25 datasets found
  1. D

    Provisional COVID-19 Deaths: Focus on Ages 0-18 Years

    • data.cdc.gov
    • data.virginia.gov
    • +5more
    csv, xlsx, xml
    Updated Jun 28, 2023
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    NCHS/DVS (2023). Provisional COVID-19 Deaths: Focus on Ages 0-18 Years [Dataset]. https://data.cdc.gov/widgets/nr4s-juj3?mobile_redirect=true
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    csv, xml, xlsxAvailable download formats
    Dataset updated
    Jun 28, 2023
    Dataset authored and provided by
    NCHS/DVS
    License

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

    Description

    Effective June 28, 2023, this dataset will no longer be updated. Similar data are accessible from CDC WONDER (https://wonder.cdc.gov/mcd-icd10-provisional.html).

    Deaths involving coronavirus disease 2019 (COVID-19) with a focus on ages 0-18 years in the United States.

  2. 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
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    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

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

    • healthdata.gov
    • odgavaprod.ogopendata.com
    • +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/CDC/Rates-of-COVID-19-Cases-or-Deaths-by-Age-Group-and/4tut-jeki
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    application/rdfxml, csv, json, application/rssxml, xml, 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

  4. D

    Provisional COVID-19 Deaths by Sex and Age

    • data.cdc.gov
    csv, xlsx, xml
    Updated Sep 27, 2023
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    NCHS/DVS (2023). Provisional COVID-19 Deaths by Sex and Age [Dataset]. https://data.cdc.gov/widgets/9bhg-hcku
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    xlsx, xml, csvAvailable download formats
    Dataset updated
    Sep 27, 2023
    Dataset authored and provided by
    NCHS/DVS
    License

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

    Description

    Effective September 27, 2023, this dataset will no longer be updated. Similar data are accessible from wonder.cdc.gov.

    Deaths involving COVID-19, pneumonia, and influenza reported to NCHS by sex, age group, and jurisdiction of occurrence.

  5. 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.

  6. New York State Statewide COVID-19 Fatalities by Age Group (Archived)

    • health.data.ny.gov
    • healthdata.gov
    • +1more
    csv, xlsx, xml
    Updated Oct 6, 2023
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    New York State Department of Health (2023). New York State Statewide COVID-19 Fatalities by Age Group (Archived) [Dataset]. https://health.data.ny.gov/Health/New-York-State-Statewide-COVID-19-Fatalities-by-Ag/du97-svf7
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    xml, csv, xlsxAvailable download formats
    Dataset updated
    Oct 6, 2023
    Dataset authored and provided by
    New York State Department of Health
    Area covered
    New York
    Description

    Note: Data elements were retired from HERDS on 10/6/23 and this dataset was archived.

    This dataset includes the cumulative number and percent of healthcare facility-reported fatalities for patients with lab-confirmed COVID-19 disease by reporting date and age group. This dataset does not include fatalities related to COVID-19 disease that did not occur at a hospital, nursing home, or adult care facility. The primary goal of publishing this dataset is to provide users with information about healthcare facility fatalities among patients with lab-confirmed COVID-19 disease.

    The information in this dataset is also updated daily on the NYS COVID-19 Tracker at https://www.ny.gov/covid-19tracker.

    The data source for this dataset is the daily COVID-19 survey through the New York State Department of Health (NYSDOH) Health Electronic Response Data System (HERDS). Hospitals, nursing homes, and adult care facilities are required to complete this survey daily. The information from the survey is used for statewide surveillance, planning, resource allocation, and emergency response activities. Hospitals began reporting for the HERDS COVID-19 survey in March 2020, while Nursing Homes and Adult Care Facilities began reporting in April 2020. It is important to note that fatalities related to COVID-19 disease that occurred prior to the first publication dates are also included.

    The fatality numbers in this dataset are calculated by assigning age groups to each patient based on the patient age, then summing the patient fatalities within each age group, as of each reporting date. The statewide total fatality numbers are calculated by summing the number of fatalities across all age groups, by reporting date. The fatality percentages are calculated by dividing the number of fatalities in each age group by the statewide total number of fatalities, by reporting date. The fatality numbers represent the cumulative number of fatalities that have been reported as of each reporting date.

