Provisional data on death registrations and death occurrences in England and Wales, broken down by sex and age. Includes deaths due to coronavirus (COVID-19) and leading causes of death.

Age-standardised mortality rates (ASMRs) for deaths involving COVID-19 by ethnic group, England.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

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.

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.

Novel coronavirus (COVID-19) is a new strain of coronavirus first identified in Wuhan, China. Clinical presentation may range from mild-to-moderate illness to pneumonia or severe acute respiratory infection. The COVID-19 pandemic has wider impacts on individuals' health, and their use of healthcare services, than those that occur as the direct result of infection. Reasons for this may include: * Individuals being reluctant to use health services because they do not want to burden the NHS or are anxious about the risk of infection. * The health service delaying preventative and non-urgent care such as some screening services and planned surgery. * Other indirect effects of interventions to control COVID-19, such as mental or physical consequences of distancing measures. This dataset provides information on trend data regarding the wider impact of the pandemic on the number of deaths in Scotland, derived from the National Records of Scotland (NRS) weekly deaths registration data. Data show recent trends in deaths (2020), whether COVID or non-COVID related, and historic trends for comparison (five-year average, 2015-2019). The recent trend data are shown by age group and sex, and the national data are also shown by broad area deprivation category (Scottish Index of Multiple Deprivation, SIMD). This data is also available on the COVID-19 Wider Impact Dashboard. Additional data sources relating to this topic area are provided in the Links section of the Metadata below. Information on COVID-19, including stay at home advice for people who are self-isolating and their households, can be found on NHS Inform. All publications and supporting material to this topic area can be found in the weekly COVID-19 Statistical Report. The date of the next release can be found on our list of forthcoming publications.

Due to changes in the collection and availability of data on COVID-19, this dataset is no longer updated. Latest information about COVID-19 is available via the UKHSA data dashboard. The UK government publish daily data, updated weekly, on COVID-19 cases, vaccinations, hospital admissions and deaths. This note provides a summary of the key data for London from this release. Data are published through the UK Coronavirus Dashboard, last updated on 23 March 2023. This update contains: Data on the number of cases identified daily through Pillar 1 and Pillar 2 testing at the national, regional and local authority level Data on the number of people who have been vaccinated against COVID-19 Data on the number of COVID-19 patients in Hospital Data on the number of people who have died within 28 days of a COVID-19 diagnosis Data for London and London boroughs and data disaggregated by age group Data on weekly deaths related to COVID-19, published by the Office for National Statistics and NHS, is also available. Key Points On 23 March 2023 the daily number of people tested positive for COVID-19 in London was reported as 2,775 On 23 March 2023 it was newly reported that 94 people in London died within 28 days of a positive COVID-19 test The total number of COVID-19 cases identified in London to date is 3,146,752 comprising 15.2 percent of the England total of 20,714,868 cases In the most recent week of complete data (12 March 2023 - 18 March 2023) 2,951 new cases were identified in London, a rate of 33 cases per 100,000 population. This compares with 2,883 cases and a rate of 32 for the previous week In England as a whole, 29,426 new cases were identified in the most recent week of data, a rate of 52 cases per 100,000 population. This compares with 26,368 cases and a rate of 47 for the previous week Up to and including 22 March 2023 6,452,895 people in London had received the first dose of a COVID-19 vaccine and 6,068,578 had received two doses Up to and including 22 March 2023 4,435,586 people in London had received either a third vaccine dose or a booster dose On 22 March 2023 there were 1,370 COVID-19 patients in London hospitals. This compares with 1,426 patients on 15 March 2023. On 22 March 2023 there were 70 COVID-19 patients in mechanical ventilation beds in London hospitals. This compares with 72 patients on 15 March 2023. Update: From 1st July updates are weekly From Friday 1 July 2022, this page will be updated weekly rather than daily. This change results from a change to the UK government COVID-19 Dashboard which will move to weekly reporting. Weekly updates will be published every Thursday. Daily data up to the most recent available will continue to be added in each weekly update. Data summary 리소스 CSV phe_vaccines_age_london_boroughs.csv CSV 다운로드 phe_vaccines_age_london_boroughs.csv CSV phe_healthcare_admissions_age.csv CSV 다운로드

Provisional data on excess mortality (excluding COVID-19) during heat-periods in the 65 years and over age group estimates in England, including the estimated number of deaths where the death occurred within 28 days of a positive COVID-19 result and the mean central England temperature.

