Barts Health NHS Trust

CQC Registered Health or/and Social Care provider

No

DARS-NIC-400985-V3D1C-v0.17

Barts Health NHS Trust are requesting Hospital Episode Statistics (HES) data, Emergency Care Data Set (ECDS), Civil Registration Deaths data, COVID-19 Hospitalisation in England Surveillance System (CHESS) data, and COVID-19 Second Generation Surveillance System (SGSS) data. The data is requested to quantify the risk of mortality associated with SARS-CoV-2 infection among tens of thousands of NHS patients that have already had surgery, and the effect of geographical location, ethnicity, and socioeconomic deprivation. The data will also quantify the excess population mortality attributable to COVID-19 among patients with disease able to be treated by surgery, including both direct surgical deaths and indirect deaths, such as those due to cancelled procedures or delayed presentation/diagnosis due to COVID-19. Emerging data suggests that surgical patients with perioperative SARS-CoV-2 infection, identified either before or after surgery, are at very high risk of pulmonary complications (50%) and death (24%). This is more than 20 times the usual 1% risk of postoperative mortality. The largest study of surgical patients with COVID-19 comprised 1128 patients from 235 hospitals in 24 countries. However, this represents only 484 patients from the UK, so the findings may not be generalisable to NHS patients. In addition, these data were collected at the height of the pandemic and only report outcomes of surgical patients with COVID-19, so they lack reliable (COVID-19 negative) comparator. To protect patients, strict infection control procedures have been, adopted in NHS hospitals, which has severely disrupted surgical throughput. This may cause unintended harm by delaying urgent surgery, including cancer treatment. The requested data will be used to report the true risk of surgery with COVID-19 and prevent avoidable harm by providing data for policymakers and health leaders to plan the NHS strategy for a dynamic recovery of surgical services. It will also allow policymakers to balance the excess mortality associated with acquiring COVID-19 during surgical admissions, against the excess mortality due to delays in the provision of surgical treatment. Cohort: Each patient will enter the cohort on their first date of OP/A&E/APC meeting criteria. All subsequent OP/A&E/ECDS/APC data for each of those patients are needed to allow longitudinal follow-up. Linkage to civil registration death data is required for date of death estimation. The study has two core aims: 1. To describe the incidence and outcomes of COVID-19 amongst patients undergoing surgery. 2. To estimate excess mortality amongst patients living with diseases able to be treated by surgery. NOTE: The study team comprises of substantial employees from Barts Health NHS Trust and Queen Mary University of London. For aim one, the study team will divide the cohort into two groups: • COVID-surgery cohort (those undergoing surgery between 1st January 2020 to 31st August 2020). • A historical comparator cohort (those undergoing surgery between 1st April 2015 and 31st December 2020). This will allow the study team to: • Quantify the 30-day and 90-day postoperative mortality associated with COVID-19. • Investigate the influence of ethnicity, socioeconomic deprivation, sex, and age on 30-day postoperative mortality. • Map regional variation in 30-day and 90-day postoperative mortality. For aim two, the study team will divide the cohort into two groups: • COVID-outpatients cohort (those attending surgical outpatient clinics or attending A&E with a surgical condition between 1st June 2019 and 31st December 2020). • A historical comparator cohort of patients attending surgical outpatient clinics or attending A&E with a surgical condition between 1st April 2015 and 30th June 2019. This will allow the study team to: • Estimate the deficit in procedures performed amongst those presenting to surgical outpatient clinics and A&E, stratified by primary diagnosis, speciality, and age, compared to the historical comparator cohort. • Determine the rate of death amongst those presenting to surgical outpatients and A&E, accounting for procedures performed. Only pseudonymised patient-level data will be used. The following datasets are required for the aims of the project: • HES Outpatients (OP) OP data for all patients attending a surgical clinic between 1st June 2019 and 31st December 2020 and 1st April 2015 and 30th June 2019 is being requested. OP data will help capture ‘untreated disease able to be treated by surgery’ and measure the number of additional deaths indirectly caused through delaying surgery. To minimise the data requested, the study team have identified the surgical outpatient clinics that data is needed from. • HES Admitted Patient Care (APC) APC data for all patients undergoing surgery between 1st January 2020 and 31st August 2020 and 1st April 2015 and 31st December 2020 is being requested to help understand the risk of death among patients associated with COVID-19. • Emergency Care Data Set (ECDS) / Accident and Emergency (A&E) ECDS data for all patients attending A&E between 1st May 2020 and 31st December 2020 is being requested. A&E data for all patients attending A&E between 1st June 2019 and 31st December 2020 and 1st April 2015 and 30th June 2019 is being requested. This will help to further capture ‘untreated disease able to be treated by surgery’, as there may be patients who present to A&E with a disease that they would normally have surgery for, but are instead sent home due to the given hospital situation. ECDS is being requested along with A&E data as ECDS replaced A&E data in May 2020 as the primary reporting structure for emergency care data. • Civil Registration Deaths The latest available Civil Registration Deaths data and linkage using the HES:Civil Registration (Deaths) bridge will allow quantification of postoperative mortality and determine the rate of death among those presenting to surgical outpatients and A&E. • COVID-19 Hospitalisation in England Surveillance System (CHESS) The latest available CHESS data will be used to capture detailed hospital admissions data in patients requiring hospitalisation due to COVID-19 infection. Patients that are awaiting surgery are at high risk of severe COVID-19 due to both their advanced age and the burden of chronic disease. • COVID-19 Second Generation Surveillance System (SGSS) The latest available SGSS data is required for accurate testing data to determine if patients awaiting surgery have tested positive for COVID-19, which will likely impact whether the patient undergoes surgery. Patients that are awaiting surgery are at high risk of severe COVID-19 due to their advanced age and burden of chronic disease. The HES OP, APC, ECDS and A&E data will be used for both cohort selection and subsequent data set creation. This project is the second of two data applications to NHS Digital as part of the ‘COVID-19 Surgical Observatory’ study. The principal aim of the overall study is to describe the ongoing impact of COVID-19 on NHS surgical services. The study is divided into three projects, each using routinely collected hospital episode data and civil registration data from England. The first data application (“DARS-NIC-375669-J7M7F-v0”) will look to understand the recovery of NHS surgery after COVID-19 by accessing the NHS Digital Data Access Environment. This data application is focussed on achieving the other two aims as described in this application. Barts Health NHS Trust will be the data controller and also process the data at Queen Mary's, Queen Mary University of London will be the data processor. All analyses will be carried out by substantive employees of the data processors. If any employees of the data processors are found to not follow the rules of the agreement, they will face serious consequences i.e. termination of contract. The project has received funding from Barts Charity, apart from this, no other organisations or funders are involved. Data will only be accessed by the Data Controller and Data Processor and only at the approved locations. The data will be accessed, analysed, and processed within the Data Safe Haven at the Pragmatic Clinical Trials Unit, Queen Mary University of London. The Data Safe Haven (ODS code: 8HN69-PCTU) achieved ‘Standards Met’ on the DSP Toolkit (17th March 2020). All outputs will be aggregated in line with NHS Digital guidance and standard statistical disclosure methods. The findings of the project will support decision making at a national, regional, and local level across England, influencing the teams that plan care across the NHS. Only summary/aggregated level data with small numbers suppressed in line with the HES Analysis Guide will be included in the outputs and publications. No data will be used for commercial purposes and only aggregated data will be provided to third parties (e.g. in preparing reports for publication). The legal basis for processing personal data is performance of a task by a public organisation in the public interest (Article 6(1)e of the Data Protection Act 2018) and for processing special category data is Article 9(2)j of the same, as the purpose is scientific research.

