15 datasets found
  1. f

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

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

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

    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.

  2. 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
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    data.cdc.gov (2023). Rates of COVID-19 Cases or Deaths by Age Group and Vaccination Status and Second Booster Dose [Dataset]. https://healthdata.gov/CDC/Rates-of-COVID-19-Cases-or-Deaths-by-Age-Group-and/4tut-jeki
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    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

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

    • data.virginia.gov
    • healthdata.gov
    • +1more
    csv, json, rdf, xsl
    Updated Jul 20, 2023
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    Centers for Disease Control and Prevention (2023). Rates of COVID-19 Cases or Deaths by Age Group and Vaccination Status [Dataset]. https://data.virginia.gov/dataset/rates-of-covid-19-cases-or-deaths-by-age-group-and-vaccination-status
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    xsl, csv, rdf, jsonAvailable download formats
    Dataset updated
    Jul 20, 2023
    Dataset provided by
    Centers for Disease Control and Preventionhttp://www.cdc.gov/
    Description

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

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

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

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

    • statista.com
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    Statista, COVID-19 deaths reported in the U.S. as of June 14, 2023, by age [Dataset]. https://www.statista.com/statistics/1191568/reported-deaths-from-covid-by-age-us/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 1, 2020 - Jun 14, 2023
    Area covered
    United States
    Description

    Between the beginning of January 2020 and June 14, 2023, of the 1,134,641 deaths caused by COVID-19 in the United States, around 307,169 had occurred among those aged 85 years and older. This statistic shows the number of coronavirus disease 2019 (COVID-19) deaths in the U.S. from January 2020 to June 2023, by age.

  5. Deaths by vaccination status, England

    • ons.gov.uk
    • cy.ons.gov.uk
    xlsx
    Updated Aug 25, 2023
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    Office for National Statistics (2023). Deaths by vaccination status, England [Dataset]. https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/deathsbyvaccinationstatusengland
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    xlsxAvailable download formats
    Dataset updated
    Aug 25, 2023
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    Age-standardised mortality rates for deaths involving coronavirus (COVID-19), non-COVID-19 deaths and all deaths by vaccination status, broken down by age group.

  6. Data_Sheet_1_Early effects of COVID-19 on maternal and child health service...

    • frontiersin.figshare.com
    bin
    Updated Jun 21, 2023
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    Orvalho Augusto; Timothy Roberton; Quinhas Fernandes; Sérgio Chicumbe; Ivan Manhiça; Stélio Tembe; Bradley H. Wagenaar; Laura Anselmi; Jon Wakefield; Kenneth Sherr (2023). Data_Sheet_1_Early effects of COVID-19 on maternal and child health service disruption in Mozambique.docx [Dataset]. http://doi.org/10.3389/fpubh.2023.1075691.s001
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    binAvailable download formats
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    Frontiers Mediahttp://www.frontiersin.org/
    Authors
    Orvalho Augusto; Timothy Roberton; Quinhas Fernandes; Sérgio Chicumbe; Ivan Manhiça; Stélio Tembe; Bradley H. Wagenaar; Laura Anselmi; Jon Wakefield; Kenneth Sherr
    License

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

    Area covered
    Mozambique
    Description

    This article is part of the Research Topic ‘Health Systems Recovery in the Context of COVID-19 and Protracted Conflict'IntroductionAfter the World Health Organization declared COVID-19 a pandemic, more than 184 million cases and 4 million deaths had been recorded worldwide by July 2021. These are likely to be underestimates and do not distinguish between direct and indirect deaths resulting from disruptions in health care services. The purpose of our research was to assess the early impact of COVID-19 in 2020 and early 2021 on maternal and child healthcare service delivery at the district level in Mozambique using routine health information system data, and estimate associated excess maternal and child deaths.MethodsUsing data from Mozambique's routine health information system (SISMA, Sistema de Informação em Saúde para Monitoria e Avaliação), we conducted a time-series analysis to assess changes in nine selected indicators representing the continuum of maternal and child health care service provision in 159 districts in Mozambique. The dataset was extracted as counts of services provided from January 2017 to March 2021. Descriptive statistics were used for district comparisons, and district-specific time-series plots were produced. We used absolute differences or ratios for comparisons between observed data and modeled predictions as a measure of the magnitude of loss in service provision. Mortality estimates were performed using the Lives Saved Tool (LiST).ResultsAll maternal and child health care service indicators that we assessed demonstrated service delivery disruptions (below 10% of the expected counts), with the number of new users of family planing and malaria treatment with Coartem (number of children under five treated) experiencing the largest disruptions. Immediate losses were observed in April 2020 for all indicators, with the exception of treatment of malaria with Coartem. The number of excess deaths estimated in 2020 due to loss of health service delivery were 11,337 (12.8%) children under five, 5,705 (11.3%) neonates, and 387 (7.6%) mothers.ConclusionFindings from our study support existing research showing the negative impact of COVID-19 on maternal and child health services utilization in sub-Saharan Africa. This study offers subnational and granular estimates of service loss that can be useful for health system recovery planning. To our knowledge, it is the first study on the early impacts of COVID-19 on maternal and child health care service utilization conducted in an African Portuguese-speaking country.

