23 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
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    NCHS/DVS (2023). Provisional COVID-19 Deaths: Focus on Ages 0-18 Years [Dataset]. https://data.cdc.gov/widgets/nr4s-juj3?mobile_redirect=true
    Explore at:
    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
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Centers for Disease Control and Prevention (2023). Rates of COVID-19 Cases or Deaths by Age Group and Vaccination Status [Dataset]. https://data.virginia.gov/dataset/rates-of-covid-19-cases-or-deaths-by-age-group-and-vaccination-status
    Explore at:
    xsl, csv, rdf, jsonAvailable download formats
    Dataset updated
    Jul 20, 2023
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Description

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

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

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

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

    • healthdata.gov
    • odgavaprod.ogopendata.com
    • +1more
    csv, xlsx, xml
    Updated Jun 16, 2023
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    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
    Explore at:
    xlsx, csv, xmlAvailable 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
    • datahub.hhs.gov
    • +4more
    csv, xlsx, xml
    Updated Sep 27, 2023
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    NCHS/DVS (2023). Provisional COVID-19 Deaths by Sex and Age [Dataset]. https://data.cdc.gov/widgets/9bhg-hcku
    Explore at:
    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
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Office for National Statistics (2023). Deaths by vaccination status, England [Dataset]. https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/deathsbyvaccinationstatusengland
    Explore at:
    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

    • datasetcatalog.nlm.nih.gov
    • scielo.figshare.com
    Updated Aug 6, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Soares, Karllian Kerlen Simonelli; Jabor, Pablo Medeiros; Zandonade, Eliana; Goncalves Jr, Etereldes; Maciel, Ethel Leonor Noia; do Prado, Thiago Nascimento (2022). COVID-19 in children in Espirito Santo State – Brazil [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0000201081
    Explore at:
    Dataset updated
    Aug 6, 2022
    Authors
    Soares, Karllian Kerlen Simonelli; Jabor, Pablo Medeiros; Zandonade, Eliana; Goncalves Jr, Etereldes; Maciel, Ethel Leonor Noia; do Prado, Thiago Nascimento
    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.

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

    • plos.figshare.com
    • datasetcatalog.nlm.nih.gov
    xls
    Updated Jun 1, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    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
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 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

    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.

  8. Table_2_Decrease in COVID-19 adverse outcomes in adults during the Delta and...

    • frontiersin.figshare.com
    docx
    Updated Jun 2, 2023
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Lenin Domínguez-Ramírez; Itzel Solis-Tejeda; Jorge Ayon-Aguilar; Antonio Mayoral-Ortiz; Francisca Sosa-Jurado; Rosana Pelayo; Gerardo Santos-López; Paulina Cortes-Hernandez (2023). Table_2_Decrease in COVID-19 adverse outcomes in adults during the Delta and Omicron SARS-CoV-2 waves, after vaccination in Mexico.docx [Dataset]. http://doi.org/10.3389/fpubh.2022.1010256.s003
    Explore at:
    docxAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    Frontiers Mediahttp://www.frontiersin.org/
    Authors
    Lenin Domínguez-Ramírez; Itzel Solis-Tejeda; Jorge Ayon-Aguilar; Antonio Mayoral-Ortiz; Francisca Sosa-Jurado; Rosana Pelayo; Gerardo Santos-López; Paulina Cortes-Hernandez
    License

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

    Description

    Mexico, one of the countries severely affected by COVID-19, accumulated more than 5. 1 all-cause excess deaths/1,000 inhabitants and 2.5 COVID-19 confirmed deaths/1,000 inhabitants, in 2 years. In this scenario of high SARS-CoV-2 circulation, we analyzed the effectiveness of the country's vaccination strategy that used 7 different vaccines from around the world, and focused on vaccinating the oldest population first. We analyzed the national dataset published by Mexican health authorities, as a retrospective cohort, separating cases, hospitalizations, deaths and excess deaths by wave and age group. We explored if the vaccination strategy was effective to limit severe COVID-19 during the active outbreaks caused by Delta and Omicron variants. Vaccination of the eldest third of the population reduced COVID-19 hospitalizations, deaths and excess deaths by 46–55% in the third wave driven by Delta SARS-CoV-2. These adverse outcomes dropped 74–85% by the fourth wave driven by Omicron, when all adults had access to vaccines. Vaccine access for the pregnant resulted in 85–90% decrease in COVID-19 fatalities in pregnant individuals and 80% decrease in infants 0 years old by the Omicron wave. In contrast, in the rest of the pediatric population that did not access vaccination before the period analyzed, COVID-19 hospitalizations increased >40% during the Delta and Omicron waves. Our analysis suggests that the vaccination strategy in Mexico has been successful to limit population mortality and decrease severe COVID-19, but children in Mexico still need access to SARS-CoV-2 vaccines to limit severe COVID-19, in particular those 1–4 years old.

