64 datasets found
  1. Coronavirus and clinically extremely vulnerable (CEV) people in England

    • ons.gov.uk
    • cy.ons.gov.uk
    xlsx
    Updated May 13, 2022
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    Office for National Statistics (2022). Coronavirus and clinically extremely vulnerable (CEV) people in England [Dataset]. https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/datasets/coronavirusandclinicallyextremelyvulnerablepeopleinengland
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    xlsxAvailable download formats
    Dataset updated
    May 13, 2022
    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

    Analysis of people previously considered to be clinically extremely vulnerable (CEV) in England during the coronavirus (COVID-19) pandemic, including their behaviours and mental and physical well-being.

  2. Meeting minutes of the Vaccine Benefit Risk Expert Working Group from the...

    • gov.uk
    Updated Dec 19, 2024
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    Medicines and Healthcare products Regulatory Agency (2024). Meeting minutes of the Vaccine Benefit Risk Expert Working Group from the Covid-19 Pandemic - 20 September 2022 to 5 May 2023 [Dataset]. https://www.gov.uk/government/publications/meeting-minutes-of-the-vaccine-benefit-risk-expert-working-group-from-the-covid-19-pandemic-20-september-2022-to-5-may-2023
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    Dataset updated
    Dec 19, 2024
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Medicines and Healthcare products Regulatory Agency
    Description

    As part of our ongoing mission to improve transparency, we are publishing minutes taken from meetings of the Commission on Human Medicines’ Vaccine Benefit Risk Expert Working Group (VBREWG) between 25 August 2020 and 5 May 2023. The VBREWG meetings focused on evaluating the safety, efficacy, and overall benefits versus risks of vaccines, providing expert advice and recommendations on licensing and regulatory action.

    Under Section 40 and 43 of the Freedom of Information Act respectively, personal data of individuals and commercially sensitive information has been redacted from these minutes.

  3. Risk of death from Coronavirus in England and Wales 2020 by ethnicity

    • statista.com
    Updated Jul 4, 2024
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    Statista (2024). Risk of death from Coronavirus in England and Wales 2020 by ethnicity [Dataset]. https://www.statista.com/statistics/1115584/coronavirus-death-risk-rate-in-the-uk-by-ethnicity/
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    Dataset updated
    Jul 4, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Mar 2, 2020 - Apr 2, 2020
    Area covered
    England, Wales, United Kingdom
    Description

    Black men and women in the United Kingdom were four times more likely to die from Coronavirus than white people of the same gender as of April 2020. Several other ethnic groups were also at an increased risk from Coronavirus than the white population, with men of Bangladeshi or Pakistani origin 3.6 times more likely, and women 3.4 more likely to die from Coronavirus.

    For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

  4. Odds ratios for risk of coronavirus-related deaths by ethnic group, England...

    • cy.ons.gov.uk
    • ons.gov.uk
    xlsx
    Updated May 7, 2020
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    Office for National Statistics (2020). Odds ratios for risk of coronavirus-related deaths by ethnic group, England and Wales [Dataset]. https://cy.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/oddsratiosforriskofcoronavirusrelateddeathsbyethnicgroupenglandandwales
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    xlsxAvailable download formats
    Dataset updated
    May 7, 2020
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

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

    Description

    Odds ratios for the risk of dying from the coronavirus (COVID-19) by ethnicity in England and Wales.

  5. Views towards COVID-19 vaccine use in the United Kingdom 2020, by age

    • flwrdeptvarieties.store
    • statista.com
    Updated Dec 20, 2023
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    Conor Stewart (2023). Views towards COVID-19 vaccine use in the United Kingdom 2020, by age [Dataset]. https://flwrdeptvarieties.store/?_=%2Ftopics%2F6112%2Fcoronavirus-covid-19-in-the-uk%2F%23zUpilBfjadnZ6q5i9BcSHcxNYoVKuimb
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    Dataset updated
    Dec 20, 2023
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Conor Stewart
    Area covered
    United Kingdom
    Description

    In December 2020, a survey carried out in the United Kingdom (UK) found that 87 percent of those aged 75 years of age were willing to take the COVID-19 vaccine and will take the vaccine as soon as it was offered to them. The highest support for taking vaccination was reported in the oldest age groups who are most at risk from the effects of contracting the coronavirus. On the other hand, 18 percent of those aged between 35 and 44 years said they did not want to be vaccinated and will do their best to avoid immunization, even if they were asked to do so by the NHS. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

  6. c

    UCL COVID-19 Social Study, 2020-2022

    • datacatalogue.cessda.eu
    Updated Nov 29, 2024
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    Fancourt, D., University College London; Bu, F., University College London; Paul, E., University College London; Steptoe, A., University College London (2024). UCL COVID-19 Social Study, 2020-2022 [Dataset]. http://doi.org/10.5255/UKDA-SN-9001-1
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    Dataset updated
    Nov 29, 2024
    Dataset provided by
    Department of Behavioural Science and Health
    Authors
    Fancourt, D., University College London; Bu, F., University College London; Paul, E., University College London; Steptoe, A., University College London
    Time period covered
    Mar 21, 2020 - Mar 22, 2022
    Area covered
    United Kingdom
    Variables measured
    Individuals, National
    Measurement technique
    Self-administered questionnaire: Web-based (CAWI)
    Description

    Abstract copyright UK Data Service and data collection copyright owner.


