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1000 Titel
  • Patterns and predictors of sick leave after Covid-19 and long Covid in a national Swedish cohort
1000 Autor/in
  1. Westerlind, Emma |
  2. Palstam, Annie |
  3. Sunnerhagen, Katharina S. |
  4. Persson, Hanna C. |
1000 Erscheinungsjahr 2021
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2021-05-31
1000 Erschienen in
1000 Quellenangabe
  • 21(1):1023
1000 Copyrightjahr
  • 2021
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1186/s12889-021-11013-2 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8164957/ |
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1000 Abstract/Summary
  • Background!#!The impact of Covid-19 and its long-term consequences is not yet fully understood. Sick leave can be seen as an indicator of health in a working age population, and the present study aimed to investigate sick-leave patterns after Covid-19, and potential factors predicting longer sick leave in hospitalised and non-hospitalised people with Covid-19.!##!Methods!#!The present study is a comprehensive national registry-based study in Sweden with a 4-month follow-up. All people who started to receive sickness benefits for Covid-19 during March 1 to August 31, 2020, were included. Predictors of sick leave ≥1 month and long Covid (≥12 weeks) were analysed with logistic regression in the total population and in separate models depending on inpatient care due to Covid-19.!##!Results!#!A total of 11,955 people started sick leave for Covid-19 within the inclusion period. The median sick leave was 35 days, 13.3% were on sick leave for long Covid, and 9.0% remained on sick leave for the whole follow-up period. There were 2960 people who received inpatient care due to Covid-19, which was the strongest predictor of longer sick leave. Sick leave the year prior to Covid-19 and older age also predicted longer sick leave. No clear pattern of socioeconomic factors was noted.!##!Conclusions!#!A substantial number of people are on sick leave due to Covid-19. Sick leave may be protracted, and sick leave for long Covid is quite common. The severity of Covid-19 (needing inpatient care), prior sick leave, and age all seem to predict the likelihood of longer sick leave. However, no socioeconomic factor could clearly predict longer sick leave, indicating the complexity of this condition. The group needing long sick leave after Covid-19 seems to be heterogeneous, indicating a knowledge gap.
1000 Sacherschließung
gnd 1206347392 COVID-19
lokal Humans
lokal Sick Leave
lokal Aged [MeSH]
lokal Humans [MeSH]
lokal COVID-19
lokal Cohort Studies [MeSH]
lokal COVID-19 [MeSH]
lokal Sweden/epidemiology
lokal Sweden/epidemiology [MeSH]
lokal Aged
lokal SARS-CoV-2 [MeSH]
lokal Public Health, Environmental and Occupational Health
lokal SARS-CoV-2
lokal Cohort Studies
lokal Sick Leave [MeSH]
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  1. https://frl.publisso.de/adhoc/uri/V2VzdGVybGluZCwgRW1tYQ==|https://frl.publisso.de/adhoc/uri/UGFsc3RhbSwgQW5uaWU=|https://frl.publisso.de/adhoc/uri/U3VubmVyaGFnZW4sIEthdGhhcmluYSBTLg==|https://frl.publisso.de/adhoc/uri/UGVyc3NvbiwgSGFubmEgQy4=
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