19 datasets found
  1. D

    Provisional COVID-19 Deaths by Sex and Age

    • data.cdc.gov
    • healthdata.gov
    • +2more
    application/rdfxml +5
    Updated Sep 27, 2023
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    NCHS/DVS (2023). Provisional COVID-19 Deaths by Sex and Age [Dataset]. https://data.cdc.gov/widgets/9bhg-hcku?mobile_redirect=true
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    csv, application/rdfxml, xml, json, tsv, application/rssxmlAvailable 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.

  2. Single year of age and average age of death of people whose death was due to...

    • ons.gov.uk
    xlsx
    Updated Aug 23, 2023
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    Office for National Statistics (2023). Single year of age and average age of death of people whose death was due to or involved coronavirus (COVID-19) [Dataset]. https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/singleyearofageandaverageageofdeathofpeoplewhosedeathwasduetoorinvolvedcovid19
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    xlsxAvailable download formats
    Dataset updated
    Aug 23, 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

    Provisional deaths registration data for single year of age and average age of death (median and mean) of persons whose death involved coronavirus (COVID-19), England and Wales. Includes deaths due to COVID-19 and breakdowns by sex.

  3. 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

  4. O

    MD COVID-19 - Confirmed Deaths by Age Distribution

    • opendata.maryland.gov
    • datasets.ai
    • +1more
    application/rdfxml +5
    Updated Mar 18, 2025
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    Maryland Department of Health Vital Statistics Administration, MDH VSA (2025). MD COVID-19 - Confirmed Deaths by Age Distribution [Dataset]. https://opendata.maryland.gov/Health-and-Human-Services/MD-COVID-19-Confirmed-Deaths-by-Age-Distribution/ix2d-fenx
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    csv, application/rdfxml, json, tsv, xml, application/rssxmlAvailable download formats
    Dataset updated
    Mar 18, 2025
    Dataset authored and provided by
    Maryland Department of Health Vital Statistics Administration, MDH VSA
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Area covered
    Maryland
    Description

    Note: Starting April 27, 2023 updates change from daily to weekly.

    Summary The cumulative number of confirmed COVID-19 deaths among Maryland residents by age: 0-9; 10-19; 20-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80+; Unknown.

    Description The MD COVID-19 - Confirmed Deaths by Age Distribution data layer is a collection of the statewide confirmed COVID-19 related deaths that have been reported each day by the Vital Statistics Administration by designated age ranges. A death is classified as confirmed if the person had a laboratory-confirmed positive COVID-19 test result. Some data on deaths may be unavailable due to the time lag between the death, typically reported by a hospital or other facility, and the submission of the complete death certificate. Probable deaths are available from the MD COVID-19 - Probable Deaths by Age Distribution data layer.

    Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

  5. D

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

    • data.cdc.gov
    • data.virginia.gov
    • +2more
    application/rdfxml +5
    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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    application/rssxml, csv, xml, tsv, json, application/rdfxmlAvailable 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.

  6. a

    Cumulative COVID-19 Mortality

    • egis-lacounty.hub.arcgis.com
    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • +2more
    Updated Dec 21, 2023
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    County of Los Angeles (2023). Cumulative COVID-19 Mortality [Dataset]. https://egis-lacounty.hub.arcgis.com/datasets/cumulative-covid-19-mortality
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    Dataset updated
    Dec 21, 2023
    Dataset authored and provided by
    County of Los Angeles
    Area covered
    Description

    Deaths were determined to be COVID-associated if they met the Department of Public Health's surveillance definition at the time of death.The cumulative COVID-19 mortality rate can be used to measure the most severe impacts of COVID-19 in a community. There have been documented inequities in COVID-19 mortality rates by demographic and geographic factors. Black and Brown residents, seniors, and those living in areas with higher rates of poverty have all been disproportionally impacted.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

  7. 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.

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

    • www150.statcan.gc.ca
    • ouvert.canada.ca
    • +2more
    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.