    The UCL COVID-19 Social Study at University College London (UCL) was launched on 21 March 2020. Led by Dr Daisy Fancourt and Professor Andrew Steptoe from the Department of Behavioural Science and Health, the team designed the study to track in real-time the psychological and social impact of the virus across the UK.

    The study quickly became the largest in the country, growing to over 70,000 participants and providing rare and privileged insight into the effects of the pandemic on people’s daily lives. Through our participants’ remarkable two-year commitment to the study, 1.2 million surveys were collected over 105 weeks, and over 100 scientific papers and 44 public reports were published.

    During COVID-19, population mental health has been affected both by the intensity of the pandemic (cases and death rates), but also by lockdowns and restrictions themselves. Worsening mental health coincided with higher rates of COVID-19, tighter restrictions, and the weeks leading up to lockdowns. Mental health then generally improved during lockdowns and most people were able to adapt and manage their well-being. However, a significant proportion of the population suffered disproportionately to the rest, and stay-at-home orders harmed those who were already financially, socially, or medically vulnerable. Socioeconomic factors, including low SEP, low income, and low educational attainment, continued to be associated with worse experiences of the pandemic. Outcomes for these groups were worse throughout many measures including mental health and wellbeing; financial struggles;self-harm and suicide risk; risk of contracting COVID-19 and developing long Covid; and vaccine resistance and hesitancy. These inequalities existed before the pandemic and were further exacerbated by COVID-19, and such groups remain particularly vulnerable to the future effects of the pandemic and other national crises.

    Further information, including reports and publications, can be found on the UCL COVID-19 Social Study website.


    Main Topics:

    The study asked baseline questions on the following:

    • Demographics, including year of birth, sex, ethnicity, relationship status, country of dwelling, urban/rural dwelling, type of accommodation, housing tenure, number of adults and children in the household, household income, education, employment status, pet ownership, and personality.
    • Health and health behaviours, including pre-existing physical health conditions, diagnosed mental health conditions, pregnancy, smoking, alcohol consumption, physical activity, caring responsibilities, usual social behaviours, and social network size.

    It also asked repeated questions at every wave on the following:

    • COVID-19 status, including whether the respondent had had COVID-19, whether they had come into likely contact with COVID-19, current isolation status and motivations for isolation, length of isolation, length of time not leaving the home, length of time not contacting others, trust in government, trust in the health service, adherence to health advice, and experience of adverse events due to COVID-19 (including severe illness within the family, bereavement, redundancy, or financial difficulties).
    • Mental health, including wellbeing, depression, anxiety, which factors were causing stress, sleep quality, loneliness, social isolation, and changes in health behaviours such as smoking, drinking and exercise.
    • How people were spending their time whilst in isolation, including questions on working, functional household activities, care, and schooling of any children in the household, hobbies, and relaxation.

    Certain waves of the study also included one-off modules on topics including volunteering behaviours, locus of control, frustrations and expectations, coping styles, fear of COVID-19, resilience, arts and creative engagement, life events, weight, gambling behaviours, mental health diagnosis, use of financial support, faith and religion, relationships, neighbourhood satisfaction, healthcare usage, discrimination experiences, life changes, optimism, long COVID and COVID-19 vaccination.

  7. f

    Table_6_Knowledge, perceived risk, and attitudes towards COVID-19 protective...

    • frontiersin.figshare.com
    docx
    Updated May 31, 2023
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    Erica Jane Cook; Elizabeth Elliott; Louisa Donald; Alfredo Gaitan; Gurch Randhawa; Sally Cartwright; Muhammad Waqar; Chimeme Egbutah; Ifunanya Nduka; Andy Guppy; Nasreen Ali (2023). Table_6_Knowledge, perceived risk, and attitudes towards COVID-19 protective measures amongst ethnic minorities in the UK: A cross-sectional study.DOCX [Dataset]. http://doi.org/10.3389/fpubh.2022.1060694.s006
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    docxAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    Frontiers
    Authors
    Erica Jane Cook; Elizabeth Elliott; Louisa Donald; Alfredo Gaitan; Gurch Randhawa; Sally Cartwright; Muhammad Waqar; Chimeme Egbutah; Ifunanya Nduka; Andy Guppy; Nasreen Ali
    License