  7. n

    Coronavirus (Covid-19) Data in the United States

    • nytimes.com
    • openicpsr.org
    • +2more
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    New York Times, Coronavirus (Covid-19) Data in the United States [Dataset]. https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html
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    Dataset provided by
    New York Times
    Description

    The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

    Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

    We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

    The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

  8. f

    COVID-19 in children in Espirito Santo State – Brazil

    • scielo.figshare.com
    • datasetcatalog.nlm.nih.gov
    tiff
    Updated Jun 1, 2023
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    Ethel Leonor Noia Maciel; Pablo Medeiros Jabor; Etereldes Goncalves Jr; Karllian Kerlen Simonelli Soares; Thiago Nascimento do Prado; Eliana Zandonade (2023). COVID-19 in children in Espirito Santo State – Brazil [Dataset]. http://doi.org/10.6084/m9.figshare.20443728.v1
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    tiffAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    SciELO journals
    Authors
    Ethel Leonor Noia Maciel; Pablo Medeiros Jabor; Etereldes Goncalves Jr; Karllian Kerlen Simonelli Soares; Thiago Nascimento do Prado; Eliana Zandonade
    License

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

    Area covered
    State of Espírito Santo, Brazil
    Description

    Abstract Objectives: to characterize school-aged children, adolescents, and young people’s profile and their associations with positive COVID-19 test results. Methods: an observational and descriptive study of secondary data from the COVID-19 Panel in Espírito Santo State in February to August 2020. People suspected of COVID-19, in the 0–19-years old age group, were included in order to assess clinical data and demographic and epidemiological factors associated with the disease. Results: in the study period, 27,351 COVID-19 notification were registered in children, adolescents, and young people. The highest COVID-19 test confirmation was found in Caucasians and were 5-14 years age group. It was also observed that headache was the symptom with the highest test confirmation. Infection in people with disabilities was more frequent in the confirmed cases. The confirmation of cases occurred in approximately 80% of the notified registrations and 0.3% of the confirmed cases, died. Conclusion: children with confirmed diagnosis for COVID-19 have lower mortality rates, even though many were asymptomatic. To control the chain of transmission and reduce morbidity and mortality rates, it was necessaryto conduct more comprehensive research and promote extensive testing in the population.

  9. c

    The COVID Tracking Project

    • covidtracking.com
    google sheets
    + more versions
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    The COVID Tracking Project [Dataset]. https://covidtracking.com/
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    google sheetsAvailable download formats
    Description

    The COVID Tracking Project collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data we can collect for the novel coronavirus, SARS-CoV-2. We attempt to include positive and negative results, pending tests, and total people tested for each state or district currently reporting that data.

    Testing is a crucial part of any public health response, and sharing test data is essential to understanding this outbreak. The CDC is currently not publishing complete testing data, so we’re doing our best to collect it from each state and provide it to the public. The information is patchy and inconsistent, so we’re being transparent about what we find and how we handle it—the spreadsheet includes our live comments about changing data and how we’re working with incomplete information.

    From here, you can also learn about our methodology, see who makes this, and find out what information states provide and how we handle it.

  10. f

    Examples of the different approaches to mitigate transmission of COVID-19...

    • plos.figshare.com
    • datasetcatalog.nlm.nih.gov
    xls
    Updated Jun 1, 2023
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    Lucy Bray; Bernie Carter; Lucy Blake; Holly Saron; Jennifer A. Kirton; Fanny Robichaud; Marla Avila; Karen Ford; Begonya Nafria; Maria Forsner; Stefan Nilsson; Andrea Chelkowski; Andrea Middleton; Anna-Clara Rullander; Janet Mattsson; Joanne Protheroe (2023). Examples of the different approaches to mitigate transmission of COVID-19 and provide information to children about COVID-19 (coronavirus) within the participating countries during the time of the study. [Dataset]. http://doi.org/10.1371/journal.pone.0246405.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Lucy Bray; Bernie Carter; Lucy Blake; Holly Saron; Jennifer A. Kirton; Fanny Robichaud; Marla Avila; Karen Ford; Begonya Nafria; Maria Forsner; Stefan Nilsson; Andrea Chelkowski; Andrea Middleton; Anna-Clara Rullander; Janet Mattsson; Joanne Protheroe
    License

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

    Description

    Examples of the different approaches to mitigate transmission of COVID-19 and provide information to children about COVID-19 (coronavirus) within the participating countries during the time of the study.

