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Depression is the most prevailing mental health problem nowadays and researchers expect it to deepen especially in most developed countries due to constantly aging population. This development would pose extreme financial burden on public finance. The SHARE project provides a huge variety of data sets including a mental health module used to form the 12-item EURO-D scale, which serves as a common metrics for depressions. However, in wave 7 this module includes respondents from only 12 countries. Consequently, this paper aims to construct data-driven indices reflecting the level of depression in Europe. We employ factor analysis and as a result, we derive two latent variables. High positive values of the first one indicate lack of depression, while high positive values of the second variable might be indicative for presence of serious depressive symptoms. We then inspect cross-national differences in terms of these two latent variables. Our major findings suggest that geographic location tends to have some effect on the presence of depressive symptoms among elderly people in Europe. [ABSTRACT FROM AUTHOR]

Aging population across developed countries is a problem that no nation has faced before. It puts severe financial burden on every public budget. Non-financial burdens are also common. Depression turns out to be the most common mental disease. Studying the features, that could boost the exhibition of depressive symptoms, and those, that could protect individuals from them, would have significant effect on the precision of public health policies. That is the aim of the paper. We base our research on wave 7 data of the Survey of Health, Aging, and Retirement in Europe that for the first time summarizes information about 27 European countries and Israel. As a preliminary step in our analysis, we construct two latent variables used to quantify the level of depression. On the basis of extensive literature review, we identify a long list of potential features and apply lasso regression so as to select those of them that contribute most in explaining the variance of each of the latent variables. Delivered results indicate that among the most common features, leading to poor mental health, are working conditions, self-perceived health and the presence of functional limitations. On the other hand, educational attendance, lack of chronic diseases and sport activities are among the factors that protect from exhibiting depression symptoms. [ABSTRACT FROM AUTHOR]

Several static models for description of the Covid-19 pandemic are considered, analyzed and compared. The best models relative to this pandemic (generalized fractional-power model and generalized inverse tangent model) are new. These and other non-symmetric models are user friendly and show a good agreement with the official data for the pandemic in Europe. The best models give less than 1% error for a 20-day prediction period and less than 1.5% error for a 40-day prediction period. Recommendations for the use of the models are made. The results obtained may be used in the analysis of possible new waves of Covid-19 in different countries, in Europe and worldwide. Such waves seem more and more likely. [ABSTRACT FROM AUTHOR]