English Is It! (ELT Training Series) Vol. 16

The Research group From English Acquisition to English Learning and Teaching is registered at the Institute of Professional Development Teaching (IDP-ICE), at the University of Barcelona. The group, founded and led by Lourdes Montoro (September 2013 - June 2021), has involved 28 teachers and professionals. 7 of them have been members of the group, and, together with 21 guest authors, have presented their work in the publication which she also created, and coordinated to fulfill the objectives of the pedagogical project which she had devised: English Is It! (ELT Training Series (Vols. 1-16)

Risk Factors for COVID-19 in Inflammatory Bowel Disease: A National, ENEIDA-Based Case–Control Study (COVID-19-EII)

(1) Scant information is available concerning the characteristics that may favour the acquisition of COVID-19 in patients with inflammatory bowel disease (IBD). Therefore, the aim of this study was to assess these differences between infected and noninfected patients with IBD. (2) This nationwide case-control study evaluated patients with inflammatory bowel disease with COVID-19 (cases) and without COVID-19 (controls) during the period March-July 2020 included in the ENEIDA of GETECCU. (3) A total of 496 cases and 964 controls from 73 Spanish centres were included. No differences were found in the basal characteristics between cases and controls. Cases had higher comorbidity Charlson scores (24% vs. 19%; p = 0.02) and occupational risk (28% vs. 10.5%; p < 0.0001) more frequently than did controls. Lockdown was the only protective measure against COVID-19 (50% vs. 70%; p < 0.0001). No differences were found in the use of systemic steroids, immunosuppressants or biologics between cases and controls. Cases were more often treated with 5-aminosalicylates (42% vs. 34%; p = 0.003). Having a moderate Charlson score (OR: 2.7; 95%CI: 1.3-5.9), occupational risk (OR: 2.9; 95%CI: 1.8-4.4) and the use of 5-aminosalicylates (OR: 1.7; 95%CI: 1.2-2.5) were factors for COVID-19. The strict lockdown was the only protective factor (OR: 0.1; 95%CI: 0.09-0.2). (4) Comorbidities and occupational exposure are the most relevant factors for COVID-19 in patients with IBD. The risk of COVID-19 seems not to be increased by immunosuppressants or biologics, with a potential effect of 5-aminosalicylates, which should be investigated further and interpreted with caution

Somos diversidad. Actividades para la formación de profesionales de la educación formal y no formal en diversidad sexual, familiar, corporal y de expresión e identidad de género

Este manual se presenta como una “caja de herramientas” donde acudir en busca de recursos y actividades didácticas para elaborar formaciones en diversidad sexual, familiar, corporal y de expresión e identidad de género, dirigidas a profesionales que trabajan con jóvenes. En este sentido, son materiales que se pueden adaptar a las necesidades de cada formación y a distintos niveles de conocimiento, tanto de los grupos participantes, como de la persona que dinamice las actividades y que son lo suficientemente flexibles para que puedan ser moldeados y utilizados según los recursos temporales y espaciales que presente cada propuestaformativa. “Somos diversidad” ofrece un total de 44 actividades articuladas en 5 módulos temáticos. Abrazar la diversidad como una oportunidad educativa Transformarse para transformar: afectividad, diferencia y diversidad Sexualidades Corporalidades, identidades y expresiones de género Diversidad familiar Cada módulo ofrece un índice inicial, una breve bienvenida donde se reflejan la justificación y objetivos del módulo, una serie de actividades y un apartado de bibliografía citada y consultada. En cada actividad se detalla su duración estimada, los objetivos propuestos, los recursos necesarios, las indicaciones para su desarrollo, y se aportan finalmente los materiales específicos necesarios para realizarlas. Este manual es el resultado de la actividad “Juventud y LGTBI+: abrazar la diversidad en la educación no formal y formal” dentro del Plan de Actividades Transnacionales (TCA) del programa Erasmus+: Juventud en Acción, organizada por el Injuve y el Grupo de Investigación “Antropología, Diversidad y Convivencia” de la Universidad Complutense de Madrid

Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

Global Retinoblastoma Presentation and Analysis by National Income Level.

