Methods
Settings and study population
The present study is a registry-based study with data from the SSIA, the Swedish National Board of Health and Welfare, and Statistics Sweden, based on the unique Swedish personal identification number. It presents data on sick leave due to Covid-19 that started between March 1 and August 31, 2020, with follow-up for 4 months. A patient has been involved in the study process as a partner in research.
Inclusion criteria for the study population were the following: being registered with sickness benefits due to Covid-19, which were defined as the International Statistical Classification of Diseases (ICD) [
17] code U07, starting within the inclusion period. Codes U (U00-U49) are used by WHO for provisional assignment of new diseases of uncertain etiology (Please see this link:
https://icd.who.int/browse10/2019/en#/U07.1).
The SSIA is the public authority in Sweden that makes decisions on sick leave and pays sickness benefits, and it provided the study population and sick leave data for the present study. All working people in Sweden are eligible for sickness benefits from the SSIA if deemed to have reduced work ability due to sickness, regardless of citizenship or place of residence. Furthermore, self-employment, parental leave, and unemployment (after previous employment) also entitle one to sickness benefits. The employer provides sick pay during the first 2 weeks of sickness absence; thereafter, the SSIA pays sickness benefits. If a person is unemployed, sickness benefits are provided by the SSIA from the start. In the present study, receiving sickness benefits regardless of amount is defined as sick leave.
The National Board of Health and Welfare provided data on date of death during the study period from the Cause of Death Register, which records all cases of death that have been registered in Sweden. Data from the National Patient Register, which includes all inpatient care in Sweden, were used to investigate hospital stay due to Covid-19.
Statistics Sweden holds registries of all people registered in Sweden. Statistics Sweden provided data on sociodemographic variables to the study.
Variables
The sick-leave period in the present study includes at least one period of sickness benefits due to Covid-19 diagnosis. Other predefined related diagnoses were merged with the Covid-19 sick leave if the gap of non-registration between sick leaves was ≤2 weeks. The related diagnoses are shown in additional Table
1 and included, for example, unspecified virus infections, fever, and postviral fatigue syndrome, but also a second sick-leave registration for Covid-19 diagnosis. In cases of sick pay provided by the employer, these were also merged with the sick-leave period. The sick-leave period could comprise a maximum of 122 days (4 months of follow-up). For predictive analyses, sick leave was dichotomised into sick leave ≥1 month (≥30 days): yes/no, and sick leave for long Covid (≥ 12 weeks, in line with the WHO definition [
16]): yes/no.
Sick leave prior to Covid-19 was defined as either being on sick leave for one period of at least 28 days between March 1, 2019, and the date of first Covid-19 sick leave registration, or being on sick leave at least six times during the same period of time.
The SSIA register includes the employment status for which a person receives the sick leave. The types of employment comprise employment (including parental leave, and combined employment and self-employment), self-employment, and unemployment (including studies).
Educational level was categorised as
primary school (≤ 9 years),
secondary school (10–12 years),
short university education (13–14 years), or
long university education (≥15 years). The educational level registered in 2019 was used. The income variable was the disposable income for each person during 2019, presented in thousands of Swedish krona SEK (1 Euro = 10.16 SEK, March 4, 2021). Income was categorised in tertiles of low, medium, and high income. Country of birth was presented as
Sweden,
Nordic countries except for Sweden,
European countries except for the Nordic countries, and
Countries outside of Europe. For marital status in 2020, married and registered partnership were both classified as
married. Likewise,
divorced and
widow/widowed meant a change from either marriage or registered partnership. Inpatient care due to Covid-19 was classified as being registered with a hospital stay of > 1 day with a registration of any of the Covid-19 diagnoses U07. The primary diagnoses are presented in additional Table
2 in the cases where U07 was not registered as the primary diagnosis.
Statistical methods
The data were processed and analysed using IBM SPSS Statistics 25. Data are presented as number and percentage (%), mean and standard deviation (SD), and median and interquartile range (IQR). The significance level (alpha) was set to 5%. To compare differences between groups, the Mann-Whitney U test and Fisher exact test were used.
To graphically present cumulative incidence of sick leave over time, Kaplan-Meier curves were used. There was no censoring, as cases of death during the study period were treated with a worst-case-scenario approach and were set at sick leave for the maximum number of days, and there was no other loss to follow-up.
Multiple logistic regression was used for predictive analysis. The regression analyses were performed on three separate populations: the total study population, the participants receiving inpatient care for Covid-19, and the participants not receiving inpatient care. Two different dependent variables were used in different models: sick leave ≥1 month and sick leave for long Covid. The independent variables were chosen based on clinical and theoretical reasoning: age, sex, educational level, income, country of birth, sick leave prior to Covid-19, employment status, marital status, and inpatient care due to Covid-19. The results are presented as odds ratio (OR), 95% confidence interval (95% CI), and
p-value in forest plots. The ordinal or continuous independent variables were tested for multicollinearity using the Spearman correlation test, with values < 0.3 being acceptable. To test the accuracy of the models, receiver operating characteristics (ROC) curves were constructed. An area under the ROC curve > 0.70 indicate acceptable accuracy [
18].
