Residential relocation leads to environmental changes, besides being likely to be influenced by sociodemographic characteristics. The relationship between them is, however, still not well described. We aimed to investigate changes in patterns of urban, natural and physical environmental exposures due to relocation and related sociodemographic determinants in a population-based cohort study of children and adolescents. We included individuals between 0 and 17 years of age, registered in primary healthcare records in Catalonia, Spain from 2006 to 2018 and categorized them as non-movers and movers (first residential change). Hierarchical clustering identified groups of individuals based on their exposure to air pollution, green spaces and built environment. Low, medium and high environmental hazard exposure clusters were identified, which were used to identify changes in environmental exposure patterns due to relocation, namely same, worse, and better environment. Multinomial logistic regression determined whether sex, age, nationality and area-level deprivation influenced these changes. From 1,316,138 individuals, 221,058 were movers. Compared to non-movers, movers were more frequently with foreign nationality (12.1% vs. 21.3%, respectively) and more likely to live in the most deprived areas at baseline (19.2% vs. 24.4%). Younger children had higher risk of moving to better or worse environments. Individuals living in areas with the highest levels of deprivation were least likely to move within the same environments (i.e., either moving to better (Odds Ratio [95% Confidence Interval] 1.39[1.34-1.44]) or worse environment (1.56[1.49-1.63])). Compared to people from Spain, those from Africa moved to similar environments (OR of moving to a better environment 0.90 (0.87-0.94) with respect to move to the same environment, and OR of moving to a worse environment 0.82 (0.78-0.85)), those from America tended to move to other environments (1.17 (1.12-1.23) better and 1.09 (1.03-1.15) worse), while those from Asia and Europe moved to better environments (1.22 (1.12-1.33) and 1.20 (1.14-1.27) respectively). No associations were found for sex. Changes in patterns of environmental exposures due to residential mobility are complex and may be determined by age, deprivation and nationality.

OBJECTIVE: Multiple treatment options are recommended for Systemic Lupus Erythematosus (SLE) by clinical guidelines. This study aimed to explore SLE treatment patterns as there is limited real-world data of SLE medication utilisation, especially in childhood-onset SLE (cSLE). METHODS: We conducted a longitudinal cohort study using five routinely collected healthcare databases from four European countries (United Kingdom, France, Germany, and Spain). We described the characteristics of adult and paediatric patients at time of SLE diagnosis. We calculated the percentage of patients commencing SLE treatments in the first month and year after diagnosis, reported number of prescriptions, starting dose, cumulative dose, and duration of each treatment, and characterised the line of therapy. RESULTS: We characterised 11,255 patients with a first diagnosis of SLE and included 5718 in our medication utilisation analyses. The majority of adult SLE patients were female (range 80-88 %), with median age of 49 to 54 years at diagnosis. In the paediatric cohort (n = 378), 66-83 % of SLE patients were female, with median age of 12 to 16 years at diagnosis. Hydroxychloroquine and glucocorticoids were common first-line treatments in both adults and children, with second-line treatments including mycophenolate mofetil and methotrexate. Few cases of monoclonal antibody use were seen in either cohort. Initial glucocorticoid dosing in paediatric patients was often higher than in adults. CONCLUSION: Treatment choices for adult SLE patients across four European countries were in line with recent therapeutic consensus guidelines. High glucocorticoid prescriptions in paediatric patients suggests the need for steroid-sparing treatment alternatives and paediatric specific guidelines.

INTRODUCTION: This study aims to investigate the relationship between deprivation, as measured by a socioeconomic deprivation index (SDI) score for census tract urban areas, and COVID-19 infections and vaccine uptake among children and adolescents before and after the vaccination rollout in Catalonia, Spain. METHODS: We conducted a population-based cohort study using primary care records. Individuals were followed 3 months before the start of the vaccination campaign in Spain and 3 months after. Children (5-11 years) and adolescents (12-15 years) with at least 1 year of prior history observation available and without missing deprivation data. For each outcome, we estimated cumulative incidence and crude Cox proportional-hazard models by SDI quintiles, and hazard ratios (HRs) of COVID-19 infection and vaccine uptake relative to the least deprived quintile, Q1. RESULTS: Before COVID-19 vaccination rollout, 290,625 children and 179,685 adolescents were analyzed. Increased HR of deprivation was associated with a higher risk of COVID-19 infection in both children [Q5: 1.55 (95% CI, 1.47-1.63)] and adolescents [Q5: 1.36 (95% CI, 1.29-1.43)]. After the rollout, this pattern changed among children, with lower risk of infection in more deprived areas [Q5: 0.62 (95% CI, 0.61-0.64)]. Vaccine uptake was higher among adolescents than children, but in both age groups, non-vaccination was more common among those living in more deprived areas (39.3% and 74.6% in Q1 vs. 26.5% and 66.9% in Q5 among children and adolescents, respectively). CONCLUSIONS: Children and adolescents living in deprived areas were at higher risk of COVID-19 non-vaccination. Socioeconomic disparities in COVID-19 infection were also evident before vaccine rollout, with a higher infection risk in deprived areas across age groups. Our findings suggest that changes in the association between deprivation and infections among children after the vaccine rollout were likely due to testing disparities.