  9. Number of deaths in care homes notified to the Care Quality Commission,...

    • ons.gov.uk
    • cy.ons.gov.uk
    xlsx
    Updated Aug 1, 2023
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    Office for National Statistics (2023). Number of deaths in care homes notified to the Care Quality Commission, England [Dataset]. https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/numberofdeathsincarehomesnotifiedtothecarequalitycommissionengland
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    xlsxAvailable download formats
    Dataset updated
    Aug 1, 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

    Provisional counts of deaths in care homes caused by coronavirus (COVID-19) by local authority. Published by the Office for National Statistics and Care Quality Commission.

  10. d

    Crisis Barometer on the COVID 19 Pandemic in Germany - Dataset - B2FIND

    • b2find.dkrz.de
    Updated Oct 22, 2023
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    (2023). Crisis Barometer on the COVID 19 Pandemic in Germany - Dataset - B2FIND [Dataset]. https://b2find.dkrz.de/dataset/e11f0c99-66a1-5f58-87f6-855353240747
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    Dataset updated
    Oct 22, 2023
    Area covered
    Germany
    Description

    The crisis barometer on the COVID-19 pandemic in Germany was conducted by USUMA on behalf of the Konrad Adenauer Foundation. During the survey period from 30.03.2020 to 04.07.2020, 4228 respondents aged 18 and over living in private households in Germany were interviewed by telephone (CATI) on the following topics: pessimism/optimism; trust in institutions, crisis competence of political parties, effects of the Corona crisis, reception of news about Corona, Corona disease, Sunday question. Respondents were selected through multi-stage random sampling from an ADM selection frame including landline and mobile numbers (dual-frame sampling). The study was conducted week-by-week as a rolling cross-section survey. Pessimism or optimism about the future in general and for Germany; party preference (Sunday question); confidence in institutions (state government of the federal state, federal government, European Union, federal armed forces, police, health authorities, authorities, courts, German Bundestag); most competent party to deal with the crisis; assessment of measures as appropriate, going too far, or not going far enough; expected extent of the impact of the Corona crisis for the respondent; reception frequency of news about the Corona crisis; respondent has contracted the Corona virus COVID 19 himself; number of people in his circle of acquaintances who have tested positive for the Corona virus. Demography: sex; age; education; employment status; federal state; number of people 18 years and older who also regularly use the cell phone used; number of cell phone numbers used to reach the respondent by phone; number of landline phone numbers; household size. Additionally coded were: respondent ID; day of interview; weighting factor. Das Krisenbarometer zur COVID-19-Pandemie in Deutschland wurde von USUMA im Auftrag der Konrad-Adenauer-Stiftung durchgeführt. Im Erhebungszeitraum vom 30.03.2020 bis 04.07.2020 wurden 4228 in Privathaushalten in Deutschland lebende Prsonen ab 18 Jahren in telefonischen Interviews (CATI) zu folgenden Themen befragt: Pessimismus/Optimismus; Institutionenvertrauen, Krisenkompetenz der Parteien, Auswirkungen der Corona-Krise, Rezeption von Nachrichten über Corona, Corona-Erkrankung, Sonntagsfrage. Die Auswahl der Befragten erfolgte durch eine mehrstufige Zufallsauswahl aus einem ADM-Auswahlrahmen unter Einschluss von Festnetz- und Mobilfunknummern (Dual-Frame Stichprobe). Die Studie wurde wochenweise als Rolling-Cross-Section Survey durchgeführt. Pessimismus oder Optimismus im Hinblick auf die Zukunft allgemein und für Deutschland; Parteipräferenz (Sonntagsfrage); Institutionenvertrauen (Landesregierung des Bundeslandes, Bundesregierung, Europäische Union, Bundeswehr, Polizei, Gesundheitsamt, Behörden, Gerichte, Deutscher Bundestag); kompetenteste Partei zur Bewältigung der Krise; Bewertung der Maßnahmen als angemessen, gehen zu weit oder gehen nicht weit genug; erwartetes Ausmaß der Auswirkungen der Corona-Krise für den Befragten; Rezeptionshäufigkeit von Nachrichten über die Corona-Krise; Befragter ist selbst am Corona-Virus COVID 19 erkrankt; Anzahl der positiv auf das Corona-Virus getesteten Menschen im Bekanntenkreis. Demographie: Geschlecht; Alter; Bildung; Erwerbsstatus; Bundesland; Anzahl Personen ab 18 Jahren, die das genutzte Handy ebenfalls regelmäßig nutzen; Anzahl der Handynummern, über die der Befragte telefonisch erreichbar ist; Anzahl der Festnetz-Rufnummern; Haushaltsgröße. Zusätzlich verkodet wurde: Befragten-ID; Befragungstag; Gewichtungsfaktor.

