Abstract
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 Open SAFELY secure analytics platform.
| Original language | English |
|---|---|
| Pages (from-to) | 1-17 |
| Number of pages | 17 |
| Journal | Wellcome Open Research |
| Volume | 5 |
| DOIs | |
| Publication status | Published - 15 Oct 2020 |
| Externally published | Yes |
Bibliographical note
Funding Information:work of B.G. on better use of data in healthcare is funded in part by: the NIHR Oxford Biomedical Research Centre, and the MRC [MR/V015737/1]. L.S. reports grants from Wellcome [202912], MRC [COV0076], NIHR [16/137/99], UKRI, British Council, GSK, British Heart Foundation (BHF) [PG/19/71/34632] and Diabetes UK and the Newton Fund [527418645] outside this work; K.B. holds a Sir Henry Dale fellowship jointly funded by Wellcome and the Royal Society [107731]; H.I.M. is funded by the NIHR Health Protection Research Unit in Immunisation (a partnership between Public Health England and LSHTM); A.Y.S.W. holds a fellowship from BHF [EPNCZQ52]; R.M. holds a Sir Henry Wellcome fellowship funded by the Wellcome Trust [201375]; E.J.W. holds grants from MRC [MR/S01442X/1, MR/R013489/1]; R.G. holds grants from NIHR [SRF-2013-06-016]; I.J.D. holds grants from NIHR [15/80/28] and GSK; H.F. holds a UKRI fellowship; B.D.N. holds an NIHR clinical lectureship. The views expressed are those of the authors and not necessarily those of the NIHR, NHS England, Public Health England or the Department of Health and Social Care.
Funding Information:
Grant information: The OpenSAFELY collaborative has received funding from the National Institute for Health Research (NIHR). The
Funding Information:
The OpenSAFELY Collaborative are grateful for all the support received from the TPP Technical Operations team throughout this work; for assistance from the information governance and database teams at NHS England and NHSX; and for additional discussions on disease characterization, codelists and methodology with H. Drysdale, N. DeVito, J. Quint. TPP provided technical expertise and infrastructure within their data centre pro bono in the context of a national emergency.
Publisher Copyright:
© 2020. The OpenSAFELY Collaborative et al.
Funding
work of B.G. on better use of data in healthcare is funded in part by: the NIHR Oxford Biomedical Research Centre, and the MRC [MR/V015737/1]. L.S. reports grants from Wellcome [202912], MRC [COV0076], NIHR [16/137/99], UKRI, British Council, GSK, British Heart Foundation (BHF) [PG/19/71/34632] and Diabetes UK and the Newton Fund [527418645] outside this work; K.B. holds a Sir Henry Dale fellowship jointly funded by Wellcome and the Royal Society [107731]; H.I.M. is funded by the NIHR Health Protection Research Unit in Immunisation (a partnership between Public Health England and LSHTM); A.Y.S.W. holds a fellowship from BHF [EPNCZQ52]; R.M. holds a Sir Henry Wellcome fellowship funded by the Wellcome Trust [201375]; E.J.W. holds grants from MRC [MR/S01442X/1, MR/R013489/1]; R.G. holds grants from NIHR [SRF-2013-06-016]; I.J.D. holds grants from NIHR [15/80/28] and GSK; H.F. holds a UKRI fellowship; B.D.N. holds an NIHR clinical lectureship. The views expressed are those of the authors and not necessarily those of the NIHR, NHS England, Public Health England or the Department of Health and Social Care. Grant information: The OpenSAFELY collaborative has received funding from the National Institute for Health Research (NIHR). The The OpenSAFELY Collaborative are grateful for all the support received from the TPP Technical Operations team throughout this work; for assistance from the information governance and database teams at NHS England and NHSX; and for additional discussions on disease characterization, codelists and methodology with H. Drysdale, N. DeVito, J. Quint. TPP provided technical expertise and infrastructure within their data centre pro bono in the context of a national emergency.
Keywords
- COVID-19
- infectious disease
- mortality
- risk prediction
- statistical methodology
ASJC Scopus subject areas
- Medicine (miscellaneous)
- General Biochemistry,Genetics and Molecular Biology
