Background: In utero exposure to bisphenols, widely used in consumer products, may alter lung development and increase the risk of respiratory morbidity in the offspring. However, evidence is scarce and mostly focused on bisphenol A (BPA) only. Objective: To examine the associations of in utero exposure to BPA, bisphenol F (BPF), and bisphenol S (BPS) with asthma, wheeze, and lung function in school-age children, and whether these associations differ by sex. Methods: We included 3,007 mother-child pairs from eight European birth cohorts. Bisphenol concentrations were determined in maternal urine samples collected during pregnancy (1999-2010). Between 7 and 11 years of age, current asthma and wheeze were assessed from questionnaires and lung function by spirometry. Wheezing patterns were constructed from questionnaires from early to mid-childhood. We performed adjusted random effects meta-analysis on individual participant data. Results: Exposure to BPA was prevalent with 90% of maternal samples containing concentrations above detection limits. BPF and BPS were found in 27% and 49% of samples. In utero exposure to BPA was associated with higher odds of current asthma (OR = 1.13, 95% CI = 1.01, 1.27) and wheeze (OR = 1.14, 95% CI = 1.01, 1.30) (p-interaction sex = 0.01) among girls, but not with wheezing patterns nor lung function neither in overall nor among boys. We observed inconsistent associations of BPF and BPS with the respiratory outcomes assessed in overall and sex-stratified analyses. Conclusion: This study suggests that in utero BPA exposure may be associated with higher odds of asthma and wheeze among school-age girls.

Introduction and objectives: Childhood obesity trends are plateauing in Spain, but limited information is available about how they differ by region. This study assessed childhood and adolescent the prevalence and incidence of overweight and obesity from 2005 to 2017 across 8 Spanish regions.
Methods: This longitudinal study used height and weight measurements from 2.5 million children aged 2 to 17 years to calculate overweight and obesity, according to the World Health Organization (WHO) guidelines. Data were obtained from The Base de datos para la Investigation Farmacoepidemiologica en Atencion Primaria, and the Information System for Research in Primary Care. Prevalence and incidence rates and trends from 2005 to 2017 were calculated and stratified by age, sex, and region.
Results: The overall obesity prevalence increased in boys and girls from age 2 (0.8%; 95%CI, 0.8-0.9 in both sexes) until peaking at age 7 in girls (17.3%; 95%CI, 17.1-17.5) and age 9 in boys (24.1%; 95%CI 23.9-24.3). The highest and lowest obesity prevalences were observed in Murcia and Navarre. Overall obesity prevalence trends decreased from 2005 to 2017 in all age-sex groups and in most regions. Highest obesity incidence rates were found in children aged 6 to 7 years, (4.5 [4.5-4.5] and 3.5 [3.5-3.5] new obesity cases per 100 person-years in boys and girls, respectively). Boys had higher prevalence and incidence rates than girls across all regions. Overweight/obesity prevalence and incidence rates and their trends were consistently higher than the obesity results, although a similar pattern was observed across sex and age.
Conclusions: Overweight and obesity prevalence slightly decreased in Spain from 2005 to 2017, but regional, sex, and age differences persisted. Because incidence peaked around the age of 6 years, it may be important to begin health promotion programs at an early age. (C) 2021 Sociedad Espanola de Cardiologia. Published by Elsevier Espana, S.L.U. All rights reserved.

Objective To investigate how trends in incidence of anxiety and depressive disorders have been affected by the COVID-19 pandemic. Design Population-based cohort study. Setting Retrospective cohort study from 2018 to 2021 using the Information System for Research in Primary Care (SIDIAP) database in Catalonia, Spain. Participants 3 640 204 individuals aged 18 or older in SIDIAP on 1 March 2018 with no history of anxiety and depressive disorders. Primary and secondary outcomes measures The incidence of anxiety and depressive disorders during the prelockdown period (March 2018-February 2020), lockdown period (March-June 2020) and postlockdown period (July 2020-March 2021) was calculated. Forecasted rates over the COVID-19 periods were estimated using negative binomial regression models based on prelockdown data. The percentage of reduction was estimated by comparing forecasted versus observed events, overall and by sex, age and socioeconomic status. Results The incidence rates per 100 000 person-months of anxiety and depressive disorders were 151.1 (95% CI 150.3 to 152.0) and 32.3 (31.9 to 32.6), respectively, during the prelockdown period. We observed an increase of 37.1% (95% prediction interval 25.5 to 50.2) in incident anxiety diagnoses compared with the expected in March 2020, followed by a reduction of 15.8% (7.3 to 23.5) during the postlockdown period. A reduction in incident depressive disorders occurred during the lockdown and postlockdown periods (45.6% (39.2 to 51.0) and 22.0% (12.6 to 30.1), respectively). Reductions were higher among women during the lockdown period, adults aged 18-34 years and individuals living in the most deprived areas. Conclusions The COVID-19 pandemic in Catalonia was associated with an initial increase in anxiety disorders diagnosed in primary care but a reduction in cases as the pandemic continued. Diagnoses of depressive disorders were lower than expected throughout the pandemic.

