| Autor: |
Richard Croker, Anna Schultze, Frank Hester, John Parry, Rafael Perera, Sam Harper, Rosalind M. Eggo, Liam Smeeth, Ewout Steyerberg, Caroline Minassian, Ruth Keogh, Karla Diaz-Ordaz, Stephen J.W. Evans, Elizabeth J. Williamson, Krishnan Bhaskaran, John Tazare, Helen I McDonald, Alex J. Walker, Sebastian Bacon, Laurie A. Tomlinson, Helen J. Curtis, Chris Bates, Caroline E. Morton, Harriet Forbes, Amir Mehrkar, Emily Nightingale, Brian D Nicholson, Richard Grieve, Dave Evans, Peter Inglesby, David Harrison, Ben Goldacre, David Leon, Jonathan Cockburn, Brian MacKenna, Rohini Mathur, Will J. Hulme, Nicholas G. Davies, Ian J. Douglas, Jessica Morley, Angel Wong, Christopher T. Rentsch |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Wellcome Open Research, Vol 5 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2398-502X |
| DOI: |
10.12688/wellcomeopenres.16353.2 |
| Popis: |
On March 11th 2020, the World Health Organization characterised COVID-19 as a pandemic. Responses to containing the spread of the virus have relied heavily on policies involving restricting contact between people. Evolving policies regarding shielding and individual choices about restricting social contact will rely heavily on perceived risk of poor outcomes from COVID-19. In order to make informed decisions, both individual and collective, good predictive models are required. For outcomes related to an infectious disease, the performance of any risk prediction model will depend heavily on the underlying prevalence of infection in the population of interest. Incorporating measures of how this changes over time may result in important improvements in prediction model performance. This protocol reports details of a planned study to explore the extent to which incorporating time-varying measures of infection burden over time improves the quality of risk prediction models for COVID-19 death in a large population of adult patients in England. To achieve this aim, we will compare the performance of different modelling approaches to risk prediction, including static cohort approaches typically used in chronic disease settings and landmarking approaches incorporating time-varying measures of infection prevalence and policy change, using COVID-19 related deaths data linked to longitudinal primary care electronic health records data within the OpenSAFELY secure analytics platform. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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