IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v22y2025i3p448-d1614734.html
   My bibliography  Save this article

Extending a COVID-19 Job Exposure Matrix: The SARS-CoV-2 or COVID-19 Job Exposure Matrix Module (SCoVJEM Module) for Population-Based Studies

Author

Listed:
  • Ximena P. Vergara

    (Heluna Health, 3300 Crossroads Pkwy. N #450, City of Industry, CA 91746, USA
    California Department of Public Health, Occupational Health Branch, 850 Marina Bay Pkwy. P-3, Richmond, CA 94804, USA)

  • Kathryn Gibb

    (California Department of Public Health, Occupational Health Branch, 850 Marina Bay Pkwy. P-3, Richmond, CA 94804, USA
    Public Health Institute, 555 12th Street, Oakland, CA 94607, USA)

  • David P. Bui

    (Heluna Health, 3300 Crossroads Pkwy. N #450, City of Industry, CA 91746, USA
    California Department of Public Health, Occupational Health Branch, 850 Marina Bay Pkwy. P-3, Richmond, CA 94804, USA)

  • Elisabeth Gebreegziabher

    (Heluna Health, 3300 Crossroads Pkwy. N #450, City of Industry, CA 91746, USA
    California Department of Public Health, Occupational Health Branch, 850 Marina Bay Pkwy. P-3, Richmond, CA 94804, USA)

  • Elon Ullman

    (Heluna Health, 3300 Crossroads Pkwy. N #450, City of Industry, CA 91746, USA
    California Department of Public Health, Occupational Health Branch, 850 Marina Bay Pkwy. P-3, Richmond, CA 94804, USA)

  • Kyle Peerless

    (California Department of Public Health, Occupational Health Branch, 850 Marina Bay Pkwy. P-3, Richmond, CA 94804, USA
    Public Health Institute, 555 12th Street, Oakland, CA 94607, USA)

Abstract

The risk of workplace SARS-CoV-2 transmission is increased by aerosolization or droplets and increased respiratory rates or increased viral stability in cold environments. Few methods exist for identifying occupational risks of SARS-CoV-2 transmission. We extended a SARS-CoV-2 job exposure matrix (JEM) into four dimensions, talking loudly (Loud) (very loud, loud, somewhat loud, or not), physical activity (PA) (high, medium or low), and cold (Cold) (cold or not) and hot environments (Hot) (hot or not), using data from the Occupational Information Network (O*NET) and a priori questions for each and noise measurements for 535 occupations. We classified 70%+ occupations as loud or very loud (74.6%); whereas 13.8% were high PA, 18.5% exposed to cold, and 23.7% exposed to hot temperatures. Applying to California 2019 workforce data to explore by race/ethnicity and sex, we found 21.2% worked in very loud and 12.6% in high PA occupations and 15.7% in cold and 17.8% hot environments. Latino workers were highly represented in very loud and high PA levels among farming (83.8 and 78.4%) and construction (58.7% and 50.3%). More males worked in each highest exposure level than females. This JEM provides aerosol transmission proxies for COVID-19 risk factors and merits investigation as a tool for epidemiologic studies.

