimi; Rodrigo Bagur; Derek Debicki; Teneille Emma Gofton; David A Steven
    License

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

    Description

    Objectives: To investigate the hypothesis that strokes occurring in patients with COVID-19 have distinctive features, we investigated stroke risk, clinical phenotypes, and outcomes in this population. Methods: We performed a systematic search resulting in 10 studies reporting stroke frequency among COVID-19 patients, which were pooled with one unpublished series from Canada. We applied random-effects meta-analyses to estimate the proportion of stroke among COVID-19. We performed an additional systematic search for cases series of stroke in COVID-19 patients (n=125) and we pooled these data with 35 unpublished cases from Canada, USA, and Iran. We analyzed clinical characteristics and in-hospital mortality stratified into age groups (<50, 50-70, >70 years). We applied cluster analyses to identify specific clinical phenotypes and their relationship with death. Results: The proportion of COVID-19 patients with stroke (1.8%, 95%CI 0.9-3.7%) and in-hospital mortality (34.4%, 95%CI 27.2-42.4%) were exceedingly high. Mortality was 67% lower in patients <50 years-old relative to those >70 years-old (OR 0.33, 95%CI 0.12-0.94, P=0.039). Large vessel occlusion was twice as frequent (46.9%) as previously reported and was high across all age groups, even in the absence of risk factors or comorbidities. A clinical phenotype characterized by older age, a higher burden of comorbidities, and severe COVID-19 respiratory symptoms, was associated with the highest in-hospital mortality (58.6%) and a 3x higher risk of death than the rest of the cohort (OR 3.52, 95%CI 1.53-8.09, P=0.003). Conclusions: Stroke is frequent among COVID-19 patients and has devastating consequences across all ages. The interplay of older age, comorbidities and severity of COVID-19 respiratory symptoms is associated with an extremely elevated mortality.

  11. Leading causes of death, total population, by age group

    • www150.statcan.gc.ca
    • ouvert.canada.ca
    • +2more
    Updated Feb 19, 2025
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    Government of Canada, Statistics Canada (2025). Leading causes of death, total population, by age group [Dataset]. http://doi.org/10.25318/1310039401-eng
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    Dataset updated
    Feb 19, 2025
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    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.

  12. Coronavirus (COVID-19) deaths in Switzerland by age group in 2023

    • statista.com
    Updated Jan 13, 2025
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    Coronavirus (COVID-19) deaths in Switzerland by age group in 2023 [Dataset]. https://www.statista.com/statistics/1110092/coronavirus-covid-19-deaths-age-group-switzerland/
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    Dataset updated
    Jan 13, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2023
    Area covered
    Switzerland
    Description

    As of January 2023, members of the Swiss population aged 80 years and older have been most vulnerable to the coronavirus (COVID-19) outbreak, with the highest number of deaths recorded in this age group. Older age groups are believed to be especially at risk.

  13. Z

    Data from: COVID-19 Mortality Risk Assessment Among Various Age Groups Using...

    • data.niaid.nih.gov
    • zenodo.org
    Updated Sep 11, 2020
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    COVID-19 Mortality Risk Assessment Among Various Age Groups Using Phylogenetic Analysis [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_4007665
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    Dataset updated
    Sep 11, 2020
    Dataset authored and provided by
    Pawan Verma
    License

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

    Description

    The datasets used for the analysis titled Phylogenomic analysis of all available COVID-19 genomes and its influence on mortality

  14. COVID-19 death rates in New York City as of December 22, 2022, by age group

    • statista.com
    Updated Dec 23, 2022
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    Statista (2022). COVID-19 death rates in New York City as of December 22, 2022, by age group [Dataset]. https://www.statista.com/statistics/1109867/coronavirus-death-rates-by-age-new-york-city/
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    Dataset updated
    Dec 23, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    New York
    Description

    The death rate in New York City for adults aged 75 years and older was around 4,135 per 100,000 people as of December 22, 2022. The risk of developing more severe illness from COVID-19 increases with age, and the virus also poses a particular threat to people with underlying health conditions.