Background The World Health Organisation (WHO) has identified a range of symptomatic manifestations to aid in the clinical diagnosis of post-COVID conditions, herein referred to as post-acute COVID-19 symptoms. We conducted an international network cohort study to estimate the burden of these symptoms in North American, European, and Asian populations. Methods A federated analysis was conducted including 10 databases from the United Kingdom, Netherlands, Norway, Estonia, Spain, France, South Korea, and the United States, between September 1st 2020 and latest data availability (which varied from December 31st 2021 to February 28th 2023), covering primary and secondary care, nationwide registries, and claims data, all mapped to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). We defined two cohorts for the main analyses: a SARS-CoV-2 infection cohort [positive polymerase chain reaction (PCR) or rapid lateral flow test (LFT) result or clinical COVID-19 diagnosis] and a general population cohort. Individuals with less than 365 days of prior history or 120 days of follow-up were excluded. We estimated incidence rates (IRs) of the 25 WHO-proposed post-acute COVID-19 symptoms, considering symptoms that occurred >= 90 and <= 365 days after index date, excluding individuals with the respective symptoms 180 days prior to the index event. Stratified analyses were conducted by age and sex. Incidence rate ratios (IRRs) were calculated comparing rates in the infected cohort versus the general population. Results from the different databases were combined using random-effects meta-analyses. Findings 3,019,408 individuals were included in the infection cohort. 1,585,160 of them were female and 1,434,248 of them male. 929,351,505 individuals were included in the general population group. 461,195,036 of them were female and 466,022,004 of them male. The 1-year IR of any post-acute COVID-19 symptom in the COVID-19 infection cohort varied significantly across databases, from 4.4 (95% CI 3.8-5.1) per 100 person-years to 103.9 (95% CI 103.2-104.7). The five most common symptoms were joint pain (from 1.6 (95% CI 1.3-1.9) to 14.3 (95% CI 14.1-14.6)), abdominal pain (from 0.3 (95% CI 0.1-0.5) to 9.9 (95% CI 9.7-10.1)), gastrointestinal issues (from 0.6 (95% CI 0.4-0.9) to 13.3 (95% CI 13.1-13.6)), cough (from 0.3 (95% CI 0.2-0.5) to 9.1 (95% CI 8.9-9.3)), and anxiety (from 0.8 (95% CI 0.6-1.2) to 11.4 (95% CI 11.2-11.6)); whereas muscle spasms (from 0.01 (95% CI 0.008-0.2) to 1.7 (95% CI 1.6-1.8)), pins and needles (from 0.05 (95% CI 0.03-0.0.9) to 1.5 (95% CI 1.4-1.6)), memory issues (from 0.03 (95% CI 0.02-0.06) to 0.8 (95% CI 0.7-0.8)), cognitive dysfunction (from 0.007 (95% CI 0.004-0.01) to 0.6 (95% CI 0.4-0.8)), and altered smell and/or taste (from 0.04 (95% CI 0.03-0.04) to 0.7 (95% CI 0.6-0.8)) were least common. Incidence rates of any post-acute COVID-19 symptoms generally increased with age, with certain symptoms peaking in middle-aged adults (anxiety, depressive disorders, headache, altered smell and taste) and others in pre-school children (gastrointestinal issues and cough). Females had higher incidence rates for most symptoms. Based on the random-effects model, the infected cohort had a higher incidence of any post-acute COVID-19 symptom than the general population, with a meta-analytic incidence rate ratio (meta-IRR) of 1.4 (1-2). A similar pattern was seen for all individual symptoms. The highest meta-IRRs were depressive disorder, 2.6 (1.7-3.9); anxiety, 2.3 (1.4-3.8); allergy, 2.1 (1.7-2. 8) and sleep disorders, 2.1 (1.5-2.6). The meta-IRR for altered smell and/or taste was 1.9 (1.3-2.8). Interpretation Post-acute COVID-19 symptoms, as listed by the WHO, were commonly observed following COVID-19 infection. However, even after standardising research methods, there was significant heterogeneity in the incidence rates from different healthcare settings and geographical locations. This is the first international study of the epidemiology of post-acute COVID-19 symptoms using the WHO-listed symptoms. Its findings contibute to understand the epidemiology of this condition from a multinational approach. Limitations of this study include the lack of consensus of the post-acute COVID-19 definition, as well as the difficulty to capture the impact on daily life of the post-acute COVID-19 symptoms in the available datasets.

PurposeThe generation of representative disease phenotypes is important for ensuring the reliability of the findings of observational studies. The aim of this manuscript is to outline a reproducible framework for reliable and traceable phenotype generation based on real world data for use in the Data Analysis and Real-World Interrogation Network (DARWIN EU). We illustrate the use of this framework by generating phenotypes for two diseases: pancreatic cancer and systemic lupus erythematosus (SLE).MethodsThe phenotyping process involves a 14-steps process based on a standard operating procedure co-created by the DARWIN EU Coordination Centre in collaboration with the European Medicines Agency. A number of bespoke R packages were utilised to generate and review codelists for two phenotypes based on real world data mapped to the OMOP Common Data Model.ResultsCodelists were generated for both pancreatic cancer and SLE, and cohorts were generated in six OMOP-mapped databases. Diagnostic checks were performed, which showed these cohorts had broadly similar incidence and prevalence figures to previously published literature, despite significant inter-database variability. Co-occurrent symptoms, conditions, and medication use were in keeping with pre-specified clinical descriptions based on previous knowledge.ConclusionsOur detailed phenotyping process makes use of bespoke tools and allows for comprehensive codelist generation and review, as well as large-scale exploration of the characteristics of the resulting cohorts. Wider use of structured and reproducible phenotyping methods will be important in ensuring the reliability of observational studies for regulatory purposes.