Together with the growing availability of data from electronic records from healthcare providers and healthcare systems, an assessment of associations between different environmental parameters (e.g., pollution levels and meteorological data) and hospitalizations, morbidity, and mortality has become possible. This study aimed to assess the association of air pollution and hospitalizations using a large database comprising almost all hospitalizations in Poland. This time-series analysis has been conducted in five cities in Poland (Warsaw, Białystok, Bielsko-Biała, Kraków, Gdańsk) over a period of almost 4 years (2014–2017, 1255 days), covering more than 20 million of hospitalizations. The hospitalizations have been extracted from the National Health Fund registries as daily summaries. Correlation analysis and distributed lag nonlinear models have been used to investigate for statistically relevant associations of air pollutants on hospitalizations, trying by various methods to minimize potential bias from atmospheric parameters, days of the week, bank holidays, etc. A statistically significant increase of respiratory disease hospitalizations has been detected after peaks of particulate matter concentrations (particularly PM 2.5 , between 0.9 and 4.5% increase per 10 units of pollutant increase, and PM 10 , between 0.9 and 3.5% per 10 units of pollutant increase), with a typical time lag between the pollutant peak and the event of 2 to 6 days. For other pollution parameters and other types of hospitalizations (e.g., cardiovascular events, eye and skin diseases, etc.), a weaker and ununiform correlations were recorded. Ambient air pollution exposure increases are associated with a short-term increase of hospitalizations due to respiratory tract diseases. The most prominent effect was recorded with the correlation of PM 2.5 and PM 10 . There is only weak evidence indicating that such short-term associations exist between peaks of air pollution concentrations and increased hospitalizations for other (e.g., cardiovascular) diseases. The obtained information could be used to better predict hospitalization patterns and costs for the healthcare system and perhaps trigger additional vigilance on particulate matter pollution in the cities.

Very few publications have compared different study designs investigating the short-term effects of air pollutants on healthcare visits and hospitalizations for respiratory tract diseases. This study describes, using two different study designs (a case-crossover design and a time-series analysis), the association of air pollutants and respiratory disease hospitalizations. The study has been conducted on 5 cities in Poland on a timeline of almost 4 years. DLNM and regression models were both used for the assessment of the short-term effects of air pollution peaks on respiratory hospitalizations. Both case-crossover and time-series studies equally revealed a positive association between air pollution peaks and hospitalization occurrences. Results were provided in the form of percentage increase of a respiratory visit/hospitalization, for each 10-μg/m 3 increment in single pollutant level for both study designs. The most significant estimated % increases of hospitalizations linked to increase of 10 μg/m 3 of pollutant have been recorded in general with particulate matter, with highest values for 24 h PM 2.5 in Warsaw (6.4%, case-crossover; 4.5%, time series, respectively) and in Białystok (5.6%, case-crossover; 4.5%, time series, respectively). The casecrossover analysis results have shown a larger CI in comparison to the results of the time-series analysis, while the lag days were easier to identify with the case-crossover design. The trends and the overlap of the results occurring from both methods are good and show applicability of both study designs to air pollution effects on short-term hospitalizations.

All measurements in the paper are to be understood as expressed in mg/m 3 , it means that in Table 2, Fig. 1, Table 6 and two instances in the results sections in paragraph 3 (when citing the NO 2 concentration) all "ppb" concentrations should have been instead labelled as "mg/m 3 ".

This paper analyzes the impact of R&D activities in an oligopoly on consumer surplus and social welfare. We use a two-stage model to analyze the behavior of duopolists at the research level, and in the final-product market, under the assumption of linear and quadratic cost functions. Three options for firm competition are considered: 1) Cournot competition at both stages; 2) cooperation at the R&D stage and Cournot competition in the final-product market; and 3) cooperation at both stages. Numerical simulations for various levels of R&D spillovers are conducted to analyze the welfare effects of firm decisions. We conclude that for high levels of technological spillovers, total welfare is highest when firms engage in cooperation at the R&D stage, and compete in the final product market, independent of the shape of cost functions. However, the functional form of production costs has a qualitative impact on welfare when firms fully compete.

In this empirical study (n = 102), the authors set out to investigate the relation between altruism (measured with the use of individual social discounting rates) and patience (measured with the use of delays) under the conditions of financial losses. Thanks to the titration algorithm by Holt et al. (2003), and the Area Under the Curve indicator by Myerson et al. (2001), the study could apply discounting procedures to assess the altruism and patience of individuals. It was found that altruism and patience can be but do not have to be positively correlated in the domain of economic losses. It turns out that the occurrence of a positive correlation between altruism and patience (as often reported in the behavioural economics literature devoted to economic gains) depends on the temporal structure of the discounting task (temporal context of choice). When both the loss for the decision maker and another person from the social distance scale is delayed (shifted in time), positive correlations between altruism and patience are not observed. The latter finding is novel and nuances the behavioural economics knowledge on the relation between altruism and patience in economic choices.