36 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. S

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

    • splitgraph.com
    Updated Jul 20, 2023
    + more versions
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    cdc-gov (2023). Rates of COVID-19 Cases or Deaths by Age Group and Vaccination Status [Dataset]. https://www.splitgraph.com/cdc-gov/rates-of-covid19-cases-or-deaths-by-age-group-and-3rge-nu2a
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    application/vnd.splitgraph.image, application/openapi+json, jsonAvailable download formats
    Dataset updated
    Jul 20, 2023
    Authors
    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

    Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

    See the Splitgraph documentation for more information.

  4. 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.

  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. 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
    Brazil, State of Espírito Santo
    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.

  7. 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.

  8. 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.

  9. f

    Data_Sheet_1_One vaccine to counter many diseases? Modeling the economics of...

    • frontiersin.figshare.com
    • datasetcatalog.nlm.nih.gov
    docx
    Updated May 31, 2023
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    Angela Y. Chang; Peter Aaby; Michael S. Avidan; Christine S. Benn; Stefano M. Bertozzi; Lawrence Blatt; Konstantin Chumakov; Shabaana A. Khader; Shyam Kottilil; Madhav Nekkar; Mihai G. Netea; Annie Sparrow; Dean T. Jamison (2023). Data_Sheet_1_One vaccine to counter many diseases? Modeling the economics of oral polio vaccine against child mortality and COVID-19.docx [Dataset]. http://doi.org/10.3389/fpubh.2022.967920.s001
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    docxAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    Frontiers
    Authors
    Angela Y. Chang; Peter Aaby; Michael S. Avidan; Christine S. Benn; Stefano M. Bertozzi; Lawrence Blatt; Konstantin Chumakov; Shabaana A. Khader; Shyam Kottilil; Madhav Nekkar; Mihai G. Netea; Annie Sparrow; Dean T. Jamison
    License

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

    Description

    IntroductionRecent reviews summarize evidence that some vaccines have heterologous or non-specific effects (NSE), potentially offering protection against multiple pathogens. Numerous economic evaluations examine vaccines' pathogen-specific effects, but less than a handful focus on NSE. This paper addresses that gap by reporting economic evaluations of the NSE of oral polio vaccine (OPV) against under-five mortality and COVID-19.Materials and methodsWe studied two settings: (1) reducing child mortality in a high-mortality setting (Guinea-Bissau) and (2) preventing COVID-19 in India. In the former, the intervention involves three annual campaigns in which children receive OPV incremental to routine immunization. In the latter, a susceptible-exposed-infectious-recovered model was developed to estimate the population benefits of two scenarios, in which OPV would be co-administered alongside COVID-19 vaccines. Incremental cost-effectiveness and benefit-cost ratios were modeled for ranges of intervention effectiveness estimates to supplement the headline numbers and account for heterogeneity and uncertainty.ResultsFor child mortality, headline cost-effectiveness was $650 per child death averted. For COVID-19, assuming OPV had 20% effectiveness, incremental cost per death averted was $23,000–65,000 if it were administered simultaneously with a COVID-19 vaccine

  10. CDPHE Viral Respiratory COVID19 Deaths by County

    • data-cdphe.opendata.arcgis.com
    Updated Oct 15, 2024
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    Colorado Department of Public Health and Environment (2024). CDPHE Viral Respiratory COVID19 Deaths by County [Dataset]. https://data-cdphe.opendata.arcgis.com/datasets/cdphe-viral-respiratory-covid19-deaths-by-county-
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    Dataset updated
    Oct 15, 2024
    Dataset authored and provided by
    Colorado Department of Public Health and Environmenthttps://cdphe.colorado.gov/
    Description

    The Colorado COVID-19 Deaths by County dataset contains statistics for deaths due to COVID-19 per county for counties with a population greater than 15,000 people and is reported on Colorado’s Viral Respiratory Diseases data website. The data in this file updates each Wednesday and includes the following fields for Colorado county's total deaths due to COVID-19 in the State of Colorado reported from Colorado Vital Statistics for the year of 2023:County: (All 64 Colorado counties, Unknown)Year: (2023)Deaths_Due_to_COVID_19: (death count)publish_dateFor more information, data definitions, and context, please visit Colorado’s Viral Respiratory Diseases data website (https://cdphe.colorado.gov/viral-respiratory-diseases-report).

  11. d

    COVID Brazil Pediatric numbers (Cases, Deaths, Intensive care use,...