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

    Area covered
    United Kingdom
    Description

    BackgroundMinority ethnic groups are at increased risk of COVID-19 related mortality or morbidity yet continue to have a disproportionally lower uptake of the vaccine. The importance of adherence to prevention and control measures to keep vulnerable populations and their families safe therefore remains crucial. This research sought to examine the knowledge, perceived risk, and attitudes toward COVID-19 among an ethnically diverse community.MethodsA cross-sectional self-administered questionnaire was implemented to survey ethnic minority participants purposefully recruited from Luton, an ethnically diverse town in the southeast of England. The questionnaire was structured to assess participants knowledge, perceived risk, attitudes toward protective measures as well as the sources of information about COVID-19. The questionnaire was administered online via Qualtrics with the link shared through social media platforms such as Facebook, Twitter, and WhatsApp. Questionnaires were also printed into brochures and disseminated via community researchers and community links to individuals alongside religious, community and outreach organisations. Data were analysed using appropriate statistical techniques, with the significance threshold for all analyses assumed at p = 0.05.Findings1,058 participants (634; 60% females) with a median age of 38 (IQR, 22) completed the survey. National TV and social networks were the most frequently accessed sources of COVID-19 related information; however, healthcare professionals, whilst not widely accessed, were viewed as the most trusted. Knowledge of transmission routes and perceived susceptibility were significant predictors of attitudes toward health-protective practises.Conclusion/recommendationImproving the local information provision, including using tailored communication strategies that draw on trusted sources, including healthcare professionals, could facilitate understanding of risk and promote adherence to health-protective actions.

  8. Self-reported coronavirus (COVID-19) infections and associated symptoms,...

    • cy.ons.gov.uk
    • ons.gov.uk
    xlsx
    Updated Apr 25, 2024
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    Office for National Statistics (2024). Self-reported coronavirus (COVID-19) infections and associated symptoms, England and Scotland [Dataset]. https://cy.ons.gov.uk/redir/eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpbmRleCI6NiwicGFnZVNpemUiOjEwLCJwYWdlIjoxLCJ1cmkiOiIvcGVvcGxlcG9wdWxhdGlvbmFuZGNvbW11bml0eS9oZWFsdGhhbmRzb2NpYWxjYXJlL2NvbmRpdGlvbnNhbmRkaXNlYXNlcy9kYXRhc2V0cy9zZWxmcmVwb3J0ZWRjb3JvbmF2aXJ1c2NvdmlkMTlpbmZlY3Rpb25zYW5kYXNzb2NpYXRlZHN5bXB0b21zZW5nbGFuZGFuZHNjb3RsYW5kIiwibGlzdFR5cGUiOiJkYXRhbGlzdCJ9.nr6OefmHqMJwWGTKo4JnlfoPDpMxoctEdal9-aIMfOQ
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    xlsxAvailable download formats
    Dataset updated
    Apr 25, 2024
    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

    In-depth analysis of Winter Coronavirus (COVID-19) Infection Study data looking at trends in self-reported symptoms of coronavirus (COVID-19), including ongoing symptoms and associated risk factors.

  9. Final model for risk of severe COVID-19.

    • plos.figshare.com
    xls
    Updated Jun 11, 2023
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    Gillian S. Dite; Nicholas M. Murphy; Richard Allman (2023). Final model for risk of severe COVID-19. [Dataset]. http://doi.org/10.1371/journal.pone.0247205.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 11, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Gillian S. Dite; Nicholas M. Murphy; Richard Allman
    License

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

    Description

    Final model for risk of severe COVID-19.

  10. Likeliness to take a coronavirus vaccine in the UK as of November 2020, by...

    • statista.com
    Updated Dec 2, 2020
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    Statista (2020). Likeliness to take a coronavirus vaccine in the UK as of November 2020, by age [Dataset]. https://www.statista.com/statistics/1189978/uk-likeliness-to-take-coronavirus-vaccine-by-age/
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    Dataset updated
    Dec 2, 2020
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Nov 19, 2020 - Nov 20, 2020
    Area covered
    United Kingdom
    Description

    In November 2020, 42 percent of survey respondents in the United Kingdom reported they would be very likely to receive a COVID-19 vaccine when it becomes available, while 24 percent said they would be fairly like to take a vaccine. The highest support for receiving the vaccine was found among those aged 65 years of age, the age group most at risk from the complications arising from COVID-19, with 64 percent of over 65s reporting they would be very likely to be immunized.

    The latest number of cases in the UK can be found here. For further information about the coronavirus pandemic, please visit our dedicated Facts and Figures page.

  11. h

    openEHR suspected COVID-19 risk assessment.v0

    • ckm.highmed.org
    • ckm.openehr.org
    xml
    Updated Feb 27, 2020
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    (2020). openEHR suspected COVID-19 risk assessment.v0 [Dataset]. https://ckm.highmed.org/ckm/templates/1246.169.687
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    xmlAvailable download formats
    Dataset updated
    Feb 27, 2020
    License

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

    Description

    To record the information required to evaluate the potential risk of Covid-19 infection, as part of professional screening or self-assessment.

    This is based on - The current NHS-111 UK self-assessment app at https://111.nhs.uk/covid-19 - A similar risk assessment app developed for pre-hospital admission by DIPS.no - Public Health England COVID-19: investigation and initial clinical management of possible cases https://www.gov.uk/government/publications/wuhan-novel-coronavirus-initial-investigation-of-possible-cases

    The exact risk factors are subject to continual update as the disease progresses.

