Key points

By February 2021 more than 4 million people across the UK had been identified as clinically extremely vulnerable to COVID-19 and advised to shield. 

Our briefing shows the scale of the challenge of ensuring that the most clinically vulnerable to COVID-19 are kept safe, and in providing high-quality health and social care during the pandemic. It also indicates that there are substantial unmet needs that should be prioritised to ensure that the mental and physical health of this group does not deteriorate further.  

In this briefing, we: 

  • present analysis from the Networked Data Lab on the impact the pandemic has had on the clinically extremely vulnerable population

  • assess the mental health of people identified as clinically extremely vulnerable 

  • examine the data on access to care for clinically extremely vulnerable  

  • assess the limitations to the use of an algorithm-driven approach to identifying the clinically extremely vulnerable population which were exacerbated by poor availability of high-quality data.

Context

Over 2 million people were identified in March and April 2020 as being clinically extremely vulnerable (CEV) and contacted and asked to stay at home. In an unprecedented use of NHS data systems, CEV people were identified using an algorithm that was applied centrally to electronic health records, alongside local clinical input. This was achieved over a very challenging timescale, using incomplete and inconsistent data and within an uncertain environment when scientific understanding of the impact of the virus was rapidly changing. As scientific understanding improved, the number of people identified as CEV increased and by February 2021 over 4 million people had been identified. 

Despite rapid action to identify and support the CEV population, the COVID-19 pandemic resulted in extremely high rates of infection, hospital admission and death in this group. The CEV population experienced an all-cause mortality rate comparatively higher than the all-cause mortality rate of an age-matched sample of the general population. This reflects their ill health and clinical vulnerability to the virus. However, it also reflects the impracticality of isolating from the wider community and the extent of COVID-19 transmission in the UK. 

The pandemic led to deteriorations in the mental health of the CEV population, and additional support is now needed to prevent any long-term impacts on their health and wellbeing. People asked to shield were more likely to suffer from mental health conditions than the general population and more likely to seek help from the NHS during the pandemic.  

The CEV population was particularly affected by changes to NHS services during the pandemic and there is a strong argument for now prioritising its care to prevent poor outcomes. 

There are limitations to the use of an algorithm-driven approach to identifying the CEV population which were exacerbated by poor availability of high-quality data. Not all CEV individuals were identified through the centrally developed algorithm due to lack of linked data or incomplete medical records, and many people would have been missed had they not been added to the shielded patient list by local clinicians. These approaches taken to identifying people resulted in significant variation across local areas in terms of when people were identified and, as a result, what services and support they had access to. Further investment in data sharing and improving data quality is essential to ensure that in the event of a future health emergency, it is possible to identify individuals quickly, accurately and consistently, and to enable rapid planning and delivery of relevant support.  

About this briefing

By using the linked data sets that our Networked Data Lab partners have invested in and developed over time, it has been possible to demonstrate some of the valuable and actionable insights that can be gathered when a more complete picture of the local population is available. 

Our briefing shows the scale of the challenge of ensuring that the most clinically vulnerable to COVID-19 are kept safe, and in providing high-quality health and social care during the pandemic. It also indicates that there are substantial unmet needs that should be prioritised to ensure that the mental and physical health of this group does not deteriorate further.  

Programme

The Networked Data Lab

Programme

A collaborative network of analytical teams across the UK working together using linked datasets on...

This briefing has been written by the Networked Data Lab (NDL). This is a Health Foundation funded collaboration of advanced analytical teams across the UK working together on shared challenges and promoting the use of analytics in improving health and social care. The other contributing authors from each of the partner organisations are listed below.

The Health Foundation

Jorgen Engmann, Nadia Kalam, Olivia Ross-Hurst, Rachel Tesfaye

NDL Grampian: The Aberdeen Centre for Health Data Science (ACHDS) which includes NHS Grampian and the University of Aberdeen

William P Ball*, Nicola Beech†, Corri Black*,†, Dimitra Blana*, Jaroslaw Dymiter‡, Jillian Evans†, Sharon Gordon^, Joanne Lumsden‡, Adrian Martin‡, Mintu Nath*, Graham Osler†, Magdalena Rzewuska#, Simon Sawhney*,†, Bernhard Scheliga*, Katie Wilde‡, Artur Wozniak†

NDL Leeds: Leeds CCG and Leeds City Council

Alex Brownrigg¶, Souheila Fox¶, Graham Hyde**, Alison Phiri¶

NDL Liverpool and Wirral: Liverpool CCG and Healthy Wirral Partnership

Saiqa Ahmed††, Lauren Barnett‡‡, Ben Barr‡‡, Joanne Bradburn§§, Simon Chambers***, Tim Caine¶¶, Annmarie Daley¶¶, Helen Duckworth¶¶, Matt Gilmore§§, Karen Jones¶¶, Lisa Jones¶¶, Michelle Jones¶¶, Lee Kirkham§§, David Knowles¶¶, Beverley Murray***

NDL North West London: Imperial College Health Partners (ICHP), Institute of Global Health Innovation (IGHI), Imperial College London (ICL), and North West London CCGs

Hutan Ashrafian†††, Alex Bottle‡‡‡, Matthew Chisambi§§§, Anna Lawrence-Jones14, Melanie Leis¶¶¶, Kavitha Saravanakumar****, Samantha Scholtz††††, Sara Sekelj§§§, Tomasz Szymanski§§§, Jordan Wallace§§§

NDL Wales: Public Health Wales, Population Data Science Swansea University, SAIL Databank, Digital Health and Care Wales and Social Care Wales

Jiao Song«, Ashley Akbari≈, Laura Bentley«, Lynsey Cross≈, Joanna Dundon>, Paul Howells>, Gareth John>, Gwyndaf Parry∞, Tomos Smith«, Lisa Trigg∞

Key

* Aberdeen Centre for Health Data Science, University of Aberdeen, Aberdeen, Scotland
† National Health Service Grampian, Aberdeen, Scotland
‡ Grampian Data Safe Haven (DaSH), University of Aberdeen, Aberdeen, Scotland
^ Centre for Health Data Science, University of Aberdeen
# Health Services Research Unit and The Centre for Health Data Science, University of Aberdeen
¶ Leeds CCG
** Leeds City Council
†† Public Advisor
‡‡ University of Liverpool
§§ Wirral CCG
¶¶ Liverpool CCG
*** Wirral Metropolitan Borough Council
††† Imperial College Healthcare NHS Trust
‡‡‡ Imperial College London
§§§ Imperial College Health Partners
¶¶¶ Institute of Global Health Innovation, Imperial
College London
**** North West London Health and Care Partnership
†††† West London NHS Trust
« Public Health Wales
> Digital Health and Care Wales
≈ Population Data Science Swansea University,
SAIL Databank
∞ Social Care Wales (SCW)

Further reading

Press release

Research reveals devastating and lasting impact of the pandemic on those asked to shield

20 October 2021
Press release

Health Foundation statement on Networked Data Lab research surrounding clinically extremely...

Chart

How has hospital use among those clinically extremely vulnerable to COVID-19 been impacted by the pandemic?

Chart

Using secondary care data linked to the shielded patient list, this analysis explores the pandemic's...

Article

Local analyses on those clinically extremely vulnerable to COVID-19

Article

The Networked Data Lab partners have each carried out further analyses on the impact of shielding...

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