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.

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.

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.

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.

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.

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.

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

Final model for risk of severe COVID-19.

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.

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.

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.

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.

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.

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.

Characteristics of cases and controls and unadjusted odds ratios for risk of severe COVID-19.

The 2019 novel coronavirus (2019-nCoV), otherwise known as COVID-19, is an infectious disease first identified in the city of Wuhan, capital of Hubei province in China. Infections have since been reported worldwide resulting in an unprecedented international response that amongst other containment measures, lead to the widespread suspension of many sporting fixtures worldwide. During a representative survey of the British adult population, undertaken between the 3rd and 4th June 2020, respondents were asked their opinion on the level of risk expected to be accepted by professional sports men and women should be expected to accept so that sporting competitions can resume.
The responses to this survey have subsequently been categorized into into two groups characterized by the NRS social grades of the respondents. The NRS social grades are a system of demographic classification used in the United Kingdom. The grades are grouped here into ABC1 and C2DE; these are taken to equate to middle class and working class, respectively.

Although an equal share of respondents within the C2DE social grade group held the opinion that professional athletes should accept either no risk or a small amount of risk, of respondents within the ABC1 social grade group, 38 percent held the opinion that athletes should accept a small amount of risk whereas only 28 percent held the opinion that athletes should accept 'no risk'.

/statistics/779014/working-class-social-class-opinions-great-britain-uk/ /statistics/1088542/educational-backgrounds-of-british-professional-athletes-by-sport-and-gender/

Populations in the UK by risk of testing positive for COVID-19 from the Coronavirus (COVID-19) Infection Survey.

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.

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

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.