  9. o

    Action plan on COVID-19 vaccination for children and teens aged 12 to under...

    • data.opendevelopmentmekong.net
    Updated Aug 3, 2021
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    (2021). Action plan on COVID-19 vaccination for children and teens aged 12 to under 18 - Laws OD Mekong Datahub [Dataset]. https://data.opendevelopmentmekong.net/dataset/action-plan-on-covid-19-vaccination-for-children-and-teens-aged-12-to-under-18
    Explore at:
    Dataset updated
    Aug 3, 2021
    Description

    The action plan for vaccination aims to protect Cambodians, protect the health system and reduce the socio-economic impact by minimizing the number of cases, illnesses, and deaths caused by COVID-19. The plan also aims to reduce the incidence, severity and mortality of COVID-19 and counteract the threat of new variants, especially Delta by providing COVID-19 vaccines to children and teenagers aged 12 to under 18 years, which is about 2 millions by the end of November 2021.

  10. 2

    YL

    • datacatalogue.ukdataservice.ac.uk
    Updated Apr 22, 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    University of Oxford, Young Lives (2024). YL [Dataset]. http://doi.org/10.5255/UKDA-SN-9251-1
    Explore at:
    Dataset updated
    Apr 22, 2024
    Dataset provided by
    UK Data Servicehttps://ukdataservice.ac.uk/
    Authors
    University of Oxford, Young Lives
    Time period covered
    Jan 1, 1900 - Dec 31, 2021
    Area covered
    India, Ethiopia, Peru, Vietnam
    Description
    The Young Lives survey is an innovative long-term project investigating the changing nature of childhood poverty in four developing countries. The study is being conducted in Ethiopia, India, Peru and Vietnam and has tracked the lives of 12,000 children over a 20-year period, through 5 (in-person) survey rounds (Round 1-5) and, with the latest survey round (Round 6) conducted over the phone in 2020 and 2021 as part of the Listening to Young Lives at Work: COVID-19 Phone Survey.

    Round 1 of Young Lives surveyed two groups of children in each country, at 1 year old and 5 years old. Round 2 returned to the same children who were then aged 5 and 12 years old. Round 3 surveyed the same children again at aged 7-8 years and 14-15 years, Round 4 surveyed them at 12 and 19 years old, and Round 5 surveyed them at 15 and 22 years old. Thus the younger children are being tracked from infancy to their mid-teens and the older children through into adulthood, when some will become parents themselves.

    The 2020 phone survey consists of three phone calls (Call 1 administered in June-July 2020; Call 2 in August-October 2020 and Call 3 in November-December 2020) and the 2021 phone survey consists of two additional phone calls (Call 4 in August 2021 and Call 5 in October-December 2021) The calls took place with each Young Lives respondent, across both the younger and older cohort, and in all four study countries (reaching an estimated total of around 11,000 young people).

    The Young Lives survey is carried out by teams of local researchers, supported by the Principal Investigator and Data Manager in each country.

    Further information about the survey, including publications, can be downloaded from the Young Lives website.


    Young Lives research has expanded to explore linking geographical data collected during the rounds to external datasets. Matching Young Lives data with administrative and geographic datasets significantly increases the scope for research in several areas, and may allow researchers to identify sources of exogenous variation for more convincing causal analysis on policy and/or early life circumstances.

    Young Lives: Data Matching Series, 1900-2021 includes the following linked datasets:

    1. Climate Matched Datasets (four YL study countries): Community-level GPS data has been matched with temperature and precipitation data from the University of Delaware. Climate variables are offered at the community level, with a panel data structure spanning across years and months. Hence, each community has a unique value of precipitation (variable PRCP) and temperature (variable TEMP), for each year and month pairing for the period 1900-2017.

    2. COVID-19 Matched Dataset (Peru only): The YL Phone Survey Calls data has been matched with external data sources (The Peruvian Ministry of Health and the National Information System of Deaths in Peru). The matched dataset includes the total number of COVID cases per 1,000 inhabitants, the total number of COVID deaths by district and per 1,000 inhabitants; the total number of excess deaths per 1,000 inhabitants and the number of lockdown days in each Young Lives district in Peru during August 2020 to December 2021.

