A SARS-CoV-2 outbreak in a plastics manufacturing plant.

BACKGROUND: A SARS-CoV-2 outbreak with an attack rate of 14.3% was reported at a plastics manufacturing plant in England. METHODS: Between 23rd March and 13th May 2021, the COVID-OUT team undertook a comprehensive outbreak investigation, including environmental assessment, surface sampling, molecular and serological testing, and detailed questionnaires, to identify potential SARS-CoV-2 transmission routes, and workplace- and worker-related risk factors. RESULTS: While ventilation, indicated using real-time CO2 proxy measures, was generally adequate on-site, the technical office with the highest localized attack rate (21.4%) frequently reached peaks in CO2 of 2100ppm. SARS-CoV-2 RNA was found in low levels (Ct ≥35) in surface samples collected across the site. High noise levels (79dB) were recorded in the main production area, and study participants reported having close work contacts (73.1%) and sharing tools (75.5%). Only 20.0% of participants reported using a surgical mask and/or FFP2/FFP3 respirator at least half the time and 71.0% expressed concerns regarding potential pay decreases and/or unemployment due to self-isolation or workplace closure. CONCLUSIONS: The findings reinforce the importance of enhanced infection control measures in manufacturing sectors, including improved ventilation with possible consideration of CO2 monitoring, utilising air cleaning interventions in enclosed environments, and provision of good-quality face masks (i.e., surgical masks or FFP2/FFP3 respirators) especially when social distancing cannot be maintained. Further research on the impacts of job security-related concerns is warranted.

Associations between an international COVID-19 job exposure matrix and SARS-CoV-2 infection among 2 million workers in Denmark.

OBJECTIVES: This study investigates the associations between the Danish version of a job exposure matrix for COVID-19 (COVID-19-JEM) and Danish register-based SARS-CoV-2 infection information across three waves of the pandemic. The COVID-19-JEM consists of four dimensions on transmission: two on mitigation measures, and two on precarious work characteristics. METHODS: The study comprised 2 021 309 persons from the Danish working population between 26 February 2020 and 15 December 2021. Logistic regression models were applied to assess the associations between the JEM dimensions and overall score and SARS-CoV-2 infection across three infection waves, with peaks in March-April 2020, December-January 2021, and February-March 2022. Sex, age, household income, country of birth, wave, residential region and during wave 3 vaccination status were accounted for. RESULTS: Higher risk scores within the transmission and mitigation dimensions and the overall JEM score resulted in higher odds ratios (OR) of a SARS-CoV-2 infection. OR attenuated across the three waves with ranges of 1.08-5.09 in wave 1, 1.06-1.60 in wave 2, and 1.05-1.45 in those not (fully) vaccinated in wave 3. In wave 3, no associations were found for those fully vaccinated. In all waves, the two precarious work dimensions showed weaker or inversed associations. CONCLUSIONS: The COVID-19-JEM is a promising tool for assessing occupational exposure to SARS-CoV-2 and other airborne infectious agents that mainly spread between people who are in close contact with each other. However, its usefulness depends on applied restrictions and the vaccination status in the population of interest.

Exploring the relationship between job characteristics and infection: Application of a COVID-19 job exposure matrix to SARS-CoV-2 infection data in the United Kingdom.

OBJECTIVE: This study aimed to assess whether workplace exposures as estimated via a COVID-19 job exposure matrix (JEM) are associated with SARS-CoV-2 in the UK. METHODS: Data on 244 470 participants were available from the Office for National Statistics Coronavirus Infection Survey (CIS) and 16 801 participants from the Virus Watch Cohort, restricted to workers aged 20-64 years. Analysis used logistic regression models with SARS-CoV-2 as the dependent variable for eight individual JEM domains (number of workers, nature of contacts, contact via surfaces, indoor or outdoor location, ability to social distance, use of face covering, job insecurity, and migrant workers) with adjustment for age, sex, ethnicity, index of multiple deprivation (IMD), region, household size, urban versus rural area, and health conditions. Analyses were repeated for three time periods (i) February 2020 (Virus Watch)/April 2020 (CIS) to May 2021), (ii) June 2021 to November 2021, and (iii) December 2021 to January 2022. RESULTS: Overall, higher risk classifications for the first six domains tended to be associated with an increased risk of infection, with little evidence of a relationship for domains relating to proportion of workers with job insecurity or migrant workers. By time there was a clear exposure-response relationship for these domains in the first period only. Results were largely consistent across the two UK cohorts. CONCLUSIONS: An exposure-response relationship exists in the early phase of the COVID-19 pandemic for number of contacts, nature of contacts, contacts via surfaces, indoor or outdoor location, ability to social distance and use of face coverings. These associations appear to have diminished over time.