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 8, 2023
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    Morato, Eric Grossi (2023). COVID Brazil Pediatric numbers (Cases, Deaths, Intensive care use, Hospitalization) dataset Mar/2020 to Aug/2022 [Dataset]. http://doi.org/10.7910/DVN/TVEGFW
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    Dataset updated
    Nov 8, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Morato, Eric Grossi
    Time period covered
    Mar 1, 2020 - Aug 7, 2022
    Area covered
    Brazil
    Description

    Since the president of Brazil, in an interview at Flow podcast on August 10, 2022, stated that in COVID, children are asymptomatic, almost never hospitalized, and rarely needed intensive care, which is a huge and dangerous lie. Based on Brazilian Health SUS data provided by the Bolsonaro government itself, we prove the ignorance and risk of mixing ideology and feelings with science and medicine. The most dangerous ignorance is not unknowing, but believing that they have knowledge, being miles away from it. Dataset provided by the opendataSUS platform with all patients notified with a diagnosis of COVID in Brazil between Jan/20 and Aug/22 under 12 years old. Number of cases, hospital admissions, ICU admissions and deaths.

  12. 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
    Explore at:
    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.

  13. Deaths involving COVID-19 by local area and deprivation

    • ons.gov.uk
    • cy.ons.gov.uk
    xlsx
    Updated May 1, 2020
    + more versions
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    Office for National Statistics (2020). Deaths involving COVID-19 by local area and deprivation [Dataset]. https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/deathsinvolvingcovid19bylocalareasanddeprivation
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    xlsxAvailable download formats
    Dataset updated
    May 1, 2020
    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

    Provisional counts of the number of deaths and age-standardised mortality rates involving the coronavirus (COVID-19) between 1 March and 17 April 2020 in England and Wales. Figures are provided by age, sex, geographies down to local authority level and deprivation indices.

  14. 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
    Vietnam, Indonesia, Nepal, United Kingdom, India, Sri Lanka, South Africa, Ghana, Gambia
    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.

  15. 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.

  16. 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.

  17. f

    Table_1_Case Report: SARS-CoV-2 Mother-to-Child Transmission and Fetal Death...

    • datasetcatalog.nlm.nih.gov
    • frontiersin.figshare.com
    • +1more
    Updated Aug 16, 2021
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    Avvad-Portari, Elyzabeth; Chimelli, Leila; da Cunha, Antonio José Ledo Alves; Amim, Joffre; Rehen, Stevens; Prata-Barbosa, Arnaldo; Tovar-Moll, Fernanda; Mendes, Mayara Abud; de Oliveira-Szejnfeld, Patrícia Soares; Marinho, Penélope Saldanha; Gomes, Ismael Carlos; Goldman, Suzan Menasce; de Oliveira, Mariana Barros Genuíno; Guimarães, Marilia Zaluar; Souza, Letícia Rocha Q.; da Matta Andreiuolo, Felipe (2021). Table_1_Case Report: SARS-CoV-2 Mother-to-Child Transmission and Fetal Death Associated With Severe Placental Thromboembolism.DOCX [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0000842304
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    Dataset updated
    Aug 16, 2021
    Authors
    Avvad-Portari, Elyzabeth; Chimelli, Leila; da Cunha, Antonio José Ledo Alves; Amim, Joffre; Rehen, Stevens; Prata-Barbosa, Arnaldo; Tovar-Moll, Fernanda; Mendes, Mayara Abud; de Oliveira-Szejnfeld, Patrícia Soares; Marinho, Penélope Saldanha; Gomes, Ismael Carlos; Goldman, Suzan Menasce; de Oliveira, Mariana Barros Genuíno; Guimarães, Marilia Zaluar; Souza, Letícia Rocha Q.; da Matta Andreiuolo, Felipe
    Description

    SARS-CoV-2 infection during pregnancy is not usually associated with significant adverse effects. However, in this study, we report a fetal death associated with mild COVID-19 in a 34-week-pregnant woman. The virus was detected in the placenta and in an unprecedented way in several fetal tissues. Placental abnormalities (MRI and anatomopathological study) were consistent with intense vascular malperfusion, probably the cause of fetal death. Lung histopathology also showed signs of inflammation, which could have been a contributory factor. Monitoring inflammatory response and coagulation in high-risk pregnant women with COVID-19 may prevent unfavorable outcomes, as shown in this case.

  18. Deaths and age-specific mortality rates, by selected grouped causes

    • www150.statcan.gc.ca
    • ouvert.canada.ca
    • +1more
    Updated Feb 19, 2025
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    Government of Canada, Statistics Canada (2025). Deaths and age-specific mortality rates, by selected grouped causes [Dataset]. http://doi.org/10.25318/1310039201-eng
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    Dataset updated
    Feb 19, 2025
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    Number of deaths and age-specific mortality rates for selected grouped causes, by age group and sex, 2000 to most recent year.

  19. 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.

  20. f

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

    • frontiersin.figshare.com
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    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.

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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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