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. U.S. opinion on the health risk severity of COVID-19 in the U.S., Jan. 2021,...

    • statista.com
    Updated May 15, 2024
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    Statista (2024). U.S. opinion on the health risk severity of COVID-19 in the U.S., Jan. 2021, by age [Dataset]. https://www.statista.com/statistics/1112470/us-opinion-covid-risk-by-age/
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    Dataset updated
    May 15, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 28, 2021 - Jan 31, 2021
    Area covered
    United States
    Description

    According to a survey in January 2021, respondents over the age of 65 are more likely to think the coronavirus is a severe health risk in the U.S. compared to those aged 18 to 34 years of age. This statistic shows the extent to which adult U.S. registered voters think the coronavirus is a health risk in the U.S. by age group, as of January 31, 2021.

  4. 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.

  5. 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

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

    • 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 and Second Booster Dose [Dataset]. https://healthdata.gov/dataset/Rates-of-COVID-19-Cases-or-Deaths-by-Age-Group-and/4tut-jeki
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    xml, json, csv, tsv, application/rdfxml, application/rssxmlAvailable 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. Share of U.S. COVID-19 cases resulting in hospitalization from...

    • statista.com
    Updated Jul 27, 2022
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    Statista (2022). Share of U.S. COVID-19 cases resulting in hospitalization from Feb.12-Mar.16, by age [Dataset]. https://www.statista.com/statistics/1105402/covid-hospitalization-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

    In the United States between February 12 and March 16, 2020, the percentage of COVID-19 patients hospitalized with the disease increased with age. Findings estimated that up to 70 percent of adults aged 85 years and older were hospitalized.

    Who is at higher risk from COVID-19? The same study also found that coronavirus patients aged 85 and older were at the highest risk of death. There are other risk factors besides age that can lead to serious illness. People with pre-existing medical conditions, such as diabetes, heart disease, and lung disease, can develop more severe symptoms. In the U.S. between January and May 2020, case fatality rates among confirmed COVID-19 patients were higher for those with underlying health conditions.

    How long should you self-isolate? As of August 24, 2020, more than 16 million people worldwide had recovered from COVID-19 disease, which includes patients in health care settings and those isolating at home. The criteria for discharging patients from isolation varies by country, but asymptomatic carriers of the virus can generally be released ten days after their positive case was confirmed. For patients showing signs of the illness, they must isolate for at least ten days after symptom onset and also remain in isolation for a short period after the symptoms have disappeared.

  8. f

    Output of data cleaning from Age differences in COVID-19 risk-taking, and...

    • rs.figshare.com
    html
    Updated May 30, 2023
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    Kelly Wolfe; Miroslav Sirota; Alasdair D. F. Clarke (2023). Output of data cleaning from Age differences in COVID-19 risk-taking, and the relationship with risk attitude and numerical ability [Dataset]. http://doi.org/10.6084/m9.figshare.16677655.v2
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    htmlAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    The Royal Society
    Authors
    Kelly Wolfe; Miroslav Sirota; Alasdair D. F. Clarke
    License

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

    Description

    This document contains the output of the data cleaning for this project.

  9. The marginal relative risk of each stage of disease collected from published...

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Abigail L. Horn; Lai Jiang; Faith Washburn; Emil Hvitfeldt; Kayla de la Haye; William Nicholas; Paul Simon; Maryann Pentz; Wendy Cozen; Neeraj Sood; David V. Conti (2023). The marginal relative risk of each stage of disease collected from published studies on COVID-19 and conditional relative risk estimated by the risk model for each risk factor on rates of hospitalization given infection, (H|I); ICU admission given hospitalization, (Q|H); and death given ICU admission, (D|Q) (95% credible interval). [Dataset]. http://doi.org/10.1371/journal.pone.0253549.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Abigail L. Horn; Lai Jiang; Faith Washburn; Emil Hvitfeldt; Kayla de la Haye; William Nicholas; Paul Simon; Maryann Pentz; Wendy Cozen; Neeraj Sood; David V. Conti
    License

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

    Description

    The reference group is individuals with no comorbidity, , and non-smoking.

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

    • data.virginia.gov
    • healthdata.gov
    • +1more
    csv, json, rdf, xsl
    Updated Jul 20, 2023
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    Centers for Disease Control and Prevention (2023). Rates of COVID-19 Cases or Deaths by Age Group and Vaccination Status [Dataset]. https://data.virginia.gov/dataset/rates-of-covid-19-cases-or-deaths-by-age-group-and-vaccination-status
    Explore at:
    xsl, csv, rdf, jsonAvailable download formats
    Dataset updated
    Jul 20, 2023
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.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

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

    • plos.figshare.com
    • figshare.com
    xls
    Updated Jun 9, 2023
    + more versions
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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.