    What is the death toll in NYC? The first coronavirus-related death in New York City was recorded on March 11, 2020. Since then, the total number of confirmed deaths has reached 37,452 while there have been 2.6 million positive tests for the disease. The number of daily new deaths in New York City has fallen sharply since nearly 600 residents lost their lives on April 7, 2020. A significant number of fatalities across New York State have been linked to long-term care facilities that provide support to vulnerable elderly adults and individuals with physical disabilities.

    The impact on the counties of New York State Nearly every county in the state of New York has recorded at least one death due to the coronavirus. Outside of New York City, the counties of Nassau, Suffolk, and Westchester have confirmed over 11,500 deaths between them. When analyzing the ratio of deaths to county population, Rockland had one of the highest COVID-19 death rates in New York State in 2021. The county, which has approximately 325,700 residents, had a death rate of around 29 per 10,000 people in April 2021.

  15. COVID-19 severity and mortality according to a-priori risk and DMT class.

    • plos.figshare.com
    • figshare.com
    xls
    Updated Jun 9, 2023
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    Gabriel Bsteh; Hamid Assar; Harald Hegen; Bettina Heschl; Fritz Leutmezer; Franziska Di Pauli; Christiane Gradl; Gerhard Traxler; Gudrun Zulehner; Paulus Rommer; Peter Wipfler; Michael Guger; Christian Enzinger; Thomas Berger (2023). COVID-19 severity and mortality according to a-priori risk and DMT class. [Dataset]. http://doi.org/10.1371/journal.pone.0255316.t003
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    xlsAvailable download formats
    Dataset updated
    Jun 9, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Gabriel Bsteh; Hamid Assar; Harald Hegen; Bettina Heschl; Fritz Leutmezer; Franziska Di Pauli; Christiane Gradl; Gerhard Traxler; Gudrun Zulehner; Paulus Rommer; Peter Wipfler; Michael Guger; Christian Enzinger; Thomas Berger
    License

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

    Description

    COVID-19 severity and mortality according to a-priori risk and DMT class.

  16. l

    Data from: All-Cause Mortality

    • geohub.lacity.org
    • egis-lacounty.hub.arcgis.com
    • +1more
    Updated Dec 21, 2023
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    County of Los Angeles (2023). All-Cause Mortality [Dataset]. https://geohub.lacity.org/datasets/lacounty::all-cause-mortality
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    Dataset updated
    Dec 21, 2023
    Dataset authored and provided by
    County of Los Angeles
    Area covered
    Description

    Death rate has been age-adjusted by the 2000 U.S. standard populaton. All-cause mortality is an important measure of community health. All-cause mortality is heavily driven by the social determinants of health, with significant inequities observed by race and ethnicity and socioeconomic status. Black residents have consistently experienced the highest all-cause mortality rate compared to other racial and ethnic groups. During the COVID-19 pandemic, Latino residents also experienced a sharp increase in their all-cause mortality rate compared to White residents, demonstrating a reversal in the previously observed mortality advantage, in which Latino individuals historically had higher life expectancy and lower mortality than White individuals despite having lower socioeconomic status on average. The disproportionately high all-cause mortality rates observed among Black and Latino residents, especially since the onset of the COVID-19 pandemic, are due to differences in social and economic conditions and opportunities that unfairly place these groups at higher risk of developing and dying from a wide range of health conditions, including COVID-19.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

  17. h

    Supporting data for "Excess mortality during the COVID-19 pandemic in Hong...

