Long-Term Health Outcomes Related to COVID 

The long-term consequences of COVID are unknown, yet they are vital to the health of all people. Currently the recording of long COVID is not comprehensively captured in health records, making it difficult to identify the longer-term health outcomes of COVID. Yet, it’s thought up to 2 million people are living with this condition in the UK. 

Project aims 

Through this research project, led by King’s College London, we want to do two things: 

• Get a better understanding of the long-term health effects of COVID to better treat those living with this condition. 

• Capture symptom data directly from patients and compare this to symptoms recorded on their electronic health record to make symptom tracking more effective. 

Participants of the study will be recruited from the COVID Symptom Study Biobank (CSSB). The CSSB was established at King’s College London during the 2020 pandemic to understand the impact and effects of COVID. 

We will then link a person’s self-reported data with their Electronic Health Record (EHR) through the UK Longitudinal Linkage Collaboration (UKLLC).  

Our research study will improve outcomes for those living with COVID. It will also act as a model on how to link patient generated data with NHS data to help future research. 

You can hear regular updates from the research team at Kings College London by attending one of our monthly webinars.

Latest updates

Our second driver project looks at the long-term health effects of long COVID.

Although it has been 5 and a half years since the start of the COVD-19 pandemic, Long COVID has affected and continues to affect millions of people across the UK and internationally.

This project links study data with data stored on electronic NHS healthcare record data through the UK Longitudinal Linkage Collaboration – a platform that allows us to link and carry out analysis.

The project uses three key data sources, combined through the UK Longitudinal Linkage Collaboration (UKLLC):

  COVID Symptom Study App Data

• Launched in 2020 for daily symptom tracking.
• Provides detailed time-series data on symptom onset, duration, and testing results.

 Study & Survey Data

• Includes multiple surveys and health assessments collected after recruitment into the biobank.
• Captures pre-pandemic health, newly diagnosed conditions, and self-reported symptoms.

Electronic Health Records (EHR)

• Accessed via UKLLC to link GP records, hospital admissions, A&E visits, planned care, specialist registers, and mortality data.

From these data sources, we want to do 2 two types of analysis.

We want to firstly describe in detail the types and the frequency of different health outcomes for people who have had or still living with, with long COVID.

We want to look at any emergency or unplanned care within hospital setting such as looking at the A&E information, A&E data sets and the admitted patient care data sets as well as other planned care for use of data of healthcare services like in going in for clinic visits and or other treatments.

This will give us information about health conditions that have been new or newly diagnosed or onset after the start of the pandemic.

And then we want to compare the likelihood of different outcomes for people with different histories of COVID-19. This involves comparing people who have had long COVID to people who’ve never had COVID or had relatively short or mild cases of COVID. From this, we want to create a risk trajectory looking at neurological and psychiatric outcomes for people with COVID versus the respiratory infections.

The second analysis will focus on comparing self-reported diagnoses of long COVID with official NHS reporting. From this, we will investigate discrepancies between the two data sources. This follows other work suggesting long COVID is underreported in clinical records compared to survey estimates.