Importance: Early diagnosis of retinoblastoma, the most common intraocular cancer, can save both a child's life and vision. However, anecdotal evidence suggests that many children across the world are diagnosed late. To our knowledge, the clinical presentation of retinoblastoma has never been assessed on a global scale. Objectives: To report the retinoblastoma stage at diagnosis in patients across the world during a single year, to investigate associations between clinical variables and national income level, and to investigate risk factors for advanced disease at diagnosis. Design, Setting, and Participants: A total of 278 retinoblastoma treatment centers were recruited from June 2017 through December 2018 to participate in a cross-sectional analysis of treatment-naive patients with retinoblastoma who were diagnosed in 2017. Main Outcomes and Measures: Age at presentation, proportion of familial history of retinoblastoma, and tumor stage and metastasis. Results: The cohort included 4351 new patients from 153 countries; the median age at diagnosis was 30.5 (interquartile range, 18.3-45.9) months, and 1976 patients (45.4%) were female. Most patients (n = 3685 [84.7%]) were from low- and middle-income countries (LMICs). Globally, the most common indication for referral was leukocoria (n = 2638 [62.8%]), followed by strabismus (n = 429 [10.2%]) and proptosis (n = 309 [7.4%]). Patients from high-income countries (HICs) were diagnosed at a median age of 14.1 months, with 656 of 666 (98.5%) patients having intraocular retinoblastoma and 2 (0.3%) having metastasis. Patients from low-income countries were diagnosed at a median age of 30.5 months, with 256 of 521 (49.1%) having extraocular retinoblastoma and 94 of 498 (18.9%) having metastasis. Lower national income level was associated with older presentation age, higher proportion of locally advanced disease and distant metastasis, and smaller proportion of familial history of retinoblastoma. Advanced disease at diagnosis was more common in LMICs even after adjusting for age (odds ratio for low-income countries vs upper-middle-income countries and HICs, 17.92 [95% CI, 12.94-24.80], and for lower-middle-income countries vs upper-middle-income countries and HICs, 5.74 [95% CI, 4.30-7.68]). Conclusions and Relevance: This study is estimated to have included more than half of all new retinoblastoma cases worldwide in 2017. Children from LMICs, where the main global retinoblastoma burden lies, presented at an older age with more advanced disease and demonstrated a smaller proportion of familial history of retinoblastoma, likely because many do not reach a childbearing age. Given that retinoblastoma is curable, these data are concerning and mandate intervention at national and international levels. Further studies are needed to investigate factors, other than age at presentation, that may be associated with advanced disease in LMICs

The global retinoblastoma outcome study : a prospective, cluster-based analysis of 4064 patients from 149 countries

DATA SHARING : The study data will become available online once all analyses are complete.BACKGROUND : Retinoblastoma is the most common intraocular cancer worldwide. There is some evidence to suggest that major differences exist in treatment outcomes for children with retinoblastoma from different regions, but these differences have not been assessed on a global scale. We aimed to report 3-year outcomes for children with retinoblastoma globally and to investigate factors associated with survival. METHODS : We did a prospective cluster-based analysis of treatment-naive patients with retinoblastoma who were diagnosed between Jan 1, 2017, and Dec 31, 2017, then treated and followed up for 3 years. Patients were recruited from 260 specialised treatment centres worldwide. Data were obtained from participating centres on primary and additional treatments, duration of follow-up, metastasis, eye globe salvage, and survival outcome. We analysed time to death and time to enucleation with Cox regression models. FINDINGS : The cohort included 4064 children from 149 countries. The median age at diagnosis was 23·2 months (IQR 11·0–36·5). Extraocular tumour spread (cT4 of the cTNMH classification) at diagnosis was reported in five (0·8%) of 636 children from high-income countries, 55 (5·4%) of 1027 children from upper-middle-income countries, 342 (19·7%) of 1738 children from lower-middle-income countries, and 196 (42·9%) of 457 children from low-income countries. Enucleation surgery was available for all children and intravenous chemotherapy was available for 4014 (98·8%) of 4064 children. The 3-year survival rate was 99·5% (95% CI 98·8–100·0) for children from high-income countries, 91·2% (89·5–93·0) for children from upper-middle-income countries, 80·3% (78·3–82·3) for children from lower-middle-income countries, and 57·3% (52·1-63·0) for children from low-income countries. On analysis, independent factors for worse survival were residence in low-income countries compared to high-income countries (hazard ratio 16·67; 95% CI 4·76–50·00), cT4 advanced tumour compared to cT1 (8·98; 4·44–18·18), and older age at diagnosis in children up to 3 years (1·38 per year; 1·23–1·56). For children aged 3–7 years, the mortality risk decreased slightly (p=0·0104 for the change in slope). INTERPRETATION : This study, estimated to include approximately half of all new retinoblastoma cases worldwide in 2017, shows profound inequity in survival of children depending on the national income level of their country of residence. In high-income countries, death from retinoblastoma is rare, whereas in low-income countries estimated 3-year survival is just over 50%. Although essential treatments are available in nearly all countries, early diagnosis and treatment in low-income countries are key to improving survival outcomes.The Queen Elizabeth Diamond Jubilee Trust and the Wellcome Trust.https://www.thelancet.com/journals/langlo/homeam2023Paediatrics and Child Healt

