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Objectives: This study aimed at estimating the real-life impact of vaccination on COVID-19 mortality, with adjustment for SARS-CoV-2 variants spread and other factors across Europe and Israel., Study Design: Time series analysis., Methods: Time series analysis of the daily number of COVID-19 deaths was performed using non-linear Poisson mixed regression models. Variables such as variants' frequency, demographics, climate, health, and mobility characteristics of thirty-two countries between January 2020 and April 2021 were considered as potentially relevant adjustment factors., Results: The analysis revealed that vaccination efficacy in terms of protection against deaths was 72%, with a lower reduction of the number of deaths for B.1.1.7 vs non-B.1.1.7 variants (70% and 78%, respectively). Other factors significantly related to mortality were arrivals at airports, mobility change from the prepandemic level, and temperature., Conclusions: Our study confirms a strong effectiveness of COVID-19 vaccination based on real-life public data, although lower than expected from clinical trials. This suggests the absence of indirect protection for non-vaccinated individuals. Results also show that vaccination effectiveness against mortality associated with the B.1.1.7 variant is slightly lower than that with other variants. Lastly, this analysis confirms the role of mobility reduction, within and between countries, as an effective way to reduce COVID-19 mortality and suggests the possibility of seasonal variations in COVID-19 incidence., (Copyright © 2021 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.)

Objectives: The purpose of this study was to determine predictors of the height of coronavirus disease 2019 (COVID-19) daily deaths' peak and time to the peak, to explain their variability across European countries., Study Design: For 34 European countries, publicly available data were collected on daily numbers of COVID-19 deaths, population size, healthcare capacity, government restrictions and their timing, tourism and change in mobility during the pandemic., Methods: Univariate and multivariate generalised linear models using different selection algorithms (forward, backward, stepwise and genetic algorithm) were analysed with height of COVID-19 daily deaths' peak and time to the peak as dependent variables., Results: The proportion of the population living in urban areas, mobility at the day of first reported death and number of infections when borders were closed were assessed as significant predictors of the height of COVID-19 daily deaths' peak. Testing the model with a variety of selection algorithms provided consistent results. Total hospital bed capacity, population size, the number of foreign travellers and the day of border closure were found to be significant predictors of time to COVID-19 daily deaths' peak., Conclusions: Our analysis demonstrated that countries with higher proportions of the population living in urban areas, countries with lower reduction in mobility at the beginning of the pandemic and countries having more infected people when closing borders experienced a higher peak of COVID-19 deaths. Greater bed capacity, bigger population size and later border closure could result in delaying time to reach the deaths' peak, whereas a high number of foreign travellers could accelerate it., (Copyright © 2021 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.)

Purpose: Chronic rhinosinusitis (CRS) is one of the most common diseases in the modern society. In recent years endoscopic sinus surgery (ESS) has become the treatment of choice for patients with CRS refractory to medical therapy. ESS proved to be successful in most, but not all patients with CRS. Currently there is no direct method available to distinguish between patients who are likely to benefit from ESS and those who are not. The aim of this study was to build multidimensional models (artificial neural networks) to predict early outcomes of ESS in individual patients., Material/methods: The study group comprised of 115 patients operated for CRS in the Department of Otolaryngology, Jagiellonian University Collegium Medicum, Cracow. The neural models were created using the Statistica Neural Network computer software package. The models required only information easily achievable for every patient before surgery. Consequently, the models could be readily applicable in everyday clinical practice. To define the results of surgery three different mathematical descriptions were compared. The models' predictions were compared with the actual results of surgery 3-6 months postoperatively., Results: The models were able to predict the early outcome of surgery in 90% of the patients but their quality depended on mathematical representation of the surgery result. The best models were characterized by 93% sensitivity and 86% specificity., Conclusions: The results of ESS depend on many factors, so reliable outcome prognoses can be produced only by multidimensional models. Artificial neural networks are a promising multidimensional tool facilitating clinical decision making in patients with CRS., (Copyright © 2014 Medical University of Bialystok. Published by Elsevier Urban & Partner Sp. z o.o. All rights reserved.)