PURPOSES : The purpose of this study is to verify traffic accident injury severity factors for elderly drivers and the relative relationship of these factors. METHODS : To verify the complicated relationship among traffic accident injury severity factors, this study employed a structural equation model (SEM). To develop the SEM structure, only the severity of human injuries was considered; moreover, the observed variables were selected through confirmatory factor analysis (CFA). The number of fatalities, serious injuries, moderate injuries, and minor injuries were selected for observed variables of severity. For latent variables, the accident situation, environment, and vehicle and driver factors were respectively defined. Seven observed variables were selected among the latent variables. RESULTS : This study showed that the vehicle and driver factor was the most influential factor for accident severity among the latent factors. For the observed variable, the type of vehicle, type of accident, and status of day or night for each latent variable were the most relative observed variables for the accident severity factor. To verify the validity of the SEM, several model fitting methods, including ${\chi}^2/df$, GFI, AGFI, CFI, and others, were applied, and the model produced meaningful results. CONCLUSIONS : Based on an analysis of results of traffic accident injury severity for elderly drivers, the vehicle and driver factor was the most influential one for injury severity. Therefore, education tailored to elderly drivers is needed to improve driving behavior of elderly driver.
Software defect severity is very important in projects with limited historical data or new projects. But general software defect prediction is very difficult to collect the label information of the training set and cross-project defect prediction must have a lot of data. In this paper, an unclassified data set with defect severity is clustered according to the distribution ratio. And defect severity-based prediction model is proposed by way of labeling. Proposed model is applied CLAMI in JM1, PC4 with the least ambiguity of defect severity-based NASA dataset. And it is evaluated the value of ACC compared to original data. In this study experiment result, proposed model is improved JM1 0.15 (15%), PC4 0.12(12%) than existing defect severity-based prediction models.
Although accident data from the National Police Agency and insurance companies do not know the vehicle safety, the damage level information can be obtained from the data managed by the bus credit association or the bus company itself. So the accident severity was analyzed based on the side impact accidents using accident repair cost. K-means clustering analysis separated the cost of accident repair into 'minor', 'moderate', 'severe', and 'very severe'. In addition, the side impact accident severity was analyzed by using an ordered logit model. As a result, it is appeared that the longer the repair period, the greater the impact on the severity of the side impact accident. Also, it is appeared that the higher the number of collision points, the greater the impact on the severity of the side impact accident. In addition, oblique collisions of the angle of impact were derived to affect the severity of the accident less than right angle collisions. Finally, the absence of opponent vehicle and large commercial vehicles involved accidents were shown to have less impact on the side impact accident severity than passenger cars.
Objectives: To evaluate the performance of models to predict AMI patients death using severity adjustment measures in Korea. Methods: Medical records of 861 patients treated by AMI in 7 general hospitals during 1996 and 1997 were reviewed by trained nurses. We measured the severity of patients by APACHE III, MedisGroups, CSI and DS. Using each severity method a predictive mortality for each patient was calculated from a logistic regression model including the severity score. The statistical performance of each severity method model was evaluated by using c-statistics and R2. For each hospital, z scores compared actual and expected mortality rates. Results: The overall in-hospital mortality was 14.5%, ranged from 10.0% to 22.2%. The distributions of severity scores for each method was significantly different by hospitals. The four severity-adjusted models to predict AMI patients death varied in their statistical performance for discrimination power of patients death. Order of Severity-adjusted mortality rates and z scores by four severity measures was different. Conclusion: Severity-adjusted mortality rates of AMI patients might be applied as an indicator for hospital performance evaluation in Korea. Because different severity methods frequently produce different impressions about relative hospital performance, more studies has to be done to use it as quality indicator and more attention should be paid to select appropriate severity measures.
PURPOSES : This study drew factors affecting motorcycle accidents in Seoul by severity using an ordered probit model and aimed to analyze and verify the drawn influence factors. METHODS : As the severity of the accidents could be classified into three types (fatal injury, serious injury and minor injury), this study drew the factors affecting accidents by a comparative analysis employing an ordered probit model, removed the variables that would not secure significance sequentially to construct a model with high explanatory power regarding the factors affecting the severity of motorcycle accidents, and calculated the marginal effect of each factor to understand the degree of each factor's impact on the severity. First, Model 1 put in all variables; Model 2 was constructed by removing the variables of the road surface conditions that could not meet the level of significance (p=0.608); Model 3 was constructed by removing gender variable (p=0.423); and Model 4 was constructed finally by removing age variable (p=0.320). RESULTS : As a result of an analysis, statistically significant variables were time of occurrence, type of accident, road alignment and motorcycle displacement, and it turned out that the impacts on the severity were in the following order: a road alignment of left downhill, the type of motorcycle-to-vehicle accidents and a road alignment of a flatland on the left. The significance of the models was tested using the likelihood ratio, the level of significance and suitability statistics about them, and as a result of the test, the significance level and suitability of the constructed models were all excellent. In addition, the model accuracy indicating the accuracy of a predicted value compared to that of the value actually observed was 70.3% for minor injury; 70.1% for serious injury; and 68.6% for fatal injury, and the overall accuracy was 70.2%, which was very high. CONCLUSIONS : As a result of an analysis of motorcycle accidents in Seoul through the ordered probit model and the marginal effect, it turned out that their severity increased in nighttime accidents as compared to daytime ones and gradually increased in the order of motorcycle-to-vehicle accidents, motorcycle-to-person ones and the ones involving motorcycle only. As a result of an analysis, the severity of accidents in road alignments of left downhill, left flatland and straight downhill increased as compared to those in a road alignment of straight flatland and that the severity of accidents of motorcycles with a displacement larger than 50cc was higher than that of those with a displacement smaller than 50cc.