Définition et validation d’algorithmes d’identification des cas de diabète dans les bases de données médico-administratives à partir de la cohorte Constances et application à l’étude de l’évolution de la prévalence et de l’incidence du diabète dans le SNDS

Contexte: Le Système National de Données Santé (SNDS) est une base de données médico-administratives (BDMA) comprenant des informations sur les remboursements de soins en ville, sur les hospitalisations en secteur public et privé et sur les décès de l’ensemble de la population résidant en France. Il s’agit d’une des sources de données majeures du dispositif de surveillance épidémiologique du diabète en France. Cette source d’informations de type Big data offre un vaste potentiel en termes de surveillance épidémiologique qui ne peut être développé qu’après avoir levé les défis méthodologiques associés au recours à ces outils.Objectifs: Les objectifs de cette thèse sont d’utiliser les données de la cohorte Constances pour améliorer le système de surveillance du diabète basé sur le SNDS et développer de nouveaux outils en appliquant la méthodologie Machine Learning.Résultats: Dans un premier temps, l’étude de validation des algorithmes d’identification des cas de diabète, à partir de la cohorte Constances, a montré qu’ils avaient d’excellentes performances que ce soit pour le diabète connu ou traité pharmacologiquement. L’algorithme basé sur les remboursements de traitements antidiabétiques a été retenu pour l’étude de l’évolution de l’épidémie du diabète en France dans le SNDS. Entre 2010 et 2017, une légère augmentation de la prévalence et une diminution de l’incidence sur la période 2012-2017, ont été observées chez les adultes âgées 45 ans ou plus. Ensuite, une méthodologie de type Machine Learning a été appliquée aux données de la cohorte Constances afin de développer un algorithme de typage du diabète. Un modèle d’analyse discriminante linéaire a été retenu, basé sur le nombre de remboursements d’insuline à action rapide, d’insuline de longue durée et de biguanides au cours des 12 mois. En utilisant la même méthodologie, deux autres algorithmes ont été développés pour identifier les cas de diabète non diagnostiqué et les cas du prédiabète. Ces deux algorithmes étaient basé sur des modèles de régression logistique. Le premier algorithme retenait 5 variables (âge, sexe et nombre de remboursements sur 12 mois de bilans lipidiques, de dosages de glycémie et de consultations d’un médecin généraliste) et le deuxième retenait 6 variables (âge, sexe et nombre de remboursements de dosages d’antigène prostatique spécifique, de glycémie et d’HbA1C et de bilans lipidiques).Conclusion: Les BDMAs, telles que le SNDS, représentent une opportunité pour la surveillance épidémiologique du diabète, élément central pour le déploiement des programmes de prévention et des politiques de santé publique.Context: The National health data system (Système National de Données Santé, SNDS) is a health-administrative database (HAD) comprising information on reimbursements of dispensed out-of-hospital health care, on public and private hospital stays and on deaths for the whole population living in France. It is one of the main data sources of the diabetes epidemiological surveillance system in France. As Big Data source of information, it offers a huge potential in terms of epidemiological surveillance and it can only be exploited through specific tools which faced in 2016 several methodological challenges.Objectives: The objectives of this thesis were to use the data from the CONSTANCES cohort to improve the classical tools for diabetes surveillance in the SNDS and to develop new tools through Machine Learnings methods.Results: First, the validation of the diabetes case definition algorithms using the CONSTANCES cohort showed they had excellent performances in identifying diagnosed cases and pharmacologically treated cases. After retaining the most suitable algorithm relative to our purpose, it was applied to the entire SNDS to study the evolution of the diabetes epidemic in France. Between 2010 and 2017, prevalence rates slightly increased while incidence rates decreased over the period 2012-2017, among adults aged 45 years or older.Machine Learning methods were applied to the data from the CONSTANCES cohort allowed to develop a high performant type1/type 2 classification algorithm. A linear discriminant model based on the number of reimbursements over the last 12 months of fast-acting insulin, long-acting insulin and biguanides was retained. Another two algorithms for identifying undiagnosed diabetes cases and prediabetes cases were developed with the same methodology. Both algorithms were logistic regression models. The undiagnosed diabetes algorithm was based on 5 variables (age, sex and number of reimbursement in the last 12 months of tests for lipid profile, screening tests for glucose and general practitioner consultations) and the prediabetes algorithm on 6 variables (age, sex and number of reimbursements in the last 12 months of specific antigen screening tests, HbA1c screening tests, tests for lipid profile and screening tests for glucose).Conclusion: HADs such as the SNDS represent an opportunity for diabetes surveillance which is a key element for the development of prevention programs and public health policies