  11. Deaths registered weekly in England and Wales, provisional

    • ons.gov.uk
    • cy.ons.gov.uk
    xlsx
    Updated Mar 26, 2025
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    Office for National Statistics (2025). Deaths registered weekly in England and Wales, provisional [Dataset]. https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/weeklyprovisionalfiguresondeathsregisteredinenglandandwales
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    xlsxAvailable download formats
    Dataset updated
    Mar 26, 2025
    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 registered in England and Wales, by age, sex, region and Index of Multiple Deprivation (IMD), in the latest weeks for which data are available.

  12. g

    COVID-19 statistics individuals aged 70 and over residing outside an...

    • gimi9.com
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    COVID-19 statistics individuals aged 70 and over residing outside an institution by security region by date | gimi9.com [Dataset]. https://gimi9.com/dataset/eu_40508e17-7296-4f39-ad25-8ddd0c904087
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    Description

    For English, see below Per 1 januari 2023 verzamelt het RIVM geen aanvullende informatie meer. Dit heeft als gevolg dat we vanaf 1 januari 2023 geen gegevens over besmettingen bij thuiswonende 70-plussers meer rapporteren . Beschrijving bestand: - Dit bestand bevat de volgende aantallen: (aantal nieuw gemelde) positief geteste thuiswonende individuen van 70 jaar en ouder, naar veiligheidsregio, per datum van de positieve testuitslag. - (aantal nieuw gemelde) positief geteste overleden thuiswonende individuen van 70 jaar en ouder, naar veiligheidsregio, per datum waarop patiënt overleden is. De aantallen betreffen COVID-19 meldingen sinds de registratie van (woon)instelling in OSIRIS met ingang van vragenlijst 5 (01-07-2020). Bij meldingen vanaf 01-07-2020 wordt geregistreerd of de patiënt woonachtig is in een instelling. Meldingen vanaf 01-07-2020 worden aangemerkt als thuiswonende individuen van 70 jaar en ouder indien deze volgens de gegevens bekend bij de GGD: • Niet wonend zijn in een instelling EN • Een leeftijd hebben van 70 jaar of ouder EN • De persoon niet werkzaam is en geen zorgmedewerker is Personen waarvan de woonvoorziening/instelling niet vermeld is kunnen alsnog uitgesloten worden als thuiswonende individuen van 70 jaar en ouder indien zij: • Op basis van hun 6 cijferige postcode gekoppeld kan worden aan een bekende locatie van een gehandicaptenzorginstelling of verpleeghuis OF • ‘Gehandicaptenzorginstelling’ of ‘Verpleeghuis’ als vermelde locatie van de besmetting hebben. OF • Op basis van de inhoud van vrije tekstvelden gelinkt kunnen worden aan een gehandicaptenzorginstelling of verpleeghuis. Het bestand is als volgt opgebouwd: Een set records per datum van met voor elke datum: • Een record voor elke veiligheidsregio (inclusief ‘Onbekend’) van Nederland, ook als voor de betreffende veiligheidsregio geen meldingen zijn. De aantallen zijn dan 0 (nul). • Veiligheidsregio is onbekend wanneer een record niet toe te wijzen is aan één unieke veiligheidsregio. Er is in dit bestand ook een datum 01-01-1900 opgenomen voor statistieken waarvan de bijbehorende datum onbekend is. Hieronder wordt beschreven hoe de variabelen zijn gedefinieerd. Beschrijving van de variabelen: Version: Versienummer van de dataset. Dit versienummer wordt aangepast (+1) wanneer de inhoud van de dataset structureel wordt gewijzigd (dus niet de dagelijkse update of een correctie op record niveau. Ook de corresponderende metadata in RIVMdata (https://data.rivm.nl) wordt dan gewijzigd. Versie 