Analysis of plant construction accidents and loss estimation using insurance loss records

There are many risks and uncertainties in plant construction projects, because of their complexity, difficulty in loss prediction and size of construction being large. The risk management of plant construction projects should not be relied solely on experiences and intuition of the contractors or the construction managers as it has been in the past. Therefore, a new quantitative and empirical risk analysis is required, in order for the development of a risk assessment using risk indicators for the plant construction projects. This research used the insurance payout record from a global insurance company to reflect the actual quantitative loss in the risk assessment model for plant construction project. The researchers adopted the geographic information as well as construction information (construction phase and commissioning phase, schedule rate, total duration), as the independent variables, which found to be statistically significant in the analysis in this study. It was found that the relationship between damage ratio and the valid variables was statistically significant, and thus, the damage model is also statistically significant. This research suggests that the regression model containing such valid independent variables could be beneficial in terms of providing foundational guidelines for the plant construction project risk analysis

A quantitative risk assessment development using risk indicators for predicting economic damages in construction sites of South Korea

The purpose of this study is to suggest a quantitative risk assessment approach for construction sites using risk indicators to predict economic damages. The frequency of damage in building construction has recently increased, and the associated costs have been increased as well. Although a request for a damage estimation model has been extended, the industry still lacks quantitative and comprehensive research that reveals the physical relationship between damage and risk indicators. To address that issue, we use an insurance company’s payouts from construction site claims in South Korea to reflect the real financial damage. We adopted a multiple regression method to define the risk indicators: geographic vulnerability, natural hazards, capability, and general project information. The results and findings of this research will be accepted as an essential guideline for developing a construction risk estimation model

Quality of Life in Metabolic Syndrome Patients Based on the Risk of Obstructive Sleep Apnea

Despite the impact of metabolic syndrome (MetS) and obstructive sleep apnea (OSA) on a sizeable proportion of the global population, the difference in the quality of life (QoL) between a group without risk factors for OSA and a group with risk factors for OSA among individuals with MetS is currently unclear. This study aimed to identify the determinants of QoL in patients with MetS with and without OSA risk factors and to analyze differences between these two groups. Data were extracted from the 2020 Korea National Health and Nutrition Examination Survey (KNHANES). The Rao–Scott χ2 test was performed to evaluate differences in baseline characteristics based on OSA risk factors. A t-test was performed to evaluate differences in the baseline QoL, and linear regression analysis was performed to identify the effect on the QoL of the two groups. The factors affecting QoL in the low-risk group included age, education level, and depression. The factors affecting QoL in the high-risk group were physical activity and depression. These results suggest that nursing interventions should be devised according to patients’ characteristics to help improve their QoL

Factors Associated with Hepatitis C Antibody Positivity in Korean Adults: A Cross-Sectional Study Based on 2013–2018 Korea National Health and Nutrition Examination Survey

This study aimed to provide basic data on the prevention of hepatitis C infection by identifying factors related to it based on the data from the National Health and Nutrition Examination Survey (KNHANES). The sixth (2013–2015) and seventh (2016–2018) Korean National Health and Nutrition Examination Surveys conducted by the Korean Disease Control and Prevention Agency were analyzed. This is a population-based, nationally representative, multistage, cross-sectional survey of noninstitutionalized persons in Korea. Multivariate regression analysis was used to assess the significance of the variables. A total of 32,942 persons aged >20 years were selected for this study. Among them, 282 tested positive for hepatitis C antibodies, while 32,660 tested negative. Of the 282 persons who tested positive, 48.6% were men and 51.4% were women. The factors associated with hepatitis C infection were age, education level, self-rated health status, and liver cirrhosis. Therefore, there is a need to educate people and implement preventive programs based on age and education levels to reduce the incidence of hepatitis C infections. In addition, it is necessary to include hepatitis C screening as part of the National Health Examination to diagnose hepatitis C infections

Revealing building vulnerability to windstorms through an insurance claim payout prediction model: a case study in South Korea

