The number of deaths registered in England and Wales due to and involving coronavirus (COVID-19). Breakdowns include age, sex, region, local authority, Middle-layer Super Output Area (MSOA), indices of deprivation and place of death. Includes age-specific and age-standardised mortality rates.

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.

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

Provisional counts of the number of deaths registered in England and Wales, including deaths involving coronavirus (COVID-19), by local authority, health board and place of death in the latest weeks for which data are available. The occurrence tabs in the 2021 edition of this dataset were updated for the last time on 25 October 2022.

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.

Provisional counts of the number of deaths registered in England and Wales, by age, sex, region and Index of Multiple Deprivation (IMD), in the latest weeks for which data are available.

PIONEER: The impact of ethnicity and multi-morbidity on COVID-related outcomes; a primary care supplemented hospitalised dataset Dataset number 3.0

Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 65million cases and more than 1.5 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS) and death. Evidence suggests that older patients, those from some ethnic minority groups and those with multiple long-term health conditions have worse outcomes. This secondary care COVID dataset contains granular demographic and morbidity data, supplemented from primary care records, to add to the understanding of patient factors on disease outcomes.

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 and 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 and 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 and 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.

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.

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

This indicator is designed to accompany the SHMI publication. The SHMI includes all deaths reported of patients who were admitted to non-specialist acute trusts in England and either died while in hospital or within 30 days of discharge. Deaths related to COVID-19 are excluded from the SHMI. A contextual indicator on the percentage of deaths reported in the SHMI which occurred in hospital and the percentage which occurred outside of hospital is produced to support the interpretation of the SHMI. Notes: 1. For discharges in the reporting period April 2024 - July 2024, almost all of the records for Wirral University Teaching Hospital NHS Foundation Trust (trust code RBL) have been submitted without an NHS number. This will have affected the linkage of the HES data to the ONS death registrations data and may have resulted in a smaller number of deaths occurring outside hospital within 30 days of discharge being identified for this trust than would have otherwise been the case. The results for this trust should therefore be interpreted with caution. This issue was only discovered after publication and this note was added on 20/12/2024. 2. There is a shortfall in the number of records for North Middlesex University Hospital NHS Trust (trust code RAP), Northumbria Healthcare NHS Foundation Trust (trust code RTF), and The Shrewsbury and Telford Hospital NHS Trust (trust code RXW). Values for these trusts are based on incomplete data and should therefore be interpreted with caution. 3. A number of trusts are now submitting Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS) rather than the Admitted Patient Care (APC) dataset. The SHMI is calculated using APC data. Removal of SDEC activity from the APC data may impact a trust’s SHMI value and may increase it. More information about this is available in the Background Quality Report. 4. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of this page.

Background. Chronic obstructive pulmonary disease (COPD) is a debilitating lung condition characterised by progressive lung function limitation. COPD is an umbrella term and encompasses a spectrum of pathophysiologies including chronic bronchitis, small airways disease and emphysema. COPD caused an estimated 3 million deaths worldwide in 2016, and is estimated to be the third leading cause of death worldwide. The British Lung Foundation (BLF) estimates that the disease costs the NHS around £1.9 billion per year. COPD is therefore a significant public health challenge. This dataset explores the impact of hospitalisation in patients with COPD during the COVID pandemic.

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. There are particularly high rates of physical inactivity, obesity, smoking & diabetes. The West Midlands has a high prevalence of COPD, reflecting the high rates of smoking and industrial exposure. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS.

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”.

Scope: All hospitalised patients admitted to UHB during the COVID-19 pandemic first wave, curated to focus on COPD. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes ICD-10 & SNOMED-CT codes pertaining to COPD and COPD exacerbations, as well as all co-morbid conditions. Serial, structured data pertaining to process of care (timings, staff grades, specialty review, wards), presenting complaint, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, nebulisers, antibiotics, inotropes, vasopressors, organ support), all outcomes. Linked images available (radiographs, CT).

Available supplementary data: More extensive data including wave 2 patients in non-OMOP form. 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.

Data on excess death during the coronavirus pandemic in young people.