    Further information is available in the PDF reports included in the study documentation.

  11. COVID-19 cohort on children with cancer: delay in treatment and increased...

    • scielo.figshare.com
    xls
    Updated Jun 1, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Ana Luiza Magalhães de Andrade Lima; Maria do Céu Diniz Borborema; Ana Paula Rodrigues Matos; Kaline Maria Maciel de Oliveira; Maria Júlia Gonçalves Mello; Mecneide Mendes Lins (2023). COVID-19 cohort on children with cancer: delay in treatment and increased frequency of deaths [Dataset]. http://doi.org/10.6084/m9.figshare.14285688.v1
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    SciELOhttp://www.scielo.org/
    Authors
    Ana Luiza Magalhães de Andrade Lima; Maria do Céu Diniz Borborema; Ana Paula Rodrigues Matos; Kaline Maria Maciel de Oliveira; Maria Júlia Gonçalves Mello; Mecneide Mendes Lins
    License

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

    Description

    Abstract Objectives: to describe epidemiological characteristics and deaths in children with cancer and COVID-19 at a reference hospital in Recife, Brazil. Methods: cohort involving children under the age of 19 underwent cancer treatment during April to July 2020. During the pandemic, real-time reverse transcriptase polymerase chain reaction assay (RT-PCR) for severe acute respiratory syndrome coronavirus 2 (SARS -CoV-2) in nasal / oropharyngeal swab were collected in symptomatic patients or before hospitalization. Those with detectable results were included in this cohort study. The outcomes were delayed on cancer treatment and death. Descriptive analysis was performed and presented in preliminary results. Results: 48 children participated in the cohort, mostly with hematological neoplasms (66.6%.),69% were male, median age was 5.5 years. The most frequent symptoms were fever (58.3%) and coughing (27.7%);72.9% required hospitalization, 20% had support in ICU and 10.5% on invasive ventilatory assistance.66.6% of the patients had their oncological treatment postponed, 16.6% died within 60 days after confirmation of SARS-CoV-2 infection. Conclusions: COVID-19 led a delay in the oncological treatment for children with cancer and a higher mortality frequency when compared to the historical series of the service. It would be important to analyze the risk factors to determine the survival impact.

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

    • www150.statcan.gc.ca
    • ouvert.canada.ca
    • +1more
    Updated Feb 19, 2025
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Government of Canada, Statistics Canada (2025). Leading causes of death, total population, by age group [Dataset]. http://doi.org/10.25318/1310039401-eng
    Explore at:
    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.

  13. V

    Dataset from Randomised Evaluation of COVID-19 Therapy

    • data.niaid.nih.gov
    Updated May 20, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    IDDO; Richard Haynes; Peter W Horby (2025). Dataset from Randomised Evaluation of COVID-19 Therapy [Dataset]. http://doi.org/10.25934/PR00009091
    Explore at:
    Dataset updated
    May 20, 2025
    Dataset provided by
    University of Oxford
    Authors
    IDDO; Richard Haynes; Peter W Horby
    Area covered
    Gambia, Nepal, Indonesia, India, Ghana, South Africa, United Kingdom, Vietnam, Sri Lanka
    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.

  14. f

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

    • datasetcatalog.nlm.nih.gov
    Updated Sep 11, 2023
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    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
    Explore at:
    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.

  15. d

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

    • search.dataone.org
    • borealisdata.ca
    Updated Oct 30, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    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
    Explore at:
    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. Prevalence of malaria in adiyan, Ogun, Nigeria

    • kaggle.com
    zip
    Updated Aug 8, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Esperat Bamgbose (2023). Prevalence of malaria in adiyan, Ogun, Nigeria [Dataset]. https://www.kaggle.com/datasets/esperatbamgbose/prevalence-of-malaria-in-adiyan-ogun-nigeria/discussion
    Explore at:
    zip(941 bytes)Available download formats
    Dataset updated
    Aug 8, 2023
    Authors
    Esperat Bamgbose
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Area covered
    Nigeria, Ogun State
    Description

    Malaria is a major cause of illness and death particularly among children under five years. It was estimated that more than one million children living in Africa especially, in remote areas with poor access to health services die annually from the direct and indirect effects of malaria.