2 update (25 januari 2022): • Er is een bijgewerkte lijst met bekende verpleeg- of verzorgingshuislocaties en particuliere woonzorgcentra van de koepelorganisatie Patiëntenfederatie Nederland ontvangen op 03-12-2021. Op 25-01-2022 is deze bijgewerkte lijst in gebruik genomen voor de vaststelling of individuen woonachtig zijn in een instelling. Versie 3 update (8 februari 2022) • Vanaf 8 februari 2022 worden de positieve SARS-CoV-2 testuitslagen rechtstreeks vanuit CoronIT aan het RIVM gemeld. Ook worden de testuitslagen van andere testaanbieders (zoals Testen voor Toegang) en zorginstellingen (zoals ziekenhuizen, verpleeghuizen en huisartsen) die hun positieve SARS-CoV-2 testuitslagen via het Meldportaal van GGD GHOR invoeren rechtstreeks aan het RIVM gemeld. Meldingen die onderdeel zijn van de bron- en contactonderzoek steekproef en positieve SARS-CoV-2 testuitslagen van zorginstellingen die via zorgmail aan de GGD worden gemeld worden wel via HPZone aan het RIVM gemeld. Vanaf 8 februari wordt de datum van de positieve testuitslag gebruikt en niet meer de datum van melding aan de GGD. Versie 4 update (24 maart 2022): • In versie 4 van deze dataset zijn records samengesteld volgens de gemeente herindeling van 24 maart 2022. Zie beschrijving van de variabele security_region_code voor meer informatie. Versie 5 update (2 augustus 2022): • De indeling van personen als zelfstandig wonende personen van 70 jaar en ouders is niet toe gepast op meldingen die sinds 8 februari 2022 alleen via een alternatieve meldroute bij het RIVM binnenkwamen. Van 8 februari t/m 1 augustus 2022 is hierdoor het aantal meldingen van zelfstandig wonende personen van 70 jaar en ouders met ongeveer 14% onderschat. Vanaf 2 augustus 2022 wordt deze indeling met terugwerkende kracht bijgewerkt. Versie 6 update (1 september 2022): - Vanaf 1 september 2022 wordt de data niet meer iedere werkdag geüpdatet, maar op dinsdagen en vrijdagen. De data wordt op deze dagen met terugwerkende kracht bijgewerkt voor de andere dagen. - Vanaf 1 september 2022 is deze dataset opgesplitst in twee delen. Het eerste deel bevat de data vanaf het begin van de pandemie tot en met 3 oktober 2021 (week 39) en bevat ‘tm’ in de bestandsnaam. Deze data wordt niet meer geüpdatet. Het tweede deel bevat de data vanaf 4 oktober 2021 (week 40) en wordt iedere dinsdag en vrijdag geüpdatet. Date_of_report: Datum en tijd waarop het databestand is aangemaakt door het RIVM. Date_of_statistic_reported: De datum die gebruikt wordt voor het rapporteren van de thuiswonende 70plus statistiek. Deze kan voor iedere gerapporteerde statistiek anders zijn, namelijk: • Voor [Total_cases_reported] is dat de datum van de positieve testuitslag. • Voor [Total_deceased_reported] is dat de datum waarop de patiënten zijn overleden. Security_region_code: Veiligheidsregiocode. De code van de veiligheidsregio gebaseerd op de woonplaats van de patiënt. Indien de woonplaats niet bekend is, wordt de veiligheidsregio gebaseerd op de GGD die de melding heeft gedaan, behalve voor veiligheidsregio Midden- en West-Brabant en Brabant-Noord aangezien voor deze regio’s GGD en veiligheidsregio niet vergelijkbaar zijn. Zie ook: https://www.cbs.nl/nl-nl/cijfers/detail/84721NED?q=Veiligheid Vanaf 24 maart 2022 is dit bestand samengesteld volgens de gemeente indeling van 24 maart 2022. Gemeente Weesp is opgegaan in gemeente Amsterdam. Met deze indeling is de veiligheidsregio Gooi- en Vechtstreek kleiner geworden en de veiligheidsregio Amsterdam-Amstelland groter; GGD Amsterdam is groter geworden en GGD Gooi- en Vechtstreek is kleiner geworden (Gemeentelijke indeling op 1 januari 2022 (cbs.nl). Security_region_name: Veiligheidsregionaam. Veiligheidsregionaam