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Sick leave due to COVID-19 during the first pandemic wave in France, 2020

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  • Additional Information
    • Contributors:
      Laboratoire Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS); Conservatoire National des Arts et Métiers CNAM (CNAM); HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM); Epidémiologie et modélisation de la résistance aux antimicrobiens - Epidemiology and modelling of bacterial escape to antimicrobials (EMAE); Institut Pasteur Paris (IP)-Université Paris Cité (UPCité); Centre de recherche en épidémiologie et santé des populations (CESP); Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay; Université Paris-Saclay; Pasteur-Cnam Risques infectieux et émergents (PACRI); Institut Pasteur Paris (IP)-Conservatoire National des Arts et Métiers CNAM (CNAM); HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Paris Cité (UPCité); Université de Rennes (UR); Arènes: politique, santé publique, environnement, médias (ARENES); Université de Rennes (UR)-Institut d'Études Politiques IEP - Rennes-École des Hautes Études en Santé Publique EHESP (EHESP)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS); Recherche sur les services et le management en santé (RSMS); Université de Rennes (UR)-École des Hautes Études en Santé Publique EHESP (EHESP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS); École des Hautes Études en Santé Publique EHESP (EHESP); Département Méthodes quantitatives en santé publique (METIS); Imperial College London; None; Collaboration internationale
    • Publication Information:
      HAL CCSD
      BMJ Publishing Group
    • Publication Date:
      2023
    • Collection:
      EHESP - Productions scientifiques de l'Ecole des hautes études en santé publique
    • Abstract:
      International audience ; Objectives To quantify the burden of COVID-19-related sick leave during the first pandemic wave in France, accounting for sick leaves due to symptomatic COVID-19 (‘symptomatic sick leaves’) and those due to close contact with COVID-19 cases (‘contact sick leaves’). Methods We combined data from a national demographic database, an occupational health survey, a social behaviour survey and a dynamic SARS-CoV-2 transmission model. Sick leave incidence from 1 March 2020 to 31 May 2020 was estimated by summing daily probabilities of symptomatic and contact sick leaves, stratified by age and administrative region. Results There were an estimated 1.70M COVID-19-related sick leaves among France’s 40M working-age adults during the first pandemic wave, including 0.42M due to COVID-19 symptoms and 1.28M due to COVID-19 contacts. There was great geographical variation, with peak daily sick leave incidence ranging from 230 in Corse (Corsica) to 33 000 in Île-de-France (the greater Paris region), and greatest overall burden in regions of north-eastern France. Regional sick leave burden was generally proportional to local COVID-19 prevalence, but age-adjusted employment rates and contact behaviours also contributed. For instance, 37% of symptomatic infections occurred in Île-de-France, but 45% of sick leaves. Middle-aged workers bore disproportionately high sick leave burden, owing predominantly to greater incidence of contact sick leaves. Conclusions France was heavily impacted by sick leave during the first pandemic wave, with COVID-19 contacts accounting for approximately three-quarters of COVID-19-related sick leaves. In the absence of representative sick leave registry data, local demography, employment patterns, epidemiological trends and contact behaviours can be synthesised to quantify sick leave burden and, in turn, predict economic consequences of infectious disease epidemics.
    • Relation:
      info:eu-repo/semantics/altIdentifier/pmid/36914254; hal-04062254; https://univ-rennes.hal.science/hal-04062254; https://univ-rennes.hal.science/hal-04062254/document; https://univ-rennes.hal.science/hal-04062254/file/oemed-2022-108451.full.pdf; PUBMED: 36914254; PUBMEDCENTRAL: PMC10176331
    • Accession Number:
      10.1136/oemed-2022-108451
    • Online Access:
      https://doi.org/10.1136/oemed-2022-108451
      https://univ-rennes.hal.science/hal-04062254
      https://univ-rennes.hal.science/hal-04062254/document
      https://univ-rennes.hal.science/hal-04062254/file/oemed-2022-108451.full.pdf
    • Rights:
      http://creativecommons.org/licenses/by-nc/ ; info:eu-repo/semantics/OpenAccess
    • Accession Number:
      edsbas.833CB78B