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The COVID-19 pandemic changed the lives of people all around the world, e.g. affected mental health and the functioning of several families. The main goal of the research presented in this paper is to assess the influence of the COVID-19 pandemic on the mental health of members of households. The research was performed on the basis of a data set from the COVID Impact Survey carried out by the Data Foundation think tank in 2020 (during the first wave of COVID-19 pandemic) in the USA among adult respondents. The survey used 6,768 observations. The authors estimated a model of logistic regression and models based on data mining methods, such as decision trees, XG Boost, k-nearest neighbours method, artificial neural networks and a support vector machine. Cluster analysis made it possible to divide respondents into groups showing their characteristic features and problems, and the constructed model took into account their mental issues and the relationship between those issues and the financial situation of households. The results demonstrate that isolation, remote education and work and limited physical activity contribute to the worsening of mental health of the population.

Micro, small and medium-sized enterprises (SME) play a significant role in the economy. According to the European Commission data, the SME sector in 2018 represented 99.8% of all enterprises in Europe, and likewise in Poland, therefore it is vital to have access to information regarding this sector and its economic situation on the lowest possible level of aggregation. The aim of the study described in this paper is to assess as to what degree data collected in public registers in Poland, including the information compiled by the Social Insurance Institution (ZUS) register, can constitute a source of information about the situation and development of the SME sector at the local level. The paper presents the economic situation of the SME sector in Europe, and also a range of information collected in ZUS registers relating to micro enterprises (including the self-employed enterpreneurs), small and medium-sized enterprises. Information concerning the SME sector in Kujawsko-Pomorskie Voivodship as of December 2018 is used in the research. The results confirm that register data enables the assessment of the economic situation of micro, small and medium- sized enterprises at the level of gminas (municipalities, communes), as per the OECD recommendations to broaden knowledge about the condition of the sector at a local level.

The impact the last financial crisis had on the small- and medium-sized enterprises (SMEs) sector varied across countries, affecting them on different levels and to a different extent. The economic situation in Poland during and after the financial crisis was quite stable compared to other EU member states. SMEs represent one of the most important segments of the economy of every country. Therefore, it is crucial to develop a prediction model which easily adapts to the characteristics of SMEs. Since the Altman Z-Score model was devised, numerous studies on bankruptcy prediction have been written. Most of them involve the application of traditional methods, including linear discriminant analysis (LDA), logistic regression and probit analysis. However, most recent studies in the area of bankruptcy prediction focus on more advanced methods, such as case-based reasoning, genetic algorithms and neural networks. In this paper, the effectiveness of LDA and SVM predictions were compared. A sample of SMEs was used in the empirical analysis, financial ratios were utilised and non-financial factors were taken account of. The hypothesis assuming that multidimensional discrimination was more effective was verified on the basis of the obtained results.

Micro, small and medium enterprises (MSMEs) represent more than 99% of enterprises in Europe. Therefore, knowledge about this sector, also in the spatial context is important to understand the patterns of economic and social development. The main goal of this article is an analysis of spatial conditions and the situation of MSMEs on a local level using combined sources of information. This includes data collected in the Social Insurance Institution and Tax registers in Poland, which provides information on the employment, wages, revenues and taxes paid by the MSMEs on a local level as well as contextual statistical information. The data is used for a diagnosis of spatial circumstances and discussion of conditions influencing the status of the MSMEs sector in a selected region (voivodeship) in Poland. Taxonomy methods including factor analysis and clustering methods based on k-means and SOM Kohonen were used for selecting significant information and grouping of the local units according to the situation of the MSMEs. There are eight factors revealed in principal component analysis and five clusters of local units distinguished using these factors. These include two clusters with a high share of rural local units and two clusters with a high share of rural-urban and urban local units. Additionally, there was an outstanding cluster with only two dominant urban local units. Factors show differences between clusters in the situation of MSMEs sector and infrastructure. Different spatial conditions in different regions influence the situation of MSMEs.

