A register and questionnaire study of long-term general health symptoms following SARS-CoV-2 vaccination in Denmark.

Many individuals who refuse COVID-19 vaccination have concerns about long-term side effects. Here, we report findings on self-reported symptoms from a Danish survey- and register study. The study included 34,868 vaccinated primary course recipients, 95.8% of whom received mRNA vaccines, and 1,568 unvaccinated individuals. Participants had no known history of SARS-CoV-2 infection. Using g-computation on logistic regression, risk differences (RDs) for symptoms between vaccinated and unvaccinated persons were estimated with adjustments for possible confounders. Within six weeks after vaccination, higher risks were observed for physical exhaustion (RD 4.9%, 95% CI 1.1% to 8.4%), fever or chills (RD 4.4%, 95% CI 2.1% to 6.7%), and muscle/joint pain (RD 7.0%, 95% CI 3.1% to 10.7%), compared to unvaccinated individuals. Beyond twenty-six weeks, risks were higher among the vaccinated for sleeping problems (RD 3.0, 95% 0.2 to 5.8), fever or chills (RD 2.0, 95% CI 0.4 to 3.6), reduced/altered taste (RD 1.2, 95% CI 0.2 to 2.3) and shortness of breath (RD 2.6, 95% CI 0.9 to 4.0). However, when examining pre-omicron responses only, the difference for reduced/altered taste was significant. As expected, the risk of experiencing physical exhaustion, fever or chills, and muscle/joint pain was higher among persons who responded within six weeks of completing the primary course. No significant differences were observed for the 7-25-week period after vaccination. Associations for the period beyond 26 weeks must be interpreted with caution and in the context of undetected SARS-CoV-2 infection, wide confidence intervals, and multiple testing. Overall, we observe no concerning signs of long-term self-reported physical, cognitive, or fatigue symptoms after vaccination.

Machine learning identifies risk factors associated with long-term sick leave following COVID-19 in Danish population.

BACKGROUND: Post COVID-19 condition (PCC) can lead to considerable morbidity, including prolonged sick-leave. Identifying risk groups is important for informing interventions. We investigated heterogeneity in the effect of SARS-CoV-2 infection on long-term sick-leave and identified subgroups at higher risk. METHODS: We conducted a hybrid survey and register-based retrospective cohort study of Danish residents who tested positive for SARS-CoV-2 between November 2020 and February 2021 and a control group who tested negative, with no known history of SARS-CoV-2. We estimated the causal risk difference (RD) of long-term sick-leave due to PCC and used the causal forest method to identify individual-level heterogeneity in the effect of infection on sick-leave. Sick-leave was defined as >4 weeks of full-time sick-leave from 4 weeks to 9 months after the test. RESULTS: Here, in a cohort of 88,818 individuals, including 37,482 with a confirmed SARS-CoV-2 infection, the RD of long-term sick-leave is 3.3% (95% CI 3.1% to 3.6%). We observe a high degree of effect heterogeneity, with conditional RDs ranging from -3.4% to 13.7%. Age, high BMI, depression, and sex are the most important variables explaining heterogeneity. Among three-way interactions considered, females with high BMI and depression and persons aged 36-45 years with high BMI and depression have an absolute increase in risk of long-term sick-leave above 10%. CONCLUSIONS: Our study supports significant individual-level heterogeneity in the effect of SARS-CoV-2 infection on long-term sick-leave, with age, sex, high BMI, and depression identified as key factors. Efforts to curb the PCC burden should consider multimorbidity and individual-level risk.

The burden of post COVID-19 condition varies from one person to another due to individual characteristics such as age, sex, and having single- or multiple pre-existing conditions. Sick leave following initial SARS-CoV-2 infection is one way to quantify this burden. However, to what extent the combinations of these characteristics impact the risk of post-acute sick leave is not well understood. Here, using a machine learning method, we observe that persons infected with SARS-CoV-2 have an increased risk of taking long-term sick leave compared to persons with no history of infection. Age, high BMI, sex, and depression explained substantial effect variation on the risk of long-term sick leave after infection. This knowledge may be used to help inform patient-targeted interventions.

A hybrid register and questionnaire study of Covid-19 and post-acute sick leave in Denmark.

Post-acute sick leave is an underexplored indicator of the societal burden of SARS-CoV-2. Here,  we report findings about self-reported sick leave and risk factors thereof from a hybrid survey and register study, which include 37,482 RT-PCR confirmed SARS-CoV-2 cases and 51,336 test-negative controls who were tested during the index- and alpha-dominant waves. We observe that an additional 33 individuals per 1000 took substantial sick leave following acute infection compared to persons with no known history of infection, where substantial sick leave is defined as >1 month of sick leave within the period 1-9 months after the RT-PCR test date. Being female, 50-65 years, or having certain pre-existing health conditions such as obesity, chronic lung diseases, and fibromyalgia each increase risk for taking substantial sick leave. Altogether, these results may help motivate improved diagnostic and treatment options for persons living with post-Covid conditions.

A standardised protocol for relative SARS-CoV-2 variant severity assessment, applied to Omicron BA.1 and Delta in six European countries, October 2021 to February 2022.

Several SARS-CoV-2 variants that evolved during the COVID-19 pandemic have appeared to differ in severity, based on analyses of single-country datasets. With decreased testing and sequencing, international collaborative studies will become increasingly important for timely assessment of the severity of new variants. Therefore, a joint WHO Regional Office for Europe and ECDC working group was formed to produce and pilot a standardised study protocol to estimate relative case-severity of SARS-CoV-2 variants during periods when two variants were co-circulating. The study protocol and its associated statistical analysis code was applied by investigators in Denmark, England, Luxembourg, Norway, Portugal and Scotland to assess the severity of cases with the Omicron BA.1 virus variant relative to Delta. After pooling estimates using meta-analysis methods (random effects estimates), the risk of hospital admission (adjusted hazard ratio (aHR) = 0.41; 95% confidence interval (CI): 0.31-0.54), admission to intensive care unit (aHR = 0.12; 95% CI: 0.05-0.27) and death (aHR = 0.31; 95% CI: 0.28-0.35) was lower for Omicron BA.1 compared with Delta cases. The aHRs varied by age group and vaccination status. In conclusion, this study demonstrates the feasibility of conducting variant severity analyses in a multinational collaborative framework and adds evidence for the reduced severity of the Omicron BA.1 variant.