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Introduction: SARS-CoV-2 infection causes acute COVID-19 and may result in post-COVID syndrome (PCS). We aimed to investigate how clinicians diagnose PCS and identify associated clinical and demographic characteristics. Methods: We analysed multiregistry data of all SARS-CoV-2 test-positive individuals in Sweden (n=1 057 174) between 1 February 2020 and 25 May 2021. We described clinical characteristics that prompt PCS diagnosis in outpatient and inpatient settings. In total, there were 6389 individuals with a hospital inpatient or outpatient diagnosis for PCS. To understand symptomatology, we examined individuals diagnosed with PCS at least 3 months after COVID-19 onset (n=6389) and assessed factors associated with PCS diagnosis. Results: Mechanical ventilation correlated with PCS (OR 114.7, 95% CI 105.1 to 125.3) compared with no outpatient/inpatient contact during initial COVID-19. Dyspnoea (13.4%), malaise/fatigue (8%) and abnormal pulmonary diagnostic imaging findings (4.3%) were the most common features linked to PCS. We compared clinical features of PCS with matched controls (COVID-19 negative, n=23 795) and COVID-19 severity-matched patients (COVID-19 positive, n=25 556). Hypertension associated with PCS cohort (26.61%) than in COVID-19-negative (OR 17.16, 95% CI 15.23 to 19.3) and COVID-19-positive (OR 9.25, 95% CI 8.41 to 10.16) controls, although most individuals received this diagnosis before COVID-19. Dyspnoea was the second most common feature in the PCS cohort (17.2%), and new to the majority compared with COVID-19-negative (OR 54.16, 95% CI 42.86 to 68.45) and COVID-19-positive (OR 18.7, 95% CI 16.21 to 21.57) controls. Conclusions: Our findings highlight factors Swedish physicians associate with PCS.

Background Paediatric-onset immune-mediated inflammatory diseases (pIMID) show more aggressive phenotypes than when diagnosed in adults. However, data on mortality are often extrapolated from adult studies. Aim To estimate the effect of pIMID on mortality. Methods In a population-based cohort study using the nationwide Danish healthcare registers, we included all patients diagnosed with pIMID in Denmark from 1980 to 2018. PIMID were defined as ICD codes indicative of autoimmune hepatitis, primary sclerosing cholangitis, Crohn's disease, ulcerative colitis, juvenile idiopathic arthritis, lupus erythematosus, or vasculitis registered before age 18 years. All-cause mortality was the primary outcome; cause-specific mortality was the secondary outcome. We used Cox survival analysis to estimate hazard ratios (HR), and Aalen survival analysis to estimate rate differences. Results We included 11,581 individuals diagnosed with pIMID and 99,665 reference individuals, accounting for 1,371,994 person-years of follow-up. Median and interquartile (IQR) age at diagnosis was 12.6 (7.9–15.9) years. During follow-up, 152 patients with pIMID and 316 reference individuals died; adjusted HR (aHR) was 3.8 (95% confidence interval [CI] 3.1–4.7). This corresponded to 6.9 (95% CI: 5.3–8.5) additional deaths per 10,000 person-years. The strongest associations were found for gastrointestinal diseases (aHR 22.8; 95% CI 9.6–64.1), gastrointestinal cancers (aHR 19.2; 95% CI 5.0–74.2) and lymphoproliferative disorders (aHR 6.8; 95% CI 2.8–16.8). Conclusion Patients diagnosed with pIMID have a fourfold higher risk of mortality when followed into early adulthood compared with reference individuals. This underlines the severe disease course of pIMID and highlights the need for multidisciplinary care., Background: Paediatric-onset immune-mediated inflammatory diseases (pIMID) show more aggressive phenotypes than when diagnosed in adults. However, data on mortality are often extrapolated from adult studies. Aim: To estimate the effect of pIMID on mortality. Methods: In a population-based cohort study using the nationwide Danish healthcare registers, we included all patients diagnosed with pIMID in Denmark from 1980 to 2018. PIMID were defined as ICD codes indicative of autoimmune hepatitis, primary sclerosing cholangitis, Crohn's disease, ulcerative colitis, juvenile idiopathic arthritis, lupus erythematosus, or vasculitis registered before age 18 years. All-cause mortality was the primary outcome; cause-specific mortality was the secondary outcome. We used Cox survival analysis to estimate hazard ratios (HR), and Aalen survival analysis to estimate rate differences. Results: We included 11,581 individuals diagnosed with pIMID and 99,665 reference individuals, accounting for 1,371,994 person-years of follow-up. Median and interquartile (IQR) age at diagnosis was 12.6 (7.9–15.9) years. During follow-up, 152 patients with pIMID and 316 reference individuals died; adjusted HR (aHR) was 3.8 (95% confidence interval [CI] 3.1–4.7). This corresponded to 6.9 (95% CI: 5.3–8.5) additional deaths per 10,000 person-years. The strongest associations were found for gastrointestinal diseases (aHR 22.8; 95% CI 9.6–64.1), gastrointestinal cancers (aHR 19.2; 95% CI 5.0–74.2) and lymphoproliferative disorders (aHR 6.8; 95% CI 2.8–16.8). Conclusion: Patients diagnosed with pIMID have a fourfold higher risk of mortality when followed into early adulthood compared with reference individuals. This underlines the severe disease course of pIMID and highlights the need for multidisciplinary care.