    Note that a critical part of the information, exposure locations, has been left open, so as to allow the list to be updated very regularly and in alignment with local or national policy.

    We have decided to leave in 'older' questions such as 'Exposure to birds in China' until such time that we get clear professional guidance that these are no longer necessary or useful.

  12. COVID-19 vaccine effectiveness estimated using Census 2021 variables,...

    • statistics.ukdataservice.ac.uk
    xlsx
    Updated Mar 8, 2023
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    Office for National Statistics; National Records of Scotland; Northern Ireland Statistics and Research Agency; UK Data Service. (2023). COVID-19 vaccine effectiveness estimated using Census 2021 variables, England: 31 March 2021 to 20 March 2022 [Dataset]. https://statistics.ukdataservice.ac.uk/dataset/covid-19-vaccine-effectiveness-estimated-using-census-2021-variables-england
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    xlsxAvailable download formats
    Dataset updated
    Mar 8, 2023
    Dataset provided by
    UK Data Servicehttps://ukdataservice.ac.uk/
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    http://reference.data.gov.uk/id/open-government-licencehttp://reference.data.gov.uk/id/open-government-licence

    Area covered
    England
    Description

    Estimates of the risk of hospital admission for coronavirus (COVID-19) and death involving COVID-19 by vaccination status, overall and by age group, using anonymised linked data from Census 2021. Experimental Statistics.

    Outcome definitions

    For this analysis, we define a death as involving COVID-19 if either of the ICD-10 codes U07.1 (COVID-19, virus identified) or U07.2 (COVID-19, virus not identified) is mentioned on the death certificate. Information on cause of death coding is available in the User Guide to Mortality Statistics. We use date of occurrance rather than date of registration to give the date of the death.

    We define COVID-109 hospitalisation as an inpatient episode in Hospital Episode Statistics where the primary diagnosis was COVID-19, identified by the ICD-19 codes (COVID-19, virus identified) or U07.2 (COVID-19, virus not identified). Where an individual had experienced more than one COVID-19 hospitalisation, the earliest that occurred within the study period was used. We define the date of COVID-19 hospitalisation as the start of the hospital episode.

    ICD-10 code

    U07.1 :

    COVID-19, virus identified

    U07.2:

    COVID-19, virus not identified

    Vaccination status is defined by the dose and the time since the last dose received

    Unvaccinated:

    no vaccination to less than 21 days post first dose

    First dose 21 days to 3 months:

    more than or equal to 21 days post second dose to earliest of less than 91 days post first dose or less than 21 days post second dose

    First dose 3+ months:

    more than or equal to 91 days post first dose to less than 21 days post second dose

    Second dose 21 days to 3 months:

    more than or equal to 21 days post second dose to earliest of less than 91 days post second dose or less than 21 days post third dose

    Second dose 3-6 months:

    more than or equal to 91 days post second dose to earliest of less than 182 days post second dose or less than 21 days post third dose

    Second dose 6+ months:

    more than or equal to 182 days post second dose to less than 21 days post third dose

    Third dose 21 days to 3 months:

    more than or equal to 21 days post third dose to less than 91 days post third dose

    Third dose 3+ months:

    more than or equal to 91 days post third dose

    Model adjustments

    Three sets of model adjustments were used

    Age adjusted:

    age (as a natural spline)

    Age, socio-demographics adjusted:

    age (as a natural spline), plus socio-demographic characteristics (sex, region, ethnicity, religion, IMD decile, NSSEC category, highest qualification, English language proficiency, key worker status)

    Fully adjusted:

    age (as a natural spline), plus socio-demographic characteristics (sex, region, ethnicity, religion, IMD decile, NSSEC category, highest qualification, English language proficiency, key worker status), plus health-related characteristics (disability, self-reported health, care home residency, number of QCovid comorbidities (grouped), BMI category, frailty flag and hospitalisation within the last 21 days.

    Age

    Age in years is defined on the Census day 2021 (21 March 2021). Age is included in the model as a natural spline with boundary knots at the 10th and 90th centiles and internal knots at the 25th, 50th and 75th centiles. The positions of the knots are calculated separately for the overall model and for each age group for the stratified model.

  13. Deaths by vaccination status, England

    • ons.gov.uk
    • cy.ons.gov.uk
    xlsx
    Updated Aug 25, 2023
    + more versions
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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.

  14. h

    Deeply-phenotyped hospital COVID patients: severity, acuity, therapies,...

