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Objectives: This study aimed to explore how the Greenlandic population experienced the course of both acute and long-term COVID-19. It was motivated by the unique epidemiologic situation in Greenland, with delayed community transmission of SARS-CoV-2 relative to the rest of the world. Methods: In a survey among 310 Greenlandic adults, we assessed the association between previous SARS-CoV-2 infection and overall health outcomes by administering three repeated questionnaires over 12 months after infection, with a response rate of 41% at the 12-month follow-up. The study included 128 individuals with confirmed SARS-CoV-2 infection from January/February 2022 and 182 test-negative controls. Participants were recruited through personal approaches, phone calls, and social media platforms. Results: A total of 53.7% of 162 participants who were test-positive recovered within 4 weeks and 2.5% were hospitalized due to SARS-CoV-2. The most common symptoms were fatigue and signs of mild upper respiratory tract infection. Less than 5% reported sick leave above 2 weeks after infection. Compared with participants who were test-negative, there was an increased risk of reporting fatigue (risk differences 25.4%, 95% confidence interval 8.8-44.0) and mental exhaustion (risk differences 23.4%, 95% confidence interval 4.8-42.2) up to 12 months after a positive test. Conclusions: Our results indicate that during a period dominated by the Omicron variant, Greenlanders experienced a mild acute course of COVID-19, with quick recovery, minimizing the impact on sick leave. Long COVID may be present in Greenlanders, with symptoms persisting up to 12 months after infection. However, it is important to consider the small sample size and modest response rate as limitations when interpreting the results.

Linda Aagaard Rasmussen,1 Niels Lyhne Christensen,2 Anne Winther-Larsen,3 Susanne Oksbjerg Dalton,4,5 Line Flytkjær Virgilsen,1 Henry Jensen,1 Peter Vedsted1 1Research Unit for General Practice, Aarhus, Denmark; 2Department of Pulmonary Medicine and Allergy, Aarhus University Hospital, Aarhus, Denmark; 3Department of Clinical Biochemistry, Aarhus University Hospital, Aarhus, Denmark; 4Survivorship and Inequality in Cancer, Danish Cancer Society Research Center, Copenhagen, Denmark; 5Department of Clinical Oncology & Palliative Care, Zealand University Hospital, Næstved, DenmarkCorrespondence: Linda Aagaard Rasmussen, Research Unit for General Practice, Bartholins Allé 2, Aarhus C, 8000, Denmark, Tel +45 8716 8365, Email linda.rasmussen@ph.au.dkIntroduction: Recurrence of cancer is not routinely registered in Danish national health registers. This study aimed to develop and validate a register-based algorithm to identify patients diagnosed with recurrent lung cancer and to estimate the accuracy of the identified diagnosis date.Material and Methods: Patients with early-stage lung cancer treated with surgery were included in the study. Recurrence indicators were diagnosis and procedure codes recorded in the Danish National Patient Register and pathology results recorded in the Danish National Pathology Register. Information from CT scans and medical records served as the gold standard to assess the accuracy of the algorithm.Results: The final population consisted of 217 patients; 72 (33%) had recurrence according to the gold standard. The median follow-up time since primary lung cancer diagnosis was 29 months (interquartile interval: 18– 46). The algorithm for identifying a recurrence reached a sensitivity of 83.3% (95% CI: 72.7– 91.1), a specificity of 93.8% (95% CI: 88.5– 97.1), and a positive predictive value of 87.0% (95% CI: 76.7– 93.9). The algorithm identified 70% of the recurrences within 60 days of the recurrence date registered by the gold stan

