Impact of COVID-19 Pandemic on Sleep Quality, Stress Level and Health-Related Quality of Life-A Large Prospective Cohort Study on Adult Danes

The everyday lives of Danish inhabitants have been affected by the COVID-19 pandemic, e.g., by social distancing, which was employed by the government in March 2020 to prevent the spread of SARS-CoV-2. Moreover, the pandemic has entailed economic consequences for many people. This study aims to assess changes in physical and mental health-related quality of life (MCS, PCS), in stress levels, and quality of sleep during the COVID-19 pandemic and to identify factors that impact such changes, using a prospective national cohort study including 26,453 participants from the Danish Blood Donor Study who answered a health questionnaire before the pandemic and during the pandemic. Descriptive statistics, multivariable linear and multinomial logistic regression analyses were applied. A worsening of MCS and quality of sleep was found, and an overall decrease in stress levels was observed. PCS was decreased in men and slightly increased in women. The extent of health changes was mainly affected by changes in job situation, type of job, previous use of anti-depressive medication and the participants’ level of personal stamina. Thus, living under the unusual circumstances that persisted during the COVID-19 pandemic has had a negative impact on the health of the general population. This may, in time, constitute a public health problem

Contour-based digital elevation modeling of watershed erosion and sedimentation: Erosion and sedimentation estimation tool (EROSET)

An erosion and sedimentation model, erosion and sedimentation estimation tool (EROSET), was developed and applied to a watershed in Happy Valley, South Australia. The model simulates the dynamics of event runoff, soil detachment, and transport processes. The erosion and sedimentation model is able to predict watershed erosion and deposition for storm events at an element as well as watershed scale. The model was developed and incorporated into an existing rainfall-runoff model based on a contour-based digital elevation framework. It combines the use of the USLE data source and extended erosion and transportation modeling into a distributed and intra storm erosion and deposition analysis. This results in storm-based, time-variant, distributed erosion and deposition modeling in the watershed for both storm-based and long-term sediment estimation. The modeling can better enable land managers to identify the areas in a watershed where erosion and deposition may occur. The modeled processes and results can be related to total storm erosion estimated by MUSLE, although they operate on different temporal and spatial frames. Satisfactory modeling results were obtained with very limited calibration which compares well with other studies.H. Sun, P. S. Cornish and T. M. Daniel

The effect of FOXA2 rs1209523 on glucose-related phenotypes and risk of type 2 diabetes in Danish individuals

<p>Abstract</p> <p>Background</p> <p>Variations within the <it>FOXA </it>family have been studied for a putative contribution to the risk of type 2 diabetes (T2D), and recently the minor T-allele of <it>FOXA2 </it>rs1209523 was reported to associate with decreased fasting plasma glucose levels in a study using a weighted false discovery rate control procedure to enhance the statistical power of genome wide association studies in detecting associations between low-frequency variants and a given trait.</p> <p>Thus, the primary aim of this study was to investigate whether the minor T-allele of rs1205923 in <it>FOXA2 </it>associated with 1) decreased fasting plasma glucose and 2) a lower risk of developing T2D. Secondly, we investigated whether rs1205923 in <it>FOXA2 </it>associated with other glucose-related phenotypes.</p> <p>Methods</p> <p>The variant was genotyped in Danish individuals from four different study populations using KASPar^®PCR SNP genotyping system. We examined for associations of the <it>FOXA2 </it>genotype with fasting plasma glucose and estimates of insulin release and insulin sensitivity following an oral glucose tolerance test in 6,162 Danish individuals from the population-based Inter99 study while association with T2D risk was assessed in 10,196 Danish individuals including four different study populations.</p> <p>Results</p> <p>The <it>FOXA2 </it>rs1209523 was not associated with fasting plasma glucose (effect size (β) = -0.03 mmol/l (95%CI: -0.07; 0.01), <it>p </it>= 0.2) in glucose-tolerant individuals from the general Danish population. Furthermore, when employing a case-control setting the variant showed no association with T2D (odds ratio (OR) = 0.82 (95%CI: 0.62-1.07), <it>p </it>= 0.1) among Danish individuals. However, when we performed the analysis in a subset of 6,022 non-obese individuals (BMI < 30 kg/m²) an association with T2D was observed (OR = 0.68 (95%CI: 0.49-0.94), <it>p </it>= 0.02). Also, several indices of insulin release and β-cell function were associated with the minor T-allele of <it>FOXA2 </it>rs1209523 in non-obese individuals.</p> <p>Conclusions</p> <p>We failed to replicate association of the minor T-allele of <it>FOXA2 </it>rs1209523 with fasting plasma glucose in a population based sample of glucose tolerant individuals. More extensive studies are needed in order to fully elucidate the potential role of <it>FOXA2 </it>in glucose homeostasis.</p

Regression spline bivariate probit models: A practical approach to testing for exogeneity

Bivariate probit models can deal with a problem usually known as endogeneity. This issue is likely to arise in observational studies when confounders are unobserved. We are concerned with testing the hypothesis of exogeneity (or absence of endogeneity) when using regression spline recursive and sample selection bivariate probit models. Likelihood ratio and gradient tests are discussed in this context and their empirical properties investigated and compared with those of the Lagrange multiplier and Wald tests through a Monte Carlo study. The tests are illustrated using two datasets in which the hypothesis of exogeneity needs to be tested

Prognostic Integrated Image-Based Immune and Molecular Profiling in Early-Stage Endometrial Cancer

Optimum risk stratification in early-stage endometrial cancer (EC) combines clinicopathological factors and the molecular EC classification defined by The Cancer Genome Atlas (TCGA). It is unclear whether analysis of intratumoral immune infiltrate improves this. We developed a machine-learning image-based algorithm to quantify density of CD8+ and CD103+ immune cells in tumor epithelium and stroma in 695 stage I endometrioid ECs from the PORTEC-1&amp;-2 trials. The relationship between immune cell density and clinicopathological/molecular factors was analyzed by hierarchical clustering and multiple regression. The prognostic value of immune infiltrate by cell type and location was analyzed by univariable and multivariable Cox regression, incorporating the molecular EC classification. Tumor-infiltrating immune cell density varied substantially between cases, and more modestly by immune cell type and location. Clustering revealed three groups with high, intermediate and low densities, with highly significant variation in the proportion of molecular EC subgroups between them. Univariable analysis revealed intraepithelial CD8+ cell density as the strongest predictor of EC recurrence; multivariable analysis confirmed this was independent of pathological factors and molecular subgroup. Exploratory analysis suggested this association was not uniform across molecular subgroups, but greatest in tumors with mutant p53 and absent in DNA mismatch repair deficient cancers. Thus, this work identified that quantification of intraepithelial CD8+ cells improved upon the prognostic utility of the molecular EC classification in early-stage EC