  11. f

    DataSheet_1_The impact of immunocompromise on outcomes of COVID-19 in...

    • figshare.com
    • datasetcatalog.nlm.nih.gov
    pdf
    Updated Aug 25, 2023
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    James Greenan-Barrett; Samuel Aston; Claire T. Deakin; Coziana Ciurtin (2023). DataSheet_1_The impact of immunocompromise on outcomes of COVID-19 in children and young people—a systematic review and meta-analysis.pdf [Dataset]. http://doi.org/10.3389/fimmu.2023.1159269.s001
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    pdfAvailable download formats
    Dataset updated
    Aug 25, 2023
    Dataset provided by
    Frontiers
    Authors
    James Greenan-Barrett; Samuel Aston; Claire T. Deakin; Coziana Ciurtin
    License

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

    Description

    BackgroundDespite children and young people (CYP) having a low risk for severe coronavirus disease 2019 (COVID-19) outcomes, there is still a degree of uncertainty related to their risk in the context of immunodeficiency or immunosuppression, primarily due to significant reporting bias in most studies, as CYP characteristically experience milder or asymptomatic COVID-19 infection and the severe outcomes tend to be overestimated.MethodsA comprehensive systematic review to identify globally relevant studies in immunosuppressed CYP and CYP in general population (defined as younger than 25 years of age) up to 31 October 2021 (to exclude vaccinated populations) was performed. Studies were included if they reported the two primary outcomes of our study, admission to intensive therapy unit (ITU) and mortality, while data on other outcomes, such as hospitalization and need for mechanical ventilation were also collected. A meta-analysis estimated the pooled proportion for each severe COVID-19 outcome, using the inverse variance method. Random effects models were used to account for interstudy heterogeneity.FindingsThe systematic review identified 30 eligible studies for each of the two populations investigated: immunosuppressed CYP (n = 793) and CYP in general population (n = 102,022). Our meta-analysis found higher estimated prevalence for hospitalization (46% vs. 16%), ITU admission (12% vs. 2%), mechanical ventilation (8% vs. 1%), and increased mortality due to severe COVID-19 infection (6.5% vs. 0.2%) in immunocompromised CYP compared with CYP in general population. This shows an overall trend for more severe outcomes of COVID-19 infection in immunocompromised CYP, similar to adult studies.InterpretationThis is the only up-to-date meta-analysis in immunocompromised CYP with high global relevance, which excluded reports from hospitalized cohorts alone and included 35% studies from low- and middle-income countries. Future research is required to characterize individual subgroups of immunocompromised patients, as well as impact of vaccination on severe COVID-19 outcomes.Systematic Review RegistrationPROSPERO identifier, CRD42021278598.

  12. V

    Dataset from Randomised Evaluation of COVID-19 Therapy

    • data.niaid.nih.gov
    Updated May 20, 2025
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    IDDO; Richard Haynes; Peter W Horby (2025). Dataset from Randomised Evaluation of COVID-19 Therapy [Dataset]. http://doi.org/10.25934/PR00009091
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    Dataset updated
    May 20, 2025
    Dataset provided by
    University of Oxford
    Authors
    IDDO; Richard Haynes; Peter W Horby
    Area covered
    Sri Lanka, Vietnam, South Africa, Nepal, Gambia, India, United Kingdom, Ghana, Indonesia
    Description

    RECOVERY is a randomised trial investigating whether treatment with Lopinavir-Ritonavir, Hydroxychloroquine, Corticosteroids, Azithromycin, Colchicine, IV Immunoglobulin (children only), Convalescent plasma, Casirivimab+Imdevimab, Tocilizumab, Aspirin, Baricitinib, Infliximab, Empagliflozin, Sotrovimab, Molnupiravir, Paxlovid or Anakinra (children only) prevents death in patients with COVID-19.

  13. f

    Table1_Clinical outcomes of COVID-19 and influenza in hospitalized children...