  7. HMPPS COVID-19 statistics : July 2021

    • gov.uk
    • s3.amazonaws.com
    Updated Aug 13, 2021
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    Ministry of Justice (2021). HMPPS COVID-19 statistics : July 2021 [Dataset]. https://www.gov.uk/government/statistics/hmpps-covid-19-statistics-july-2021
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    Dataset updated
    Aug 13, 2021
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Ministry of Justice
    Description

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

    Data includes:

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

    Pre-release access

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

    Ministry of Justice:

    Lord Chancellor and Secretary of State for Justice; Parliamentary Under Secretary of State; Permanent Secretary; Minister and Permanent Secretary Private Secretaries (x9); Special Advisors (x2); Director General for Policy and Strategy Group; Deputy Director of Data and Evidence as a Service - interim; Head of Profession, Statistics; Head of Prison Safety and Security Statistics; Head of News; Deputy Head of News and relevant press officers (x3).

    HM Prison and Probation Service:

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

    Related links

    Update on COVID-19 in prisons

    Prison estate expanded to protect NHS from coronavirus risk

    Measures announced to protect NHS from coronavirus risk in prisons

  8. f

    Observed coverage disruption of selected SRMNCH services due to the COVID-19...

    • plos.figshare.com
    • datasetcatalog.nlm.nih.gov
    xls
    Updated May 31, 2023
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    Aatekah Owais; Arjumand Rizvi; Muhammad Jawwad; Susan Horton; Jai K. Das; Catherine Merritt; Ralfh Moreno; Atnafu G. Asfaw; Paul Rutter; Phuong H. Nguyen; Purnima Menon; Zulfiqar A. Bhutta (2023). Observed coverage disruption of selected SRMNCH services due to the COVID-19 pandemic in South Asia, Jan 2020 to Jun 2021. [Dataset]. http://doi.org/10.1371/journal.pgph.0001567.t001
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    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS Global Public Health
    Authors
    Aatekah Owais; Arjumand Rizvi; Muhammad Jawwad; Susan Horton; Jai K. Das; Catherine Merritt; Ralfh Moreno; Atnafu G. Asfaw; Paul Rutter; Phuong H. Nguyen; Purnima Menon; Zulfiqar A. Bhutta
    License

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

    Area covered
    Asia, South Asia
    Description

    Observed coverage disruption of selected SRMNCH services due to the COVID-19 pandemic in South Asia, Jan 2020 to Jun 2021.

  9. 2

    YL

    • datacatalogue.ukdataservice.ac.uk
    Updated Apr 22, 2024
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    University of Oxford, Young Lives (2024). YL [Dataset]. http://doi.org/10.5255/UKDA-SN-9251-1
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    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
    Ethiopia, India, 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.

  10. A severity level for patients with COVID-19 infection; Adapted from...

    • plos.figshare.com
    xls
    Updated Jun 14, 2023
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    Chanapai Chaiyakulsil; Paskorn Sritipsukho; Araya Satdhabudha; Pornumpa Bunjoungmanee; Auchara Tangsathapornpong; Phakatip Sinlapamongkolkul; Naiyana Sritipsukho (2023). A severity level for patients with COVID-19 infection; Adapted from reference no 10. [Dataset]. http://doi.org/10.1371/journal.pone.0267035.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 14, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Chanapai Chaiyakulsil; Paskorn Sritipsukho; Araya Satdhabudha; Pornumpa Bunjoungmanee; Auchara Tangsathapornpong; Phakatip Sinlapamongkolkul; Naiyana Sritipsukho
    License

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

    Description

    A severity level for patients with COVID-19 infection; Adapted from reference no 10.

  11. f

    Estimated number of additional adolescent pregnancies, maternal and neonatal...