Background: The decline in global child mortality is an important public health achievement, yet child mortality remains disproportionally high in many low-income countries like Guinea-Bissau. The persisting high mortality rates necessitate targeted research to identify vulnerable subgroups of children and formulate effective interventions. Objective: This study aimed to discover subgroups of children at an elevated risk of mortality in the urban setting of Bissau, Guinea-Bissau, West Africa. By identifying these groups, we intend to provide a foundation for developing targeted health interventions and inform public health policy. Methods: We used data from the health and demographic surveillance site, Bandim Health Project, covering 2003 to 2019. We identified baseline variables recorded before children reached the age of 6 weeks. The focus was on determining factors consistently linked with increased mortality up to the age of 3 years. Our multifaceted methodological approach incorporated spatial analysis for visualizing geographical variations in mortality risk, causally adjusted regression analysis to single out specific risk factors, and machine learning techniques for identifying clusters of multifactorial risk factors. To ensure robustness and validity, we divided the data set temporally, assessing the persistence of identified subgroups over different periods. The reassessment of mortality risk used the targeted maximum likelihood estimation (TMLE) method to achieve more robust causal modeling. Results: We analyzed data from 21,005 children. The mortality risk (6 weeks to 3 years of age) was 5.2% (95% CI 4.8%-5.6%) for children born between 2003 and 2011, and 2.9% (95% CI 2.5%-3.3%) for children born between 2012 and 2016. Our findings revealed 3 distinct high-risk subgroups with notably higher mortality rates, children residing in a specific urban area (adjusted mortality risk difference of 3.4%, 95% CI 0.3%-6.5%), BACKGROUND: The decline in global child mortality is an important public health achievement, yet child mortality remains disproportionally high in many low-income countries like Guinea-Bissau. The persisting high mortality rates necessitate targeted research to identify vulnerable subgroups of children and formulate effective interventions. OBJECTIVE: This study aimed to discover subgroups of children at an elevated risk of mortality in the urban setting of Bissau, Guinea-Bissau, West Africa. By identifying these groups, we intend to provide a foundation for developing targeted health interventions and inform public health policy. METHODS: We used data from the health and demographic surveillance site, Bandim Health Project, covering 2003 to 2019. We identified baseline variables recorded before children reached the age of 6 weeks. The focus was on determining factors consistently linked with increased mortality up to the age of 3 years. Our multifaceted methodological approach incorporated spatial analysis for visualizing geographical variations in mortality risk, causally adjusted regression analysis to single out specific risk factors, and machine learning techniques for identifying clusters of multifactorial risk factors. To ensure robustness and validity, we divided the data set temporally, assessing the persistence of identified subgroups over different periods. The reassessment of mortality risk used the targeted maximum likelihood estimation (TMLE) method to achieve more robust causal modeling. RESULTS: We analyzed data from 21,005 children. The mortality risk (6 weeks to 3 years of age) was 5.2% (95% CI 4.8%-5.6%) for children born between 2003 and 2011, and 2.9% (95% CI 2.5%-3.3%) for children born between 2012 and 2016. Our findings revealed 3 distinct high-risk subgroups with notably higher mortality rates, children residing in a specific urban area (adjusted mortality risk difference of 3.4%, 95% CI 0.3%-6.5%), children born to mothers with no prenat

There is increasing awareness of the importance of modelling life course trajectories to unravel how social, economic and health factors relate to health over time. Different methods have been developed and applied in public health to classify individuals into groups based on characteristics of their life course. However, the application and results of different methods are rarely compared. We compared the application and results of two methods to classify life course trajectories of individuals, i.e. sequence analysis and group-based multi-trajectory modeling (GBTM), using public health data. We used high-resolution Danish nationwide register data on 926,160 individuals born between 1987 and 2001, including information on the yearly occurrence of 7 childhood adversities in 2 dimensions (i.e. family poverty and family dynamics). We constructed childhood adversity trajectories from 0 to 15 years by applying (1) sequence analysis using optimal matching and cluster analysis using Ward's method and (2) GBTM using logistic and zero-inflated Poisson regressions. We identified 2 to 8 cluster solutions using both methods and determined the optimal solution for both methods. Both methods generated a low adversity, a poverty, and a consistent or high adversity cluster. The 5-cluster solution using sequence analysis additionally included a household psychiatric illness and a late adversity cluster. The 4-group solution using GBTM additionally included a moderate adversity cluster. Compared with the solution obtained through sequence analysis, the solution obtained through GBTM contained fewer individuals in the low adversity cluster and more in the other clusters. We find that the two methods generate qualitatively similar solutions, but the quantitative distributions of children over the groups are different. The method of choice depends on the type of data available and the research question of interest. We provide a comprehensive overview of important considerations and ben, There is increasing awareness of the importance of modelling life course trajectories to unravel how social, economic and health factors relate to health over time. Different methods have been developed and applied in public health to classify individuals into groups based on characteristics of their life course. However, the application and results of different methods are rarely compared. We compared the application and results of two methods to classify life course trajectories of individuals, i.e. sequence analysis and group-based multi-trajectory modeling (GBTM), using public health data. We used high-resolution Danish nationwide register data on 926,160 individuals born between 1987 and 2001, including information on the yearly occurrence of 7 childhood adversities in 2 dimensions (i.e. family poverty and family dynamics). We constructed childhood adversity trajectories from 0 to 15 years by applying (1) sequence analysis using optimal matching and cluster analysis using Ward's method and (2) GBTM using logistic and zero-inflated Poisson regressions. We identified 2 to 8 cluster solutions using both methods and determined the optimal solution for both methods. Both methods generated a low adversity, a poverty, and a consistent or high adversity cluster. The 5-cluster solution using sequence analysis additionally included a household psychiatric illness and a late adversity cluster. The 4-group solution using GBTM additionally included a moderate adversity cluster. Compared with the solution obtained through sequence analysis, the solution obtained through GBTM contained fewer individuals in the low adversity cluster and more in the other clusters. We find that the two methods generate qualitatively similar solutions, but the quantitative distributions of children over the groups are different. The method of choice depends on the type of data available and the research question of interest. We provide a comprehensive overview of important considerations and