In this study we focus on distress events of European banks over the period of 1990–2015, using unbalanced panel of 3,691 banks. We identify 132 distress events, which include actual bankruptcies as well as bailout cases. We apply CAMEL‑like bank‑level variables and control macroeconomic variables (GDP, inflation, unemployment rate). The analysis is based on traditional logistic regression and k‑means clustering. We find, that the probability of distress is connected with macroeconomic conditions via regional grouping (clustering). Bank‑level variables that were stable predictors of distress from 1 to 4 years prior to event are equity to total assets ratio (leverage) and loans to funding (liquidity). From macroeconomic factors, the GDP growth is a reasonable variable, however with differentiated impact: for 1 year distance high distress probability is connected with low GDP growth, but for 2, 3 and 4 year distance: high distress probability is conversely connected with high GDP growth. This shows the changing role of macroeconomic environment and indicates the potential impact of favorable macroeconomic conditions on building‑up systemic problems in the banking sector. Artykuł poświęcony został bankructwom banków europejskich z lat 1990–2015. Wykorzystane zostały w nim niezbilansowane dane panelowe dla 3691 banków. Zidentyfikowano 132 bankructwa – zarówno faktyczne, jak i wynikające z konieczności subwencji. Wykorzystano zmienne na poziomie banków typu CAMEL i kontrolne zmienne makroekonomiczne (PKB, inflacja, stopa bezrobocia). Analiza oparta została na tradycyjnym modelu regresji logistycznej do predykcji bankructwa i metodzie k‑średnich do grupowania. Otrzymane wyniki wskazują, że prawdopodobieństwo bankructwa jest zależne od warunków makroekonomicznych poprzez wyniki klasteryzacji. Zmienne na poziomie banków, które są stabilnym predyktorem bankructwa od roku do czterech lat przed zdarzeniem, to: kapitał do aktywów ogółem (dźwignia) oraz kredyty do funduszy (płynność). Z czynników makroekonomicznych istotne znaczenie ma PKB, ale ze zróżnicowanym wpływem: dla roku przed bankructwem wysokie prawdopodobieństwo bankructwa jest związane z niską dynamika PKB, ale dla 2, 3 i 4 lat przed bankructwem wysokie ryzyko bankructwa jest związane z wysoką dynamika PKB, czyli jest to zależność odwrotna. Pokazuje to zmienną rolę otoczenia makroekonomicznego i wskazuje na potencjalny wpływ sprzyjających warunków makroekonomicznych na powstawanie problemu systemowego w sektorze bankowym.

Default risk assessment is crucial in the banking activity. Different models were developed in the literature using the discriminant analysis, logistic regression and data mining techniques. In this paper the logistic regression was applied to verify models proposed by R. Jagiello for different sectors. As an alternative, the logistic regression model with the nominal variable SECTOR was applied on the pooled sample of enterprises. The dynamic approach using the Cox regression survival model was estimated. Including the nominal variable SECTOR only slightly increases the predictive power of the model (in the case of “defaults”). The predictive power of the Cox regression model is lower, the only advantage is the higher accuracy classification in the case of “defaulted” enterprises.

Introduction/Background Strategy of treatment in patients with recurrent ovarian cancer (OC) dependents on platinum-free interval (PFI). We aimed to compare survival in ‘platinum resistant’ OC patients who received platinum-based or non-platinum chemotherapy at first relapse in real world practice based. Methodology Data of OC patients treated with primary debulking surgery and standard chemotherapy (paclitaxel/platinum analogs) between January 1, 2011 and December 31, 2011 were extracted from Polish National Health Fund Database. Further overall survival (OS) was compared between patients who received any type of chemotherapy up to 6 months PFI. Results 17% (227/1369) patients classified as ‘platinum-resistant’ by the 6-month threshold of PFI and 29% (66/227) of those group, were treated with Pt-based chemotherapy at second line. Median OS after platinum-based 2-nd line chemotherapy was significantly longer compared to non- platinum chemotherapy group (median OS was 28.1 months, 95% CI: 23.3–42.0 vs. 16.4 months, 95% CI: 13.1–21.0; HR=0.71, 95% CI, 0.51–0.99; P=0.049). This effect was seen, among patients with PFI of 3–6 months (median OS was, 28.1 months, 95% CI: 23.1–42.0 vs 14.3 months, 95% CI: 10.4–40.5, respectively; HR=0.61, 95%CI, 0.39–0.96; P=0.0336). but not in PFI Conclusion Our results from real world data based on nationwide population cohort study shows that six-months PFI as an arbitrary threshold for subsequent Pt-based chemotherapy is questionable. Biomarkers of platinum sensitivity are needed to identify potential responders, especially in group of patients with PFI 3–6 months. Disclosure Nothing to disclose.