    • plos.figshare.com
    xls
    Updated Jun 7, 2023
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    Aatekah Owais; Arjumand Rizvi; Muhammad Jawwad; Susan Horton; Jai K. Das; Catherine Merritt; Ralfh Moreno; Atnafu G. Asfaw; Paul Rutter; Phuong H. Nguyen; Purnima Menon; Zulfiqar A. Bhutta (2023). Estimated number of additional adolescent pregnancies, maternal and neonatal deaths, low birthweight births and stunted children resulting from girls dropping out of school due to the COVID-19 pandemic in six South Asian countries. [Dataset]. http://doi.org/10.1371/journal.pgph.0001567.t003
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    xlsAvailable download formats
    Dataset updated
    Jun 7, 2023
    Dataset provided by
    PLOS Global Public Health
    Authors
    Aatekah Owais; Arjumand Rizvi; Muhammad Jawwad; Susan Horton; Jai K. Das; Catherine Merritt; Ralfh Moreno; Atnafu G. Asfaw; Paul Rutter; Phuong H. Nguyen; Purnima Menon; Zulfiqar A. Bhutta
    License

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

    Area covered
    South Asia
    Description

    Estimated number of additional adolescent pregnancies, maternal and neonatal deaths, low birthweight births and stunted children resulting from girls dropping out of school due to the COVID-19 pandemic in six South Asian countries.

  12. Demographic data based on diagnosis of radiographic pneumonia.

    • plos.figshare.com
    • figshare.com
    xls
    Updated Jun 14, 2023
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    Chanapai Chaiyakulsil; Paskorn Sritipsukho; Araya Satdhabudha; Pornumpa Bunjoungmanee; Auchara Tangsathapornpong; Phakatip Sinlapamongkolkul; Naiyana Sritipsukho (2023). Demographic data based on diagnosis of radiographic pneumonia. [Dataset]. http://doi.org/10.1371/journal.pone.0267035.t004
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    xlsAvailable download formats
    Dataset updated
    Jun 14, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Chanapai Chaiyakulsil; Paskorn Sritipsukho; Araya Satdhabudha; Pornumpa Bunjoungmanee; Auchara Tangsathapornpong; Phakatip Sinlapamongkolkul; Naiyana Sritipsukho
    License

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

    Description

    Demographic data based on diagnosis of radiographic pneumonia.

  13. Associating factors in children with pneumonia.

    • plos.figshare.com
    xls
    Updated May 30, 2023
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    Chanapai Chaiyakulsil; Paskorn Sritipsukho; Araya Satdhabudha; Pornumpa Bunjoungmanee; Auchara Tangsathapornpong; Phakatip Sinlapamongkolkul; Naiyana Sritipsukho (2023). Associating factors in children with pneumonia. [Dataset]. http://doi.org/10.1371/journal.pone.0267035.t005
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    xlsAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Chanapai Chaiyakulsil; Paskorn Sritipsukho; Araya Satdhabudha; Pornumpa Bunjoungmanee; Auchara Tangsathapornpong; Phakatip Sinlapamongkolkul; Naiyana Sritipsukho
    License

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

    Description

    Associating factors in children with pneumonia.

  14. Demographic data based on month.

    • plos.figshare.com
    xls
    Updated Jun 8, 2023
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    Chanapai Chaiyakulsil; Paskorn Sritipsukho; Araya Satdhabudha; Pornumpa Bunjoungmanee; Auchara Tangsathapornpong; Phakatip Sinlapamongkolkul; Naiyana Sritipsukho (2023). Demographic data based on month. [Dataset]. http://doi.org/10.1371/journal.pone.0267035.t003
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    Jun 8, 2023
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    PLOShttp://plos.org/
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    Chanapai Chaiyakulsil; Paskorn Sritipsukho; Araya Satdhabudha; Pornumpa Bunjoungmanee; Auchara Tangsathapornpong; Phakatip Sinlapamongkolkul; Naiyana Sritipsukho
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    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Demographic data based on month.

  15. f

    Demographic data based on site of admission.

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    • plos.figshare.com
    xls
    Updated Jun 15, 2023
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    Chanapai Chaiyakulsil; Paskorn Sritipsukho; Araya Satdhabudha; Pornumpa Bunjoungmanee; Auchara Tangsathapornpong; Phakatip Sinlapamongkolkul; Naiyana Sritipsukho (2023). Demographic data based on site of admission. [Dataset]. http://doi.org/10.1371/journal.pone.0267035.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 15, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Chanapai Chaiyakulsil; Paskorn Sritipsukho; Araya Satdhabudha; Pornumpa Bunjoungmanee; Auchara Tangsathapornpong; Phakatip Sinlapamongkolkul; Naiyana Sritipsukho
    License

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

    Description

    Demographic data based on site of admission.

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

Data_Sheet_1_Risk factors for admission to the pediatric critical care unit among children hospitalized with COVID-19 in France.docx

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docxAvailable download formats
Dataset updated
Jun 16, 2023
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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.

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