This study was conducted to propose an insight into the appropriateness of hospital length of stay(LOS) by developing a severity-adjusted LOS model for patients with pneumonia, organism unspecified. The pneumonia risk-adjustment model developed in this paper is based upon the 2006-2010 the Korean National Hospital Discharge in-depth Injury Survey. Decision tree analysis revealed that age, admission type, insurance type, and the presence of additional disorders(pleural effusion, respiratory failure, sepsis, congestive heart failure etc.) were major factors affecting the severity-adjusted model using the Clinical Classifications Software(CCS). Also there was a difference in LOS among the regional hospitals, especially the hospital LOS has not been efficiently managed in Gyeongsangbuk-do, Jeollanam-do, Jeollabuk-do, Daejeon, and Busan. To appropriately manage hospital LOS, reliable statistical information about severity-adjusted LOS should be generated on a national level to make sure that hospitals voluntarily reduce excessive LOS and manage main causes of delayed discharge.
Objectives: The purpose of the study is to investigate the musculo-skeletal pain prevalence and severity in the dental hygienists based on PRECEDE model. Methods: A self-reported questionnaire was completed by 483 dental hygienists in Gwangju from September 13 to October 12, 2013. Data were analyzed by frequency analysis, chi-square test, t-test, and multiple logistic regression analysis using SPSS 18.0 program. Musculo-skeletal pain severity was classified from 1 to 5 by PRECEDE model. The questionnaire consisted of six questions of the general characteristics of the subjects, one question of musculo-skeletal pain prevalence, one question of body part musculo-skeletal pain prevalence, one question of subjective health status, three questions of activities of daily living, six questions of working environment, one question of musculoskeletal system diseases knowledge, two questions of social support, two questions of education experience and data use method, and five questions of necessity of health education. Results: The prevalence rate of musculo-skeletal pain within a year was 83.9% and 22.8% of the dental hygienists complained of severe pain. The odds ratio of moderate pain severity was 1.99(95% CI, 1.10-3.60) and the odds ratio of unhealthiness was 3.27 (95% CI, 1.35-7.94). The odds ratio of pain severity in those working for 4-6 years was 0.21(95% CI, 0.08-0.57). The odds ratio of pain severity in those practicing 6-10 scaling cases per day was 0.33(95% CI, 0.17-0.65). The odds ratio of pain severity in wrist turning and bending was 3.56(95% CI, 1.19-10.62). Conclusions: The muscolu-skeletal pain severity in the dental hygienists was closely associated with subjective health condition, work duration, the number of scaling practice activity, and a treatment posture. Regular physical checkup for the dental hygienists will improve the musculo-skeletal pain due to scaling practice.
Background: Workers are often exposed to hazardous heat due to their work environment, leading to various injuries. As a result of climate change, heat-related injuries (HRIs) are becoming more problematic. This study aims to identify critical contributing factors to the severity of occupational HRIs. Methods: This study analyzed historical injury reports from the Occupational Safety and Health Administration (OSHA). Contributing factors to the severity of HRIs were identified using text mining and model-free machine learning methods. The Multinomial Logit Model (MNL) was applied to explore the relationship between impact factors and the severity of HRIs. Results: The results indicated a higher risk of fatal HRIs among middle-aged, older, and male workers, particularly in the construction, service, manufacturing, and agriculture industries. In addition, a higher heat index, collapses, heart attacks, and fall accidents increased the severity of HRIs, while symptoms such as dehydration, dizziness, cramps, faintness, and vomiting reduced the likelihood of fatal HRIs. Conclusions: The severity of HRIs was significantly influenced by factors like workers' age, gender, industry type, heat index , symptoms, and secondary injuries. The findings underscore the need for tailored preventive strategies and training across different worker groups to mitigate HRIs risks.
Objective : Health insurers and policy makers are increasingly examining the hospital mortality rate as an indicator of hospital quality and performance. To be meaningful, a risk-adjustment of the death rates must be implemented. This study reviewed 5 severity measurement methods and applied them to the same data set to determine whether judgments regarding the severity-adjusted hospital mortality rates were sensitive to the specific severity measure. Methods : The medical records of 584 patients who underwent coronary artery bypass graft surgery in 6 general hospitals during 1996 and 1997 were reviewed by trained nurses. The MedisGroups, Disease Staging, Computerized Severity Index, APACHE III and KDRG were used to quantify severity of the patients. The predictive probability of death was calculated for each patient in the sample from a multivariate logistic regression model including the severity score, age and sex to evaluate the hospitals' performance, the ratio of the observed number of deaths to the expected number for each hospital was calculated. Results : The overall in-hospital mortality rate was 7.0%, ranging from 2.7% to 15.7% depending on the particular hospital. After the severity adjustment, the mortality rates for each hospital showed little difference according to the severity measure. The 5 severity measurement methods varied in their statistical performance. All had a higher c statistic and $R^2$ than the model containing only age and sex. There was a little difference in the relative hospital performance evaluation by the severity measure. Conclusion : These results suggest that judgments regarding a hospital's performance based on severity adjusted mortality can be sensitive to the severity measurement method. Although the 5 severity measures regarding hospital performance concurred, more often than would be expected by chance, the assessment of an individual hospital mortality rates varied by the different severity measurement method used.
The aim of this paper is to propose fighter aircraft's mission severity model which can be used as one of key factors for designing a structure and determining design life of KF-X. The mission severity is a quantitative data of flight loads and expressed by Nz(Vertical Load Factor) exceedances or occurrences. The severities of the flight loads depended on the circumstances of the countries which operate fighter aircraft. In this paper we have studied on Nz occurrences/exceedances of ROKAF fighter aircraft to generate mission severity model for the KF-X. The analyses of flight data were accomplished by using the Matlab.
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