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. 2021 May 31;21(1):1023.
doi: 10.1186/s12889-021-11013-2.

Patterns and predictors of sick leave after Covid-19 and long Covid in a national Swedish cohort

Emma Westerlind  1 , Annie Palstam  1 , Katharina S Sunnerhagen  1 , Hanna C Persson  2
Affiliations

Patterns and predictors of sick leave after Covid-19 and long Covid in a national Swedish cohort

Emma Westerlind et al. BMC Public Health. .

Abstract

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.

Keywords: Covid-19; Follow-up; Long Covid; SARS-CoV2; Sick leave.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Cumulative incidence of sick leave during the study period. Total study population (left) and divided according to inpatient care due to Covid-19 (right)
Fig. 2
Fig. 2
Predictors of sick leave ≥1 month, and long Covid. There were 7813 persons on sick leave ≥1 month, and 4009 < 1 month; area under the ROC curve: 0.586. There were 1574 persons on sick leave for long Covid, and 10,248 not on sick leave for long Covid; area under the ROC curve: 0.692. Abbreviations: OR, odds ratio; CI, confidence interval
Fig. 3
Fig. 3
Predictors of sick leave ≥1 month, and long Covid, for participants’ receiving inpatient care. There were 2202 persons on sick leave ≥1 month, and 704 < 1 month; area under the ROC-curve: 0.607. There were 785 persons on sick leave for long Covid, and 2121 not on sick leave for long Covid; area under the ROC-curve: 0.617. Abbreviations: OR, odds ratio; CI, confidence interval
Fig. 4
Fig. 4
Predictors of sick leave ≥1 month, and long Covid, for participants’ not receiving inpatient care. There were 5611 persons on sick leave ≥1 month, and 3305 < 1 month; area under the ROC-curve: 0.550. There were 789 persons on sick leave for long Covid, and 8127 not on sick leave for long Covid; area under the ROC-curve: 0.609. Abbreviations: OR, odds ratio; CI, confidence interval
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