ABSTRACT
IntroductionThis study aims to explore the impact of COVID-19 vaccination on critical care by examining associations between vaccination and admission to critical care with COVID-19 during Englands Delta wave, by age group, dose, and over time. MethodsWe used linked routinely-collected data to conduct a population cohort study of patients admitted to adult critical care in England for management of COVID-19 between 1 May and 15 December 2021. Included participants were the whole population of England aged 18 years or over (44.7 million), including 10,141 patients admitted to critical care with COVID-19. The intervention was vaccination with one, two, or a booster/three doses of any COVID-19 vaccine. ResultsCompared with unvaccinated patients, vaccinated patients were older (median 64 years for patients receiving two or more doses versus 50 years for unvaccinated), with higher levels of severe comorbidity (20.3% versus 3.9%) and immunocompromise (15.0% versus 2.3%). Compared with patients who were unvaccinated, those vaccinated with two doses had a relative risk reduction (RRR) of between 90.1% (patients aged 18-29, 95% CI, 86.8% to 92.7%) and 95.9% (patients aged 60-69, 95% CI, 95.5% to 96.2%). Waning was only observed for those aged 70+, for whom the RRR reduced from 97.3% (91.0% to 99.2%) to 86.7% (85.3% to 90.1%) between May and December but increased again to 98.3% (97.6% to 98.8%) with a booster/third dose. ConclusionImportant demographic and clinical differences exist between vaccinated and unvaccinated patients admitted to critical care with COVID-19. While not a causal analysis, our findings are consistent with a substantial and sustained impact of vaccination on reducing admissions to critical care during Englands Delta wave, with evidence of waning predominantly restricted to those aged 70+.
ABSTRACT
ObjectivesTo compare approaches for obtaining relative and absolute estimates of risk of 28-day COVID-19 mortality for adults in the general population of England in the context of changing levels of circulating infection. DesignThree designs were compared. (A) case-cohort which does not explicitly account for the time-changing prevalence of COVID-19 infection, (B) 28-day landmarking, a series of sequential overlapping sub-studies incorporating time-updating proxy measures of the prevalence of infection, and (C) daily landmarking. Regression models were fitted to predict 28-day COVID-19 mortality. SettingWorking on behalf of NHS England, we used clinical data from adult patients from all regions of England held in the TPP SystmOne electronic health record system, linked to Office for National Statistics (ONS) mortality data, using the OpenSAFELY platform. ParticipantsEligible participants were adults aged 18 or over, registered at a general practice using TPP software on 1st March 2020 with recorded sex, postcode and ethnicity. 11,972,947 individuals were included, and 7,999 participants experienced a COVID-19 related death. The study period lasted 100 days, ending 8th June 2020. PredictorsA range of demographic characteristics and comorbidities were used as potential predictors. Local infection prevalence was estimated with three proxies: modelled based on local prevalence and other key factors; rate of A&E COVID-19 related attendances; and rate of suspected COVID-19 cases in primary care. Main outcome measuresCOVID-19 related death. ResultsAll models discriminated well between patients who did and did not experience COVID-19 related death, with C-statistics ranging from 0.92-0.94. Accurate estimates of absolute risk required data on local infection prevalence, with modelled estimates providing the best performance. ConclusionsReliable estimates of absolute risk need to incorporate changing local prevalence of infection. Simple models can provide very good discrimination and may simplify implementation of risk prediction tools in practice.
