Publications by authors named "S S R Crossfield"
Routine use of genetic data in healthcare is much-discussed, yet little is known about its performance in epidemiological models including traditional risk factors. Using severe COVID-19 as an exemplar, we explore the integration of polygenic risk scores (PRS) into disease models alongside sociodemographic and clinical variables. PRS were optimized for 23 clinical variables and related traits previously-associated with severe COVID-19 in up to 450,449 UK Biobank participants, and tested in 9,560 individuals diagnosed in the pre-vaccination era.
View Article and Find Full Text PDFPurpose: Assessing the long-term impact of cancer on people's lives is challenging due to confounding issues such as aging and comorbidities. We aimed to investigate this impact by comparing the outcomes of cancer survivors with a matched control cohort.
Methods: This was a cross-sectional survey of breast, colorectal and ovarian cancer survivors approximately 5 years post-diagnosis and a cohort of age, sex and social deprivation-matched controls who had never had a cancer diagnosis.
Article Synopsis
- The study aimed to identify risk factors (clinical, socio-demographic, and genetic) for severe COVID-19 outcomes like hospitalization or death in a sample of 9560 UK Biobank participants diagnosed in 2020.
- The research discovered that age, obesity, male sex, smoking habits, and living in deprived areas heightened the risk of severe illness, while an optimized polygenic risk score (PRS) highlighted genetic influences related to immune response pathways.
- The findings suggest that integrating genetic data with standard clinical and socio-demographic factors can enhance understanding of severe COVID-19 outcomes, especially before the vaccination era.
Introduction: More people are living with and beyond a cancer diagnosis. There is limited understanding of the long-term effects of cancer and cancer treatment on quality of life and personal and household finances when compared to people without cancer. In a separate protocol we have proposed to link de-identified data from electronic primary care and hospital records for a large population of cancer survivors and matched controls.
View Article and Find Full Text PDFArticle Synopsis
- Linked healthcare data has significant potential for improving knowledge and services, but it raises ethical and legal issues around privacy and confidentiality that need to be addressed effectively.
- The research developed a clear data flow protocol to create pseudonymous and anonymous datasets, which was successfully implemented in the Comprehensive Patient Records study in Leeds, UK.
- The framework created not only received ethical approval but also provides a robust method that enhances current standards for data protection, ensuring compliance while facilitating research.