Non-tuberculous mycobacteria (NTM) are ubiquitous environmental bacteria that can induce pulmonary and non-pulmonary diseases in susceptible persons. It is reported that the prevalence of NTM diseases is increasing in developed countries, but this differs by regions and countries. NTM species distribution and the rate of diseases caused by NTM vary widely in the historical territories of Moravia and Silesia (Czech Republic). This epidemiologic study of NTM diseases covers the period 2012–2018, reviews isolates obtained from patients with clinical disease and investigates correlations with related socio-economic and environmental factors. Individual NTM patients were included only once during the studied period and results were presented as incidence rate per year. The most frequently isolated NTM meeting the microbiological and clinical criteria in the study were the Mycobacterium avium-intracellulare complex, followed by Mycobacterium kansasii and Mycobacterium xenopi. A previously described endemic incidence of M. kansasii in the Karviná district and M. xenopi in the Ostrava district was also observed in this study. The incidence of NTM patients in the whole studied territory was 1.10/100,000 inhabitants (1.33/100,000 in men and 0.88/100,000 in women). The annual incidence of lymphadenitis in children (≤5 years of age) was 2.35/100,000 of the population of children during the 7 year period but increased in the year 2018 to 5.95/100,000. The rate of human tuberculosis in the studied area was 1.97/100,000 inhabitants. The incidence of NTM pulmonary diseases correlated with a lower socio-economic status (r = 0.63) and a higher concentration of benzo[a]pyrene pollution in the air (r = 0.64).

Quality of life and life satisfaction are topics that currently receive a great deal of attention across the globe. Many approaches exist, which use both qualitative and quantitative methods, to capture these phenomena. Historically, quality of life was measured exclusively by economic indicators. However, it is indisputable that other factors influence people’s life satisfaction, which is captured by subjective survey-based data. By contrast, objective data can easily be obtained and cover a wider range, in terms of population and area. In this research, the multiple fuzzy linear regression model is applied in order to explain the relationship between subjective life satisfaction and selected objective indicators used to evaluate quality of life. The great advantage of the fuzzy model lies in its ability to capture uncertainty, which is undoubtedly associated with the vague concept of subjective life satisfaction. The main outcome of the paper is the detection of indicators that have a statistically significant relationship with life satisfaction. Subsequently, a pan-European sub-national prediction of life satisfaction after the consideration of the most relevant input indicators was proposed, including the uncertainty associated with the prediction of such a phenomenon. The study revealed significant regional differences and similarities between the originally reported satisfaction of life and the predicted one. With the help of spatial and non-spatial statistics supported by visual analysis, it is possible to assess life satisfaction more precisely, while taking into account the ambiguity of the perception of life satisfaction. Additionally, predicted values supplemented with the uncertainty measure (fuzzy approach) and the synthesis of results in the form of European typology help to compare and contrast the results in a more useful manner than in existing studies.

The accompanying Main Map shows forest vulnerability zones calculated for the Czech Republic, covering a total area of almost 79,000 km². Calculating forest vulnerability zones over such a large area requires a unique approach due to its complexity. The map includes additional information on forest areas and topography. The model of forest vulnerability zones (FVZ) constitutes an alternative to existing zones of forest health hazard caused by emissions. It was created based on subjective classification of existing incidence of damage in forests. Moreover, the model of forest vulnerability zones estimates the risk of forest health degradation caused by abiotic factors.