Quantifying the risk of workplace COVID-19 clusters in terms of commuter, workplace, and population characteristics

作者：Christopher E. Overton, Rachel Abbey, Tarrion Baird, Rachel Christie, Owen Daniel, Julie Day, Matthew Gittins, Owen Jones, Robert S. Paton, Maria Tang, Tom Ward, Jack Wilkinson, Camilla Woodrow-Hill, T.L. Aldridge, Yiqun Chen · 发表于：arXiv (Cornell University) · 年份：2023 · DOI：10.48550/arxiv.2305.08745 · 被引用次数：2 · 研究领域：COVID-19 epidemiological studies、Air Quality and Health Impacts、COVID-19 and Mental Health

Objectives: To identify and quantify risk factors that contribute to clusters of COVID-19 in the workplace. Methods: We identified clusters of COVID-19 cases in the workplace and investigated the characteristics of the individuals, the workplaces, the areas they work, and the methods of commute to work, through data linkages based on Middle Layer Super Output Areas (MSOAs) in England between 20/06/2021 and 20/02/2022. We estimated associations between potential risk factors and workplace clusters, adjusting for plausible confounders identified using a Directed Acyclic Graph (DAG). Results: For most industries, increased physical proximity in the workplace was associated with increased risk of COVID-19 clusters, while increased vaccination was associated with reduced risk. Commuter demographic risk factors varied across industry, but for the majority of industries, a higher proportion of black/african/caribbean ethnicities, and living in deprived areas, was associated with increased cluster risk. A higher proportion of commuters in the 60-64 age group was associated with reduced cluster risk. There were significant associations between gender, work commute methods, and staff contract type with cluster risk, but these were highly variable across industries. Conclusions: This study has used novel national data linkages to identify potential risk factors of workplace COVID-19 clusters, including possible protective effects of vaccination and increased physical distance at work. T...