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Smith, David R M; Jijón, Sofía; Oodally, Ajmal; Shirreff, George; Aït Bouziad, Karim; Ante-Testard, Pearl Anne; Bastard, Jonathan; Bouziri, Hanifa; Daouda, Oumou Salama; Duchemin, Tom; Godon-Rensonnet, Anne-Sophie; Henriot, Paul; Houri, Yasmine; Neynaud, Hélène; Perozziello, Anne; Thonon, Frédérique; Crépey, Pascal; Dab, William; Jean, Kévin; Temime, Laura
Occupational and environmental medicine (London, England), 05/2023, Letnik: 80, Številka: 5Journal Article
ObjectivesTo 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’).MethodsWe 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.ResultsThere 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.ConclusionsFrance 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.
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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Povezave do osebnih bibliografij avtorjev | Povezave do podatkov o raziskovalcih v sistemu SICRIS |
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Vir: Osebne bibliografije
in: SICRIS
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