Data on clinically extremely vulnerable people in England during the coronavirus (COVID-19) pandemic from the Shielding Behavioural Survey. Includes information on their behaviours and well-being since receiving shielding guidance.

This is a release of management information for anonymous summary data for those patients that have been identified on the Shielded Patient List (SPL). Its purpose is to make the summary data available to a wider audience, as open data, to enable a broad base of users to perform analysis from it. The purpose behind releasing this data is to present regional and local data to allow for its use in public health. It will also allow for greater analysis, modelling and planning to be performed using the latest data, to aid in the response to the pandemic. We will update this weekly and we would welcome your feedback to help us develop our open data sets. The data that is published is based on version 74 of the SPL clinical methodology, with the data extracted as at 21 September 2021.

The Chief Medical Officer (CMO) for England, working with the CMOs of the devolved nations and other senior clinicians, commissioned NHS Digital to produce a list of people at “high risk” of complications from COVID-19, who should be shielded for at least 12 weeks. The CMO for Wales commissioned a collaboration of national bodies in Wales (NWIS, DU, NWSSP, PHW) to identify “high risk” people for the Welsh population, based largely on the NHS Digital methodology. This list is referred to as the Shielded Patient List (SPL).

The “high risk” list was defined as a subset of a wider group of people who may be “at risk”. Specific advice applies to these groups, currently this advice is: • “At Risk” – large group normally at risk from the flu - should practice strict social distancing • “At high risk” – a smaller sub-group (circa 70k), defined by CMO – should practice complete social “shielding” NHS Digital have described the methodology that has been used to identify patients who meet the high risk criteria due to their inclusion in one or more of the disease groups.

As there are differences in some of the systems used across the devolved nations, nuanced differences in application and interpretation of CMO guidance, this document describes the Welsh methodology.

https://nwis.nhs.wales/coronavirus/coronavirus-content/coronavirus-documents/covid-19-high-risk-shielded-patient-list-identification-methodology/

Dataset received it's final update in March 2022

Unequal impact of COVID-19: BAME disproportionality Camden Demographics of Shielded Population by location age ethnicity deprivation gender GPs and reason for shielding.

This study set out to investigate, using qualitative methodology, the experiences of spouses of people with Amyotrophic Lateral Sclerosis (ALS) during the coronavirus pandemic, with particular focus on spouse distress and cognitive and behavioral change in people with ALS (pwALS). Qualitative semi-structured interviews of nine spouses of pwALS living in England were conducted between 11/09/2020 and 20/04/2021, focusing on spouses’ perspectives of how their lives and the lives of pwALS were affected by the pandemic and related lockdowns. Interviews were subject to thematic analysis. Four superordinate themes were identified from the spouses’ interviews: (i) pandemic behaviors, which encompassed accounts of cautious behavior, relaxation of cautious behavior, and other people’s attitudes to shielding the person with ALS; (ii) changes to daily life caused by the pandemic and progression of ALS; (iii) distress in spouses, which included anxiety, depression, and burden; and (iv) ALS-related behavioral impairment. Spouses also provided mixed accounts of telehealth care, pointing out its convenience but some felt that face-to-face appointments were preferable. While many reactions to the pandemic reported by spouses of pwALS may have been similar to those of the general population or other vulnerable groups, interviews indicated the potential for the pandemic to have made more apparent certain aspects of behavioral change in pwALS with which carers may require support. Clinicians need to acknowledge spouses’ concerns about the potential limitations of remote clinical consultations, enquire about cognitive and behavioral change, and consider how input should be best provided in such limiting circumstances.

Unequal impact of COVID-19: BAME disproportionality The shielded population are those that have been defined by Government on medical grounds as medically vulnerable due to a clinical condition that puts them at High risk of developing complications from COVID-19 infection. Those recommended to shield include: • Organ transplant recipients • Pregnant women with congenital heart conditions • Those with rare diseases such as homozygous sickle cell, SCID and others • Those on immunosuppression therapies • People with specific cancers or those with cancer undergoing chemo/radiotherapy. Camden has so far received the contact details of almost 8,000 residents identified by central Government, with more records likely in future. The details received do not contain the personal characteristics of those on the list, although we have received an overview from North Central London NHS. (note: above is broken down into shielder’s location and BAME ward profile, shielding population by ethnicity, shielded food need and dietary requirements).

CVD-COVID-UK, co-ordinated by the British Heart Foundation (BHF) Data Science Centre (https://bhfdatasciencecentre.org/), is one of the NIHR-BHF Cardiovascular Partnership’s National Flagship Projects.

CVD-COVID-UK aims to understand the relationship between COVID-19 and cardiovascular diseases through analyses of de-identified, pseudonymised, linked, nationally collated health datasets across the four nations of the UK. The consortium has over 400 members across more than 50 institutions including data custodians, data scientists and clinicians, all of whom have signed up to an agreed set of principles with an inclusive, open and transparent ethos.