    • datahub.hku.hk
    Updated Oct 30, 2024
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    Shuqi Xu (2024). Supporting data for "Excess mortality during the COVID-19 pandemic in Hong Kong and South Korea" [Dataset]. http://doi.org/10.25442/hku.27273840.v1
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    Dataset updated
    Oct 30, 2024
    Dataset provided by
    HKU Data Repository
    Authors
    Shuqi Xu
    License

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

    Area covered
    Hong Kong
    Description

    Results data for the thesis on estimating the age-, sex-, cause-specific excess mortality during the COVID-19 pandemic in Hong Kong and South Korea.Thesis abstractBackgroundFew studies used a consistent methodology and adjusted for the risk of influenza-like illness (ILI) in historical mortality trends when estimating and comparing the cause-specific excess mortality (EM) during the COVID-19 pandemic. Previous studies demonstrated that excess mortality was widely reported from CVD and among the elderly. This study aims to estimate and compare the overall, age-, sex-, and cause-specific excess mortality during the COVID-19 pandemic in Hong Kong (HK) and South Korea (SK) with consideration of the impact of ILI.MethodsIn this population-based study, we first fitted a generalized additive model to the monthly mortality data from Jan 2010 to Dec 2019 in HK and SK before the COVID-19 pandemic. Then we applied the fitted model to estimate the EM from Jan 2020 to Dec 2022. The month index was modelled with a natural cubic spline. Akaike information criterion (AIC) was used to select the number of knots for the spline and inclusion of covariates such as monthly mean temperature, absolute humidity, ILI consultation rate, and the proxy for flu activity.FindingsFrom 2020 to 2022, the EM in HK was 239.8 (95% CrI: 184.6 to 293.9) per 100,000 population. Excess mortality from respiratory diseases (RD) (ICD-10 code: J00-J99), including COVID-19 deaths coded as J98.8, was 181.3 (95% CrI: 149.9 to 210.4) per 100,000. Except for RD, the majority of the EM in HK was estimated from cardiovascular diseases (CVD) (22.4% of the overall EM), influenza and pneumonia (16.2%), ischemic heart disease (8.9%), ill-defined causes (8.6%) and senility (6.7%). No statistically significant reduced deaths were estimated among other studied causes.From 2020 to 2022, the EM in SK was 204.7 (95% CrI: 161.6 to 247.2) per 100,000 population. Of note, COVID-19 deaths in SK were not included in deaths from RD but were recorded with the codes for emergency use as U07.1 or U07.2. The majority of the EM was estimated from ill-defined causes (32.0% of the overall EM), senility (16.6%), cerebrovascular disease (6.8%) and cardiovascular diseases (6.1%). Statistically significant reduction in mortality with 95 CrI lower than zero was estimated from vascular, other and unspecified dementia (-26.9% of expected deaths), influenza and pneumonia (-20.7%), mental and behavioural disorders (-18.8%) and respiratory diseases (-7.7%).InterpretationExcluding RD in HK which includes COVID-19 deaths, the majority of the EM in HK and SK was from CVD and senility. Mortality from influenza and pneumonia was estimated to have a statistically significant increase in HK but a decrease in SK probability due to different coding practices. HK had a heavier burden of excess mortality in the elderly age group 70-79 years and 80 years or above, while SK had a heavier burden in the age group of 60-69 years. Both HK and SK have a heavier burden of excess mortality from males than females. Better triage systems for identifying high-risk people of the direct or indirect impact of the epidemic are needed to minimize preventable mortality.

  18. Risk of COVID-19 death in CYP with a SARS-CoV-2 positive test within 100...

    • plos.figshare.com
    xls
    Updated Jun 21, 2023
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    Marta Bertran; Zahin Amin-Chowdhury; Hannah G. Davies; Hester Allen; Tom Clare; Chloe Davison; Mary Sinnathamby; Giulia Seghezzo; Meaghan Kall; Hannah Williams; Nick Gent; Mary E. Ramsay; Shamez N. Ladhani; Godwin Oligbu (2023). Risk of COVID-19 death in CYP with a SARS-CoV-2 positive test within 100 days by demographics (adjusted and unadjusted odds ratios). [Dataset]. http://doi.org/10.1371/journal.pmed.1004118.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Marta Bertran; Zahin Amin-Chowdhury; Hannah G. Davies; Hester Allen; Tom Clare; Chloe Davison; Mary Sinnathamby; Giulia Seghezzo; Meaghan Kall; Hannah Williams; Nick Gent; Mary E. Ramsay; Shamez N. Ladhani; Godwin Oligbu
    License

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

    Description

    Risk of COVID-19 death in CYP with a SARS-CoV-2 positive test within 100 days by demographics (adjusted and unadjusted odds ratios).

