51 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

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

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

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

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

  3. 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.

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

    • statista.com
    • tokrwards.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.

  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. Number of COVID-19 deaths in the United States as of March 10, 2023, by...

    • statista.com
    • tokrwards.com
    Updated Mar 28, 2023
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    Statista (2023). Number of COVID-19 deaths in the United States as of March 10, 2023, by state [Dataset]. https://www.statista.com/statistics/1103688/coronavirus-covid19-deaths-us-by-state/
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    Dataset updated
    Mar 28, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    As of March 10, 2023, there have been 1.1 million deaths related to COVID-19 in the United States. There have been 101,159 deaths in the state of California, more than any other state in the country – California is also the state with the highest number of COVID-19 cases.

    The vaccine rollout in the U.S. Since the start of the pandemic, the world has eagerly awaited the arrival of a safe and effective COVID-19 vaccine. In the United States, the immunization campaign started in mid-December 2020 following the approval of a vaccine jointly developed by Pfizer and BioNTech. As of March 22, 2023, the number of COVID-19 vaccine doses administered in the U.S. had reached roughly 673 million. The states with the highest number of vaccines administered are California, Texas, and New York.

    Vaccines achieved due to work of research groups Chinese authorities initially shared the genetic sequence to the novel coronavirus in January 2020, allowing research groups to start studying how it invades human cells. The surface of the virus is covered with spike proteins, which enable it to bind to human cells. Once attached, the virus can enter the cells and start to make people ill. These spikes were of particular interest to vaccine manufacturers because they hold the key to preventing viral entry.

  8. f

    Data from: Multisystem Inflammatory Syndrome and COVID-19 in children and...

    • scielo.figshare.com
    tiff
    Updated Jun 3, 2023
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    Erly Catarina de Moura; Fabrício Vieira Cavalcante; Luciana Gonzaga de Oliveira; Ivana Cristina de Holanda Cunha Barreto; Geraldo Magela Fernandes; Gustavo Saraiva Frio; Leonor Maria Pacheco Santos (2023). Multisystem Inflammatory Syndrome and COVID-19 in children and adolescents: epidemiological aspects, Brazil, 2020-20211 [Dataset]. http://doi.org/10.6084/m9.figshare.21087419.v1
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    tiffAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    SciELO journals
    Authors
    Erly Catarina de Moura; Fabrício Vieira Cavalcante; Luciana Gonzaga de Oliveira; Ivana Cristina de Holanda Cunha Barreto; Geraldo Magela Fernandes; Gustavo Saraiva Frio; Leonor Maria Pacheco Santos
    License

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

    Area covered
    Brazil
    Description

    ABSTRACT This study describes epidemiological aspects of the Multisystemic Inflammatory Syndrome in Children (MIS-C) associated with COVID-19 and mortality by COVID-19 in children (0-9 years old) and adolescents (10-19 years old). The data sources, for 2020-2021, were the Epidemiological Surveillance System for MIS-C and Mortality Information System for COVID-19, both managed by the Ministry of Health. There were 1,503 cases, more frequent in children (77%) than in adolescents (23%), and 93 reported deaths due to MIS-C in 26 of the 27 States of the Country. The highest number of cases in children was reported in São Paulo (268), but the highest incidence took place in the Federal District (7.8 per 100,000 inhabitants). The rate of deaths due to MIS-C was 5.4% in children and 8.7% in adolescents. There were 2,329 deaths due to COVID-19 in the population under 20 years old, with a higher rate in adolescents (4.4 per 100,000 inhabitants) than in children (2.7); the highest rate occurred in Roraima. We recommend intensifying immunization against COVID-19 in such population, increasing protection against the negative effects of COVID-19 and MIS-C, which may have short, medium and/or long-term consequences, so as not to compromise the full integration of these citizens into society.

  9. 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.

  10. 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

  11. HMPPS COVID-19 statistics : February 2021

    • gov.uk
    • s3.amazonaws.com
    Updated Mar 12, 2021
    + more versions
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    Ministry of Justice (2021). HMPPS COVID-19 statistics : February 2021 [Dataset]. https://www.gov.uk/government/statistics/hmpps-covid-19-statistics-february-2021
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    Dataset updated
    Mar 12, 2021
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Ministry of Justice
    Description

    The HM Prison and Probation Service (HMPPS) COVID-19 statistics provides monthly data on the HMPPS response to COVID-19. It addresses confirmed cases of the virus in prisons and the Youth Custody Service sites, deaths across HMPPS service users and mitigating action being taken to limit the spread of the virus and save lives.

    Data includes:

    • Deaths where prisoners, children in custody or probation service users have died having tested positive for COVID-19 or where there was a clinical assessment that COVID-19 was a contributory factor in their death.
    • Confirmed COVID-19 cases in prisoners and children in custody (i.e. positive tests).
    • Narrative on capacity management data for prisons.