The dataset consists of quantitative data derived mainly from international datasets (ILO, WHO), supplemented by data from national datasets and modelled data to complete missing values. It shows the statistical data we collated and used to calculate estimates of Covid-19 deaths among migrant health care workers and includes details on how missing information was imputed. It includes spreadsheet estimates for India, Nigeria, Mexico, and the UK for excess and reported Covid-19 deaths amongst foreign-born workers and for all workers in the human health and social work sector and in three specific health occupations: doctors, nurses, and midwives. For each group the spreadsheets provide a basic estimate and an age-sex standardised estimate.

This project aims to give proper attention to the place of migrant workers in health care systems during the Covid-19 pandemic. Migrant workers are of substantial and growing significance in many countries' health and care systems and are key to realising the global goal of universal health care, so it is vital that we understand much better how Covid-19 is impacting on them.

The project's overarching research questions are, in the relation to Covid-19, what risks do migrant health care workers experience, what are the pressures on resilient and sustainable health care workforces, and how are stakeholders responding to these risks and pressures?

We develop a research method to estimate Covid-19 migrant health care worker mortality rates and trial the method, undertaking statistical analysis and modelling using quantitative data drawn from WHO and OECD data and other demographic and bio-statistical data as available.

In addition to strengthening the methodological techniques and empirical evidence base on the risks of Covid-19 infection and death among migrant health care workers our project also tracks, through documentary analysis, collective responses to such risks and challenges to sustainable health workforces for universal health coverage.

This project is attuned to the urgent need for high quality data and for 'real world' solutions-focused Covid-19 research forged from collaboration. We are focused on the immediate application of proof-of concept findings to a rapidly-evolving global health crisis.

This open data publication has moved to COVID-19 Statistical Data in Scotland (from 02/11/2022) Novel coronavirus (COVID-19) is a new strain of coronavirus first identified in Wuhan, China. Clinical presentation may range from mild-to-moderate illness to pneumonia or severe acute respiratory infection. This dataset provides information on demographic characteristics (age, sex, deprivation) of confirmed novel coronavirus (COVID-19) cases, as well as trend data regarding the wider impact of the virus on the healthcare system. Data includes information on primary care out of hours consultations, respiratory calls made to NHS24, contact with COVID-19 Hubs and Assessment Centres, incidents received by Scottish Ambulance Services (SAS), as well as COVID-19 related hospital admissions and admissions to ICU (Intensive Care Unit). Further data on the wider impact of the COVID-19 response, focusing on hospital admissions, unscheduled care and volume of calls to NHS24, is available on the COVID-19 Wider Impact Dashboard. There is a large amount of data being regularly published regarding COVID-19 (for example, Coronavirus in Scotland - Scottish Government and Deaths involving coronavirus in Scotland - National Records of Scotland. Additional data sources relating to this topic area are provided in the Links section of the Metadata below. Information on COVID-19, including stay at home advice for people who are self-isolating and their households, can be found on NHS Inform. All publications and supporting material to this topic area can be found in the weekly COVID-19 Statistical Report. The date of the next release can be found on our list of forthcoming publications. Data visualisation is available to view in the interactive dashboard accompanying the COVID-19 Statistical Report. Please note information on COVID-19 in children and young people of educational age, education staff and educational settings is presented in a new COVID-19 Education Surveillance dataset going forward.

Pre-existing conditions of people who died due to COVID-19, broken down by country, broad age group, and place of death occurrence, usual residents of England and Wales.

The COVID-19 Health Inequalities Monitoring in England (CHIME) tool brings together data relating to the direct impacts of coronavirus (COVID-19) on factors such as mortality rates, hospital admissions, confirmed cases and vaccinations.

By presenting inequality breakdowns - including by age, sex, ethnic group, level of deprivation and region - the tool provides a single point of access to:

• show how inequalities have changed during the course of the pandemic and what the current cumulative picture is
• bring together data in one tool to enable users to access and use the intelligence more easily
• provide indicators with a consistent methodology across different data sets to facilitate understanding
• support users to identify and address inequalities within their areas, and identify priority areas for recovery

In the March 2023 update, data has been updated for deaths, hospital admissions and vaccinations. Data on inequalities in vaccination uptake within upper tier local authorities has been added to the tool for the first time. This replaces data for lower tier local authorities, published in December 2022, allowing the reporting of a wider range of inequality breakdowns within these areas.