Research analysis of small enterprises are still rare, due to lack of individual level data. Small enterprise failures are connected not only with their financial situation abut also with non-financial factors. In recent research we tend to apply more and more complex models. However, it is not so obvious that increasing complexity increases the effectiveness. In this paper the sample of 806 small enterprises were analyzed. Qualitative factors were used in modeling. Some simple and more complex models were estimated, such as logistic regression, decision trees, neural networks, gradient boosting, and support vector machines. Two hypothesis were verified: (i) not only financial ratios but also non-financial factors matter for small enterprise survival, and (ii) advanced statistical models and data mining techniques only insignificantly increase the prediction accuracy of small enterprise failures. Results show that simple models are as good as more complex model. Data mining models tend to be overfitted. Most important financial ratios in predicting small enterprise failures were: operating profitability of assets, current assets turnover, capital ratio, coverage of short-term liabilities by equity, coverage of fixed assets by equity, and the share of net financial surplus in total liabilities. Among non-financial factors only two of them were important: the sector of activity and employment.

Credit risk management is a key element in bank management. For credit risk management, statistical models are used, the so‑called scoring and rating models. For enterprise risk assessment, rating models are used. Rating models consist of quantitative models (based on financial ratios) and qualitative models (based on a questionnaire). For estimation of quantitative models, econometric and statistical models are used, mainly logistic regression models. In this paper, statistical models for quantitative assessment are presented, including an empirical example based on the sample of data for SMEs made available by one of Polish banks. A logistic regression model with a nominal variable – the region of activity, including territorial differences, was used. The construction of rating model was presented, including the sector and region of activity. Zarządzanie ryzykiem kredytowym stanowi kluczowy element w zarządzaniu bankiem. Do zarządzania ryzykiem kredytowym wykorzystywane są modele statystyczne tzw. Modele scoringowe i ratingowe. Do oceny ryzyka kredytowego przedsiębiorstw wykorzystuje się modele ratingowe. Składową modeli ratingowych są modele ilościowe (oparte na wskaźnikach finansowych) oraz modele jakościowe (oparte na ankiecie jakościowej). Do budowy modeli ilościowych wykorzystuje się modele statystyczne i ekonometryczne, głównie modele regresji logistycznej. W artykule omówione zostały modele statystyczne do oceny ilościowej wraz z przykładem empirycznym opartym na danych dla próby MŚP udostępnionej przez jeden z polskich banków. Wykorzystano model regresji logistycznej ze zmienną nominalną – region działalności, uwzględniający zróżnicowanie terytorialne. Pokazana została konstrukcja modelu uwzględniającego zarówno branże działalności, jak i region działalności.

Taking into consideration the weakness of the models based on discrimination function (Z-score) proposed by Altman within the conditions of polish economy some attempts were taken in the 90s to adjust these models to the reality of post-communist economy. The initial interest in the models of multivariate discriminant analysis was extended by logistic regression models and then also by neural networks and decision trees. In the recent years some attempts were also taken to apply models of the event history analysis. Rating models based on developed bankruptcy risk models are basic element in credit risk management. Paper focuses on the critical assessment of statistical methods applied and points out the advantages and disadvantages of various approaches toward the estimation of models. Empirical comparative analysis were conducted based on the sample of enterprises. The possible application of statistical models in credit risk assessment of enterprises (rating models) was pointed out. Dostrzegając słabość modeli opartych na funkcji dyskryminacyjnej Z-score zaproponowanej przez Altmana w warunkach gospodarki polskiej podjęto w latach 90. próby dostosowania tych modeli do realiów gospodarki post-komunistycznej. Początkowe zainteresowanie modelami wielowymiarowej analizy dyskryminacyjnej poszerzono o modele regresji logistycznej a później również o sieci neuronowe i drzewa decyzyjne. W ostatnich latach podjęto również próby zastosowania modeli analizy historii zdarzeń. Modele ratingowe oparte na wypracowanych modelach upadłości stanowią kluczowy element w zarządzaniu ryzykiem kredytowym. W artykule podjęto próbę krytycznej oceny stosowanych metod statystycznych oraz wskazano na zalety i wady różnych podejść do budowy modeli. Przeprowadzono porównawczą analizę empiryczną na próbie przedsiębiorstw. Wskazano na możliwość wykorzystania modeli statystycznych do oceny ryzyka kredytowego przedsiębiorstw (modele ratingowe).

