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Background : This study aimed to identify the current risk factors for coronavirus disease 2019 severity and examine its association with medication use. Methods : We used data from a large United States electronic health record database to conduct an anonymized cohort study of 171,491 patients with coronavirus disease 2019. The study was conducted from January 1, 2020, to August 27, 2021. Data on age, race, sex, history of diseases, and history of medication prescriptions were analyzed using the Cox proportional hazards model analysis to calculate hazard ratios for hospitalization and severe risk. Results: Factors that increased the risk of hospitalization and critical care were age ≥ 65 years, male sex, type 2 diabetes, hypertension, interstitial pneumonia, and cardiovascular disease. In particular, age ≥ 65 years significantly increased the risk of hospitalization (hazard ratio, 2.81 [95% confidence interval, 2.58–3.07]; P < 0.001) and critical care (hazard ratio, 3.45 [2.88–4.14]; P < 0.001). In contrast, patients with hyperlipidemia had a reduced risk. However, patients with hyperlipidemia who were not taking statins had a significantly increased risk of hospitalization (hazard ratio, 1.24 [1.16–1.34]; P < 0.001). Sodium-glucose cotransporter-2 inhibitors, angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, glucocorticoids, and statins significantly reduced the risk of hospitalization and critical care. The risk of hospitalization and critical care increased in patients of all ethnicities with type 2 diabetes. The factors that significantly increased the risk of hospitalization in all regions were older age, hypertension, chronic obstructive pulmonary disease, and cardiovascular disease. Conclusion: This study identified factors that increase or reduce the risk of severe coronavirus disease. The provision of appropriate drug treatment and modification of lifestyle-related risk factors may reduce coronavirus disease severity. [ABSTRACT FROM AUTHOR]

The impact of overlapping risk factors on coronavirus disease (COVID-19) severity is unclear. To evaluate the impact of type 2 diabetes (T2D) and obesity on COVID-19 severity, we conducted a cohort study with 28,095 anonymized COVID-19 patients using data from the COVID-19 Research Database from January 1, 2020 to November 30, 2020. The mean age was 50.8 ± 17.5 years, and 11,802 (42%) patients were male. Data on age, race, sex, T2D complications, antidiabetic medication prescription, and body mass index ≥ 30 kg/m2 (obesity) were analysed using Cox proportional hazard models, with hospitalization risk and critical care within 30 days of COVID-19 diagnosis as the main outcomes. The risk scores were 0–4 for age ≥ 65 years, male sex, T2D, and obesity. Among the participants, 11,294 (61.9%) had obesity, and 4445 (15.8%) had T2D. T2D, obesity, and male sex were significantly associated with COVID-19 hospitalization risk. Regarding hospitalization risk scores, compared with those for hospitalization risk score 0 and critical care risk score 0, hazard ratios [95% confidence intervals] were 19.034 [10.470–34.600] and 55.803 [12.761–244.015] (P < 0.001) (P < 0.001), respectively, for risk score 4. Complications from diabetes and obesity increased hospitalization and critical care risks for COVID-19 patients. [ABSTRACT FROM AUTHOR]

This paper examines peak external wind pressures acting on walls of low-rise buildings using various parameters. Results indicate that positive pressure distribution is relatively uniform regardless of parameters considered and its magnitude decreases as averaging area size becomes larger. Large suction pressure distribution occurs at lateral edges of walls and its magnitude does not average out with increasing averaging area size as rapidly as for positive pressure. Based on these observations, we find that zoning of wall area for design purposes is only necessary for wall suction pressures. We further find that it is more suitable to base zoning on building height to some extent. When the present results are compared with the design values in the AIJ Recommendations for Loads and Buildings in Japan, the design values significantly underestimate positive wall pressures for small averaging areas, but the degree of underestimation diminishes as area becomes larger. Similarly, design values underestimate negative wall pressures up to an area of 10 m², but begin to overestimate past that point. A similar observation was made for a comparison with ASCE7-10; however, the degree of overestimation of negative code values at larger areas was smaller than the one observed in the comparison with the AIJ Recommendations. [ABSTRACT FROM AUTHOR]

Abstract: Farmland values are influenced not only by agricultural production but also by several other economic and environmental factors. Among the attributes that contribute value to farmland are certain amenity characteristics. Using farm-level data this study examines the impact of natural amenity on farmland values in the United States. In contrast to previous studies that assume a homogeneous relationship across the entire distribution of farmland values, this study uses quantile regression to estimate the empirical model. Our quantile regression analysis offers some insightful results. Natural amenity is positively correlated with farmland values and its impact is often more pronounced at higher price range of farmland. On the other hand, land retirement programs like CRP and WLP have positive impacts on farmland values but, at the lower quantiles. Direct farm payments have a significantly positive effect on farmland values and the impact increases monotonically across quantiles. Finally, policy implications are discussed. [Copyright &y& Elsevier]

Purpose – The purpose of this paper is to assess the impacts of GM crop adoption on the profitability of farms operated by young and/or beginning farmers and ranchers (YBFR). Design/methodology/approach – This research uses weighted quantile regression analysis in conjunction with 2004-2006 Agricultural Resource Management Survey to evaluate the impact of GM crop adoption on financial performance of farms operated by YBFR. The methodology employed in this study corrects for the simultaneity of technology adoption and farm financial performance. Findings – As expected, the impact of GM crop adoption on profitability is positively affected by the scale of operation and leverage. On the other hand, off-farm employment by "beginning" farmers has a negative impact on farm's profitability if they choose to adopt GM crops. Finally, quantile regression results from a farm household study shows that the model performs better at the higher quantile of the distribution. Research limitations/implications – This study helps to determine whether the adoption of GM crops increases the profitability of farms operated by "beginning" farmers. In addition, it explores the impact of other factors (such as farm, operator, demographic, and financial characteristics) on the profitability of farms operated by "beginning" farmers. Practical implications – Computing the profitability of adoption decisions for YBFR will provide significant information to YBFR that they can use in constructing their farm operations strategic business plan and future decisions regarding farming operations. Originality/value – Existing research does not examine the impact of GM crops adoption on farm profitability of YBFR. Furthermore, YBFR operators face significant challenges in making their operations financially viable, owing to lack of access to capital and land. [ABSTRACT FROM AUTHOR]