In this review, Early Career Members of the European Respiratory Society (ERS) and the Chairs of the ERS Assembly 7: Paediatrics present the highlights in paediatric respiratory medicine from the ERS International Congress 2021. The eight scientific Groups of this Assembly cover respiratory physiology and sleep, asthma and allergy, cystic fibrosis (CF), respiratory infection and immunology, neonatology and intensive care, respiratory epidemiology, bronchology, and lung and airway development. We here describe new developments in lung function testing and sleep-disordered breathing diagnosis, early life exposures affecting pulmonary function in children and effect of COVID-19 on sleep and lung function. In paediatric asthma, we present the important role of the exposome in asthma development, and how biologics can provide better outcomes. We discuss new methods to assess distal airways in children with CF, as some details remain blind when using the lung clearance index. Moreover, we summarise the new ERS guidelines for bronchiectasis management in children and adolescents. We present interventions to reduce morbidity and monitor pulmonary function in newborns at risk of bronchopulmonary dysplasia and long-term chronic respiratory morbidity of this disease. In respiratory epidemiology, we characterise primary ciliary dyskinesia, identify early life determinants of respiratory health and describe the effect of COVID-19 preventive measures on respiratory symptoms. Also, we describe the epidemiology of interstitial lung diseases, possible consequences of tracheomalacia and a classification of diffuse alveolar haemorrhage in children. Finally, we highlight that the characterisation of genes and pathways involved in the development of a disease is essential to identify new biomarkers and therapeutic targets.

A lossless, one-shot and privacy-preserving distributed algorithm was revealed for fitting linear mixed models on multi-site data. The algorithm was applied to a study of 120,609 COVID-19 patients using only minimal aggregated data from each of 14 sites.
Linear mixed models are commonly used in healthcare-based association analyses for analyzing multi-site data with heterogeneous site-specific random effects. Due to regulations for protecting patients’ privacy, sensitive individual patient data (IPD) typically cannot be shared across sites. We propose an algorithm for fitting distributed linear mixed models (DLMMs) without sharing IPD across sites. This algorithm achieves results identical to those achieved using pooled IPD from multiple sites (i.e., the same effect size and standard error estimates), hence demonstrating the lossless property. The algorithm requires each site to contribute minimal aggregated data in only one round of communication. We demonstrate the lossless property of the proposed DLMM algorithm by investigating the associations between demographic and clinical characteristics and length of hospital stay in COVID-19 patients using administrative claims from the UnitedHealth Group Clinical Discovery Database. We extend this association study by incorporating 120,609 COVID-19 patients from 11 collaborative data sources worldwide.

OBJECTIVE To study the association between covid-19 vaccines, SARS-CoV-2 infection, and risk of immune mediated neurological events. DESIGN Population based historical rate comparison study and self-controlled case series analysis. SETTING Primary care records from the United Kingdom, and primary care records from Spain linked to hospital data. PARTICIPANTS 8 330 497 people who received at least one dose of covid-19 vaccines ChAdOx1 nCoV-19, BNT162b2, mRNA-1273, or Ad.26.COV2.S between the rollout of the vaccination campaigns and end of data availability (UK: 9 May 2021; Spain: 30 June 2021). The study sample also comprised a cohort of 735 870 unvaccinated individuals with a first positive reverse transcription polymerase chain reaction test result for SARS-CoV-2 from 1 September 2020, and 14 330 080 participants from the general population. MAIN OUTCOME MEASURES Outcomes were incidence of Bell’s palsy, encephalomyelitis, Guillain-Barre syndrome, and transverse myelitis. Incidence rates were estimated in the 21 days after the first vaccine dose, 90 days after a positive test result for SARS-CoV-2, and between 2017 and 2019 for background rates in the general population cohort. Indirectly standardised incidence ratios were estimated. Adjusted incidence rate ratios were estimated from the self-controlled case series. RESULTS The study included 4 376 535 people who received ChAdOx1 nCoV-19, 3 588 318 who received BNT162b2, 244 913 who received mRNA-1273, and 120 731 who received Ad26.CoV.2; 735 870 people with SARS-CoV-2 infection; and 14 330 080 people from the general population. Overall, post-vaccine rates were consistent with expected (background) rates for Bell’s palsy, encephalomyelitis, and Guillain-Barre syndrome. Self-controlled case series was conducted only for Bell’s palsy, given limited statistical power, but with no safety signal seen for those vaccinated. Rates were, however, higher than expected after SARS-CoV-2 infection. For example, in the data from the UK, the standardised incidence ratio for Bell’s palsy was 1.33 (1.02 to 1.74), for encephalomyelitis was 6.89 (3.82 to 12.44), and for Guillain-Barre syndrome was 3.53 (1.83 to 6.77). Transverse myelitis was rare (<5 events in all vaccinated cohorts) and could not be analysed. CONCLUSIONS No safety signal was observed between covid-19 vaccines and the immune mediated neurological events of Bell's palsy, encephalomyelitis, GuillainBarre syndrome, and transverse myelitis. An increased risk of Bell's palsy, encephalomyelitis, and GuillainBarre syndrome was, however, observed for people with SARS-CoV-2 infection.