    This is not a large dataset. The data in the dataset was gathered for four years by a data analyst from 2019-2022. This entails many purchases of antimalarial drugs for children, teenagers, and adults. It was produced manually from sales records and with inventory software in KOTZ PETHABAM PHARMACY LTD, Ogun state, Nigeria. The dataset has twelve columns and four rows.

    ****Task**** - Analyze the dataset of adults, teenagers and children.

    • Give your observations about each category and which year or month shows a drastic increase in the number of cases of malaria.

    • Analyze pre-covid malaria cases, post-covid and during covid malaria cases.

    • How can the dataset be used to help community pharmacy to stock antimalaria drugs?

    • Does seasons and weather condition have an impact on cases of malaria?

  17. f

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

    • frontiersin.figshare.com
    docx
    Updated Jun 16, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    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
    Explore at:
    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

    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.

  18. o

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

    • open.ottawa.ca
    • hamhanding-dcdev.opendata.arcgis.com
    • +1more
    Updated Nov 4, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    City of Ottawa (2025). COVID-19 Outbreaks in Ottawa Healthcare, Childcare and Educational Establishments [Dataset]. https://open.ottawa.ca/datasets/covid-19-outbreaks-in-ottawa-healthcare-childcare-and-educational-establishments/api
    Explore at:
    Dataset updated
    Nov 4, 2025
    Dataset authored and provided by
    City of Ottawa
    Area covered
    Ottawa
    Description

    Date created: Data effective February 2020. Uploaded to Open Data on September 22, 2020.Update frequency: DailyAccuracy: Points of consideration for interpretation of the data: The data was extracted by Ottawa Public Health from The COVID-19 Ottawa Database (The COD). The COD is a dynamic disease reporting system that allows for ongoing updates to data previously entered. The data extracted from The COD 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.Attributes Data fields:Facility Name – text Type 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-19 Total Deaths – total number of people with confirmed COVID-19 who died Author: OPH Epidemiology TeamAuthor email: OPH-Epidemiology@ottawa.caMaintainer Organization: Epidemiology & Evidence, Ottawa Public Health

  19. f

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

    • figshare.com
    • datasetcatalog.nlm.nih.gov
    pdf
    Updated Aug 25, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    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
    Explore at:
    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.

  20. Table_2_Epidemiological and Clinical Characteristics of COVID-19 in...

    • frontiersin.figshare.com
    • figshare.com
    bin
    Updated Jun 10, 2023
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Bingbing Li; Shan Zhang; Ruili Zhang; Xi Chen; Yong Wang; Changlian Zhu (2023). Table_2_Epidemiological and Clinical Characteristics of COVID-19 in Children: A Systematic Review and Meta-Analysis.DOCX [Dataset]. http://doi.org/10.3389/fped.2020.591132.s003
    Explore at:
    binAvailable download formats
    Dataset updated
    Jun 10, 2023
    Dataset provided by
    Frontiers Mediahttp://www.frontiersin.org/
    Authors
    Bingbing Li; Shan Zhang; Ruili Zhang; Xi Chen; Yong Wang; Changlian Zhu
    License

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

    Description

    Given the relatively low rate and limited publicly available data regarding children with SARS-CoV-2 infection, this knowledge gap should be addressed with urgency. This systematic review with meta-analysis aimed to evaluate the epidemiological spectrum and clinical characteristics of children infected with SARS-CoV-2. Relevant international and Chinese public databases were systematically searched to identify all case studies from January 1, 2020 to May 7, 2020. This study consisted of 96 studies involving 7004 cases. The mean age of pediatric cases was 6.48 years (95% CI 52.0–77.5), 90% had household contact, and 66% presented with mild to moderate clinical syndromes. The main symptoms were fever (47%, 95% CI 41–53%) and cough (42%, 95% CI 36–48%). About 23% of children were asymptomatic, 27% had comorbidity, and 29% had a co-infection. The pooled mean incubation period was 9.57 days (95% CI 7.70–11.44). The shedding of SARS-CoV-2 in the upper respiratory tract lasted 11.43 days, and 75% of patients had virus particles in their stool. A total of 34% of the children had neutropenia and 26% had lymphocytosis. Interferon-alpha (81%) was the most commonly used antiviral drug in the children. The discharge and death rates were 79 and 1%. In conclusion, the transmissibility of pediatric COVID-19 should be not ignored because of the relatively long incubation period, shedding duration, and mild clinical syndromes.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
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

Explore at:
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.

Search
Clear search
Close search
Google apps
Main menu