  12. Age and social vulnerability in the context of Coronavirus COVID-19

    • coronavirus-disasterresponse.hub.arcgis.com
    • coronavirus-resources.esri.com
    Updated Mar 20, 2020
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    Esri’s Disaster Response Program (2020). Age and social vulnerability in the context of Coronavirus COVID-19 [Dataset]. https://coronavirus-disasterresponse.hub.arcgis.com/documents/fa916e123b7044c69d020a9f3e0a45d1
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    Dataset updated
    Mar 20, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri’s Disaster Response Program
    Description

    Age and social vulnerability in the context of Coronavirus COVID-19 (ArcGIS Blog).How to map the confluence of COVID-19 risk factors for US counties._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

  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. d

    MD COVID-19 - Vaccinations by Age Distribution

    • catalog.data.gov
    • opendata.maryland.gov
    Updated Sep 2, 2022
    + more versions
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    opendata.maryland.gov (2022). MD COVID-19 - Vaccinations by Age Distribution [Dataset]. https://catalog.data.gov/dataset/md-covid-19-vaccinations-by-age-distribution
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    Dataset updated
    Sep 2, 2022
    Dataset provided by
    opendata.maryland.gov
    Description

    WARNING: This asset has been deprecated and will no longer be updated (Last Updated April 14, 2022). Summary The cumulative number of COVID-19 vaccinations by age groupings: 0-9; 10-19; 20-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80+; Unknown. Description MD COVID-19 - Vaccinations by Age Distribution data layer is a collection of COVID-19 vaccinations that have been reported each day into ImmuNet. 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.

  15. Depression risk due to COVID-19 in South Korea 2022, by age group

    • statista.com
    Updated Aug 21, 2022
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    Statista (2022). Depression risk due to COVID-19 in South Korea 2022, by age group [Dataset]. https://www.statista.com/statistics/1270136/south-korea-covid19-depression-risk-by-age-group/
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    Dataset updated
    Aug 21, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Mar 2020 - Jun 2022
    Area covered
    South Korea
    Description

    According to a survey conducted in South Korea in June of 2022, South Koreans in their thirties were at a roughly 24 percent risk of depression due to the coronavirus (COVID-19) pandemic. Generally, younger age groups were at higher risk of depression than older age groups, and seem to be more negatively affected by the COVID-19 pandemic in terms of mental health. Overall, as of June 2022, South Koreans were at a 16.9 percent risk of developing depression.

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

  16. COVID-19 and the Experiences of Populations at Greater Risk: Wave 4 General...

    • icpsr.umich.edu
    ascii, delimited, r +3
    Updated Oct 19, 2023
    + more versions
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    Chandra, Anita (2023). COVID-19 and the Experiences of Populations at Greater Risk: Wave 4 General Population, United States, 2020-2021 [Dataset]. http://doi.org/10.3886/ICPSR38737.v1
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    stata, delimited, ascii, sas, r, spssAvailable download formats
    Dataset updated
    Oct 19, 2023
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    Chandra, Anita
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/38737/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/38737/terms

    Area covered
    United States
    Description

    In the context of COVID-19, RAND and the Robert Wood Johnson Foundation partnered again to build from the National Survey of Health Attitudes to implement a longitudinal survey to understand how these health views and values have been affected by the experience of the pandemic, with particular focus on populations deemed vulnerable or underserved, including people of color and those from low- to moderate-income backgrounds. The questions in this COVID-19 survey focused specifically on experiences related to the pandemic (e.g., financial, physical, emotional), how respondents viewed the disproportionate impacts of the pandemic, whether and how respondents' views and priorities regarding health actions and investments are changing (including the roles of government and the private sector), and how general values about such issues as freedom and racism may be related to pandemic views and response expectations. This study includes the results for Wave 4 for the general population. Demographic information includes sex, marital status, household size, race and ethnicity, family income, employment status, age, and census region.

  17. h

    Deeply-phenotyped hospital COVID patients: severity, acuity, therapies,...

    • healthdatagateway.org
    unknown
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    This publication uses data from PIONEER, an ethically approved database and analytical environment (East Midlands Derby Research Ethics 20/EM/0158), Deeply-phenotyped hospital COVID patients: severity, acuity, therapies, outcomes [Dataset]. https://healthdatagateway.org/dataset/145
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    unknownAvailable download formats
    Dataset authored and provided by
    This publication uses data from PIONEER, an ethically approved database and analytical environment (East Midlands Derby Research Ethics 20/EM/0158)
    License

    https://www.pioneerdatahub.co.uk/data/data-request-process/https://www.pioneerdatahub.co.uk/data/data-request-process/

    Description

    PIONEER: Deeply-phenotyped hospital COVID patients: severity, acuity, therapies, outcomes Dataset number 4.0

    Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 6 million cases& more than 1.5 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS)& death. There is a pressing need for tools to stratify patients, to identify those at greatest risk. Acuity scores are composite scores which help identify patients who are more unwell to support & prioritise clinical care. There are no validated acuity scores for COVID-19 & it is unclear whether standard tools are accurate enough to provide this support. This secondary care COVID dataset contains granular demographic, morbidity, serial acuity and outcome data to inform risk prediction tools in COVID-19.

    PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 & 2.

    EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. UHB has cared for >5000 COVID admissions to date.

    Scope: All COVID swab confirmed hospitalised patients to UHB from January – May 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes but also primary care records& clinic letters. Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes. Linked images available (radiographs, CT, MRI, ultrasound).

    Available supplementary data: Health data preceding & following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data.

    Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

  18. H

    COVID-19 Sex-Disaggregated Data Tracker

    • data.humdata.org
    csv
    Updated Apr 26, 2024
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    Global Health 50/50 (inactive) (2024). COVID-19 Sex-Disaggregated Data Tracker [Dataset]. https://data.humdata.org/dataset/covid-19-sex-disaggregated-data-tracker
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    csvAvailable download formats
    Dataset updated
    Apr 26, 2024
    Dataset provided by
    Global Health 50/50 (inactive)
    License

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

    Description

    Understanding gender is essential to understanding the risk factors of poor health, early death and health inequities. The COVID-19 outbreak is no different. At this point in the pandemic, we are unable to provide a clear answer to the question of the extent to which sex and gender are influencing the health outcomes of people diagnosed with COVID-19. However, experience and evidence thus far tell us that both sex and gender are important drivers of risk and response to infection and disease.

    In order to understand the role gender is playing in the COVID-19 outbreak, countries urgently need to begin both collecting and publicly reporting sex-disaggregated data. At a minimum, this should include the number of cases and deaths in men and women.

    In collaboration with CNN, Global Health 50/50 began compiling publicly available sex-disaggregated data reported by national governments to date and is exploring how gender may be driving the higher proportion of reported deaths in men among confirmed cases so far.

    For more, please visit: http://globalhealth5050.org/covid19

  19. u

    People who are at high risk for severe illness from COVID-19

    • data.urbandatacentre.ca
    • ouvert.canada.ca
    • +1more
    Updated Oct 1, 2024
    + more versions
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    (2024). People who are at high risk for severe illness from COVID-19 [Dataset]. https://data.urbandatacentre.ca/dataset/gov-canada-44a4c02d-6a85-4cb7-a57f-8a9ed90d3acb
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    Dataset updated
    Oct 1, 2024
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Description

    While diseases can make anyone sick, some Canadians are more at risk of developing severe complications from an illness due to underlying medical conditions and age. If you are at risk for complications, you can take action to reduce your risk of getting sick from COVID-19.

  20. dataset related to article "LESSONS LEARNED FROM THE LESSONS LEARNED IN...

    • zenodo.org
    • data.niaid.nih.gov
    Updated Jan 12, 2023
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    Alessia Marcassoli; Alessia Marcassoli; Matilde Leonardi; Matilde Leonardi; Marco Passavanti; Marco Passavanti; Valerio De Angelis; Valerio De Angelis; Enrico Bentivegna; Enrico Bentivegna; Paolo Martelletti; Paolo Martelletti; Alberto Raggi; Alberto Raggi (2023). dataset related to article "LESSONS LEARNED FROM THE LESSONS LEARNED IN PUBLIC HEALTH DURING THE FIRST YEARS OF COVID-19 PANDEMIC" in press [Dataset]. http://doi.org/10.5281/zenodo.7525742
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    Dataset updated
    Jan 12, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Alessia Marcassoli; Alessia Marcassoli; Matilde Leonardi; Matilde Leonardi; Marco Passavanti; Marco Passavanti; Valerio De Angelis; Valerio De Angelis; Enrico Bentivegna; Enrico Bentivegna; Paolo Martelletti; Paolo Martelletti; Alberto Raggi; Alberto Raggi
    License

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

    Description

    THE DATABASE CONTAINS THE DATA EXTRACTION FORM USED TO RECORD DATA FROM THE STUDIES INCLUDED IN THE REVIEW, WHICH JOURNAL, YEAR, AUTHORS, TITLE, GEOGRAPHIC AREA OF THE STUDY (WHO REGIONS), AND PARTICIPANTS’ INFORMATION (NUMBER AND MEAN AGE) WERE REPORTED. IN THIS FORM, DATA HAVE BEEN DIVIDED INTO 10 MAIN SECTIONS, ONE FOR EACH WHO PUBLIC HEALTH PILLAR. IN EACH SECTION, A DEFINITION OF THE PUBLIC HEALTH AREA COVERED BY THE PILLAR, THE TOTAL NUMBER OF ARTICLES LINKED TO THE PILLAR, THE TOTAL NUMBER OF AVAILABLE LESSONS CONNECTED TO EACH PILLAR, AS WELL AS THE TOTAL NUMBER OF REFERENCES TO EACH LESSON LEARNED WITHIN EACH PILLAR (WHICH CONSTITUTES THE MAIN RESULT) WAS INCLUDED.

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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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