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ObjectivesTo explore associations between COVID-19 vaccination and admission to critical care with COVID-19 in England, by age group, dose, and over time.
ApproachWe conducted a population cohort study by linking the Case Mix Programme national clinical audit of adult intensive care to the National Immunisation Management System, combined with population denominators from the Office for National Statistics. We compared patient characteristics and estimated rates of critical care admission by vaccination status and age group and explored variation in admission rates and relative protection over time. Finally, we used quantitative bias analysis to explore sensitivity to potential linkage error.
ResultsBetween 1 May and 15 December 2021 (England's Delta wave), 11,431 patients were admitted to adult critical care units in England with COVID-19, of whom 10,141 were included in the cohort. Vaccinated patients were older, with higher levels of severe comorbidity and immunocompromise. Unvaccinated patients were greatly overrepresented, with people who had received two or more doses being at least 92% less likely to be admitted compared with those who were unvaccinated, for all ages, once the second dose had been rolled out. Those aged 50 years or older who had received a booster/third dose were 98-99% less likely to be admitted compared with unvaccinated. Our analysis was robust to linkage error, but closely related analyses could have been exquisitely sensitive to missed links.
ConclusionOur findings are consistent with a high degree of vaccine-acquired protection against critical care admission, with little waning in the first 6-9 months for those aged under 70. While our analysis was robust to linkage error, significant care is warranted with related analyses of linked vaccination and outcome databases.

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, R 2 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.

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, R(2) 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.

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.