    In this release information on COVID-19 related deaths and confirmed COVID-19 cases at prison and Youth Custody Service establishment level up to 31 January 2021.

    Pre-release access

    The bulletin was produced and handled by the ministry’s analytical professionals and production staff. For the bulletin pre-release access of up to 24 hours is granted to the following persons:

    Ministry of Justice:

    Lord Chancellor and Secretary of State for Justice; Parliamentary Under Secretary of State; Permanent Secretary; Minister and Permanent Secretary Private Secretaries (x8); Special Advisors (x2); Director General for Policy and Strategy Group; Deputy Director of Data and Evidence as a Service; Head of Profession, Statistics; Head of Prison Safety and Security Statistics; Head of News; Deputy Head of News and relevant press officers (x2).

    HM Prison and Probation Service:

    Chief Executive Officer; Director General Prisons; Chief Executive and Director General Private Secretaries and Heads of Office (x4); Deputy Director of COVID-19 HMPPS Response; Deputy Director Joint COVID 19 Strategic Policy Unit (x2); Director General of Probation and Wales; Executive Director Probation and Women; Executive Director of Youth Custody Service; Executive Director HMPPS Wales; Executive Director, Performance Directorate; Head of Health, Social Care and Substance Misuse Services; Head of Capacity Management and Custodial Capacity Manager.

    Related links

    Update on COVID-19 in prisons

    Prison estate expanded to protect NHS from coronavirus risk

    Measures announced to protect NHS from coronavirus risk in prisons

  12. 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.

  13. m

    COVID-19 reporting

    • mass.gov
    Updated Mar 4, 2020
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    Executive Office of Health and Human Services (2022). COVID-19 reporting [Dataset]. https://www.mass.gov/info-details/covid-19-reporting
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    Dataset updated
    Mar 4, 2020
    Dataset provided by
    Department of Public Health
    Executive Office of Health and Human Services
    Area covered
    Massachusetts
    Description

    The COVID-19 dashboard includes data on city/town COVID-19 activity, confirmed and probable cases of COVID-19, confirmed and probable deaths related to COVID-19, and the demographic characteristics of cases and deaths.

  14. Data from: Comparison of pandemic excess mortality in 2020-2021 across...

    • zenodo.org
    bin, csv
    Updated May 19, 2022
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    Michael Levitt; Francesco Zonta; John Ioannidis; Michael Levitt; Francesco Zonta; John Ioannidis (2022). Comparison of pandemic excess mortality in 2020-2021 across different empirical calculations [Dataset]. http://doi.org/10.5281/zenodo.6545130
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    csv, binAvailable download formats
    Dataset updated
    May 19, 2022
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Michael Levitt; Francesco Zonta; John Ioannidis; Michael Levitt; Francesco Zonta; John Ioannidis
    License

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

    Description

    Different modeling approaches can be used to calculate excess deaths for the COVID-19 pandemic period. We compared 6 calculations of excess deaths (4 previously published and two new ones that we performed with and without age-adjustment) for 2020-2021. With each approach, we calculated excess deaths metrics and the ratio R of excess deaths over recorded COVID-19 deaths. The main analysis focused on 33 high-income countries with weekly deaths in the Human Mortality Database (HMD at mortality.org) and reliable death registration. Secondary analyses compared calculations for other countries, whenever available. Across the 33 high-income countries, excess deaths were 2.0-2.8 million without age-adjustment, and 1.6-2.1 million with age-adjustment with large differences across countries. In our analyses after age-adjustment, 8 of 33 countries had no overall excess deaths; there was a death deficit in children; and 0.478 million (29.7%) of the excess deaths were in people <65 years old. In countries like France, Germany, Italy, and Spain excess death estimates differed 2 to 4-fold between highest and lowest figures. The R values’ range exceeded 0.3 in all 33 countries. In 16 of 33 countries, the range of R exceeded 1. In 25 of 33 countries some calculations suggest R>1 (excess deaths exceeding COVID-19 deaths) while others suggest R<1 (excess deaths smaller than COVID-19 deaths). Inferred data from 4 evaluations for 42 countries and from 3 evaluations for another 98 countries are very tenuous Estimates of excess deaths are analysis-dependent and age-adjustment is important to consider. Excess deaths may be lower than previously calculated.

  15. 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.

  16. V

    Dataset from Randomised Evaluation of COVID-19 Therapy

    • data.niaid.nih.gov
    Updated May 20, 2025
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    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
    IDDO
    Authors
    Richard Haynes; Peter W Horby
    Area covered
    Gambia, Ghana, South Africa, Indonesia, Sri Lanka, India, United Kingdom, Nepal, Vietnam
    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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

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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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