Assessing the impact of COVID-19 on the clinically extremely vulnerable population

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.