is gebaseerd op de Veiligheidsregiocode. Zie ook: https://www.rijksoverheid.nl/onderwerpen/veiligheidsregios-en-crisisbeheersing/veiligheidsregios Total_cases_reported: Het aantal nieuwe bij de GGD gemelde COVID-19 besmette thuiswonende 70-plussers op [Date_of_statistic_reported]. Het werkelijke aantal COVID-19 besmette thuiswonende 70-plussers is hoger dan het aantal meldingen in de surveillance, omdat niet iedereen met een mogelijke besmetting getest wordt. Bovendien is niet van iedere melding bekend of dit een thuiswonende 70-plusser betreft. Total_deceased_reported: Het aantal bij de GGD gemelde thuiswonende 70-plussers dat is overleden aan COVID-19 op [Date_of_statistic_reported]. Het werkelijke aantal overleden thuiswonende 70-plusser dat is overleden aan COVID-19 is hoger dan het aantal meldingen in de surveillance, omdat niet alle overleden patiënten getest worden en overlijdens niet wettelijk meldingsplichtig zijn. Bovendien is niet van iedere melding bekend of dit een thuiswonende 70-plusser betreft. Correcties die in meldingen in het bronsysteem OSIRIS worden gedaan kunnen ook leiden tot correcties in dit databestand. Aantallen die in het verleden door het RIVM zijn gepubliceerd kunnen in dat geval afwijken van de aantallen in dit databestand. Dit bestand bevat dus altijd de aantallen op basis van de meest actuele gegevens in het bronsysteem OSIRIS. In het CSV-bestand wordt als scheidingsteken een ‘;’ puntkomma gebruikt. Er staan geen lege regels in het bestand. Hieronder de kolomnamen en de typen waarden in het CSV-bestand: • Version: Bestaande uit een enkel geheel getal (integer). Is voor elke rij altijd gevuld. Voorbeeld: 2. • Date_of_report: Geschreven in formaat JJJJ-MM-DD HH:MM. Is voor elke rij altijd gevuld. Voorbeeld: 2020-10-16 10:00. • Date_of_statistic_reported: Geschreven in formaat JJJJ-MM-DD. Is voor elke rij altijd gevuld. Voorbeeld: 2020-10-09. • Security_region_code: Bestaande uit ‘VR’ gevolgd door twee cijfers. Kan ook leeg zijn indien de regio onbekend is. Voorbeeld: VR01. • Security_region_name: Bestaande uit een character string. Is voor elke rij altijd gevuld. Voorbeeld: Midden- en West-Brabant. • Total_cases_reported: Bestaande uit enkel hele getallen (integer). Is voor elke rij altijd gevuld. Voorbeeld: 12. • Total_deceased_reported: Bestaande uit enkel hele getallen (integer). Is voor elke rij altijd gevuld. Voorbeeld: 8. Covid-19 statistics for persons aged 70 and older living outside an institution, by security region and date As of 1 January 2023, the RIVM will no longer collect additional information. As a result, from January 1, 2023, we will no longer report data on infections among people over 70 living at home. File description: This file contains the following numbers: - Number of newly reported persons aged 70 and older living at home who tested positive, by security region, by date of the positive test result. - Number of newly reported deceased persons aged 70 and older living at home who tested positive, by security region, by date on which the patient died. The numbers concern COVID-19 reports since the registration of the (residential) institution in OSIRIS with effect from questionnaire 5 (01-07-2020). For reports from 01-07-2020, it is recorded whether the patient lives in an institution. For reports from 01-07-2020 persons aged 70 and older are considered to be living at home if, according to the information known to the PHS, they: • were not living in an institution AND • Are aged 70 years or older AND • The person is not employed and is not a healthcare worker Persons whose residential facility/institution is not listed can still be excluded as being an