Discussion
In this registry-based national cohort, the median duration of sickness benefits due to Covid-19 was more than 1 month (35 days). Furthermore, more than one out of ten subjects were on sick leave for more than 12 weeks, defined as long Covid. Sick leave due to Covid-19 is an unexplored area, and increased knowledge is essential for a national and global perspective. The results from this study show that Covid-19 places a large burden not only on affected people and the health-care system, but also on the health insurance system. A large proportion of people with Covid-19 had a protracted course of sick leave, in both the present study and a published preprint [
12], and a long-term perspective is needed for these patients. The actual prevalence of long Covid is unclear both globally and in Sweden [
16], but the present study suggests that it affects a substantial proportion of patients, as 13% of the people with sickness benefit due to Covid-19 were on sick leave for long Covid. To the best of our knowledge, this is the first study to estimate long Covid and its consequences from sick-leave data. In a preprint study [
12] using self-reported symptoms as a measure of long Covid, the majority reported that their ability to work was reduced for several months after Covid-19, but 27% were working as before. This indicates that the actual prevalence of long Covid is even higher in Sweden than the number on sick leave for long Covid presented in this study.
The need for inpatient care due to Covid-19 was a significant predictor of sick leave ≥1 month, and it increased by more than three-fold the odds of needing sick leave for long Covid compared with no inpatient care due to Covid-19. It is likely that a patient who requires extensive care due to critical Covid-19 illness will have restrictions in function for a long time [
19]. Five years after being treated at an intensive care unit for acute respiratory distress syndrome, people still had decreased levels of physical function [
20]. In line with the present findings, hospitalisation has also been shown to predict protracted symptoms after Covid-19 [
11], and long-term symptoms exist in both hospitalised and non-hospitalised Covid-19 patients in a preprint study [
13]. People suffering from long Covid seem to be a heterogeneous group [
13], including both those who have been critically ill in the acute phase and those with non-critical illness, and it may be speculated that the consequences of Covid-19 differ in the two groups. Possible sex differences are one factor to consider.
Prior studies have repeatedly reported that men have more severe Covid-19, with higher mortality, a need for more medical care, and worse outcomes [
21]. In this study, in line with previous research, the patients who received inpatient care due to Covid-19 were mostly men (64%). Furthermore, the proportion of men was significantly higher than the proportion on women in the group on sick leave for long Covid compared with the group not on sick leave for long Covid. In addition, men had higher odds of longer sick leave for Covid-19, both in the total population and in the population receiving inpatient care due to Covid-19. However, the present study comprised all people who received sickness benefits due to Covid-19 in Sweden, of whom a clear majority (60%) were women. This can be compared with the number of confirmed cases of Covid-19 in Sweden, where women represent 52% of the cases [
22]. Furthermore, in absolute numbers, there were more women than men in the long-Covid group. This is in line with previous data showing that women generally receive more sick leave in Sweden [
9] and have lower odds of returning to work after various diagnoses [
23,
24]. The patients receiving sick leave for long Covid seem to be a heterogeneous group, with both critical and non-critical acute phases, and it is important not to neglect any of them.
To be on sick leave for more than 28 days or more than six times in the year before having Covid-19 resulted in higher odds of sick leave ≥1 month and for long Covid in most of the analyses. This could reflect higher comorbidity in the people with prior sick leave, or a higher vulnerability for sick leave in general. Other studies have shown that there is an association between previous sick leave and sick leave after stroke [
25], traumatic brain injury [
26], and mental disorder [
27], and the present study indicates this also true for Covid-19.
Older age predicted sick leave ≥1 month and for long Covid in most of the analyses. Older age is highly associated with worse outcome after Covid-19 [
28]. Furthermore, it can be speculated that health-care providers, the SSIA, and employers all act to optimise the return to work for younger people.
The pandemic seems to strike differently depending on socioeconomic or ethical conditions, both globally [
29,
30] and in Sweden [
31]. Various socioeconomic factors predicted longer sick leave for Covid-19 in this study, but the results are not clear and consistent throughout the models. To get the whole picture of factors affecting post-Covid-19 outcomes, and specifically sick leave, more research is needed. Perhaps more work-related predictors have to be included, or types of persistent symptoms. Our results did not yield distinct predictions for hospitalised vs. non-hospitalised people. Perhaps other subgroups within the heterogeneous group of persons requiring long-term sick leave after Covid-19 need to be analysed in order to paint a clearer picture.
Methodology discussion and study limitations
The present cohort included only people receiving sickness benefits, which for most people means sick leave of more than 2 weeks. In addition, compared with national age-matched data, there were notably few deaths in the cohort [
32]. This could perhaps be explained by the risk of people dying before being registered for sickness benefits for Covid-19, since the medical certificate for sick leave is usually written retrospectively on referral to a different hospital ward, for patients being treated in an intensive care unit.
About one-third of the patients were classified as “Covid-19, virus undetected”, indicating insecurity in the data. Data collection was conducted in an early phase of the pandemic in Sweden, when PCR testing was limited. The authors found it most reasonable to consider the “Covid-19, virus undetected” diagnosis as a Covid-19 infection, to capture the picture of sick leave due to Covid-19. Furthermore, related diagnoses were merged into the Covid-19 sick-leave period if there was a gap shorter than 2 weeks. This decision was based on clinical experience and reasoning, to limit the risk of missing substantial sick leave due to difficulties of registration in the beginning of the pandemic. However, there is also a risk that a small number of the sick-leave periods were not due only to Covid-19. It was not possible to determine the exact prevalence of people suffering from symptoms of Covid-19 or long Covid in this study.
The ROC curves indicate that the accuracy of the regression models was low, so the predictions should be interpreted with caution.
Acknowledgments
The authors would like to acknowledge the register holder, the Swedish Social Insurance Agency, the Swedish National Board of Health and Welfare, and Statistics Sweden for providing data. We would also like to thank our patient partner, Helena Strömberg, for her knowledgeable contribution to the research question and discussion of the results, and Adam Viktorisson for figure illustrations.
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