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

Hengel, Karen M Oude; Burdorf, Alex; Pronk, Anjoeka; Schlünssen, Vivi; Stokholm, Zara Ann; Kolstad, Henrik Albert; Veldhoven, Karin van; Basinas, Ioannis; Tongeren, Martie van; Peters, Susan

doi:10.5271/sjweh.3998

Cited by 58 publications

(81 citation statements)

References 20 publications

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“…Results of Cox regression for 13 categories of essential worker compared to non-essential workers. Time to first infection adjusted for (1) age and sex (2) age, sex, IMD, ethnic group, region (3) age, sex, IMD, ethnic group, region, household size, rural or urban location, health conditions. Model uses 3772517 observations from 312 304 participants.…”

Section: Resultsmentioning

confidence: 99%

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Occupational differences in SARS-CoV-2 infection: Analysis of the UK ONS Coronavirus (COVID-19) Infection Survey

Rhodes

Pearce

Mueller

et al. 2022

Preprint

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BackgroundConsiderable concern remains about how occupational SARS-CoV-2 risk has evolved during the COVID-19 pandemic. We aimed to ascertain which occupations had the greatest risk of SARS-CoV-2 infection and explore how relative differences varied over the pandemic.MethodsAnalysis of cohort data from the UK Office of National Statistics Coronavirus (COVID-19) Infection Survey from April 2020 to November 2021. This survey is designed to be representative of the UK population and uses regular PCR testing. Cox and multilevel logistic regression to compare SARS-CoV-2 infection between occupational/sector groups, overall and by four time periods with interactions, adjusted for age, sex, ethnicity, deprivation, region, household size, urban/rural neighbourhood and current health conditions.ResultsBased on 3,910,311 observations from 312,304 working age adults, elevated risks of infection can be seen overall for social care (HR 1.14; 95% CI 1.04 to 1.24), education (HR 1.31; 95% CI 1.23 to 1.39), bus and coach drivers (1.43; 95% CI 1.03 to 1.97) and police and protective services (HR 1.45; 95% CI 1.29 to 1.62) when compared to non-essential workers. By time period, relative differences were more pronounced early in the pandemic. For healthcare elevated odds in the early waves switched to a reduction in the later stages. Education saw raises after the initial lockdown and this has persisted. Adjustment for covariates made very little difference to effect estimates.ConclusionsElevated risks among healthcare workers have diminished over time but education workers have had persistently higher risks. Long-term mitigation measures in certain workplaces may be warranted.What is already known on this topicSome occupational groups have observed increased rates of disease and mortality relating to COVID-19.What this study addsRelative differences between occupational groups have varied during different stages of the COVID-19 pandemic with risks for healthcare workers diminishing over time and workers in the education sector seeing persistent elevated risks.How this study might affect research, practice or policyIncreased long term mitigation such as ventilation should be considered in sectors with a persistent elevated risk. It is important for workplace policy to be responsive to evolving pandemic risks.

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Section: Resultsmentioning

confidence: 99%

“…Hence, it is important that we better understand occupational risks in order to inform policy and practice. Risk of COVID-19 disease in the workplace will be a consequence of exposure to SARS-CoV-2 virus; workplace factors known to be related to exposure include ventilation, ability to social distance and number of daily contacts (3, 4).…”

Section: Introductionmentioning

confidence: 99%

Occupational differences in SARS-CoV-2 infection: Analysis of the UK ONS Coronavirus (COVID-19) Infection Survey

Rhodes

Pearce

Mueller

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…[14] An English working Group developed a control banding matrix to help employers assess the risks of COVID-19 infection during the pandemic, [15] and researchers from Denmark, Netherlands and United Kingdom also developed a JEM defining relevant exposure and workplace characteristics related to exposure to the SARS-CoV2. [16] Differences in country and design would be interesting to compare in future studies. Some limitations of the JEM approach exist.…”

Section: Discussionmentioning

confidence: 99%

Mat-O-Covid: Validation of a SARS-CoV-2 Job Exposure Matrix (JEM) Using Data from a National Compensation System for Occupational Covid-19

Descatha¹,

Sembajwe²,

Gilbert³

et al. 2022

Preprint

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Background. We aimed to assess the validity of the Mat-O-Covid Job Exposure Matrix (JEM) on SARS-CoV2 using compensation data from the French National Health Insurance compensation system for occupational-related COVID-19. Methods. Deidentified compensation data for occupational COVID-19 in France were obtained between August 2020 and August 2021. The acceptance was considered as the reference. Mat-O-Covid is an expert based French JEM on workplace exposure to SARS-CoV2. Bivariate and multivariate models were used to study the association between the exposure assessed by Mat-O-Covid and the reference, as well as the Area Under Curves (AUC), sensitivity, specificity, predictive values, and likelihood ratios. Results. In the 1140 cases included, there was a close association between the Mat-O-Covid index and the reference (p&lt;0.0001). The overall predictivity was good, with an AUC of 0.78 and an optimal threshold at 13 per thousand. Using Youden’s J statistic resulted in 0.67 sensitivity and 0.87 specificity. Both positive and negative likelihood ratios were significant: respectively 4.9 [2.4-6.4] and 0.4 [0.3-0.4]. Discussion. It was possible to assess Mat-O-Covid’s validity using data from the national compensation system for occupational COVID-19. Though further studies are needed, Mat-O-Covid exposure assessment appears to be accurate enough to be used in research.

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“…However, it is challenging to obtain individual exposure data on SARS-CoV-2 infections in large populations, if not impossible considering the time scales involved. Therefore, different tools have been developed during the pandemic to identify levels of COVID-19 risk by occupation to guide policymakers, occupational safety and health (OSH) practitioners, and employers during the pandemic ( Baker et al , 2020 ; Zhang, 2021 ; Oude Hengel et al , 2022 ; Williams et al , 2022 ). Job Exposure Matrices (JEM) have been developed for a wide range of exposures that occur in the workplace and convert occupations into estimates of exposure ( Kromhout and Vermeulen, 2001 ; Peters, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…Job Exposure Matrices (JEM) have been developed for a wide range of exposures that occur in the workplace and convert occupations into estimates of exposure ( Kromhout and Vermeulen, 2001 ; Peters, 2020 ). Oude Hengel et al (2022) developed an international COVID-19 Job Exposure Matrix (COVID-19-JEM), consisting of eight dimensions associated with risk of transmission, mitigation measures, and precarious work, to enhance the investigation of the role of the workplace in the spread of SARS-CoV-2 infections and subsequent cases of COVID-19 disease ( Oude Hengel et al , 2022 ). This COVID-19-JEM was developed by occupational exposure experts from three countries—i.e.…”

Section: Introductionmentioning

confidence: 99%

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

Peters

Schlünssen

Veldhoven

et al. 2022

Annals of Work Exposures and Health

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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.

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

Cited by 58 publications

References 20 publications

Occupational differences in SARS-CoV-2 infection: Analysis of the UK ONS Coronavirus (COVID-19) Infection Survey

Occupational differences in SARS-CoV-2 infection: Analysis of the UK ONS Coronavirus (COVID-19) Infection Survey

Mat-O-Covid: Validation of a SARS-CoV-2 Job Exposure Matrix (JEM) Using Data from a National Compensation System for Occupational Covid-19

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

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