What does Covid-19 look like when the emergency is over—but its effects are still showing up at work? In this Danish study, infection was linked to a clear increase in taking more than a month of sick leave. As societies have gradually adjusted to living with SARS-CoV-2, research has shifted from rapid emergency assessments to retrospective analyses of the health and economic burden countries have sustained.

Quantifying the societal impact of post-Covid conditions has been difficult because diagnosis can be hard to reach due to symptom variability. It is also challenging to disentangle the post-acute effects of SARS-CoV-2 infection from symptoms of pre-existing health conditions.

It remains unclear how post-acute effects affect daily living and quality of life at the societal level, how this differs by variant and vaccination status, and which groups are most at risk of prolonged illness. The study aimed to evaluate the association between SARS-CoV-2 infection and post-acute sick leave.

It also aimed to explore the possible impact of age, sex, and pre-existing health conditions on that association. Specifically, post-acute sick leave was examined after infections during the index- and alpha-dominant periods in Denmark. The comparison was between persons infected with SARS-CoV-2 and persons with no known history of SARS-CoV-2 infection.

This cohort study merged nationwide survey and register data. In Denmark, all residents are assigned a unique identifier, the CPR number, in the Danish Civil Registration System. The CPR number is commonly used to link individual-level data from varying data sources.

A summary of the study population and the variables used from survey responses and register data is presented in Fig. 1. In panel C, the horizontal bars show obesity as the most prevalent listed condition, followed by depression and high blood pressure.

The shortest bars are for fibromyalgia and chronic fatigue syndrome, while the remaining conditions fall between these extremes. The prevalence of substantial sick leave among test-positives and test-negatives was compared using risk differences, which give the difference between the risk of an outcome in the exposed and unexposed groups.

Parametric g-computation on logistic regression was used to estimate risk differences, with ninety-five percent confidence intervals obtained using bootstrap random resampling with one thousand iterations. The comparison was adjusted for age, sex, Charlson Comorbidity Index, self-reported chronic illness, and education level.

Risk differences for substantial sick leave were expressed in percentage points. Of 294,035 invited people, 106,917 fully completed a questionnaire nine months after testing positive or negative for SARS-CoV-2, giving a response rate of 36.4 percent.

After exclusions, the study population consisted of 88,818 individuals, including 37,482 with SARS-CoV-2 infection confirmed by a positive RT-PCR test. Overall, 64.3 percent of participants were female, and the mean age was 45 years, with a standard deviation of 13.8 years.

Less than one percent had received one or more doses of a SARS-CoV-2 vaccine, and 16.6 percent were identified as obese from self-reported height and weight. The prevalence of substantial sick leave was 1.4 percent among test-negatives compared with 4.5 percent among test-positives.

Among test-negatives, substantial sick leave was similar across age groups. Conversely, among test-positives, the prevalence of substantial sick leave increased with age. Among test-positives who took substantial sick leave, 21.1 percent had received a hospital-registered diagnosis with sequelae of SARS-CoV-2.

In panel C, the red positive-test curve rises across the age groups, while the gray negative-test curve stays comparatively level after a modest early increase. The separation is especially visible from ages thirty to thirty-nine onward, with the widest gap around ages fifty to fifty-nine.

Risk differences were greater for females, at 3.9 percentage points, than for males, at 2.1 percentage points. For people aged fifty to sixty-five and for all pre-existing health conditions except chronic fatigue syndrome and anxiety, estimated risk differences were higher than in the general population.

The largest risk differences were observed for people with fibromyalgia, at 10.6 percentage points; COPD or other lung disease, at 6.8 percentage points; and diabetes, at 5.9 percentage points. Obesity had a risk difference of 5.7 percentage points, larger than the 3.3 percentage points in the general population.

Table three is a forest plot showing risk differences in substantial sick leave after SARS-CoV-2 infection, comparing positive participants with test-negative participants. In the full population, the risk difference was three point three percentage points, with a ninety-five percent confidence interval from three point zero to three point five.

Estimates were three point nine for females and two point one for males, while participants with fibromyalgia had an estimate of ten point six, illustrating how results varied across demographic and pre-existing health groups. The main limitations were the study’s self-reporting nature and potential participation and recall bias.

The duration of self-reported sick leave could not be directly attributed to long Covid symptoms because the questionnaire asked about sick leave in general rather than sick leave associated with SARS-CoV-2 infection. Register-based sick leave benefits were a possible alternative, but DREAM primarily covers benefits granted after thirty days of sickness absence.

Because register data largely exclude shorter absences, they are not well-suited for capturing fluctuating illness as a possible consequence of long Covid. The main takeaway is that test-positive participants had more substantial sick leave, especially females, people aged fifty to sixty-five, and people with certain pre-existing conditions.

That makes sick leave a useful indicator of post-Covid’s broader burden.