Validation of a COVID-19 Job Exposure Matrix (COVID-19-JEM) for Occupational Risk of a SARS-CoV-2 Infection at Work: Using Data of Dutch Workers.

OBJECTIVES: A COVID-19 Job Exposure Matrix (COVID-19-JEM) has been developed, consisting of four dimensions on transmission, two on mitigation measures, and two on precarious work. This study aims to validate the COVID-19-JEM by (i) comparing risk scores assigned by the COVID-19-JEM with self-reported data, and (ii) estimating the associations between the COVID-19-JEM risk scores and self-reported COVID-19. METHODS: Data from measurements 2 (July 2020, n = 7690) and 4 (March 2021, n = 6794) of the Netherlands Working Conditions Survey-COVID-19 (NWCS-COVID-19) cohort study were used. Responses to questions related to the transmission risks and mitigation measures of Measurement 2 were used to calculate self-reported risk scores. These scores were compared with the COVID-19-JEM attributed risk scores, by assessing the percentage agreement and weighted kappa (κ). Based on Measurement 4, logistic regression analyses were conducted to estimate the associations between all COVID-19-JEM risk scores and self-reported COVID-19 (infection in general and infected at work). RESULTS: The agreement between the COVID-19-JEM and questionnaire-based risk scores was good (κ ≥ 0.70) for most dimensions, except work location (κ = 0.56), and face covering (κ = 0.41). Apart from the precarious work dimensions, higher COVID-19-JEM assigned risk scores had higher odds ratios (ORs; ranging between 1.28 and 1.80) on having had COVID-19. Associations were stronger when the infection were thought to have happened at work (ORs between 2.33 and 11.62). CONCLUSIONS: Generally, the COVID-19-JEM showed a good agreement with self-reported infection risks and infection rates at work. The next step is to validate the COVID-19-JEM with objective data in the Netherlands and beyond.

Occupational differences in COVID-19 incidence, severity, and mortality in the United Kingdom: Available data and framework for analyses

There are important differences in the risk of SARS-CoV-2 infection and death depending on occupation. Infections in healthcare workers have received the most attention, and there are clearly increased risks for intensive care unit workers who are caring for COVID-19 patients. However, a number of other occupations may also be at an increased risk, particularly those which involve social care or contact with the public. A large number of data sets are available with the potential to assess occupational risks of COVID-19 incidence, severity, or mortality. We are reviewing these data sets as part of the Partnership for Research in Occupational, Transport, Environmental COVID Transmission (PROTECT) initiative, which is part of the National COVID-19 Core Studies. In this report, we review the data sets available (including the key variables on occupation and potential confounders) for examining occupational differences in SARS-CoV-2 infection and COVID-19 incidence, severity and mortality. We also discuss the possible types of analyses of these data sets and the definitions of (occupational) exposure and outcomes. We conclude that none of these data sets are ideal, and all have various strengths and weaknesses. For example, mortality data suffer from problems of coding of COVID-19 deaths, and the deaths (in England and Wales) that have been referred to the coroner are unavailable. On the other hand, testing data is heavily biased in some periods (particularly the first wave) because some occupations (e.g. healthcare workers) were tested more often than the general population. Random population surveys are, in principle, ideal for estimating population prevalence and incidence, but are also affected by non-response. Thus, any analysis of the risks in a particular occupation or sector (e.g. transport), will require a careful analysis and triangulation of findings across the various available data sets.

Investigation of a SARS-CoV-2 Outbreak at an Automotive Manufacturing Site in England.

Workplace-related outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continue to occur globally. The manufacturing sector presents a particular concern for outbreaks, and a better understanding of transmission risks are needed. Between 9 March and 24 April 2021, the COVID-19 (coronavirus disease 2019) Outbreak Investigation to Understand Transmission (COVID-OUT) study undertook a comprehensive investigation of a SARS-CoV-2 outbreak at an automotive manufacturing site in England. The site had a total of 266 workers, and 51 SARS-CoV-2 infections. Overall, ventilation, humidity, and temperature at the site were assessed to be appropriate for the number of workers and the work being conducted. The company had implemented a number of infection control procedures, including provision of face coverings, spacing in the work, and welfare areas to allow for social distancing. However, observations of worker practices identified lapses in social distancing, although all were wearing face coverings. A total of 38 workers, including four confirmed cases, participated in the COVID-OUT study. The majority of participants received COVID-19 prevention training, though 42.9% also reported that their work required close physical contact with co-workers. Additionally, 73.7% and 34.2% had concerns regarding reductions in future income and future unemployment, respectively, due to self-isolation. This investigation adds to the growing body of evidence of SARS-CoV-2 outbreaks from the manufacturing sector. Despite a layered COVID-19 control strategy at this site, cases clustered in areas of high occupancy and close worker proximity.