Introduction: Individuals with multimorbidity often receive high numbers of hospital outpatient services in concurrent trajectories. Nevertheless, little is known about factors associated with initiating new hospital outpatient trajectories; identified as the continued use of outpatient contacts for the same medical condition. Purpose: To investigate whether the number of chronic conditions and sociodemographic characteristics in adults with multimorbidity is associated with entering a hospital outpatient trajectory in this population. Methods: This population-based register study included all adults in Denmark with multimorbidity on January 1, 2018. The exposures were number of chronic conditions and sociodemographic characteristics, and the outcome was the rate of starting a new outpatient trajectory during 2018. Analyses were stratified by the number of existing outpatient trajectories. We used Poisson regression analysis, and results were expressed as incidence rates and incidence rate ratios with 95% confidence intervals. We followed the individuals during the entire year of 2018, accounting for person-time by hospitalization, emigration, and death. Results: Incidence rates for new outpatient trajectories were highest for individuals with low household income and ≥3 existing trajectories and for individuals with ≥3 chronic conditions and in no already established outpatient trajectory. A high number of chronic conditions and male gender were found to be determinants for initiating a new outpatient trajectory, regardless of the number of existing trajectories. Low educational level was a determinant when combined with 1, 2, and ≥3 existing trajectories, and increasing age, western ethnicity, and unemployment when combined with 0, 1, and 2 existing trajectories. Conclusion: A high number of chronic conditions, male gender, high age, low educational level and unemployment were determinants for initiation of an outpatient trajectory. The rate was modified by the

Introduction: Recurrence of cancer is not routinely registered in Danish national health registers. This study aimed to develop and validate a register-based algorithm to identify patients diagnosed with recurrent lung cancer and to estimate the accuracy of the identified diagnosis date. Material and Methods: Patients with early-stage lung cancer treated with surgery were included in the study. Recurrence indicators were diagnosis and procedure codes recorded in the Danish National Patient Register and pathology results recorded in the Danish National Pathology Register. Information from CT scans and medical records served as the gold standard to assess the accuracy of the algorithm. Results: The final population consisted of 217 patients; 72 (33%) had recurrence according to the gold standard. The median follow-up time since primary lung cancer diagnosis was 29 months (interquartile interval: 18– 46). The algorithm for identifying a recurrence reached a sensitivity of 83.3% (95% CI: 72.7– 91.1), a specificity of 93.8% (95% CI: 88.5– 97.1), and a positive predictive value of 87.0% (95% CI: 76.7– 93.9). The algorithm identified 70% of the recurrences within 60 days of the recurrence date registered by the gold standard method. The positive predictive value of the algorithm decreased to 70% when the algorithm was simulated in a population with a recurrence rate of 15%. Conclusion: The proposed algorithm demonstrated good performance in a population with 33% recurrences over a median of 29 months. It can be used to identify patients diagnosed with recurrent lung cancer, and it may be a valuable tool for future research in this field. However, a lower positive predictive value is seen when applying the algorithm in populations with low recurrence rates., Introduction: Recurrence of cancer is not routinely registered in Danish national health registers. This study aimed to develop and validate a register-based algorithm to identify patients diagnosed with recurrent lung cancer and to estimate the accuracy of the identified diagnosis date. Material and Methods: Patients with early-stage lung cancer treated with surgery were included in the study. Recurrence indicators were diagnosis and procedure codes recorded in the Danish National Patient Register and pathology results recorded in the Danish National Pathology Register. Information from CT scans and medical records served as the gold standard to assess the accuracy of the algorithm. Results: The final population consisted of 217 patients; 72 (33%) had recurrence according to the gold standard. The median follow-up time since primary lung cancer diagnosis was 29 months (interquartile interval: 18–46). The algorithm for identifying a recurrence reached a sensitivity of 83.3% (95% CI: 72.7–91.1), a specificity of 93.8% (95% CI: 88.5–97.1), and a positive predictive value of 87.0% (95% CI: 76.7–93.9). The algorithm identified 70% of the recurrences within 60 days of the recurrence date registered by the gold standard method. The positive predictive value of the algorithm decreased to 70% when the algorithm was simulated in a population with a recurrence rate of 15%. Conclusion: The proposed algorithm demonstrated good performance in a population with 33% recurrences over a median of 29 months. It can be used to identify patients diagnosed with recurrent lung cancer, and it may be a valuable tool for future research in this field. However, a lower positive predictive value is seen when applying the algorithm in populations with low recurrence rates.