    • datasetcatalog.nlm.nih.gov
    Updated Sep 11, 2023
    + more versions
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    Khan, Farid; Di Fusco, Manuela; McGrath, Leah J.; Lopez, Santiago M. C.; Cane, Alejandro; Reimbaeva, Maya; Welch, Verna L.; Malhotra, Deepa; Alfred, Tamuno; Moran, Mary M. (2023). Table1_Clinical outcomes of COVID-19 and influenza in hospitalized children <5 years in the US.pdf [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0001012872
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    Dataset updated
    Sep 11, 2023
    Authors
    Khan, Farid; Di Fusco, Manuela; McGrath, Leah J.; Lopez, Santiago M. C.; Cane, Alejandro; Reimbaeva, Maya; Welch, Verna L.; Malhotra, Deepa; Alfred, Tamuno; Moran, Mary M.
    Area covered
    United States
    Description

    IntroductionWe compared hospitalization outcomes of young children hospitalized with COVID-19 to those hospitalized with influenza in the United States.MethodsPatients aged 0-<5 years hospitalized with an admission diagnosis of acute COVID-19 (April 2021-March 2022) or influenza (April 2019-March 2020) were selected from the PINC AI Healthcare Database Special Release. Hospitalization outcomes included length of stay (LOS), intensive care unit (ICU) admission, oxygen supplementation, and mechanical ventilation (MV). Inverse probability of treatment weighting was used to adjust for confounders in logistic regression analyses.ResultsAmong children hospitalized with COVID-19 (n = 4,839; median age: 0 years), 21.3% had an ICU admission, 19.6% received oxygen supplementation, 7.9% received MV support, and 0.5% died. Among children hospitalized with influenza (n = 4,349; median age: 1 year), 17.4% were admitted to the ICU, 26.7% received oxygen supplementation, 7.6% received MV support, and 0.3% died. Compared to children hospitalized with influenza, those with COVID-19 were more likely to have an ICU admission (adjusted odds ratio [aOR]: 1.34; 95% confidence interval [CI]: 1.21–1.48). However, children with COVID-19 were less likely to receive oxygen supplementation (aOR: 0.71; 95% CI: 0.64–0.78), have a prolonged LOS (aOR: 0.81; 95% CI: 0.75–0.88), or a prolonged ICU stay (aOR: 0.56; 95% CI: 0.46–0.68). The likelihood of receiving MV was similar (aOR: 0.94; 95% CI: 0.81, 1.1).ConclusionsHospitalized children with either SARS-CoV-2 or influenza had severe complications including ICU admission and oxygen supplementation. Nearly 10% received MV support. Both SARS-CoV-2 and influenza have the potential to cause severe illness in young children.

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

    • www150.statcan.gc.ca
    • open.canada.ca
    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.

  15. d

    Smart Discharges Uganda Under 5: Phase I clinical data of children 0-6...

    • search.dataone.org
    • borealisdata.ca
    Updated Oct 30, 2024
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    Zhang, Cherri; Akter, Tanjila; Nguyen, Vuong; Bone, Jeff; Wiens, Matthew (2024). Smart Discharges Uganda Under 5: Phase I clinical data of children 0-6 months - Covid-19 cohort [Dataset]. http://doi.org/10.5683/SP3/QYOSW0
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    Dataset updated
    Oct 30, 2024
    Dataset provided by
    Borealis
    Authors
    Zhang, Cherri; Akter, Tanjila; Nguyen, Vuong; Bone, Jeff; Wiens, Matthew
    Area covered
    Uganda
    Description