BACKGROUND: Sunitinib (SU) and pazopanib (PZ) are standards of care for first-line treatment of metastatic renal cell carcinoma (mRCC). However, how the efficacy of these drugs translates into effectiveness on a population-based level is unknown.PATIENTS AND METHODS: We used the International mRCC Database Consortium (IMDC) to assess overall survival (OS), progression-free survival (PFS), response rate (RR) and performed proportional hazard regression adjusting for IMDC prognostic groups. Second-line OS (OS2) and second-line PFS (PFS2) were also evaluated.RESULTS: We obtained data from 7438 patients with mRCC treated with either first-line SU (n = 6519) or PZ (n = 919) with an overall median follow-up of 40.4 months (95% confidence interval [CI] 39.2-42.1). There were no significant differences in IMDC prognostic groups (p = 0.36). There was no OS difference between SU and PZ (22.3 versus 22.6 months, respectively, p = 0.65). When adjusted for IMDC criteria, the hazard ratio (HR) of death for PZ versus SU was 1.03 (95% CI 0.92-1.17, p = 0.58). There was no PFS difference between SU and PZ (8.4 versus 8.3 months, respectively, p = 0.17). When adjusted for IMDC criteria, the HR for PFS for PZ versus SU was 1.08 (95% CI 0.981-1.19, p = 0.12). There was no difference in RR between SU and PZ (30% versus 28%, respectively, p = 0.15). We also found no difference in any second-line treatment between either post-SU or post-PZ groups for OS2 (13.1 versus 11 months, p = 0.27) and PFS2 (3.7 versus 5.0 months, p = 0.07).CONCLUSIONS: We confirmed in real-world practice that SU and PZ have similar efficacy in the first-line setting for mRCC and do not affect outcomes with subsequent second-line treatment.

The scope of this paper is to construct the demographic profile of the entrepreneur. The basic question is: who has higher chances of survival? Recently the interest in firms survival in Poland has increased significantly. To cover the need for more detailed data there was panel database used. The survey covers the five-year history of enterprises' activity and the sample covers only enterprises employing initially fewer than 50 workers. In this paper only the sample of 1518 enterprises established in 2004 was selected. Only enterprises that initially did not employ any worker were selected. Cox regression semiparametric model with time constant and time varying variables was applied. Regarding demographic characteristics of the main owner a conclusion may be drawn, that an enterprise with a highly educated owner experienced in management had a lower liquidation risk. Opposite to other studies a male owner had higher risk of failure comparing to a female owner. Also an older owner (35 years and more) had higher chances to be liquidated, which is in opposite direction than expected. However this effect is time varying. The entrepreneur with management experience increases chances of survival. If a manager succeeded in acquiring the loan for a start the success is more probable. First-year investments increased the survival chances. The first year was the most important because at that time the main drive for being on the market was observed.

Ryzyko kredytowe jest jednym z najwazniejszych rodzajow ryzyka, na ktore wystawiony jest bank. Ryzyko upadlości przedsiebiorstw jest zazwyczaj modelowane z wykorzystaniem analizy dyskryminacyjnej lub regresji logistycznej. Jednakze poszukiwane są nowe sposoby i techniki efektywniejszej predykcji bankructwa. Jedną z takich metod jest analiza przezycia. Analiza prezentowana w tym artykule ma na celu porownanie nowych metod predykcji bankructwa z tradycyjnymi modelami, takimi jak regresja logistyczna. Wskazane są zalety i wady tych metod oraz propozycja rozszerzenia analizy przezycia o makrozmienne, a modelu regresji – o zmienne nominalne. Wlączenie makrozmiennych podnosi moc predykcyjną modeli. Oszacowano modele z wykorzystaniem proby polskich przedsiebiorstw MŚP zawierającej 1561 przedsiebiorstw, w tym 807 upadlości.