Clinicians report that infection control procedures designed to protect patients and staff in hospitals have led to a dramatic reduction in surgical throughput. So far, plans for the delivery of surgery have not balanced the risk of complications due to COVID-19, against accurate data describing the risk of harm due to delays in treatment for surgical patients. It is in the public’s interest that care is planned appropriately, and a balance is struck between preventing harm through infection control and treating disease through surgery. The findings of this project will be published and publicised widely. The intended audience is those who provide and plan care within the NHS, including clinicians, managers, and policymakers at all levels as well as specialist groups such as the BMJ Technology Assessment Group (TAG). The outputs will be produced by Barts Health and will provide detailed data analysis to facilitate active planning of surgical services in a way that is not currently possible, since there is no routine national reporting of surgical activity or outcomes. Reporting the regional variations in care will facilitate UK-wide learning. The geographical modelling will support local healthcare leaders in understanding how their regional challenges differ to other areas, which will be vitally important in the event of regional lockdowns. The analyses of ethnicity and socioeconomic deprivation will support NHS leaders in improving surgical care for patients at the highest risk of complications after surgery and COVID-19. The benefits can be measured once the findings are published and vital statistics are provided to NHS leaders. This is particularly important as the pandemic moves into new phases, and decisions related to care in the NHS will be continuingly reassessed. The outputs and publications produced will influence the decision-making process as the circumstances change, and this will be seen implemented within the NHS services. This will benefit societal health and wellbeing by minimising the impact of infection control measures on the delivery of surgical treatments. In the event of future peaks of COVID-19, or during the winter influenza season, the data will ensure NHS leaders can maximise surgical activity across NHS regions and minimise the detrimental impact of COVID-19 on the health of nation. Lay summaries of the findings will be developed in collaboration with patients, as the ongoing disruption to care is of concern to those waiting for care. Publication in peer reviewed journals will support resumption of care in other countries dealing with the COVID-19 pandemic and the associated disruption. Professional bodies e.g. Royal College of Anaesthetists/ The Royal College of Surgeons of England will review and integrate the findings within their reports which will be disseminated to care providers across England. The first report is expected within two months of receiving data access, with sequential reports following from this. This will be achieved for cost 6 months from data access.

05/06/2021

Health and Social Care Act 2012 - s261 - 'Other dissemination of information'

Does not include the flow of confidential data

One-Off

TRE