The mental health of staff working on intensive care units over the COVID-19 winter surge of 2020 in England: a cross sectional survey.

BACKGROUND: The COVID-19 pandemic generated a surge of critically ill patients greater than the capacity of the UK National Health Service (NHS). There have been multiple well-documented impacts associated with the national COVID-19 pandemic surge on ICU staff, including an increased prevalence of mental health disorders on a scale potentially sufficient to impair high-quality care delivery. We investigated the prevalence of five mental health outcomes; explored demographic and professional predictors of poor mental health outcomes; and describe the prevalence of functional impairment; and explore demographic and professional predictors of functional impairment in ICU staff over the 2020/2021 winter COVID-19 surge in England. METHODS: English ICU staff were surveyed before, during, and after the winter 2020/2021 surge using a survey which comprised validated measures of mental health. RESULTS: A total of 6080 surveys were completed, by ICU nurses (57.5%), doctors (27.9%), and other healthcare staff (14.5%). Reporting probable mental health disorders increased from 51% (before) to 64% (during), and then decreased to 46% (after). Younger, less experienced nursing staff were most likely to report probable mental health disorders. During and after the winter, >50% of participants met threshold criteria for functional impairment. Staff who reported probable post-traumatic stress disorder, anxiety, or depression were more likely to meet threshold criteria for functional impairment. CONCLUSIONS: The winter of 2020/2021 was associated with an increase in poor mental health outcomes and functional impairment amongst ICU staff during a period of peak caseload. These effects are likely to impact on patient care outcomes and the longer-term resilience of the healthcare workforce.

June: open-source individual-based epidemiology simulation.

We introduce June, an open-source framework for the detailed simulation of epidemics on the basis of social interactions in a virtual population constructed from geographically granular census data, reflecting age, sex, ethnicity and socio-economic indicators. Interactions between individuals are modelled in groups of various sizes and properties, such as households, schools and workplaces, and other social activities using social mixing matrices. June provides a suite of flexible parametrizations that describe infectious diseases, how they are transmitted and affect contaminated individuals. In this paper, we apply June to the specific case of modelling the spread of COVID-19 in England. We discuss the quality of initial model outputs which reproduce reported hospital admission and mortality statistics at national and regional levels as well as by age strata.