TY - JOUR
T1 - Common protocol for validation of the QCOVID algorithm across the four UK nations
AU - Kerr, Steven
AU - Robertson, Chris
AU - Nafilyan, Vahe
AU - Lyons, Ronan A
AU - Kee, Frank
AU - Cardwell, Christopher R
AU - Coupland, Carol
AU - Lyons, Jane
AU - Humberstone, Ben
AU - Hippisley-Cox, Julia
AU - Sheikh, Aziz
N1 - Funding Information:
Competing interests AS reports grants from NIHR, grants from MRC, and grants from HRR UK, during the conduct of the study. JL and RL report grants from UKRI Medical Research Council, during the conduct of the study. JH-C reports grants from John Fell Oxford University Press Research Fund, grants from Cancer Research UK (CR-UK) grant number C5255/A18085, through the Cancer Research UK Oxford Centre, grants from the Oxford Wellcome Institutional Strategic Support Fund (204826/Z/16/Z), grants from NIHR, during the conduct of the study; personal fees and other from ClinRisk, outside the submitted work; and JH-C is an unpaid director of QResearch, a not-for-profit organisation which is a partnership between the University of Oxford and EMIS Health who supply the QResearch database used for this work. Carol Coupland reports personal fees from ClinRisk, outside the submitted work. JH-C, AS and CC were members of the research team involved in the development of the QCOVID risk prediction algorithm. All other authors report no conflict of interest Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Funding Information:
Funding The validation in England will be funded by a grant from the National Institute for Health Research following a commission by the Chief Medical Officer for England. In Scotland, EAVE II is funded by the Medical Research Council (MR/ R008345/1) and supported by the Scottish Government. In Wales, Controlling COVID-19 is supported by the Medical Research Council (MR/V028367/1).
Publisher Copyright:
© 2022 Authors.
PY - 2022/6/14
Y1 - 2022/6/14
N2 - INTRODUCTION: The QCOVID algorithm is a risk prediction tool for infection and subsequent hospitalisation/death due to SARS-CoV-2. At the time of writing, it is being used in important policy-making decisions by the UK and devolved governments for combatting the COVID-19 pandemic, including deliberations on shielding and vaccine prioritisation. There are four statistical validations exercises currently planned for the QCOVID algorithm, using data pertaining to England, Northern Ireland, Scotland and Wales, respectively. This paper presents a common procedure for conducting and reporting on validation exercises for the QCOVID algorithm.METHODS AND ANALYSIS: We will use open, retrospective cohort studies to assess the performance of the QCOVID risk prediction tool in each of the four UK nations. Linked datasets comprising of primary and secondary care records, virological testing data and death registrations will be assembled in trusted research environments in England, Scotland, Northern Ireland and Wales. We will seek to have population level coverage as far as possible within each nation. The following performance metrics will be calculated by strata: Harrell's C, Brier Score, R2 and Royston's D.ETHICS AND DISSEMINATION: Approvals have been obtained from relevant ethics bodies in each UK nation. Findings will be made available to national policy-makers, presented at conferences and published in peer-reviewed journal.
AB - INTRODUCTION: The QCOVID algorithm is a risk prediction tool for infection and subsequent hospitalisation/death due to SARS-CoV-2. At the time of writing, it is being used in important policy-making decisions by the UK and devolved governments for combatting the COVID-19 pandemic, including deliberations on shielding and vaccine prioritisation. There are four statistical validations exercises currently planned for the QCOVID algorithm, using data pertaining to England, Northern Ireland, Scotland and Wales, respectively. This paper presents a common procedure for conducting and reporting on validation exercises for the QCOVID algorithm.METHODS AND ANALYSIS: We will use open, retrospective cohort studies to assess the performance of the QCOVID risk prediction tool in each of the four UK nations. Linked datasets comprising of primary and secondary care records, virological testing data and death registrations will be assembled in trusted research environments in England, Scotland, Northern Ireland and Wales. We will seek to have population level coverage as far as possible within each nation. The following performance metrics will be calculated by strata: Harrell's C, Brier Score, R2 and Royston's D.ETHICS AND DISSEMINATION: Approvals have been obtained from relevant ethics bodies in each UK nation. Findings will be made available to national policy-makers, presented at conferences and published in peer-reviewed journal.
KW - COVID-19
KW - epidemiology
KW - public health
U2 - 10.1136/bmjopen-2021-050994
DO - 10.1136/bmjopen-2021-050994
M3 - Article
C2 - 35701053
SN - 2044-6055
VL - 12
SP - e050994
JO - BMJ Open
JF - BMJ Open
IS - 6
M1 - e050994
ER -