    This data is a subset of the Smart Discharges Uganda Under 5 years parent study and is specific to the Phase I observation cohort of children aged 0-6 months collected during the Covid-19 pandemic in 2020. Objective(s): Used as part of the Smart Discharge prediction modelling for adverse outcomes such as post-discharge death and readmission. Data Description: All data were collected at the point of care using encrypted study tablets and these data were then uploaded to a Research Electronic Data Capture (REDCap) database hosted at the BC Children’s Hospital Research Institute (Vancouver, Canada). At admission, trained study nurses systematically collected data on clinical, social and demographic variables. Following discharge, field officers contacted caregivers at 2 and 4 months by phone, and in-person at 6 months, to determine vital status, post-discharge health-seeking, and readmission details. Verbal autopsies were conducted for children who had died following discharge. . Data Processing: Created z-scores for anthropometry variables using height and weight according to WHO cutoff. Distance to hospital was calculated using latitude and longitude. Extra symptom and diagnosis categories were created based on text field in these two variables. BCS score was created by summing all individual components. Limitations: There are missing dates and the admission, discharge, and readmission dates are not in order. Ethics Declaration: This study was approved by the Mbarara University of Science and Technology Research Ethics Committee (No. 15/10-16), the Uganda National Institute of Science and Technology (HS 2207), and the University of British Columbia / Children & Women’s Health Centre of British Columbia Research Ethics Board (H16-02679). This manuscript adheres to the guidelines for STrengthening the Reporting of OBservational studies in Epidemiology (STROBE). NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

  16. f

    Data_Sheet_1_Influenza vs. COVID-19: Comparison of Clinical Characteristics...

    • frontiersin.figshare.com
    pdf
    Updated May 30, 2023
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    Almudena Laris-González; Martha Avilés-Robles; Clemen Domínguez-Barrera; Israel Parra-Ortega; José Luis Sánchez-Huerta; Karla Ojeda-Diezbarroso; Sergio Bonilla-Pellegrini; Víctor Olivar-López; Adrián Chávez-López; Rodolfo Jiménez-Juárez (2023). Data_Sheet_1_Influenza vs. COVID-19: Comparison of Clinical Characteristics and Outcomes in Pediatric Patients in Mexico City.PDF [Dataset]. http://doi.org/10.3389/fped.2021.676611.s001
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    pdfAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    Frontiers
    Authors
    Almudena Laris-González; Martha Avilés-Robles; Clemen Domínguez-Barrera; Israel Parra-Ortega; José Luis Sánchez-Huerta; Karla Ojeda-Diezbarroso; Sergio Bonilla-Pellegrini; Víctor Olivar-López; Adrián Chávez-López; Rodolfo Jiménez-Juárez
    License

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

    Area covered
    Mexico City
    Description

    Introduction: Respiratory viruses are among the leading causes of disease and death among children. Co-circulation of influenza and SARS-CoV2 can lead to diagnostic and management difficulties given the similarities in the clinical picture.Methods: This is a cohort of all children hospitalized with SARS-CoV2 infection from March to September 3rd 2020, and all children admitted with influenza throughout five flu-seasons (2013–2018) at a pediatric referral hospital. Patients with influenza were identified from the clinical laboratory database. All hospitalized patients with confirmed SARS-CoV2 infection were followed-up prospectively.Results: A total of 295 patients with influenza and 133 with SARS-CoV2 infection were included. The median age was 3.7 years for influenza and 5.3 years for SARS-CoV2. Comorbidities were frequent in both groups, but they were more common in patients with influenza (96.6 vs. 82.7%, p < 0.001). Fever and cough were the most common clinical manifestations in both groups. Rhinorrhea was present in more than half of children with influenza but was infrequent in those with COVID-19 (53.6 vs. 5.8%, p < 0.001). Overall, 6.4% percent of patients with influenza and 7.5% percent of patients with SARS-CoV2 infection died. In-hospital mortality and the need for mechanical ventilation among symptomatic patients were similar between groups in the multivariate analysis.Conclusions: Influenza and COVID-19 have a similar picture in pediatric patients, which makes diagnostic testing necessary for adequate diagnosis and management. Even though most cases of COVID-19 in children are asymptomatic or mild, the risk of death among hospitalized patients with comorbidities may be substantial, especially among infants.

  17. f

    Study Population Characteristics.

