Inflammatory bowel disease is associated with an increased risk of colorectal cancer. The study aims to identify the risk factors for ulcerative colitis-colorectal cancer and to perform a survival curve analysis of the outcome. This retrospective cohort study included 254 patients from March 2016 to October 2017. Age, age at diagnosis, follow-up time, smoking status, and family history of colorectal cancer were analyzed as risk factors for colorectal cancer. The mean patient age was 46.6 ± 16.9 years; 5.5% of the patients were smokers and 49.6% had pancolitis. Six patients (2.36%) had colorectal cancer, which was associated with age at diagnosis (odds/hazard ratio 1.059 [95% confidence interval: 1.001–1.121]; P = .04), family history of colorectal cancer (12.992 [1.611–104.7]; P = .02), and follow-up time (0.665 [0.513–0.864]; P = .002). Active smoking was the main identified risk factor, after both logistic (8.477 [1.350–53.232]; P = .02) and Cox proportional-hazards (32.484 [2.465–428.1]; P = .008) regression analysis. The risk of colorectal cancer was 3.17% at 10 years and 4.26% at 20 years of follow-up. Active smoking and family history were identified as risk factors for colorectal cancer. These findings should aid the early identification of patients who require vigorous surveillance, and prevent exposure to risk factors.

Purpose This paper aims to investigate the foreclosure discount for the German residential market in the years from 2008 to 2011. Design/methodology/approach The determinants of the foreclosure discount are estimated in a hedonic price model. The analysis is based on a unique data set compiled from three different data sources with 135,000 foreclosed properties. Findings The findings reveal that residential units in foreclosures are sold at a discount of 19 per cent compared to residential units with similar characteristics that are not in foreclosure. Second, a regional pattern can be observed, with discounts being negatively correlated to unemployment risk and liquidity. Third, the model with interaction terms shows that foreclosure discounts are linked to specific property characteristics. Fourth, these object-related risks are typically smaller than regional risks or locational risks. Research limitations/implications Given the highly fragmented system of Gutachterausschüsse in Germany, who are responsible for collecting transaction data, we were not able to directly analyze transaction data, but only a proxy for this price information. Practical implications The results can be important for financial institutions that are trying to assess the risk of lending for a specific object in a specific location. So far, banks primarily try to assess the default risk of private lenders by analyzing the debtor’s financial position and the quality of the property. The analysis provides insights into which characteristics of a property might imply additional risk, and in which region these risks are biggest. Originality/value To the best of the authors’ knowledge, this is the first attempt to analyze the foreclosure discount for the German housing market.

Background: Chronic obstructive pulmonary disease (COPD) has a functional definition. However, differences in clinical characteristics and systemic manifestations make COPD a heterogeneous disease and some manifestations have been associated with different risks of acute exacerbations, hospitalizations, and death. Objective: Therefore, the objective of the study was to evaluate possible clinical clusters in COPD at two study centers in Brazil and identify the associated exacerbation and mortality rate during 1 year of follow-up. Methods: We included patients with COPD and all underwent an evaluation composed of the Charlson Index, body mass index (BMI), current pharmacological treatment, smoking history (packs-year), history of exacerbations/hospitalizations in the last year, spirometry, six-minute walking test (6MWT), quality of life questionnaires, dyspnea, and hospital anxiety and depression scale. Blood samples were also collected for measurements of C-reactive protein (CRP), blood gases, laboratory analysis, and blood count. For the construction of the clusters, 13 continuous variables of clinical importance were considered: hematocrit, CRP, triglycerides, low density lipoprotein, absolute number of peripheral eosinophils, age, pulse oximetry, BMI, forced expiratory volume in the first second, dyspnea, 6MWD, total score of the Saint George Respiratory Questionnaire and packs-year of smoking. We used the Ward and K-means methods and determined the best silhouette value to identify similarities of individuals within the cluster (cohesion) in relation to the other clusters (separation). The number of clusters was determined by the heterogeneity values of the cluster, which in this case was determined as four clusters. Results: We evaluated 301 COPD patients and identified four different groups of COPD patients. The first cluster (203 patients) was characterized by fewer symptoms and lower functional severity of the disease, the second cluster by higher values of peripheral eosinophils, the third cluster by more systemic inflammation and the fourth cluster by greater obstructive severity and worse gas exchange. Cluster 2 had an average of 959±3 peripheral eosinophils, cluster 3 had a higher prevalence of nutritional depletion (46.1%), and cluster 4 had a higher BODE index. Regarding the associated comorbidities, we found that only obstructive sleep apnea syndrome and pulmonary thromboembolism were more prevalent in cluster 4. Almost 50% of all patients presented an exacerbation during 1 year of follow-up. However, it was higher in cluster 4, with 65% of all patients having at least one exacerbation. The mortality rate was statistically higher in cluster 4, with 26.9%, vs 9.6% in cluster 1. Conclusion: We could identify four clinical different clusters in these COPD populations, that were related to different clinical manifestations, comorbidities, exacerbation, and mortality rate. We also identified a specific cluster with higher values of peripheral eosinophils.

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We examine the potential effects of Solvency II on general portfolio efficiency, and specifically on the allocation of alternative assets by European insurers. The paper starts with a brief introduction to the Solvency II Directive, focusing on the rules for calculating the Solvency capital requirements (SCR), according to the standard formula. The following empirical analysis entails several portfolio optimizations considering six relevant asset classes for the time period from 1993-2013. We derive optimal portfolios with respect to portfolio risk and capital requirements, and finally combine both optimization problems. Our results suggest that, although the capital charges for real estate and infrastructure assets are not adequately calibrated, a significant shift of portfolio weights is not expected for the majority of European insurers. However, after Solvency II comes into effect, undercapitalized insurers may often not be capable of holding risk-optimal allocations of alternative assets.