These indicators are designed to accompany the SHMI publication. The SHMI methodology does not make any adjustment for deprivation. This is because adjusting for deprivation might create the impression that a higher death rate for those who are more deprived is acceptable. Patient records are assigned to 1 of 5 deprivation groups (called quintiles) using the Index of Multiple Deprivation (IMD). The deprivation quintile cannot be calculated for some records e.g. because the patient's postcode is unknown or they are not resident in England. Contextual indicators on the percentage of provider spells and deaths reported in the SHMI belonging to each deprivation quintile are produced to support the interpretation of the SHMI. Notes: 1. As of the July 2020 publication, COVID-19 activity has been excluded from the SHMI. The SHMI is not designed for this type of pandemic activity and the statistical modelling used to calculate the SHMI may not be as robust if such activity were included. Activity that is being coded as COVID-19, and therefore excluded, is monitored in the contextual indicator 'Percentage of provider spells with COVID-19 coding' which is part of this publication. 2. Please note that there was a fall in the overall number of spells from March 2020 due to COVID-19 impacting on activity for England and the number has not returned to pre-pandemic levels. Further information at Trust level is available in the contextual indicator ‘Provider spells compared to the pre-pandemic period’ which is part of this publication. 3. There is a shortfall in the number of records for County Durham and Darlington NHS Foundation Trust (trust code RXP), East Lancashire Hospitals NHS Trust (trust code RXR), Guy’s and St Thomas’ NHS Foundation Trust (trust code RJ1), King’s College Hospital NHS Foundation Trust (trust code RJZ) and The Princess Alexandra Hospital NHS Trust (trust code RQW). Values for these trusts are based on incomplete data and should therefore be interpreted with caution. 4. Frimley Health NHS Foundation Trust (trust code RDU) stopped submitting data to the Secondary Uses Service (SUS) during June 2022 and did not start submitting data again until April 2023 due to an issue with their patient records system. This is causing a large shortfall in records and values for this trust should be viewed in the context of this issue. 5. A number of trusts are now submitting Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS) rather than the Admitted Patient Care (APC) dataset. The SHMI is calculated using APC data. Removal of SDEC activity from the APC data may impact a trust’s SHMI value and may increase it. More information about this is available in the Background Quality Report. 6. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of this page.

Provisional counts of the number of deaths involving the coronavirus (COVID-19) within the care sector registered from 14 March 2020 to 21 January 2022.

The Care in Funerals project drew upon 67 semi-structured qualitative interviews with 68 individuals who had been bereaved, and/or worked or volunteered in deathcare and funeral provision in the UK during the COVID-19 pandemic. Interviews explored their experiences during the pandemic, evaluations of what was good and what was less good, how they responded, and suggestions of what might be improved going forward. They also examined what interviewees understood by the term 'care' in relation to funerals. All participants gave informed consent to participate. Interviews had a mean length of one hour, and were conducted using video calling software or, in some cases, telephone, between April 2021 and April 2022. This dataset consists of 63 transcripts (two interviewees were interviewed together in one case) all of which have had identifying details removed such that the participants cannot be identified. Four transcripts have been withheld as permission was not granted by participants for their inclusion in a data repository.Funeral provision in the UK was significantly disrupted when COVID-19 infection control policies constrained how and by whom bodies could be attended to and moved to burial/cremation sites; how funeral directors and celebrants could communicate with bereaved families; and possibilities for gathering for funerals, mourning and memorialising activities. The regulations generated significant distress and perceptions of injustice. They also prompted the development of new funeral practices - inviting important questions about funeral provision. Our interdisciplinary research starts from a recognition of funeral provision as a form of care (and set of caring practices) oriented towards people who have died and their bereaved family, friends and communities. It addresses neglected ethical aspects of funeral provision, including, in the context of COVID-19, questions of fairness and the moral dimensions of distress evident in family members' and funeral directors' worries about not fulfilling important responsibilities, or doing wrong, to those who have died or been bereaved. Our ethical analyses will be grounded in an ethnographic examination of changed practices and experiences that includes: (1) analysis of funeral artefacts, including online films, tribute pages, and written accounts; (2) interviews with diverse bereaved family members, funeral directors and celebrants. We will attend carefully to what people consider good and right (or not) and why in different circumstances. We will develop practical ethical analyses of post-death care that address tensions between different purposes of funerals and diverse perspectives on post-death responsibilities. Discussion events with key stakeholders will inform the development of resources for future policy and practice. Those who expressed interest were sent participant information and a consent form and offered an opportunity to discuss the study before deciding whether to take part. Interviews took place online or by telephone. We received informed consent verbally (recorded) or in writing (by email). Four researchers conducted the interviews, using shared topic guides. After broad opening questions, they followed participants’ conversational leads while covering key topics, including their experiences of funerals during the pandemic, what they felt was challenging about these funerals, and what made a funeral ‘good’. Interviewers wrote fieldnotes summarising the interview, noting key impressions and capturing any information provided ‘off tape.’ Interviews were transcribed verbatim by an external company, then checked for accuracy and anonymised by members of the research team.

Life expectancy at birth for males and females for Middle Layer Super Output Areas (MSOAs), Leicester: 2016 to 2020The average number of years a person would expect to live based on contemporary mortality rates.For a particular area and time period, it is an estimate of the average number of years a newborn baby would survive if he or she experienced the age-specific mortality rates for that area and time period throughout his or her life.Life expectancy figures have been calculated based on death registrations between 2016 to 2020, which includes the first wave and part of the second wave of the coronavirus (COVID-19) pandemic.

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

Provisional counts of the number of care home resident deaths registered in England and Wales, by region, including deaths involving coronavirus (COVID-19), in the latest weeks for which data are available.

Model estimates of deaths involving the coronavirus (COVID-19) by ethnic group for people in private households in England.