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Objectives: To identify and quantify risk factors that contribute to clustersof COVID-19 in the workplace.  Methods: We identified clusters of COVID-19 cases in the workplace andinvestigated the characteristics of the individuals, the workplaces, the areasthey work, and the methods of commute to work, through data linkages based onMiddle Layer Super Output Areas (MSOAs) in England between 20/06/2021 and20/02/2022. We estimated associations between potential risk factors andworkplace clusters, adjusting for plausible confounders identified using aDirected Acyclic Graph (DAG).  Results: For most industries, increased physical proximity in the workplacewas associated with increased risk of COVID-19 clusters, while increasedvaccination was associated with reduced risk. Commuter demographic risk factorsvaried across industry, but for the majority of industries, a higher proportionof black/african/caribbean ethnicities, and living in deprived areas, wasassociated with increased cluster risk. A higher proportion of commuters in the60-64 age group was associated with reduced cluster risk. There weresignificant associations between gender, work commute methods, and staffcontract type with cluster risk, but these were highly variable acrossindustries.  Conclusions: This study has used novel national data linkages to identifypotential risk factors of workplace COVID-19 clusters, including possibleprotective effects of vaccination and increased physical distance at work. Thesame methodological approach can be applied to wider occupational andenvironmental health research.

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