  13. MD COVID19 TotalVaccinationsAge65plusFirstandSecondSingleDose

    • hub.arcgis.com
    • coronavirus.maryland.gov
    • +3more
    Updated May 24, 2021
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    ArcGIS Online for Maryland (2021). MD COVID19 TotalVaccinationsAge65plusFirstandSecondSingleDose [Dataset]. https://hub.arcgis.com/datasets/maryland::md-covid19-totalvaccinationsage65plusfirstandsecondsingledose
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    Dataset updated
    May 24, 2021
    Dataset provided by
    Authors
    ArcGIS Online for Maryland
    Description

    SummaryThe cumulative number of COVID-19 vaccinations for persons aged 65+ within a single Maryland jurisdiction: Persons fully vaccinated and those who have received at least one dose.DescriptionThe MD COVID-19—Persons 65+ Fully Vaccinated layer represents the number of people in each Maryland jurisdiction aged 65 and older who have either received at least one dose of COVID-19 vaccine in a two-dose regimen or are fully vaccinated (have either received a single shot regimen or have completed the second dose in a two-dose regimen), reported each day into ImmuNet.COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.

  14. f

    Supplementary file 1_Burden of acute and long-term COVID-19: a nationwide...

    • frontiersin.figshare.com
    • figshare.com
    docx
    Updated Mar 18, 2025
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    Mariam Murad; Stephen L. Atkin; Pearl Wasif; Alwaleed Abdulaziz Behzad; Aamal M. J. Abdulla Husain; Roisin Leahy; Florence Lefebvre d’Hellencourt; Jean Joury; Mohamed Abdel Aziz; Srinivas Rao Valluri; Hammam Haridy; Julia Spinardi; Moe H. Kyaw; Manaf Al-Qahtani (2025). Supplementary file 1_Burden of acute and long-term COVID-19: a nationwide study in Bahrain.docx [Dataset]. http://doi.org/10.3389/fpubh.2025.1539453.s001
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    docxAvailable download formats
    Dataset updated
    Mar 18, 2025
    Dataset provided by
    Frontiers
    Authors
    Mariam Murad; Stephen L. Atkin; Pearl Wasif; Alwaleed Abdulaziz Behzad; Aamal M. J. Abdulla Husain; Roisin Leahy; Florence Lefebvre d’Hellencourt; Jean Joury; Mohamed Abdel Aziz; Srinivas Rao Valluri; Hammam Haridy; Julia Spinardi; Moe H. Kyaw; Manaf Al-Qahtani
    License

