The EpiCov database contains de-identified patient and NHS staff data from the Cambridge University Hospitals NHS Foundation Trust (CUH) Electronic Health Record systems, including scan images and laboratory results.

The database will include routinely collected information about patients diagnosed with COVID-19 or suspected of having COVID-19, and staff who have been tested for COVID-19. It will also include information about a large number of control patients who do not have a diagnosis of COVID-19.

This will allow comparisons of patients with and without COVID-19 infection who have similar symptoms to see if there are any important differences that might help us understand the best way to prevent, diagnose and treat COVID-19 infection.

Thousands of patients need to be compared to check if any differences found between them are related to age, gender or the season.

No direct personal identifiers (such as name, date of birth, contact details, hospital or NHS number) will be included in the database. All information will be extracted and de-identified through an automated process by the CUH Clinical Informatics Team who process patient data as part of their job role.

The database will facilitate a variety of research related to COVID-19 aimed at improving health, treatment or services. Research will include large data studies using advanced methods of analysis, such as machine learning, with the aim of learning about how to predict patient outcomes and the best way to treat patients based on their clinical information.

The EpiCov database will both enable local researchers to use the Electronic Health Record resource for important research, and also allow CUH to contribute well-curated data to other national and international COVID-19 projects and databases.

if you have any question about the database, please contact us on the email below:

We propose a study using anonymised national data from NHS’s electronic staff records (ESR) to explore the risk of sickness absence due to suspected Covid-19 according to ethnicity, professional role, sex and age and in relation to available antigen/antibody test results.

This will help us understand not only the differential risks of Covid-19 infection in health care workers but also the possible modifying roles of ethnicity, age and sex. We will also examine sickness absence for mental illness. In addition, in a limited number of NHS Trusts we will collect group information on hospital admissions, staff redeployment and the availability of PPE.

Accordingly the study will answer the following three questions among staff employed by NHS acute medical Trusts:

1. How have rates of sickness absence ascribed to suspected Covid-19 infection varied according to ethnicity, age, sex, and potential for occupational contact with Covid-19 as indicated by occupation and department? How are these related to available data on antigen/antibody test results?
2. How have rates of prolonged sickness absence ascribed to suspected Covid-19 infection varied according to ethnicity, age, sex, and potential for occupational contact with Covid-19 as indicated by occupation and department?
3. How have rates of sickness absence ascribed to mental illness and other causes unrelated to Covid-19, varied over the course of the epidemic as compared with 12 months earlier, and have changes differed by ethnicity, occupation and department?

The study duration will be six months.