    • datasetcatalog.nlm.nih.gov
    • plos.figshare.com
    Updated Apr 15, 2024
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    Cai, Tianxi; Verdy, Guillaume; Mowery, Danielle L.; Tibollo, Valentina; Schubert, Petra; Tan, Byorn W. L.; Avillach, Paul; Gehlenborg, Nils; Ho, Yuk-Lam; Neuraz, Antoine; Hutch, Meghan R.; Visweswaran, Shyam; Xia, Zongqi; Vidorreta, Fernando J. Sanz; Klann, Jeffrey G.; Schriver, Emily R.; Le, Trang T.; Gutiérrez-Sacristán, Alba; Omenn, Gilbert S.; Tippman, Patric; Talbert, Jeffery; Hanauer, David A.; Bryant, William A.; Henderson, Darren W.; Bonzel, Clara-Lea; Tan, Amelia L. M.; Loh, Ne Hooi Will; Spiridou, Anastasia; Makoudjou, Adeline; Bellazzi, Riccardo; Moal, Bertrand; Yuan, William; Benoit, Vincent; Malovini, Alberto; Tan, Bryce W. Q.; Chiudinelli, Lorenzo; Hong, Chuan; Cho, Kelly; Samayamuthu, Malarkodi Jebathilagam; Makwana, Simran; Abad, Zahra Shakeri Hossein; Luo, Yuan; Son, Jiyeon; Morris, Michele; Das, Priyam; González, Tomás González; Batugo, Ashley; Wang, Xuan (2024). Study Population Characteristics. [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0001344135
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    Dataset updated
    Apr 15, 2024
    Authors
    Cai, Tianxi; Verdy, Guillaume; Mowery, Danielle L.; Tibollo, Valentina; Schubert, Petra; Tan, Byorn W. L.; Avillach, Paul; Gehlenborg, Nils; Ho, Yuk-Lam; Neuraz, Antoine; Hutch, Meghan R.; Visweswaran, Shyam; Xia, Zongqi; Vidorreta, Fernando J. Sanz; Klann, Jeffrey G.; Schriver, Emily R.; Le, Trang T.; Gutiérrez-Sacristán, Alba; Omenn, Gilbert S.; Tippman, Patric; Talbert, Jeffery; Hanauer, David A.; Bryant, William A.; Henderson, Darren W.; Bonzel, Clara-Lea; Tan, Amelia L. M.; Loh, Ne Hooi Will; Spiridou, Anastasia; Makoudjou, Adeline; Bellazzi, Riccardo; Moal, Bertrand; Yuan, William; Benoit, Vincent; Malovini, Alberto; Tan, Bryce W. Q.; Chiudinelli, Lorenzo; Hong, Chuan; Cho, Kelly; Samayamuthu, Malarkodi Jebathilagam; Makwana, Simran; Abad, Zahra Shakeri Hossein; Luo, Yuan; Son, Jiyeon; Morris, Michele; Das, Priyam; González, Tomás González; Batugo, Ashley; Wang, Xuan
    Description

    Few studies examining the patient outcomes of concurrent neurological manifestations during acute COVID-19 leveraged multinational cohorts of adults and children or distinguished between central and peripheral nervous system (CNS vs. PNS) involvement. Using a federated multinational network in which local clinicians and informatics experts curated the electronic health records data, we evaluated the risk of prolonged hospitalization and mortality in hospitalized COVID-19 patients from 21 healthcare systems across 7 countries. For adults, we used a federated learning approach whereby we ran Cox proportional hazard models locally at each healthcare system and performed a meta-analysis on the aggregated results to estimate the overall risk of adverse outcomes across our geographically diverse populations. For children, we reported descriptive statistics separately due to their low frequency of neurological involvement and poor outcomes. Among the 106,229 hospitalized COVID-19 patients (104,031 patients ≥18 years; 2,198 patients <18 years, January 2020-October 2021), 15,101 (14%) had at least one CNS diagnosis, while 2,788 (3%) had at least one PNS diagnosis. After controlling for demographics and pre-existing conditions, adults with CNS involvement had longer hospital stay (11 versus 6 days) and greater risk of (Hazard Ratio = 1.78) and faster time to death (12 versus 24 days) than patients with no neurological condition (NNC) during acute COVID-19 hospitalization. Adults with PNS involvement also had longer hospital stay but lower risk of mortality than the NNC group. Although children had a low frequency of neurological involvement during COVID-19 hospitalization, a substantially higher proportion of children with CNS involvement died compared to those with NNC (6% vs 1%). Overall, patients with concurrent CNS manifestation during acute COVID-19 hospitalization faced greater risks for adverse clinical outcomes than patients without any neurological diagnosis. Our global informatics framework using a federated approach (versus a centralized data collection approach) has utility for clinical discovery beyond COVID-19.