    • healthdatagateway.org
    unknown
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    This publication uses data from PIONEER, an ethically approved database and analytical environment (East Midlands Derby Research Ethics 20/EM/0158), Deeply-phenotyped hospital COVID patients: severity, acuity, therapies, outcomes [Dataset]. https://healthdatagateway.org/dataset/145
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    unknownAvailable download formats
    Dataset authored and provided by
    This publication uses data from PIONEER, an ethically approved database and analytical environment (East Midlands Derby Research Ethics 20/EM/0158)
    License

    https://www.pioneerdatahub.co.uk/data/data-request-process/https://www.pioneerdatahub.co.uk/data/data-request-process/

    Description

    PIONEER: Deeply-phenotyped hospital COVID patients: severity, acuity, therapies, outcomes Dataset number 4.0

    Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 6 million cases& more than 1.5 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS)& death. There is a pressing need for tools to stratify patients, to identify those at greatest risk. Acuity scores are composite scores which help identify patients who are more unwell to support & prioritise clinical care. There are no validated acuity scores for COVID-19 & it is unclear whether standard tools are accurate enough to provide this support. This secondary care COVID dataset contains granular demographic, morbidity, serial acuity and outcome data to inform risk prediction tools in COVID-19.

    PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 & 2.

    EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. UHB has cared for >5000 COVID admissions to date.

    Scope: All COVID swab confirmed hospitalised patients to UHB from January – May 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes but also primary care records& clinic letters. Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes. Linked images available (radiographs, CT, MRI, ultrasound).

    Available supplementary data: Health data preceding & following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data.

    Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

  15. c

    Food Vulnerability during COVID-19, 2020-2023

    • datacatalogue.cessda.eu
    • beta.ukdataservice.ac.uk
    Updated Mar 26, 2025
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    Lambie-Mumford, H; Loopstra, R; Gordon, K; Cooper, N; Shaw, S; Perry, J (2025). Food Vulnerability during COVID-19, 2020-2023 [Dataset]. http://doi.org/10.5255/UKDA-SN-856580
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    Dataset updated
    Mar 26, 2025
    Dataset provided by
    Freelancer
    King
    University of Sheffield
    Church Action On Poverty
    Authors
    Lambie-Mumford, H; Loopstra, R; Gordon, K; Cooper, N; Shaw, S; Perry, J
    Time period covered
    Jul 8, 2020 - Jan 7, 2023
    Area covered
    United Kingdom
    Variables measured
    Organization, Household
    Measurement technique
    Mapping and monitoring food access support at a national level, across the UK. (1) Systematic desk-based mapping of national interventions. (2) Systematic desk-based search and review of existing evidence on key interventions. (3) Primary data (online interviews and workshops) with representatives of government departments, national charities, food and poverty charities and business representativesHear directly from those with lived experience of food insecurity during the pandemic. (1) Monthly panel meetings (Oct 2020-Dec 2021) using a range of participatory and creative methods through which panel members could share and reflect on their experiences and contribute to policy recommendations. Reflective conversations were also held with panel members individually. (2) Deliberative policy engagement workshops (autumn 2021) that brought the panel together with ‘policy specialists’ with direct experience of shaping policy regarding food security.Mapping and monitoring food access support at a local level. In-depth case studies of 14 local authority areas in the UK that involved: (1) Desk based mapping of local interventions (2) Primary data (online interviews and workshops) with local representatives of councils, public health, local charities, local food aid organisations, other groups supporting food access (e.g., community councils)
    Description

    This research project mapped and monitored responses to household food insecurity during the COVID-19 pandemic.

    During the COVID-19 pandemic, governments, local authorities, charities and local communities worked to ensure access to food for those facing new risks of food insecurity due to being unable to go out for food or due to income losses arising from the crisis. New schemes were developed, such as governments replacing incomes of people at risk of unemployment on account of lockdowns, providing food parcels for people asked to shield, referrals for people to receive voluntary help with grocery shopping, and free school meals replacement vouchers or cash transfers. These worked alongside existing provision for those unable to afford food – such as food banks – which have been adapting their services to continue to meet increasing demand from a range of population groups. This resulted in a complex set of support structures which developed and changed as the COVID-19 pandemic, and its impacts, evolved.

    About the project

    The project was funded by the Economic and Social Research Council (ESRC) through the UKRI Ideas to Address COVID-19 grant call and ran for two years from July 2020. The research aimed to provide collaborative monitoring and analysis of food support systems to inform food access policy and practice. The research team was led by the University of Sheffield and King’s College London alongside colleagues from Sustain: the alliance for better food and farming and Church Action on Poverty. Full details of the team are below. Collaboration with partners and stakeholders was at the heart of the project. The research team worked with stakeholders from national and local government, the civil service, third sector, NGOs as well as people who were accessing food and financial assistance during the pandemic.

    The End of project summary of key findings were published in August 2022. Details of the workpackages and research reports can be found below.

    Project work packages

    Work package 1: National level food access systems mapping and monitoring

    Looking at food access support across the UK during the COVID-19 pandemic, national level mapping and monitoring was undertaken in England, Northern Ireland, Scotland and Wales as well as at a UK level. National level stakeholders (for example from devolved governments and national voluntary organisations) from across the four nations worked with us to understand and monitor how support for food access has operated and evolved across the UK.