Please send any questions or comments to PHA-OHID@dhsc.gov.uk

As of May 2, 2023, there were roughly 687 million global cases of COVID-19. Around 660 million people had recovered from the disease, while there had been almost 6.87 million deaths. The United States, India, and Brazil have been among the countries hardest hit by the pandemic.

The various types of human coronavirus The SARS-CoV-2 virus is the seventh known coronavirus to infect humans. Its emergence makes it the third in recent years to cause widespread infectious disease following the viruses responsible for SARS and MERS. A continual problem is that viruses naturally mutate as they attempt to survive. Notable new variants of SARS-CoV-2 were first identified in the UK, South Africa, and Brazil. Variants are of particular interest because they are associated with increased transmission.

Vaccination campaigns Common human coronaviruses typically cause mild symptoms such as a cough or a cold, but the novel coronavirus SARS-CoV-2 has led to more severe respiratory illnesses and deaths worldwide. Several COVID-19 vaccines have now been approved and are being used around the world.

Provisional counts of the number of deaths and age-standardised mortality rates involving the coronavirus (COVID-19), by occupational groups, for deaths registered between 9 March and 28 December 2020 in England and Wales. Figures are provided for males and females.

OMOP dataset: Hospital COVID patients: severity, acuity, therapies, outcomes Dataset number 2.0

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 OMOP 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. This is a subset of data in OMOP format.

Scope: All COVID swab confirmed hospitalised patients to UHB from January – August 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. 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.

Available supplementary data: Health data preceding & following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data. Further OMOP data available as an additional service.

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.

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.

BackgroundThe COVID-19 pandemic has been a devastating and enduring mass-bereavement event, with uniquely difficult sets of circumstances experienced by people bereaved at this time. However, little is known about the long-term consequences of these experiences, including the prevalence of Prolonged Grief Disorder (PGD) and other conditions in pandemic-bereaved populations.MethodsA longitudinal survey of people bereaved in the UK between 16 March 2020 and 2 January 2021, with data collected at baseline (n = 711), c. 8 (n = 383), 13 (n = 295), and 25 (n = 185) months post-bereavement. Using measures of Prolonged Grief Disorder (PGD) (Traumatic Grief Inventory), grief vulnerability (Adult Attitude to Grief Scale), and social support (Inventory of Social Support), this analysis examines how participant characteristics, characteristics of the deceased and pandemic-related circumstances (e.g., restricted visiting, social isolation, social support) are associated with grief outcomes, with a focus on symptoms of PGD.ResultsAt baseline, 628 (88.6%) of participants were female, with a mean age of 49.5 (SD 12.9). 311 (43.8%) deaths were from confirmed/suspected COVID-19. Sample demographics were relatively stable across time points. 34.6% of participants met the cut-off for indicated PGD at c. 13 months bereaved and 28.6% at final follow-up. Social isolation and loneliness in early bereavement and lack of social support over time strongly contributed to higher levels of prolonged grief symptoms, while feeling well supported by healthcare professionals following the death was associated with reduced levels of prolonged grief symptoms. Characteristics of the deceased most strongly associated with lower levels of prolonged grief symptoms, were a more distant relationship (e.g., death of a grandparent), an expected death and death occurring in a care-home. Participant characteristics associated with higher levels of prolonged grief symptoms included low level of formal education and existence of medical conditions.ConclusionResults suggest higher than expected levels of PGD compared with pre-pandemic times, with important implications for bereavement policy, provision and practice now (e.g., strengthening of social and specialist support) and in preparedness for future pandemics and mass-bereavement events (e.g., guidance on infection control measures and rapid support responses).

Provisional age-standardised mortality rates for deaths due to COVID-19 by sex, English regions and Welsh health boards.

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.

For the fifth edition (August 2017), a new version of the individual data file was deposited. A Government Office Region variable has been added, and some previous health authority and socio-economic variables removed.