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

    Area covered
    Bahrain
    Description

    BackgroundCoronavirus disease 2019 (COVID-19) may lead to long-term sequelae. This study aimed to understand the acute and post-acute burden of SARS-CoV-2 infection and to identify high-risk groups for post-COVID-19 conditions (PCC).MethodsA retrospective observational study of the Bahraini population was conducted between 1 May 2021 and 30 April 2023, utilizing the national administrative database. PCC cases were defined according to WHO guidelines. All COVID-19 cases were confirmed using real-time polymerase chain reaction (PCR).ResultsOf 13,067 COVID-19 cases, 12,022 of them experienced acute COVID-19, and 1,045 of them developed PCC. Individuals with PCC tended to be older women with risk factors and instances of SARS-CoV-2 reinfection. The incidence rates per 100,000 individuals during the Alpha pandemic surge (2020), Delta pandemic surge (2021), and Omicron pandemic surge (2022) were 2.2, 137.2, and 222.5 for acute COVID-19, and 0.27, 10.5, and 19.3, respectively, for PCC cases. The death rates per 100,000 individuals during the Alpha, Delta, and Omicron pandemic surges were 3, 112, and 76, respectively, for acute COVID-19 and 1, 10, and 8, respectively, for PCC. The death rate was highest among those aged 65 and older during the Delta pandemic surge.ConclusionThese findings suggest the need for a timely national vaccination program prior to new COVID-19 surges to prevent complications related to SARS-CoV-2 infection, particularly in the older adult and in non-older adult individuals with risk factors.

  15. MD COVID19 TotalVaccinationsAge65PlusAtleast1DoseAndFullyVaccinated DataMart...

    • arc-gis-hub-home-arcgishub.hub.arcgis.com
    • hub.arcgis.com
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    Updated Mar 30, 2022
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    ArcGIS Online for Maryland (2022). MD COVID19 TotalVaccinationsAge65PlusAtleast1DoseAndFullyVaccinated DataMart [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/datasets/maryland::md-covid19-totalvaccinationsage65plusatleast1doseandfullyvaccinated-datamart/about
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    Dataset updated
    Mar 30, 2022
    Dataset provided by
    Authors
    ArcGIS Online for Maryland
    Description

    Deprecated as of 4/21/2023On 4/27/2023 several COVID-19 datasets were retired and no longer included in public COVID-19 data dissemination. For more information, visit https://imap.maryland.gov/pages/covid-dataSummaryThe cumulative number of COVID-19 vaccinations for persons aged 65+ within a single Maryland jurisdiction: Persons fully vaccinated and those who have received at least one dose.DescriptionThe MD COVID-19—Persons 65+ Fully Vaccinated layer represents the number of people in each Maryland jurisdiction aged 65 and older who have either received at least one dose of COVID-19 vaccine in a two-dose regimen or are fully vaccinated (have either received a single shot regimen or have completed the second dose in a two-dose regimen), reported each day into ImmuNet.CDC COVID10 Vaccinations in the United States,CountyCOVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.

  16. f

    Data from: S1 Data -

    • figshare.com
    • plos.figshare.com
    xlsx
    Updated Aug 22, 2024
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    Qian Huang (2024). S1 Data - [Dataset]. http://doi.org/10.1371/journal.pone.0307303.s005
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    xlsxAvailable download formats
    Dataset updated
    Aug 22, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Qian Huang
    License

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

    Description

    This study examines demographic disparities in COVID-19 exposures across older adults age 60–79 and older adults age 80 and over, and explores the factors driving these dynamics in the United States (U.S.) from January 2020 to July 2022. Spatial clusters were identified, and 14 main health determinants were synthesized from 62 pre-existing county-level variables. The study also assessed the correlation between these health determinants and COVID-19 incidence rates for both age groups during the pandemic years. Further examination of incidence rates in relation to health determinants was carried out through statistical and spatial regression models. Results show that individuals aged 80 and over had much higher hospitalization rates, death rates, and case-fatality rates in 2020–2022. Spatial results indicate that the geographical cluster of high incidence rates for both groups shifted from the Midwest at the beginning of the pandemic to the Southwest in 2022. The study revealed marked spatial, temporal, and demographic nonstationary dynamics in COVID-19 exposures, indicating that the health effects of contextual factors vary across age groups. COVID-19 incidence rates in older adults were strongly influenced by race, healthcare access, social capital, environment, household composition, and mobility. Future public health policies and mitigations should further their efforts by considering temporal and demographic nonstationarity as well as local conditions.

