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.

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.

Notes:

This publication of the SHMI relates to discharges in the reporting period February 2022 - January 2023. The SHMI is the ratio between the actual number of patients who die following hospitalisation at the trust and the number that would be expected to die on the basis of average England figures, given the characteristics of the patients treated there. The SHMI covers patients admitted to hospitals in England who died either while in hospital or within 30 days of being discharged. Deaths related to COVID-19 are excluded from the SHMI. To help users of the data understand the SHMI, trusts have been categorised into bandings indicating whether a trust's SHMI is 'higher than expected', 'as expected' or 'lower than expected'. For any given number of expected deaths, a range of observed deaths is considered to be 'as expected'. If the observed number of deaths falls outside of this range, the trust in question is considered to have a higher or lower SHMI than expected. The expected number of deaths is a statistical construct and is not a count of patients. The difference between the number of observed deaths and the number of expected deaths cannot be interpreted as the number of avoidable deaths or excess deaths for the trust. The SHMI is not a measure of quality of care. A higher than expected number of deaths should not immediately be interpreted as indicating poor performance and instead should be viewed as a 'smoke alarm' which requires further investigation. Similarly, an 'as expected' or 'lower than expected' SHMI should not immediately be interpreted as indicating satisfactory or good performance. Trusts may be located at multiple sites and may be responsible for 1 or more hospitals. A breakdown of the data by site of treatment is also provided, as well as a breakdown of the data by diagnosis group. Further background information and supporting documents, including information on how to interpret the SHMI, are available on the SHMI homepage (see Related Links). Information about the exclusion of COVID-19 from the SHMI can also be found on the same page. A link to the methodological changes statement which details the exclusion is also available in the Related Links section

The “Medical Officer of Health Reports”, provide valuable, historic insights into the state of public health in Glasgow. We utilize the reports compiled by the Medical Officer of Health (MOH) to gather information on mortality from different diseases. We catalogue annual mortality rates for different diseases for the city covering the years 1898 to 1972.

This research aims to assess the medium-run implications of COVID-19 on income and health inequality, and possible policies that aim to mitigate these effects. The medium run is important because the impacts of COVID-19 on inequality are expected to persist for many years. Understanding how inequality changes over the medium run, and assessing mitigation policies beyond the short term, requires information on the evolution of income and health inequalities several years after an outbreak. To achieve this, we will combine models typically applied to modern datasets with quantitative data from historical periods that, unlike contemporary data, cover extended post-outbreak periods. We will use records from Glasgow since the end of the 19th century, covering a period of intense and volatile economic activity, as well as multiple disease outbreaks. We choose Glasgow because it is a large city demonstrating similar inequalities to those seen today, and because administrative records for Glasgow provide detailed relevant information.

Our approach is the following. We will use a modelling framework that has been shown to be effective in capturing income inequality and the effects of recessions on this inequality. We will extend the modelling approach to also include health inequalities and ensure that both income and health inequalities are represented accurately using recent datasets. To set up the model so that it captures the effects of outbreaks on inequalities, we will use historical data from earlier times that include large disease outbreaks. The model will then allow us to examine the effects of different policy interventions for households with different socioeconomic characteristics.