Background
Metabolic syndrome (MS) is the simultaneous occurrence of a cluster of predefined cardiovascular risk factors. Although individual MS components are associated with increased risk of cancer, it is still unclear whether the association between MS and cancer differs from the association between individual MS components and cancer. The aim of this matched case-control study was to estimate the association of 13 types of cancer with (1) MS and (2) the diagnosis of 0, 1 or 2 individual MS components.
Methods
Cases included 183,248 patients >= 40 years from the SIDIAP database with incident cancer diagnosed between January 2008-December 2017. Each case was matched to four controls by inclusion date, sex and age. Adjusted conditional logistic regression models were used to evaluate the association between MS and cancer risk, comparing the effect of global MS versus having one or two individual components of MS.
Results
MS was associated with an increased risk of the following cancers: colorectal (OR: 1.28, 95%CI: 1.23-1.32), liver (OR: 1.93, 95%CI: 1.74-2.14), pancreas (OR: 1.79, 95%CI: 1.63-1.98), post-menopausal breast (OR: 1.10, 95%CI: 1.06-1.15), pre-menopausal endometrial (OR: 2.14, 95%CI: 1.74-2.65), post-menopausal endometrial (OR: 2.46, 95%CI: 2.20-2.74), bladder (OR: 1.41, 95%CI: 1.34-1.48), kidney (OR: 1.84, 95%CI: 1.69-2.00), non-Hodgkin lymphoma (OR: 1.23, 95%CI: 1.10-1.38), leukaemia (OR: 1.42, 95%CI: 1.31-1.54), lung (OR: 1.11, 95%CI: 1.05-1.16) and thyroid (OR: 1.71, 95%CI: 1.50-1.95). Except for prostate, pre-menopause breast cancer and Hodgkin and non-Hodgkin lymphoma, MS is associated with a higher risk of cancer than 1 or 2 individual MS components. Estimates were significantly higher in men than in women for colorectal and lung cancer, and in smokers than in non-smokers for lung cancer.
Conclusion
MS is associated with a higher risk of developing 11 types of common cancer, with a positive correlation between number of MS components and risk of cancer.

Background and Aims During the first peak of the COVID-19 pandemic, the Preventive Medicine Department and the Occupational Health Department at Hospital Clinic de Barcelona (HCB), a large Spanish referral hospital, developed an innovative comprehensive SARS-CoV2 Surveillance and Control System (CoSy-19) in order to preserve patients’ and health care workers’ (HCWs) safety. We aim to describe the CoSy-19 and to assess the impact in the number of contacts that new cases generated along this time. Methods Observational descriptive study of the findings of the activity of contact tracing of all cases received at the HCB during the first peak of COVID-19 in Spain (February 25th-May 3rd, 2020). Results A team of 204 professionals and volunteers performed 384 in-hospital contact-tracing studies which generated contacts, detecting 298 transmission chains which suggested preventive measures, generated around 22 000 follow-ups and more than 30 000 days of work leave. The number of contacts that new cases generated decreased during the study period. Conclusion Coordination between Preventive Medicine and Occupational Health departments and agile information systems were necessary to preserve non-COVID activity and workers safety.

Background We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient’s risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. Methods We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. Results Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.72-0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. Conclusions This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use.