Main Topics:

The survey had two separate elements: an interviewer visit and a nurse visit. At the first visit all respondents aged 13 and over were asked to give a CAPI (computed assisted) interview on a range of health related topics. Parents/Guardians of 2-12 year olds were interviewed about the child. The interview collected information relating to respondents' history of respiratory and atopic conditions, non-fatal accidents and general health. Adults were questioned about smoking and drinking behaviour. All respondents aged 8 and over were then asked to complete a booklet. For adults and young adults (from the age of 16) these self-completion documents contained further modules on general health, specifically the SF-36 and EuroQol questionnaires. 8-17 year olds completed questions on smoking and drinking experiences. At the end of the interview, all respondents were asked to have their height and weight measured. A limited amount of proxy information was obtained, where possible, about those unwilling or unable to take part in the survey.

Those who agreed to the second visit, made later by a nurse, were then surveyed about their use of prescribed medications. Then, if the respondent was willing, further anthropometric measurements (i.e. demi-span, mid-upper arm circumference) were taken, their blood pressure was measured and they provided a blood sample (which was analysed for IgE, house dust mite IgE, cotinine for adults and for children, ferritin, and haemoglobin). Children aged 4-15 were asked to give a saliva sample for the analysis of cotinine.

Data on age at death, date of death and causes of death (ICD...

Occupational registration data was linked to anonymised electronic health records maintained by the Secure Anonymised Information Linkage (SAIL) Databank in a privacy-protecting trusted research environment. We examined records for all linked care workers from 1st March 2016 to 30th November 2021.

Domiciliary Care Workers (DCWs) are employed in both public and private sectors to support adults at home. The support they provide varies but often includes personal care, which demands close contact between care worker and the person being supported. Since the start of the COVID-19 pandemic, people working across the care sectors in England and Wales have experienced higher rates of death involving COVID-19 infection. Social care workers, in both residential and domiciliary care settings, have been particularly badly affected, with rates of death involving COVID-19 approximately double that for health care workers.

We do not fully understand the full impact on domiciliary care worker mortality, how COVID-19 has affected worker health more broadly, and the risk factors which contribute to these. Existing evidence on deaths from the ONS relies on occupational classification. However, for many individuals reported as dying with some COVID-19 involvement, information on occupation is missing (18% and 40% missing for males and females respectively). The impact of COVID-19 on the health of domiciliary care workers (DCWs) is therefore likely to be considerable, including on COVID-19 infection itself, mental health, and respiratory illnesses. We aim to generate rapid high-quality evidence based on the views of care workers and by linking care workers' registration data to routine health data. We can use this information to inform public health interventions for safer working practice and additional support for care workers.

Our study will use a combination of research methods. We will use existing administrative data involving carer professional registration records as well as health care records. Our analysis of these data will be guided in part by qualitative interviews that we will conduct with domiciliary care workers in Wales. The interviews will address the experiences of care workers during the course of the pandemic.

Registration data for care workers in Wales will be securely transferred from the regulatory body, Social Care Wales (SCW) to the Secured Anonymised Information Linkage (SAIL) Databank at Swansea University. These data will be combined with anonymised health records made available from the SAIL databank. Information which could be used to identify individual care workers will be removed in this process. We expect that this will create a research database of all domiciliary care workers in Wales, approximately 17,000 individuals. From this group we will also identify about 30 care workers to be approached via SCW to take part in a qualitative interview. The interview sample will be chosen so that it includes workers from a variety of backgrounds.

In our analysis, we will describe the socio-demographic characteristics of the group of care workers in the research database, for example, their average age. We will establish the number of care workers with both suspected and confirmed COVID-19 infection. Will explore how infection with COVID-19 has impacted on key health outcomes, including whether workers were admitted to hospital or died. We will also explore the health of care workers before and during COVID-19 pandemic. We will use the information gained from interviews with care workers to guide the way we analyse the health records of the care workers. Finally, we will examine how well the results from our analysis of care workers in Wales can be used inform what may be happening for workers in other countries in the UK.

To ensure that our findings will be of most use to those working in social care, we will work with an Implementation Reference Group. The group will include key stakeholders such as representatives from regulators from across the UK. Working with this group, we will provide rapid recommendations to drive public health initiatives for care worker safety. This may include changes in working practices and longer-term service planning to support care worker health needs.