Suggested Citation

  • Ximena P. Vergara & Kathryn Gibb & David P. Bui & Elisabeth Gebreegziabher & Elon Ullman & Kyle Peerless, 2025. "Extending a COVID-19 Job Exposure Matrix: The SARS-CoV-2 or COVID-19 Job Exposure Matrix Module (SCoVJEM Module) for Population-Based Studies," IJERPH, MDPI, vol. 22(3), pages 1-18, March.
    • Handle: RePEc:gam:jijerp:v:22:y:2025:i:3:p:448-:d:1614734
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/22/3/448/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/22/3/448/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Michael J. Handel, 2016. "The O*NET content model: strengths and limitations [Stärken und Grenzen des O*NET-Models]," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 49(2), pages 157-176, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Böhm, Robert & Letmathe, Peter & Schinner, Matthias, 2023. "The monetary value of competencies: A novel method and case study in smart manufacturing," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    2. repec:eur:ejesjr:364 is not listed on IDEAS
    3. repec:eur:ejesjr:361 is not listed on IDEAS
    4. Chiarello, Filippo & Fantoni, Gualtiero & Hogarth, Terence & Giordano, Vito & Baltina, Liga & Spada, Irene, 2021. "Towards ESCO 4.0 – Is the European classification of skills in line with Industry 4.0? A text mining approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    5. Granata,Julia & Moroz,Harry Edmund & Nga Thi Nguyen, 2023. "Identifying Skills Needs in Vietnam: The Survey of Detailed Skills," Policy Research Working Paper Series 10565, The World Bank.
    6. Christenko, Aleksandr, 2022. "Automation and occupational mobility: A task and knowledge-based approach," Technology in Society, Elsevier, vol. 70(C).
    7. Gottlieb, Charles & Grobovšek, Jan & Poschke, Markus & Saltiel, Fernando, 2021. "Working from home in developing countries," European Economic Review, Elsevier, vol. 133(C).
    8. Parsons, Christopher & Reysenbach, Tyler & Wahba, Jackline, 2020. "Network Sorting and Labor Market Outcomes: Evidence from the Chaotic Dispersal of the Viet Kieu," IZA Discussion Papers 13952, Institute of Labor Economics (IZA).
    9. Sabina Szymczak, 2023. "Systematic literature review: theory on GVCs' impact on wages, employment, and productivity," GUT FME Working Paper Series A 71, Faculty of Management and Economics, Gdansk University of Technology.
    10. Raimi, Daniel & Greenspon, Jacob, 2022. "Matching Geographies and Job Skills in the Energy Transition," RFF Working Paper Series 22-25, Resources for the Future.
    11. G. Jacob Blackwood & Cindy Cunningham & Matthew Dey & Lucia Foster & Cheryl Grim & John C. Haltiwanger & Rachel L. Nesbit & Sabrina Pabilonia & Jay Stewart & Cody Tuttle & Zoltan Wolf, 2023. "Opening the Black Box: Task and Skill Mix and Productivity Dispersion," NBER Chapters, in: Technology, Productivity, and Economic Growth, National Bureau of Economic Research, Inc.
    12. Chigusa Okamoto, 2019. "The effect of automation levels on US interstate migration," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 63(3), pages 519-539, December.
    13. Moh Hosseinioun & Frank Neffke & Letian Zhang & Hyejin Youn, 2025. "Skill dependencies uncover nested human capital," Nature Human Behaviour, Nature, vol. 9(4), pages 673-687, April.
    14. Fabio Montobbio & Jacopo Staccioli & Maria Enrica Virgillito & Marco Vivarelli, 2021. "Labour-saving automation and occupational exposure: a text-similarity measure," DISCE - Working Papers del Dipartimento di Politica Economica dipe0021, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    15. Sörman, Daniel Eriksson & Stenling, Andreas & Sundström, Anna & Rönnlund, Michael & Vega-Mendoza, Mariana & Hansson, Patrik & Ljungberg, Jessica K., 2021. "Occupational cognitive complexity and episodic memory in old age," Intelligence, Elsevier, vol. 89(C).
    16. Nikolova, Milena & Lepinteur, Anthony & Cnossen, Femke, 2023. "Just Another Cog in the Machine? A Worker-Level View of Robotization and Tasks," IZA Discussion Papers 16610, Institute of Labor Economics (IZA).
    17. Jacqueline Mosomi & Amy Thornton, 2022. "Physical proximity and occupational employment change by gender during the COVID-19 pandemic," WIDER Working Paper Series wp-2022-90, World Institute for Development Economic Research (UNU-WIDER).
    18. Giuseppe De Arcangelis & Rama Dasi Mariani, 2019. "Multi-Country Tasks Measures: Beyond US-based Data and a Focus on Migration," Economics Bulletin, AccessEcon, vol. 39(3), pages 2155-2161.
    19. Fruehwirt, Wolfgang & Duckworth, Paul, 2021. "Towards better healthcare: What could and should be automated?," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    20. Francesco Roncone, 2025. "Work-Hour Instability, Occupational Mobility and Gender," Working Papers wp1201, Dipartimento Scienze Economiche, Universita' di Bologna.
    21. Christelle Khalaf & Gilbert Michaud & G. Jason Jolley, 2023. "Predicting declining and growing occupations using supervised machine learning," Journal of Computational Social Science, Springer, vol. 6(2), pages 757-780, October.
    22. Arief A. Yusuf & Reza Anglingkusumo & Andy Sumner & Putri R. Halim & Anggita C.M. Kusuma, 2020. "Routinization And The Changing Task Composition In The Labor Market: Evidence From Indonesia," Working Papers WP/06/2020, Bank Indonesia.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:22:y:2025:i:3:p:448-:d:1614734. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.