Artikel
A German job exposure matrix for COVID-19 infections (COVID-19-JEM) based on data from the Gutenberg COVID-19 Study (GCS)
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Autoren
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Karin Rossnagel
- Bundesanstalt für Arbeitsschutz und Arbeitsmedizin, Fachbereich Arbeit und Gesundheit, Berlin, Germany
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Sylvia Jankowiak
- Bundesanstalt für Arbeitsschutz und Arbeitsmedizin, Fachbereich Arbeit und Gesundheit, Berlin, Germany
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Michaela Prigge
- Bundesanstalt für Arbeitsschutz und Arbeitsmedizin, Fachbereich Arbeit und Gesundheit, Berlin, Germany
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Julian Chalabi
- Präventive Kardiologie und Medizinische Prävention, Universitätsmedizin der Johannes Gutenberg-Universität Mainz, Mainz, Germany
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Daniela Zahn
- Universitätsmedizin Mainz, Mainz, Germany
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Rieke Baumkötter
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Simge Yilmaz
- Preventive Cardiology and Preventive Medicine, Center for Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
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Pavel Dietz
- Institute of Occupational, Social, and Environmental Medicine, University Medical Center of the Johannes Gutenberg University, Mainz, Germany
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Emilio Gianicolo
- Institut für Medizinische Biometrie, Epidemiologie und Informatik der Universitätsmedizin der Johannes Gutemberg-Universität, Mainz, Italy
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Thomas Münzel
- Department of Cardiology – Cardiology I, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
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Karl Lackner
- Institute for Clinical Chemistry and Laboratory Medicine, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
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Alexander Schuster
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Manfred Beutel
- Department of Psychosomatic Medicine and Psychotherapy, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
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Martin Schütte
- Bundesanstalt für Arbeitsschutz und Arbeitsmedizin, Fachbereich Arbeit und Gesundheit, Berlin, Germany
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Philipp Wild
- Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
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Janice Hegewald
- Bundesanstalt für Arbeitsschutz und Arbeitsmedizin, Berlin, Germany
| Veröffentlicht: | 6. September 2024 |
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Gliederung
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Background: Job-Exposure Matrices (JEMs) are a common method used in occupational epidemiology to assess exposure when individual data is lacking. In 2022, experts from Denmark, the Netherlands and the United Kingdom systematically ranked the expected exposures and workplace characteristics for each job and country to construct three country-specific JEMs describing potential occupational risk factors for SARS-CoV-2 infections [1]. Thus, our aim was to develop a similar COVID-19-JEM for German pandemic working conditions using data from the Gutenberg COVID-19 Study (GCS).
Methods: Between October 2020 and June 2021, the GCS asked a population-based sample about their occupation and working conditions during the pandemic. Occupations were coded with the German Classification of Occupations 2010 (KldB 2010, revised version 2020), a hierarchical classification of occupations with five numerically coded levels.
Based on the available GCS data on working conditions, we attempted to emulate the eight risk dimensions used in [1]. These comprised four determinants of transmission risk: number of co-workers in close vicinity, type of contacts (co-workers, customers, patients), location (indoor or outdoor), contaminated work surfaces. Two dimensions of risk reduction measures: social distancing and face covering. Two dimensions, migrant workers and income insecurity, served as proxies for precarious work.
Working conditions were generally categorised as having no risk (score=0), low risk (score=1), elevated risk (score=2) or high risk (score=3). However, the variables describing location and face coverings permitted only a categorisation up to elevated risk (score=2).
Ordinal multi-level regression models (random intercept) adjusted for gender, age and the log of weekly working hours were used to predict the dimension category for each occupational category [2]. The JEM is made up of the predicted values for each occupational category from these models.
Results: Overall, data of 5749 working participants were used to construct the JEM, 50% of whom were women (n=2870). The mean age was 49.8 years and 1097 participants reported working exclusively at home.
With the available data we could emulate six of the eight original dimensions. No information was available regarding contaminated surfaces and too few migrant workers were included in the study population. Due to group sizes, the JEM could only be created for the first three levels (digits) of the KldB 2010: nine occupational areas (1-digit), 36 occupational main groups (2-digit) and 113 of 144 (78.5%) occupational groups (3-digit). The variation of the different risk scores explained by occupation (random effects) ranged from 0.51 to 2.81 in the 3-digit models.
Conclusion: This first attempt at a German COVID-19-JEM provides a data-based description of job conditions for occupational groups. However, the data was not always sufficient to adequately construct all of the risk dimension scores. Also, the sample size was too small to estimate scores for each occupational group (3-digit) or individual occupations (4-digit). Next steps could include constructing scores for missing occupational groups using other sources (e.g., BERUFENET) and testing the ability of the JEM to predict infections. A validated JEM can help identify occupations with a greater need for primary prevention.
The authors declare that they have no competing interests.
The authors declare that a positive ethics committee vote has been obtained.
References
- 1.
- Oude Hengel KM, Burdorf A, Pronk A, Schlünssen V, Stokholm ZA, Kolstad HA et al. Exposure to a SARS-CoV-2 infection at work: development of an international job exposure matrix (COVID-19-JEM). Scand J Work Environ Health. 2022;48(1):61–70.
- 2.
- Kroll LE. Construction and Validation of a General Index for Job Demands in Occupations Based on ISCO-88 and KldB-92. MDA – Methoden, Daten, Analysen. 2011;5(1):63-90.
