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Project #31:
Risk factors and prediction models for Long COVID.

  • Type: Research
  • Topic areas: Post-COVID health impacts [e.g. long COVID]

Reports of people developing long-lasting post COVID symptoms are rising, with a latest estimate of 1.1 million people living in private households in the UK (1.7% of the population), according to the Office of National Statistics. However, little is known about which groups of individuals with a history of COVID-19 are more likely to have symptoms over a long period (e.g. more than 12 weeks). Recent research showed that not all patients who self-report long COVID have been diagnosed clinically or referred to recovery services by their doctors.

This is a population based cohort study from the electronic health records using the OpenSAFELY platform. In this study, we aim to identify risk factors associated with long COVID diagnosis in the primary care records. We will investigate the following questions:

  • What are the risk factors associated with long COVID diagnosis?
  • What are the differences between risk factors for acute COVID and long COVID?
  • Can we predict or discriminate between these two types of COVID?
  • How the clinical coding of long COVID diagnosis impacts on the above analyses?

The identified risk factors will help increase the awareness of long COVID, improve the diagnosis coverage and subsequently improve the access/referral to recovery services and inform a risk prediction model to identify and prioritise care, education and rehabilitation for patients at risk of long COVID.


  • Study leads: Yinghui Wei
  • Organisation: University of Plymouth / University of Bristol