An Ecological Study of COVID-19 Infection Rates within the UK Food and Drink Processing Industry.

Abstract

ObjectivesFood processing facilities represent critical infrastructure that have stayed open during much of the COVID-19 pandemic. Understanding the burden of COVID-19 in this sector is thus important to help reduce the potential for workplace infection in future outbreaks.MethodsWe undertook a workplace survey in the UK food and drink processing sector and collected information on workplace size, characteristics (e.g. temperature, ventilation), and experience with COVID-19 (e.g. numbers of positive cases). For each site, we calculated COVID-19 case rates per month per 1000 workers. We performed an ecological analysis using negative binomial regression to assess the association between COVID-19 rates and workplace and local risk factors.ResultsRespondents from 33 companies including 66 individual sites completed the survey. COVID-19 cases were reported from the start of the pandemic up to June 2021. Respondents represented a range of industry subgroups, including grain milling/storage (n = 16), manufacture of malt (n = 14), manufacture of prepared meals (n = 12), manufacture of beverages (n = 8), distilling (n = 5), manufacture of baked goods (n = 5), and other (n = 6), with a total of 15 563 workers across all sites. Average monthly case rates per 1000 workers ranged from 0.9 in distilling to 6.1 in grain milling/storage. Incidence rate ratios were partially attenuated after adjusting for several local and workplace factors, though risks for one subgroup (grain milling/storage) remained elevated. Certain local and workplace characteristics were related to higher infection rates, such as higher deprivation (5 km only), a lower proportion of remote workers, lower proportion of workers in close proximity, and higher numbers of workers overall.ConclusionsOur analysis suggests some heterogeneity in the rates of COVID-19 across sectors of the UK food and drink processing industry. Infection rates were associated with deprivation, the proportions of remote workers and workers in close proximity, and the number of workers.