  18. a

    COVID-19 Outbreaks in Ottawa Healthcare, Childcare and Educational...

    • hub.arcgis.com
    • open.ottawa.ca
    • +1more
    Updated Sep 21, 2020
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    City of Ottawa (2020). COVID-19 Outbreaks in Ottawa Healthcare, Childcare and Educational Establishments [Dataset]. https://hub.arcgis.com/datasets/5b24f70482fe4cf1824331d89483d3d3
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    Dataset updated
    Sep 21, 2020
    Dataset authored and provided by
    City of Ottawa
    License

    https://ottawa.ca/en/city-hall/get-know-your-city/open-data#open-data-licence-version-2-0https://ottawa.ca/en/city-hall/get-know-your-city/open-data#open-data-licence-version-2-0

    Area covered
    Ottawa
    Description

    Summary of COVID-19 outbreaks in Ottawa healthcare, childcare and educational establishments, based on the most up to date information available in the the Ontario Ministry of Health Public Health Case and Contact Management Solution (CCM) .

    Accuracy: Points of consideration for interpretation of the data: The data was extracted by Ottawa Public Health from the Ontario Ministry of Health Public Health Case and Contact Management Solution (CCM) . The CCM is a dynamic disease reporting system that allows for ongoing updates to data previously entered. The data extracted from The CCM represent a snapshot at the time of extraction and may differ in previous or subsequent reports.Data are for confirmed outbreaks and the number of staff, living in Ottawa, and residents/patients/students with laboratory confirmed COVID-19 associated to each outbreak is provided. Please note, individuals may be linked to multiple outbreaks. All the outbreaks reflect the outbreak definitions at the time they were declared open:Healthcare Institutions: From April 1st 2020, 1 staff or resident case of laboratory-confirmed COVID-19 is considered an outbreak in long-term care homes (LTCH), retirement homes (RH) and other healthcare institutions (e.g. group home, assisted living, group shelter) and declared facility wide. Starting May 10th 2020, 2 staff or patient cases of laboratory-confirmed COVID-19 within a specified hospital unit within a 14-day period where both cases could have reasonably acquired their infection in hospital is considered an outbreak in a public hospital.Childcare & Education: Starting July 2020, 1 child or staff (or household member) case of laboratory-confirmed COVID-19 is considered an outbreak in a childcare establishment. Starting August 26 2020, 2 student or staff (or visitor) cases of laboratory-confirmed COVID-19 within a specified class within a 14-day period where at least one case could have reasonably acquired their infection at school (including transportation and before/after school care) is considered an outbreak in an educational establishment. Update Frequency: Tuesdays and Fridays

    Attributes: Data fields: Facility Name – textType of Facility - textLocation in Facility – textReported Date – date the COVID-19 outbreak was openedEnd Date - date the COVID-19 outbreak was closedResident/Patient/Child/Student Cases – number of residents, patients, children, or students with confirmed COVID-19Resident/Patient/Child/Student Cases – number of residents, patients, children, or students with confirmed COVID-19 who diedStaff Cases – number of staff with confirmed COVID-19Staff Deaths – number of staff with confirmed COVID-19 who diedTotal Cases – total number of people with confirmed COVID-19Total Deaths – total number of people with confirmed COVID-19 who died Contact: OPH Epidemiology Team | Epidemiology & Evidence, Ottawa Public Health

  19. f

    Data_Sheet_1_Risk factors for admission to the pediatric critical care unit...

    • frontiersin.figshare.com
    docx
    Updated Jun 16, 2023
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    Blandine Prévost; Aurélia Retbi; Florence Binder-Foucard; Aurélie Borde; Amélie Bruandet; Harriet Corvol; Véronique Gilleron; Maggie Le Bourhis-Zaimi; Xavier Lenne; Joris Muller; Eric Ouattara; Fabienne Séguret; Pierre Tran Ba Loc; Sophie Tezenas du Montcel (2023). Data_Sheet_1_Risk factors for admission to the pediatric critical care unit among children hospitalized with COVID-19 in France.docx [Dataset]. http://doi.org/10.3389/fped.2022.975826.s001
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    docxAvailable download formats
    Dataset updated
    Jun 16, 2023
    Dataset provided by
    Frontiers
    Authors
    Blandine Prévost; Aurélia Retbi; Florence Binder-Foucard; Aurélie Borde; Amélie Bruandet; Harriet Corvol; Véronique Gilleron; Maggie Le Bourhis-Zaimi; Xavier Lenne; Joris Muller; Eric Ouattara; Fabienne Séguret; Pierre Tran Ba Loc; Sophie Tezenas du Montcel
    License

