INTRODUCTION: To evaluate the prescription patterns, effectiveness, and safety of adding probiotics to Helicobacter pylori eradication therapy, in Europe. METHODS: International, prospective, noninterventional registry of the clinical practice of the European gastroenterologists. Data were collected and quality reviewed until March 2021 at AEG-REDCap. The effectiveness was evaluated by modified intention-to-treat analysis, differentiating by geographic areas. Adverse events (AEs) were categorized as mild, moderate, and severe. RESULTS: Overall, 36,699 treatments were recorded, where 8,233 (22%) were prescribed with probiotics. Probiotics use was associated with higher effectiveness in the overall analysis (odds ratio [OR] 1.631, 95% confidence interval [CI] 1.456–1.828), as well as in triple (OR 1.702, 95% CI 1.403–2.065), quadruple (OR 1.383, 95% CI 0.996–1.920), bismuth quadruple (OR 1.248, 95% CI 1.003–1.554), and sequential therapies (OR 3.690, 95% CI 2.686–5.069). Lactobacillus genus was associated with a higher therapy effectiveness in Eastern Europe when triple (OR 2.625, 95% CI 1.911–3.606) and bismuth quadruple (OR 1.587, 95% CI 1.117–2.254) first-line therapies were prescribed. In Central Europe, the use of probiotics was associated with a decrease in both the overall incidence of AEs (OR 0.656, 95% CI 0.516–0.888) and severe AEs (OR 0.312, 95% CI 0.217–0.449). Bifidobacterium genus was associated with lower overall (OR 0.725, 95% CI 0.592–0.888) and severe (OR 0.254, 95% CI 0.185–0.347) AEs, and Saccharomyces was associated with reduced overall (OR 0.54, 95% CI 0.32–0.91) and severe (OR 0.257, 95% CI 0.123–0.536) AEs under quadruple-bismuth regimen. DISCUSSION: In Europe, the use of probiotics was associated with higher effectiveness and safety of H. pylori eradication therapy. Lactobacillus improved treatment effectiveness, whereas Bifidobacterium and Saccharomyces were associated with a better safety profile.

Acknowledging a considerable literature on modeling daily temperature data, we propose a multi-level spatiotemporal model which introduces several innovations in order to explain the daily maximum temperature in the summer period over 60 years in a region containing Aragón, Spain. The model operates over continuous space but adopts two discrete temporal scales, year and day within year. It captures temporal dependence through autoregression on days within year and also on years. Spatial dependence is captured through spatial process modeling of intercepts, slope coefficients, variances, and autocorrelations. The model is expressed in a form which separates fixed effects from random effects and also separates space, years, and days for each type of effect. Motivated by exploratory data analysis, fixed effects to capture the influence of elevation, seasonality, and a linear trend are employed. Pure errors are introduced for years, for locations within years, and for locations at days within years. The performance of the model is checked using a leave-one-out cross-validation. Applications of the model are presented including prediction of the daily temperature series at unobserved or partially observed sites and inference to investigate climate change comparison.Supplementary materials accompanying this paper appear online.

This study aims to build a multistate model and describe a predictive tool for estimating the daily number of intensive care unit (ICU) and hospital beds occupied by patients with coronavirus 2019 disease (COVID-19). The estimation is based on the simulation of patient trajectories using a multistate model where the transition probabilities between states are estimated via competing risks and cure models. The input to the tool includes the dates of COVID-19 diagnosis, admission to hospital, admission to ICU, discharge from ICU and discharge from hospital or death of positive cases from a selected initial date to the current moment. Our tool is validated using 98,496 cases positive for severe acute respiratory coronavirus 2 extracted from the Aragón Healthcare Records Database from July 1, 2020 to February 28, 2021. The tool demonstrates good performance for the 7- and 14-days forecasts using the actual positive cases, and shows good accuracy among three scenarios corresponding to different stages of the pandemic: 1) up-scenario, 2) peak-scenario and 3) down-scenario. Long term predictions (two months) also show good accuracy, while those using Holt-Winters positive case estimates revealed acceptable accuracy to day 14 onwards, with relative errors of 8.8%. In the era of the COVID-19 pandemic, hospitals must evolve in a dynamic way. Our prediction tool is designed to predict hospital occupancy to improve healthcare resource management without information about clinical history of patients. Our easy-to-use and freely accessible tool (https://github.com/peterman65) shows good performance and accuracy for forecasting the daily number of hospital and ICU beds required for patients with COVID-19.

Este texto surge de la necesidad de contar con un material didáctico adecuado para la asignatura Análisis de Datos del Máster Universitario en Evaluación y Entrenamiento Físico para la Salud de la Universidad de Zaragoza. Sin embargo, las técnicas descritas aquí no se limitan al ámbito de las ciencias del deporte o de la salud, sino que son aplicables en multitud de campos de la investigación científica. Para ello, este libro ofrece una guía paso a paso para el análisis de datos con Jamovi, un software sencillo e intuitivo que está ganando rápidamente popularidad, especialmente en ámbitos relacionados con la investigación en salud y sociología, desplazando a otros programas como SPSS.