Prevalence of pre-diabetes, undiagnosed and diagnosed diabetes among adults aged 18 to 70 years in France: the CONSTANCES cohort

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Apports de l’intelligence artificielle dans la prévention du diabète : comment cibler les personnes ayant un diabète méconnu dans le Système National des Données Santé : Étude basée sur les données de la cohorte CONSTANCES

INTRODUCTION - En 2013-2014, selon les données de la cohorte Constances, 1,6% de la population française âgée de 18 à 69 ans avait un diabète méconnu. L’objectif de notre étude était de développer un algorithme pour identifier les cas de diabète méconnu dans le Système National des données de santé (SNDS) en utilisant l’intelligence artificielle. METHODES - L’algorithme a été développé à partir de la cohorte Constances dans laquelle des données d’auto-questionnaire, de questionnaire médical et des résultats biologiques sont appariés avec les données du SNDS. Nous avons utilisé une méthodologie d’apprentissage automatique supervisé composée de huit étapes. Premièrement, nous avons sélectionné la base de données (BdD) de référence, en excluant les cas de diabète connu. Parmi les 44,185 participants, nous avons identifié comme cible les cas de diabète méconnu - glycémie à jeun ≥7 mmol/l (n=655)-. Les étapes suivantes étaient : codification des variables SNDS, division de la BdD de référence en base d’entrainement et base de test, sélection des variables et entrainement, validation et sélection des algorithmes. RESULTATS - Seules 12 des 3471 variables codées étaient retenues pour leur capacité de discrimination entre la cible : diabète méconnu versus pas de diabète. L’algorithme final est un modèle de régression logistique basé sur les 5 variables les plus discriminantes : âge, sexe et nombre de remboursements (hors hôpital public) dans l’année précédente d’explorations d’une anomalie lipidique, de consultations d’un médecin généraliste et de dosages de glycémie. La spécificité, la sensibilité et la précision de l’algorithme étaient de 70%, 71 % et 69%, respectivement. CONCLUSION - L’intelligence artificielle ouvre de nombreuses perspectives en termes de prévention du diabète. Ainsi, l’identification des personnes à très haut risque permettrait de cibler les personnes à inclure dans les campagnes de prévention et de leur offrir une prise en charge spécifique

Identifying type 1 / type 2 diabetes in medico-administrative database to improve health surveillance, medical research and prevention in diabetes: Algorithm development and application

Introduction: Big data sources represent an opportunity for diabetes research. One example is the French national health data system (SNDS), gathering information on medical claims of out-of-hospital health care and hospitalizations for the entire French population (66 million). Currently, a validated algorithm based on antidiabetic drug reimbursement is able to identify people with pharmacologically-treated diabetes in the SNDS. But it cannot distinguish type 1 from type 2 diabetes. Differentiating type 1 and type 2 diabetes is crucial in diabetes surveillance, because they carry differences in their prevention, populations at risk, disease natural history, pathophysiology, management and risk of complications.This article investigates the development of a type 1/type 2 diabetes classification algorithm using artificial intelligence and its application to estimate the prevalence of type 1 and type 2 diabetes in France. Methods: The final data set comprised all diabetes cases from the CONSTANCES cohort (n = 951). A supervised machine learning method based on eight steps was used: final data set selection, target definition (type 1), coding features, final data set splitting into training and testing data sets, feature selection and training and validation and selection of algorithms. The selected algorithm was applied to SNDS data to estimate the type 1 and type 2 diabetes prevalence among adults 18–70 years of age. Results: Among the 3481 SNDS features, 14 were selected to train the different algorithms. The final algorithm was a linear discriminant analysis model based on the number of reimbursements for fast-acting insulin, long-acting insulin and biguanides over the previous year (specificity 97% and sensitivity 100%). In 2016, after adjusting for algorithm performance, type 1 and type 2 diabetes prevalence in France was estimated to be 0.3% and 4.4%, respectively. Conclusion: Our type 1/type 2 classification algorithm was found to perform well and to be applicable to any prescription or medical claims database from other countries. Artificial intelligence opens new possibilities for research and diabetes prevention