  19. d

    Pre-pandemic Alcohol consumption highly predicts Covid-19 mortality

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 14, 2023
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    Errasfa, Mourad; Mourad Errasfa (2023). Pre-pandemic Alcohol consumption highly predicts Covid-19 mortality [Dataset]. http://doi.org/10.7910/DVN/FS5TFU
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    Dataset updated
    Nov 14, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Errasfa, Mourad; Mourad Errasfa
    Description

    Pre-pandemic (data of 2019) epidemiologic and demographic data have shown that some parameters such as cancer, Alzheimer's disease, advanced age, and alcohol intake levels are positively correlated to Covid-19 mortality, instead, birth and fertility rates are negatively correlated to Covid-19 mortality. A stepwise multiple regression analysis of the above parameters against Covid-19 mortality in 32 countries from Asia, America, Africa, and Europe has generated two main predictors of Covid-19 mortality: alcohol consumption and birth/mortality ratio. A first-order equation correlated alcohol intake to Covid-19 mortality as follows; Covid-19 mortality= 0.1057 x (liters of alcohol intake) + 0.2214 (Coefficient of determination = 0.750, F value = 38.63 , P-value = 7.64x10-7). A second equation correlated (birth rate/mortality rate) to Covid-19 mortality as follows; Covid-19 mortality= - 0.3129 x (birth rate/mortality) ratio +1.638 (coefficient of determination = 0.799, F value = 51.2, P-value = 7.09x10-8). Thus, pre-pandemic alcohol consumption is a high predictor of Covid-19 mortality that should be taken into account as a serious risk factor for future safety measures against SARS-CoV-2 infection.

  20. Number of coronavirus (COVID-19) deaths in Sweden 2023, by age groups

    • statista.com
    Updated May 15, 2024
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    Statista (2024). Number of coronavirus (COVID-19) deaths in Sweden 2023, by age groups [Dataset]. https://www.statista.com/statistics/1107913/number-of-coronavirus-deaths-in-sweden-by-age-groups/
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    Dataset updated
    May 15, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 11, 2023
    Area covered
    Sweden
    Description

    As of January 11, 2023, the highest number of deaths due to the coronavirus in Sweden was among individuals aged 80 to 90 years old. In this age group there were 9,124 deaths as a result of the virus. The overall Swedish death toll was 22,645 as of January 11, 2023.

    The first case of coronavirus (COVID-19) in Sweden was confirmed on February 4, 2020. The number of cases has since risen to over 2.68 million, as of January 2023. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

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Share of U.S. COVID-19 cases resulting in death from Feb. 12 to Mar. 16, by age [Dataset]. https://www.statista.com/statistics/1105431/covid-case-fatality-rates-us-by-age-group/
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Share of U.S. COVID-19 cases resulting in death from Feb. 12 to Mar. 16, by age

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3 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Jul 27, 2022
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
Feb 12, 2020 - Mar 16, 2020
Area covered
United States
Description

Among COVID-19 patients in the United States from February 12 to March 16, 2020, estimated case-fatality rates were highest for adults aged 85 years and older. Younger people appeared to have milder symptoms, and there were no deaths reported among persons aged 19 years and under.

Tracking the virus in the United States The outbreak of a previously unknown viral pneumonia was first reported in China toward the end of December 2019. The first U.S. case of COVID-19 was recorded in mid-January 2020, confirmed in a patient who had returned to the United States from China. The virus quickly started to spread, and the first community-acquired case was confirmed one month later in California. Overall, there had been approximately 4.5 million coronavirus cases in the country by the start of August 2020.

U.S. health care system stretched California, Florida, and Texas are among the states with the most coronavirus cases. Even the best-resourced hospitals in the United States have struggled to cope with the crisis, and certain areas of the country were dealt further blows by new waves of infections in July 2020. Attention is rightly focused on fighting the pandemic, but as health workers are redirected to care for COVID-19 patients, the United States must not lose sight of other important health care issues.

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