    Work package 1 publications: Mapping responses to the risk of rising food insecurity during the COVID-19 crisis across the UK (published August 2020) Monitoring responses to the risk of rising food insecurity during the COVID-19 crisis across the UK (published December 2020) Mapping and monitoring responses to the risk of rising food insecurity during the COVID-19 crisis across the UK - Autumn 2020 to Summer 2021 (published August 2022)

    Work package 2: Participatory Policy Panel

    To fully understand food access responses, it was crucial to hear directly from those with lived experience of food insecurity during the pandemic. In partnership with Church Action on Poverty, we convened a participatory policy panel made up of people who have direct experience of a broad range of support to access food. Meeting regularly throughout the project (Oct 2020-Dec 2021), the panel used a range of participatory and creative methods to share and reflect on their experiences and contribute these to policy recommendations.

    Work package 2 publications: Navigating Storms (published October 2021) Food Experiences During COVID-19 Participatory Panel Deliberative Policy Engagement (published August 2022) Food Experiences During COVID-19 - Participatory Methods in Practice: Key Learning (published August 2022)

    Work package 3: Local area case studies

    Fourteen local areas across the UK were the focus for more in depth case study research. Working with local stakeholders in each area, the research mapped what local responses looked like and how they operated. The research followed the developments in these areas throughout the duration of the project.

    Work package 3 publications: Comparing local responses to household food insecurity during COVID-19 across the UK (March – August 2020) – Executive Summary (published July 2021) Comparing local responses to household food insecurity during COVID-19 across the UK (March – August 2020) (published July 2021). Eight local case study reports covering responses in those areas between March and August 2020: Argyll and Bute, Belfast, Cardiff, Derry and Strabane, Herefordshire, Moray, Swansea, West Berkshire (published July 2021). Local Area Case Studies – Methodological Appendix (published July 2021) Local responses to household food insecurity during COVID-19 across the UK (March – August 2020): Full report (published July 2021) Local responses to household food insecurity across the UK...

  16. COVID-19: working from home risk to marketing organizations in the UK in...

    • statista.com
    Updated Jan 5, 2023
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    Statista (2023). COVID-19: working from home risk to marketing organizations in the UK in 2020 [Dataset]. https://www.statista.com/statistics/1104706/coronavirus-home-office-risk-to-marketing-uk/
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    Dataset updated
    Jan 5, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Mar 2020
    Area covered
    United Kingdom
    Description

    Due the spread of coronavirus (COVID-19), many marketing organizations in the United Kingdom (UK) may have to start operating remotely in 2020. A survey published in mid-March revealed that 62 percent of marketers fear that their organization could be compromised by the need to work from home. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

  17. f

    Adjusted odds ratios with 95% confidence interval from multivariable...

    • plos.figshare.com
    xls
    Updated Apr 17, 2024
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    Ryan J. Lowhorn; Mohammed Chowdhury; Symon Kimitei; Sammie Haskin; Mohammad Masum; A. K. M. Fazlur Rahman (2024). Adjusted odds ratios with 95% confidence interval from multivariable logistic regression model with GEE for the outcome COVID-19 mortality. [Dataset]. http://doi.org/10.1371/journal.pone.0296895.t002
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    xlsAvailable download formats
    Dataset updated
    Apr 17, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Ryan J. Lowhorn; Mohammed Chowdhury; Symon Kimitei; Sammie Haskin; Mohammad Masum; A. K. M. Fazlur Rahman
    License

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

    Description

    Adjusted odds ratios with 95% confidence interval from multivariable logistic regression model with GEE for the outcome COVID-19 mortality.

  18. l

    Combatting gendered, sexual risks and harms online during Covid-19:...

    • figshare.le.ac.uk
    Updated Oct 11, 2023
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    Kaitlynn Mendes; Jessica Ringrose; Tanya Horeck; Elizabeth Milne (2023). Combatting gendered, sexual risks and harms online during Covid-19: Developing resources for young people, parents and schools. [Dataset]. http://doi.org/10.25392/leicester.data.16904470.v1
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    Dataset updated
    Oct 11, 2023
    Dataset provided by
    University of Leicester
    Authors
    Kaitlynn Mendes; Jessica Ringrose; Tanya Horeck; Elizabeth Milne
    License

    https://www.rioxx.net/licenses/all-rights-reserved/https://www.rioxx.net/licenses/all-rights-reserved/