The aim of this study is to develop regional vulnerability functions of buildings to estimate the loss from windstorms. Windstorms trigger critical financial damage to assets around the world. Insurance companies assess the financial risk of their exposures by employing windstorm risk assessment models. The vulnerability function in the risk assessment model is generally based on the analysis of actual damage records from insurance companies. However, the absence of detailed loss data is an obstacle to developing vulnerability functions. To fill this gap, this study provides a methodology to develop a function using an insurance company's loss data associated with windstorms. Vulnerability functions are generated based on the wind speed, line of business, and value of the property. The findings and methodology of this study offer a practical way of reflecting the real economic losses and regional vulnerability of buildings and help to develop vulnerability functions for insurance companies and emergency planners

Validity and Reliability of the Korean Version of the Holistic Nursing Competence Scale

This methodological study aimed to verify the validity and reliability of the Korean version of the Holistic Nursing Competence Scale (HNCS), which comprises five dimensions and 36 items. The English version of the HNCS was forward and backward translated and administered to 251 participants with more than a year of work experience in a general hospital. Data were analyzed using SPSS WIN 24.0(Chicago, IL, USA), and AMOS program was used for confirmatory factor analysis. Additionally, the “Task Performance Evaluation Instrument for Clinical Nurses” was used for concurrent validity. Reliability assessed using Cronbach’s α was 0.969. Convergent, discriminant, and concurrent validity were good. Average variance extracted and construct reliability ranged from 0.845 to 0.932 and 0.980 to 0.987, respectively. The model was suitable with the chi-square value being 1216.563 (df = 584, p < 0.001), and Q value being less than three. Goodness-of-fit index, root mean square residual, and root mean square error of approximation were 0.784, 0.066, and 0.066, respectively. Moreover, comparative fit index, Tucker–Lewis index, and incremental fit index were 0.913, 0.906, and 0.913, respectively. Thus, this study verified the validity and reliability of the Korean version of the HNCS. Our findings suggest that the scale is helpful in measuring and developing the holistic nursing competence of clinical nurses

Measuring Vulnerability of Typhoon in Residential Facilities: Focusing on Typhoon Maemi in South Korea

Typhoons cause severe monetary damage globally. Many global insurance companies and public agencies are currently developing and utilizing windstorm risk estimation models to calculate the level of risk and set up strategies for avoiding, mitigating, and relocating those economic risks. Hence, the usage and accuracy of the windstorm risk estimation model is becoming increasingly significant, and reflecting local vulnerabilities is essential for refined risk assessment. While key risk indicators have been recognized in practical studies of economic losses associated with windstorms, there remains a lack of comprehensive research addressing the relationship between economic losses of residential buildings for South Korea and vulnerability. This research investigates the real damage record of Typhoon Maemi from an insurance company in order to bridge this gap. The aim of this study is to define the damage indicators of typhoons and create a framework for typhoon damage function, using the damage caused by Typhoon Maemi as a representative paradigm. Basic building information and natural disaster indicators are adopted to develop the damage function. The results and metric of this research provide a pragmatic approach that helps create damage functions for insurance companies and contingency planners, reflecting the actual financial losses and local vulnerabilities of buildings. The framework and results of this study will provide a practical way to manage extreme cases of natural disasters, develop a damage function for insurers and public authorities, and reveal the real economic damage and local vulnerability of residential buildings in South Korea

Predicting hurricane wind damage by claim payout based on Hurricane Ike in Texas

The increasing occurrence of natural disasters and their related damage have led to a growing demand for models that predict financial loss. Although considerable research on the financial losses related to natural disasters has found significant predictors, there has been a lack of comprehensive study that addresses the relationship among vulnerabilities, natural disasters, and the economic losses of individual buildings. This study identifies the vulnerability indicators for hurricanes to establish a metric to predict the related financial loss. We classify hurricane-prone areas by highlighting the spatial distribution of losses and vulnerabilities. This study used a Geographical Information System (GIS) to combine and produce spatial data and a multiple regression method to establish a wind damage prediction model. As the dependent variable, we used the value of the Texas Windstorm Insurance Association (TWIA) claim payout divided by the appraised values of the buildings to predict real economic loss. As independent variables, we selected a hurricane indicator and built environment vulnerability indicators. The model we developed can be used by government agencies and insurance companies to predict hurricane wind damage