Impact of SARS-Cov-2 on Clinical Trial Unit Staff: The EPIC Observational Study

IntroductionClinical Trials Units (CTUs) are a key component of delivering non-commercial and commercial clinical research globally. Within the UK, CTUs are seen as a specialist and independent entity available to all researchers requiring support to setup, conduct and deliver clinical trials. Therefore, an involvement of a CTU is highly recommended by national regulators and positively accepted by funders, especially for drug and/or medical device and/or complex intervention trials. AimThis study aims to determine the challenges associated with the management of Covid-19 research managed via the CTU workforce, including the challenges associated with quality assurance, trial setup and data management. Additionally, this study will explore the by-stander effect on trial staff by way of evaluating the mental and physical health impact. Methods/ DesignThis is a mixed methods study. An online novel questionnaire survey study will be conducted among the UK CTU workforce. Quantitative data will be collected using the Qualtrics XM platform. We aim to recruit up to 1,500 CTU staff across the UK workforce. A subgroup sample will be randomly invited to take part in semi-structured interviews. Therefore, this survey will generate both quantitative and qualitative data inclusive of demographic data. ResultsThe findings will inform current initiatives and identify key themes for prioritising in further research to develop robust approaches to support CTU staff, including the development of a start-re-start framework for CTUs for any future pandemics relevant to developing and delivering communicable diseases and non-communicable diseases-based research. Strengths/LimitationsThe validation of the EPIC impact questionnaire used qualitative and quantitative methods which is a strength of the study. However, the study has a single timepoint to obtain data with the secondary outcome measures to be completed at two timepoints as this is an exploratory study attempting to obtain a wider data pool.