    Description

    This study sought to assess the impact of COVID-19 and social isolation on young people's experiences of online sexual risks and gendered harms during a period of increased reliance on screens. Through surveys, and focus group interviews with young people (ages 13-21) and parents/carers, and teachers, the study addressed gaps in knowledge by exploring young people's differing experiences of online sexual harassment during Covid-19, in relation to gender (girls, boys, gender non-conforming), sexuality (LGBTQI+) and other intersecting identities. Survey: We administered an online survey to 551 teens of all genders (aged 13-18), 72 parents/carers, and 47 teachers, safeguarding leads and/or school staff across schools in England. These surveys were disseminated between May and September 2021 by our charitable partner, School of Sexuality Education (SSE). The survey for teens asked participants about their experiences of online sexual and gendered risk and harm during COVID-19, and the survey for parents/carers asked participants about their understanding of social media platforms (e.g. TikTok, WhatsApp, Instagram, Snapchat, etc.), and awareness of their children’s experiences of online sexual and gendered risk and harm online during COVID-19. The survey for teachers asked questions around their students’ experiences with a range of digital harassment and abuse (including technology facilitated gender-based violence), any training they received, and if their schools have policies dealing with these issues. Focus Groups and Interviews: Enacting a rigorous mixed methodology we simultaneously used a combination of focus groups and individual interviews with teens, school staff/safeguards, and parents/carers from May-July 2021 immediately following three major UK lockdowns. We conducted 17 focus groups with 65 teens and 29 individual follow-up interviews with this sample in five comprehensive secondary schools across England. The youth focus groups were arranged according to year group and self-identified gender and included two to six participants. Most groups were either all girls or all boys with one mixed gender group aligning to a pre-existing friendship group. Focus groups used arts-based methodologies and began with an ice-breaker activity where participants were asked to write down or draw something positive and negative about social media (including gaming platforms), using templates we provided. Template options included blank display screens of Instagram, Snapchat, TikTok, Yubo, WhatsApp, YouTube, Twitter, and PS5. After 5 to 10 minutes, participants took turns describing to the group what they wrote down. The researchers then used a focus group guide to ask questions, covering topics related to teens’ online experiences of risk and harm during COVID-19, as well as the gendered dynamics of these experiences. Following the focus groups, we provided teens with the opportunity to participate in follow-up individual interviews, where we elicited more detailed accounts of topics discussed in the focus groups. In addition, we conducted a total of 17 interviews with teachers, safeguarding leads and/or school staff in the five research schools. Interviews were designed to inform policy guidance for teachers and education associations on how to improve safety procedures and reporting practices for young people. We also conducted four online focus groups with parents/carers, with a total of nine parents/carers using a convenience sample. They were not parents of children from the schools in our study. Focus groups explored parents/carers’ knowledge and awareness of social media platforms, and the extent to which parents/carers felt equipped to support their children around sexually abusive or threatening online experiences they may have had on these popular platforms. After obtaining informed consent, discussions and interviews with students, teachers, and parents/carers were digitally recorded and transcribed verbatim. To ensure confidentiality, participants used pseudonyms, and transcripts were anonymized. The study's central aim is to take this data and develop a set of interactive digital resources that provide accessible and tailored advice and information for young people, teachers, and parents, on how to stay safe online during the pandemic and beyond.

  19. c

    Health Survey for England, 2021

    • datacatalogue.cessda.eu
    • beta.ukdataservice.ac.uk
    Updated Nov 29, 2024
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    NatCen Social Research; University College London, Department of Epidemiology and Public Health (2024). Health Survey for England, 2021 [Dataset]. http://doi.org/10.5255/UKDA-SN-9319-1
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    Dataset updated
    Nov 29, 2024
    Dataset provided by
    Health Behaviour Unit
    Authors
    NatCen Social Research; University College London, Department of Epidemiology and Public Health
    Time period covered
    Jan 1, 2021 - Mar 30, 2022
    Area covered
    England
    Variables measured
    National, Individuals
    Measurement technique
    Self-administered questionnaire: Paper, Face-to-face interview: Computer-assisted (CAPI/CAMI), Clinical measurements, Self-administered questionnaire: Computer-assisted (CASI)
    Description

    Abstract copyright UK Data Service and data collection copyright owner.

    The Health Survey for England (HSE) is a series of surveys designed to monitor trends in the nation's health. It was commissioned by NHS Digital and carried out by the Joint Health Surveys Unit of the National Centre for Social Research and the Department of Epidemiology and Public Health at University College London.
    The aims of the HSE series are:
    • to provide annual data about the nation’s health;
    • to estimate the proportion of people in England with specified health conditions;
    • to estimate the prevalence of certain risk factors associated with these conditions;
    • to examine differences between population subgroups in their likelihood of having specific conditions or risk factors;
    • to assess the frequency with which particular combinations of risk factors are found, and which groups these combinations most commonly occur;
    • to monitor progress towards selected health targets
    • since 1995, to measure the height of children at different ages, replacing the National Study of Health and Growth;
    • since 1995, monitor the prevalence of overweight and obesity in children.
    The survey includes a number of core questions every year but also focuses on different health issues at each wave. Topics are revisited at appropriate intervals in order to monitor change.

    Further information about the series may be found on the NHS Digital Health Survey for England; health, social care and lifestyles webpage, the NatCen Social Research NatCen Health Survey for England webpage and the University College London Health and Social Surveys Research Group UCL Health Survey for England webpage.

    Changes to the HSE from 2015:
    Users should note that from 2015 survey onwards, only the individual data file is available under standard End User Licence (EUL). The household data file is now only included in the Special Licence (SL) version, released from 2015 onwards. In addition, the SL individual file contains all the variables included in the HSE EUL dataset, plus others, including variables removed from the EUL version after the NHS Digital disclosure review. The SL HSE is subject to more restrictive access conditions than the EUL version (see Access information). Users are advised to obtain the EUL version to see if it meets their needs before considering an application for the SL version.