Approved researchers access data within secure trusted/secure research environments (TREs/SDEs) provided by NHS England (England), the National Safe Haven (Scotland), the Secure Anonymised Information Linkage (SAIL) Databank (Wales) and the Honest Broker Service (Northern Ireland). A dashboard of datasets available in each nation’s TRE can be found here: https://bhfdatasciencecentre.org/areas/cvd-covid-uk-covid-impact/

This dataset represents the linked datasets in SAIL Databank’s TRE for Wales and contains the following datasets: • Welsh Longitudinal GP Dataset - Welsh Primary Care (Daily COVID codes only) (GPCD) • Welsh Longitudinal General Practice Dataset (WLGP) - Welsh Primary Care • Critical Care Dataset (CCDS) • Emergency Department Dataset Daily (EDDD) • Emergency Department Dataset (EDDS) • Outpatient Database for Wales (OPDW) • Outpatient Referral (OPRD) • Patient Episode Dataset for Wales (PEDW) • COVID-19 Test Results (PATD) • COVID-19 Test Trace and Protect (CTTP) - Legacy • COVID-19 Shielded People List (CVSP) • SARS-CoV-2 viral sequencing data (COG-UK data)-Lineage/Variant Data-Wales (CVSD) • Covid Vaccination Dataset (CVVD) • Annual District Death Daily (ADDD) • Annual District Death Extract (ADDE) • COVID-19 Consolidated Deaths (CDDS) • Intensive Care National Audit and Research Centre (ICCD) - Legacy - COVID only • Intensive Care National Audit and Research Centre (ICNC) • Welsh Dispensing Dataset (WDDS) - Legacy • Annual District Birth Extract (ADBE) • Maternity Indicators Dataset (MIDS) • National Community Child Health Database (NCCHD) • Care Home Dataset (CARE) • Congenital Anomaly Register and Information Service (CARS) • Referral to Treatment Times (RTTD) • SAIL Dementia e-Cohort (SDEC) • Welsh Ambulance Services NHS Trust (WASD) • Welsh Demographic Service Dataset (WDSD) • Welsh Results Reports Service (WRRS) • ONS 2011 Census Wales (CENW)

Unequal impact of COVID-19: BAME disproportionality Section 1 (slides 1 – 3): The Public Health England (PHE) review confirms the risk of mortality as a result of covid-19 by ethnicity. Data on access to care and emergency response has been taken from our local VCS partner feedback and indications from local data.) Data on the care workforce by ethnicity was taken from our local data and the Section 2 (Slides 4 – 7) covers demographic information on Black, Asian, and other or mixed ethnic people delivering direct care in the wider social care sector from the Skills for Care 2019 Social Care Workforce Review (note: factors that need to be considered are age, sex, underlying health conditions, ethnicity, and pregnancy.) Information on Camden’s ASC workforce was taken from GLA 2016-based Ethnic Group Projections - mid-2020). Demographic information on people receiving ASC support in Camden has been taken from our local service data. Section 3: (slides 8-15) sets out information on Adult Social Care activity during Covid-19 and looks at data relative to ethnicity including the ASC cohort of Camden’s shielded residents. (Service held data NOT official statistics including qualitative feedback from communities) Section 4: (Slides 16 – 18) shows information related to the Adult Social Care outcomes framework which has provided some information gathered before Covid-19 on the experiences of people who are BAME and in receipt of social care support in Camden.

The COVID-19 pandemic brought many disruptions to children’s education, including the education of children with intellectual (learning) disability and/or autism. We investigated the educational experiences of autistic children and children with an intellectual disability about a year after the COVID-19 pandemic started in the UK.

An online survey collected data during the summer/autumn of 2021 from 1,234 parents of 5 to 15 year-old children across all 4 UK countries. The study investigated school attendance and home learning experiences of children with intellectual disability and/or autistic children who were registered to attend school in 2021. The study also investigated the experience of Elective Home Education in families of children with a neurodevelopmental condition whose child was de-registered from school before and after the pandemic started in the UK in March 2020.

The study provided evidence on the impact of COVID-19 on school attendance and home education for children with a neurodevelopmental condition.Education changed dramatically due to the COVID-19 pandemic. Schools closed in 2019/20. There was compulsory return to school in September 2020 with measures in place to control infection and new regulations about COVID-19-related absences. School attendance in the first term of 2020-21 was lower compared to other years. Many children were de-registered from school. In early 2020-21, there was a second prolonged period of national school closures. The pandemic has caused many disruptions to children's education.

Children with neurodevelopmental conditions (NDCs), in particular intellectual disability and autism, are the most vulnerable of vulnerable groups. Among children with special educational needs and disabilities (SEND), children with intellectual disability and/or autism consistently struggle to meet the required standards in education. Our study will focus on these two groups of children.

Before the pandemic, many children with NDCs missed school. Then the pandemic disrupted everyone's education. Approximately one year after the pandemic started, we will investigate the educational experiences of children with NDCs.

Our project will investigate: - School absence and reasons for absence among children with intellectual disability and/or autism - Child, family, and school factors associated with school absence - Barriers and facilitators of school attendance - Parents' experiences of home schooling

An online survey will collect data from approximately 1,500 parents of 5 to 17 year-old children with NDCs across all 4 UK countries. We will recruit parents of: (i) children registered with a school in spring/summer 2021; (ii) children not registered with a school in spring/summer 2021 but who were registered with a school at the start of the pandemic in March 2020; and (iii) children not registered with a school on either date. We will collect data on school attendance for those registered with a school, and data on home learning experiences for those not registered with a school. For all children, we will collect data on their mental health.

The first analysis will investigate school absence with a focus on children registered with a school. We will summarise school absence data as well as reasons for absence as reported by the parents. The second analysis will investigate school attendance: attending school or home schooling. We will describe the children currently registered to attend school (group 1), those not currently registered who were registered in March 2020 at the start of the pandemic (group 2), and those not registered on either point (group 3). We will summarise the reasons parents give for de-registering their child from school. Our final analysis will focus on home learning support during home schooling. We will describe the types of support schools offer to school-registered students during remote learning (when students are self-isolating/shielding, or schools are closed because of lockdown). We will describe the home learning experiences of school de-registered children and parents' satisfaction with these arrangements.

We will work closely with parents of children with NDCs, seeking their advice on the study. Our team includes the Council for Disabled Children, the largest umbrella organization in the UK bringing together many charities supporting disabled children and their families. We will share the study findings widely, including key messages for policies related to the education of children with special educational needs and disabilities.

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