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Patterns and predictors of sick leave after Covid-19 and long Covid in a national Swedish cohort.
Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg; Sahlgrenska University Hospital, Gothenburg.ORCID iD: 0000-0002-7127-213x
2021 (English)In: BMC Public Health, E-ISSN 1471-2458, Vol. 21, no 1, article id 1023Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2021. Vol. 21, no 1, article id 1023
Keywords [en]
Covid-19, Follow-up, Long Covid, SARS-CoV2, Sick leave
National Category
Public Health, Global Health and Social Medicine
Identifiers
URN: urn:nbn:se:du-37428DOI: 10.1186/s12889-021-11013-2PubMedID: 34059034OAI: oai:DiVA.org:du-37428DiVA, id: diva2:1568312
Available from: 2021-06-17 Created: 2021-06-17 Last updated: 2025-10-09Bibliographically approved

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Palstam, Annie

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