    COVID-19 and the HSE:
    Due to the COVID-19 pandemic, the HSE 2020 survey was stopped in March 2020 and never re-started. There was no publication that year. The survey resumed in 2021, albeit with an amended methodology. The full HSE resumed in 2022, with an extended fieldwork period. Due to this, the decision was taken not to progress with the 2023 survey, to maximise the 2022 survey response and enable more robust reporting of data. See the NHS Digital Health Survey for England - Health, social care and lifestyles webpage for more details.


    The 2021 HSE included additional topics on physical activity, wellbeing (including loneliness), and gambling. The survey also provided updates on repeated core topics, including general health, long-standing illness, smoking and drinking.


    Main Topics:

    Core topics
    • General health
    • Longstanding illness
    • Smoking
    • Average weekly alcohol consumption
    • Drinking (heaviest day in last week)
    • Consent to data linkage (NHS central register, HES)
    • Socio-economic information: sex, age, income, education, employment etc
    • Prescribed medications (nurse)
    Additional topics
    • Social care receipt and provision
    • Provision of unpaid care
    • Dental health
    • Use of GP and counselling services
    • Eating disorders

    Measurements

    • Height and weight
    • Blood pressure (nurse)
    • Waist and hip circumference (nurse)
    • Blood sample for cholesterol, glycated haemoglobin (nurse)
    • Saliva sample (nurse)

  20. c

    Flexible Contracts and Ethnic Economic Inequalities Across Gender During the...

    • datacatalogue.cessda.eu
    • b2find.dkrz.de
    Updated Mar 26, 2025
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    Ochmann, N (2025). Flexible Contracts and Ethnic Economic Inequalities Across Gender During the UK's COVID-19 Recession, Evidence for Equality National Survey Analysis Code, 2021 [Dataset]. http://doi.org/10.5255/UKDA-SN-857254
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    Dataset updated
    Mar 26, 2025
    Dataset provided by
    University of Manchester
    Authors
    Ochmann, N
    Time period covered
    Feb 16, 2021 - Oct 31, 2021
    Area covered
    United Kingdom
    Variables measured
    Individual
    Measurement technique
    EVENS used web-based interviews and computer-assisted (CATI) telephone interviews. EVENS aimed to better represent ethnic minorities compared to existing data sets regarding the range of represented minority population groups. To cite from the online Abstract of EVENS: "....EVENS survey used an 'open' survey approach, which requires participants to opt-in to the survey instead of probability-based approaches that invite individuals to participate following their identification within a pre-defined sampling frame. This 'open' approach sought to overcome some of the limitations of probability-based methods in order to reach a large number and diverse mix of people from religious and ethnic minorities." (UK Data Service: SN-9116)
    Description

    Approximately one quarter of the UK population have a migration background (first- or second-generation immigrants). Some ethnic minority groups are more likely to be in atypical or flexible employment than the White British majority. In particular during a time of health and economic crisis, such as the COVID–19 pandemic, those ethnic groups were expected to be economically more vulnerable than other groups. This study shows the increased vulnerability of some ethnic minority groups during COVID–19 by looking at their labour market outcomes compared to White British. Specifically, we ask whether it was their disproportionate presence in flexible employment or in shut-down occupations that made some ethnic minority groups vulnerable to adverse labour market outcomes during the COVID–19 recession? Using the COVID–19 recession in the UK as a case study, we employ weighted linear probability models with 2021 data from the Evidence for Equality National Survey (EVENS) to look at changes in economic indicators across ethnic groups and gender. We report heterogeneity in flexible employment rates within the non-White group and between the non-White and the White British group. By using a conditional decomposition method, we aim to show that those ethnic minority groups who were disproportionately on flexible contracts experienced worse economic effects than the White British group. The collection consists of the Stata Do-File which can be used to reproduce the study.

    Was it their disproportionate presence in flexible employment or in shut-down occupations that made some ethnic minority groups vulnerable to adverse labour market outcomes during the COVID–19 recession? Using the COVID–19 recession in the UK as a case study, we employ weighted linear probability models with 2021 data from the Evidence for Equality National Survey (EVENS) to look at changes in economic indicators across ethnic groups and gender. We report heterogeneity in flexible employment rates within the non-White group and between the non-White and the White British group. By using a conditional decomposition method, we conclude that those ethnic minority groups who were disproportionately on flexible contracts experienced worse economic effects than the White British group.

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Office for National Statistics (2022). Coronavirus and clinically extremely vulnerable (CEV) people in England [Dataset]. https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/datasets/coronavirusandclinicallyextremelyvulnerablepeopleinengland
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Coronavirus and clinically extremely vulnerable (CEV) people in England

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13 scholarly articles cite this dataset (View in Google Scholar)
xlsxAvailable download formats
Dataset updated
May 13, 2022
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

Analysis of people previously considered to be clinically extremely vulnerable (CEV) in England during the coronavirus (COVID-19) pandemic, including their behaviours and mental and physical well-being.

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