• Journal of Navigation and Port Research
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• v.30 no.6 s.112
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• pp.551-559
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• 2006

The purpose of this study was to find out morbidity rate and pattern of disease and affect of variables related disease and medical management of seafares'. The subjects this study were 1049 seafares' were took education in Korea Institute of Maritime and Fisheries Technology. This questionnaire was focused on finding the basic data for prevention of disease and promotion health for the seafares'. The collected data were analyzed by using descriptive statistics, Chi-square, cross tab, linear regression by SPSS 10.1 package. The result of this study are as follow. 1)The morbidity rate within recent 12 months was 69.0%. 2)there were significant differences qf occurred disease in age(p<0.05), income(p<0.01), career of ship on board(p<0.01), rank(p<.01), perceived health status(p<0.01), worry of health(p<0.01), fatigue symptoms(0.01), satisfy of job(p<0.05), rest time(p<0.05) 3) Considering disease unable to work more than 4 hour, the number of those who had oral disease 7.3%> tinea 6.6%> gastric ulcer 6.4, and musculoskeletal disease group were 20.9%, which revealed the highest rate oral disease 13.6%> skin disease> 12.4%, digestive disease> 12.1%. 4) As refer to medical management, The pain above 31days 35.7%, hospitalization and treatment below 7days were each 50.2%, 42.8%, medical service were doctor's office 27.9, which revealed the highest rate.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2006.06b
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• pp.19-27
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• 2006

The purpose of this study was to find of morbidity rate and pattern of disease and affect of variables related disease and medical management of seafares' The subjects this study were 1049 seafares' who took education in Korea Institute of Maritime and Fisheries Technology. This questionnaire was focused on finding the basic data for prevention of disease and promotion health for the seafares' The collected data were analyzed by using descriptive statistics, Chi-square, cross tab, linear regression by SPSS 10.1 package. The result of this study are as follow. 1)The morbidity rate within recent 12 months was 69.0%. 2)there were significant differences of occurred disease in age(p<0.05), income(p<0.01), career of ship on board(p<0.01), rank(p<0.01), perceived health status(p<0.01), worry of health(p<0.01), fatigue symptoms(0.01), satisfy of job(p<0.05), rest time(p<0.05) 3) Considering disease unable to work more than 4 hour, the number of those who had oral disease 7.3%> tinea 6.6%> gastric ulcer 6.4, and musculoskeletal disease group were 20.9%, which revealed the highest rate oral disease 13.6%> skin disease> 12.4%, digestive disease> 12.1%. 4) As refer to medical management, The pain above 31days 35.7%, hospitalization and treatment below 7days were each 50.2%, 42.8%, medical service were doctor's office 27.9, which revealed the highest rate.

• Journal of Korean Society of Transportation
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• v.20 no.7
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• pp.155-166
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• 2002

Since 1990s. there has been an increasing number of traffic accidents at intersection. which requires more urgent measures to insure safety on intersection. This study set out to analyze the road conditions, traffic conditions and traffic operation conditions on signalized intersection. to identify the elements that would impose obstructions in safety, and to develop a traffic accident prediction model to evaluate the safety of an intersection using the cop relation between the elements and an accident. In addition, the focus was made on suggesting appropriate traffic safety policies by dealing with the danger elements in advance and on enhancing the safety on the intersection in developing a traffic accident prediction model fir a signalized intersection. The data for the study was collected at an intersection located in Wonju city from January to December 2001. It consisted of the number of accidents, the road conditions, the traffic conditions, and the traffic operation conditions at the intersection. The collected data was first statistically analyzed and then the results identified the elements that had close correlations with accidents. They included the area pattern, the use of land, the bus stopping activities, the parking and stopping activities on the road, the total volume, the turning volume, the number of lanes, the width of the road, the intersection area, the cycle, the sight distance, and the turning radius. These elements were used in the second correlation analysis. The significant level was 95% or higher in all of them. There were few correlations between independent variables. The variables that affected the accident rate were the number of lanes, the turning radius, the sight distance and the cycle, which were used to develop a traffic accident prediction model formula considering their distribution. The model formula was compared with a general linear regression model in accuracy. In addition, the statistics of domestic accidents were investigated to analyze the distribution of the accidents and to classify intersections according to the risk level. Finally, the results were applied to the Spearman-rank correlation coefficient to see if the model was appropriate. As a result, the coefficient of determination was highly significant with the value of 0.985 and the ranks among the intersections according to the risk level were appropriate too. The actual number of accidents and the predicted ones were compared in terms of the risk level and they were about the same in the risk level for 80% of the intersections.