  17. f

    Sociodemographic characteristics and treatment outcome status of...

    • figshare.com
    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Abdene Weya Kaso; Gebi Agero; Zewdu Hurissa; Taha Kaso; Helen Ali Ewune; Habtamu Endashaw Hareru; Alemayehu Hailu (2023). Sociodemographic characteristics and treatment outcome status of hospitalized patients with COVID-19 to Bokoji Hospital treatment centre, 2021. [Dataset]. http://doi.org/10.1371/journal.pone.0268280.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Abdene Weya Kaso; Gebi Agero; Zewdu Hurissa; Taha Kaso; Helen Ali Ewune; Habtamu Endashaw Hareru; Alemayehu Hailu
    License

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

    Description

    Sociodemographic characteristics and treatment outcome status of hospitalized patients with COVID-19 to Bokoji Hospital treatment centre, 2021.

  18. Data describing the role of interleukin 6 as a predictive factor for a...

    • springernature.figshare.com
    xlsx
    Updated Jun 1, 2023
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    Peter Sabaka; Alena Koščálová; Igor Straka; Július Hodosy; Róbert Lipták; Barbora Kmotorková; Mária Kachlíková; Alice Kušnírová (2023). Data describing the role of interleukin 6 as a predictive factor for a severe course of Covid-19 [Dataset]. http://doi.org/10.6084/m9.figshare.12711767.v1
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    xlsxAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    Peter Sabaka; Alena Koščálová; Igor Straka; Július Hodosy; Róbert Lipták; Barbora Kmotorková; Mária Kachlíková; Alice Kušnírová
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This dataset consists of a single .xlsx spreadsheet of data describing the role of interleukin 6 as a predictive factor for a severe course of Covid-19. The data underlie the manuscript: "Role of interleukin 6 as a predictive factor for a severe course of Covid-19: retrospective data analysis of patients from a Long-term Care Facility during Covid-19 Outbreak". The related study aimed to find an effective screening tool to identify the elderly patients in long-term care facilities who were at risk of severe illness and death due to Covid-19, and explored the role of interleukin 6 (IL-6) as a predictive factor. The spreadsheet contains demographic, clinical and biochemical variables of SARS-CoV-2 positive patients obtained during the first day of intervention in the outbreak of Covid-19 in one long-term care facility in Slovakia in April 2020. All data were obtained during initial screening on the first day of intervention.

  19. f

    Association between chronic condition count and death in hospital among...

    • plos.figshare.com
    xls
    Updated Jun 21, 2023
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    Dianne Zakaria; Samina Aziz; Sharon Bartholomew; Su-Bin Park; Cynthia Robitaille; Murray Weeks (2023). Association between chronic condition count and death in hospital among adults aged 20+ years during first acute care hospitalization with a confirmed or suspected COVID-19 diagnosis in Canada by life-course age group. [Dataset]. http://doi.org/10.1371/journal.pone.0280050.t004
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    xlsAvailable download formats
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Dianne Zakaria; Samina Aziz; Sharon Bartholomew; Su-Bin Park; Cynthia Robitaille; Murray Weeks
    License

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

    Area covered
    Canada
    Description

    Association between chronic condition count and death in hospital among adults aged 20+ years during first acute care hospitalization with a confirmed or suspected COVID-19 diagnosis in Canada by life-course age group.

  20. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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NCHS/DVS (2023). Provisional COVID-19 Deaths by Sex and Age [Dataset]. https://data.cdc.gov/widgets/9bhg-hcku?mobile_redirect=true

Provisional COVID-19 Deaths by Sex and Age

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9 scholarly articles cite this dataset (View in Google Scholar)
csv, application/rdfxml, xml, json, tsv, application/rssxmlAvailable 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.

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