Excess mortality among essential workers in England and Wales during the COVID-19 pandemic.

BACKGROUND: Exposure to SARS-CoV-2, subsequent development of COVID-19 and death from COVID-19 may vary by occupation, and the risks may be higher for those categorised as 'essential workers'. METHODS: We estimated excess mortality by occupational group and sex separately for each month in 2020 and for the entire 12 months overall. RESULTS: Mortality for all adults of working age was similar to the annual average over the previous 5 years. Monthly excess mortality peaked in April, when the number of deaths was 54.2% higher than expected and was lowest in December when deaths were 30.0% lower than expected.Essential workers had consistently higher excess mortality than other groups throughout 2020. There were also large differences in excess mortality between the categories of essential workers, with healthcare workers having the highest excess mortality and social care and education workers having the lowest. Excess mortality also varied widely between men and women, even within the same occupational group. Generally, excess mortality was higher in men. CONCLUSIONS: In summary, excess mortality was consistently higher for essential workers throughout 2020, particularly for healthcare workers. Further research is needed to examine excess mortality by occupational group, while controlling for important confounders such as ethnicity and socioeconomic status. For non-essential workers, the lockdowns, encouragement to work from home and to maintain social distancing are likely to have prevented a number of deaths from COVID-19 and from other causes.

Exposure to a SARS-CoV-2 infection at work: development of an international job exposure matrix (COVID-19-JEM).

OBJECTIVE: This study aimed to construct a job exposure matrix (JEM) for risk of becoming infected with the SARS-CoV-2 virus in an occupational setting. METHODS: Experts in occupational epidemiology from three European countries (Denmark, The Netherlands and the United Kingdom) defined the relevant exposure and workplace characteristics with regard to possible exposure to the SARS-CoV-2 virus. In an iterative process, experts rated the different dimensions of the COVID-19-JEM for each job title within the International Standard Classification of Occupations system 2008 (ISCO-08). Agreement scores, weighted kappas, and variances were estimated. RESULTS: The COVID-19-JEM contains four determinants of transmission risk [number of people, nature of contacts, contaminated workspaces and location (indoors or outdoors)], two mitigation measures (social distancing and face covering), and two factors for precarious work (income insecurity and proportion of migrants). Agreement scores ranged from 0.27 [95% confidence interval (CI) 0.25-0.29] for 'migrants' to 0.76 (95% CI 0.74-0.78) for 'nature of contacts'. Weighted kappas indicated moderate-to-good agreement for all dimensions [ranging from 0.60 (95% CI 0.60-0.60) for 'face covering' to 0.80 (95% CI 0.80-0.80) for 'contaminated workspaces'], except for 'migrants' (0.14 (95% CI -0.07-0.36). As country differences remained after several consensus exercises, the COVID-19-JEM also has a country-axis. CONCLUSIONS: The COVID-19-JEM assesses the risk at population level using eight dimensions related to SARS-COV-2 infections at work and will improve our ability to investigate work-related risk factors in epidemiological studies. The dimensions of the COVID-19-JEM could also be valuable for other future communicable diseases in the workplace.

Occupational differences in COVID-19 incidence, severity, and mortality in the United Kingdom: Available data and framework for analyses.

There are important differences in the risk of SARS-CoV-2 infection and death depending on occupation. Infections in healthcare workers have received the most attention, and there are clearly increased risks for intensive care unit workers who are caring for COVID-19 patients. However, a number of other occupations may also be at an increased risk, particularly those which involve social care or contact with the public. A large number of data sets are available with the potential to assess occupational risks of COVID-19 incidence, severity, or mortality. We are reviewing these data sets as part of the Partnership for Research in Occupational, Transport, Environmental COVID Transmission (PROTECT) initiative, which is part of the National COVID-19 Core Studies. In this report, we review the data sets available (including the key variables on occupation and potential confounders) for examining occupational differences in SARS-CoV-2 infection and COVID-19 incidence, severity and mortality. We also discuss the possible types of analyses of these data sets and the definitions of (occupational) exposure and outcomes. We conclude that none of these data sets are ideal, and all have various strengths and weaknesses. For example, mortality data suffer from problems of coding of COVID-19 deaths, and the deaths (in England and Wales) that have been referred to the coroner are unavailable. On the other hand, testing data is heavily biased in some periods (particularly the first wave) because some occupations (e.g. healthcare workers) were tested more often than the general population. Random population surveys are, in principle, ideal for estimating population prevalence and incidence, but are also affected by non-response. Thus, any analysis of the risks in a particular occupation or sector (e.g. transport), will require a careful analysis and triangulation of findings across the various available data sets.