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

    Area covered
    France
    Description

    BackgroundCOVID-19 infection is less severe among children than among adults; however, some patients require hospitalization and even critical care. Using data from the French national medico-administrative database, we estimated the risk factors for critical care unit (CCU) admissions among pediatric COVID-19 hospitalizations, the number and characteristics of the cases during the successive waves from January 2020 to August 2021 and described death cases.MethodsWe included all children (age < 18) hospitalized with COVID-19 between January 1st, 2020, and August 31st, 2021. Follow-up was until September 30th, 2021 (discharge or death). Contiguous hospital stays were gathered in “care sequences.” Four epidemic waves were considered (cut off dates: August 11th 2020, January 1st 2021, and July 4th 2021). We excluded asymptomatic COVID-19 cases, post-COVID-19 diseases, and 1-day-long sequences (except death cases). Risk factors for CCU admission were assessed with a univariable and a multivariable logistic regression model in the entire sample and stratified by age, whether younger than 2.ResultsWe included 7,485 patients, of whom 1988 (26.6%) were admitted to the CCU. Risk factors for admission to the CCU were being younger than 7 days [OR: 3.71 95% CI (2.56–5.39)], being between 2 and 9 years old [1.19 (1.00–1.41)], pediatric multisystem inflammatory syndrome (PIMS) [7.17 (5.97–8.6)] and respiratory forms [1.26 (1.12–1.41)], and having at least one underlying condition [2.66 (2.36–3.01)]. Among hospitalized children younger than 2 years old, prematurity was a risk factor for CCU admission [1.89 (1.47–2.43)]. The CCU admission rate gradually decreased over the waves (from 31.0 to 17.8%). There were 32 (0.4%) deaths, of which the median age was 6 years (IQR: 177 days–15.5 years).ConclusionSome children need to be more particularly protected from a severe evolution: newborns younger than 7 days old, children aged from 2 to 13 years who are more at risk of PIMS forms and patients with at least one underlying medical condition.

  20. Children’s self-report of how much they know about COVID-19.

    • plos.figshare.com
    xls
    Updated May 30, 2023
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    Lucy Bray; Bernie Carter; Lucy Blake; Holly Saron; Jennifer A. Kirton; Fanny Robichaud; Marla Avila; Karen Ford; Begonya Nafria; Maria Forsner; Stefan Nilsson; Andrea Chelkowski; Andrea Middleton; Anna-Clara Rullander; Janet Mattsson; Joanne Protheroe (2023). Children’s self-report of how much they know about COVID-19. [Dataset]. http://doi.org/10.1371/journal.pone.0246405.t007
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    xlsAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Lucy Bray; Bernie Carter; Lucy Blake; Holly Saron; Jennifer A. Kirton; Fanny Robichaud; Marla Avila; Karen Ford; Begonya Nafria; Maria Forsner; Stefan Nilsson; Andrea Chelkowski; Andrea Middleton; Anna-Clara Rullander; Janet Mattsson; Joanne Protheroe
    License

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

    Description

    Children’s self-report of how much they know about COVID-19.

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NCHS/DVS (2023). Provisional COVID-19 Deaths: Focus on Ages 0-18 Years [Dataset]. https://data.cdc.gov/widgets/nr4s-juj3?mobile_redirect=true

Provisional COVID-19 Deaths: Focus on Ages 0-18 Years

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3 scholarly articles cite this dataset (View in Google Scholar)
csv, xml, xlsxAvailable download formats
Dataset updated
Jun 28, 2023
Dataset authored and provided by
NCHS/DVS
License

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

Description

Effective June 28, 2023, this dataset will no longer be updated. Similar data are accessible from CDC WONDER (https://wonder.cdc.gov/mcd-icd10-provisional.html).

Deaths involving coronavirus disease 2019 (COVID-19) with a focus on ages 0-18 years in the United States.

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