Sick leave due to COVID-19 during the first pandemic wave in France, 2020.
COVID-19
Disease Outbreaks
Epidemiology
Models, Theoretical
Sick Leave
Journal
Occupational and environmental medicine
ISSN: 1470-7926
Titre abrégé: Occup Environ Med
Pays: England
ID NLM: 9422759
Informations de publication
Date de publication:
05 2023
05 2023
Historique:
received:
03
05
2022
accepted:
17
02
2023
medline:
17
4
2023
pubmed:
14
3
2023
entrez:
13
3
2023
Statut:
ppublish
Résumé
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'). 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. 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. 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.
Identifiants
pubmed: 36914254
pii: oemed-2022-108451
doi: 10.1136/oemed-2022-108451
pmc: PMC10176331
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
268-272Informations de copyright
© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
Déclaration de conflit d'intérêts
Competing interests: None declared.
Références
BMC Public Health. 2021 May 31;21(1):1023
pubmed: 34059034
Euro Surveill. 2021 Dec;26(48):
pubmed: 34857064
Front Public Health. 2020 Oct 19;8:580546
pubmed: 33194983
BMJ. 2021 Mar 3;372:n471
pubmed: 33658183
Glob Public Health. 2020 Jul;15(7):925-934
pubmed: 32396447
MMWR Morb Mortal Wkly Rep. 2020 Jul 10;69(27):853-858
pubmed: 32644979
Health Aff (Millwood). 2020 Dec;39(12):2197-2204
pubmed: 33058691
Vaccines (Basel). 2022 Mar 19;10(3):
pubmed: 35335111
Science. 2020 Jul 10;369(6500):208-211
pubmed: 32404476
J Infect Public Health. 2020 Jun;13(6):843-848
pubmed: 32493671