Summary
Research Support, Non-U.S. Gov't
- Dipstick Urine Protein, as a Predictor of Cardiovascular Mortality in Korean Men: Korea Medical Insurance Corporation Study.
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Kyoungsoo Ha, Hyeon Chang Kim, Dae Ryong Kang, Chung Mo Nam, Song Vogue Ahn, Il Suh
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J Prev Med Public Health. 2006;39(5):427-432.
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Abstract
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- OBJECTIVES
This study was to investigate if the dipstick proteinuria can predict cardiovascular mortality in a population of Korean men. METHODS: We measured urine protein and other cardiovascular risk factors in 100059 Korean men, aged between 35-59 years in 1990 and 1992. Levels of proteinuria measured by dipstick method were trace or less, 1+, 2+, and 3+ or greater. The primary outcomes were deaths from all causes, cardiovascular disease, cancer, and others in a 12 year follow-up from 1993 to 2004. RESULTS: The multivariate-adjusted relative risks (95% CI) for cardiovascular death according to the level of proteinuria (1+, 2+, 3+ and more) in 1990 examination were 2.18 (1.36-3.48), 2.55 (1.37-4.78), and 4.57 (2.16-9.66) respectively. The corresponding relative risks according to the level of proteinuria in 1992 examination were 2.49 (1.71-3.64), 2.64 (1.53-4.58), and 2.78 (1.15-6.73). The relative risks for cardiovascular death of men with proteinuria (1+ or greater) once and twice among the examinations were 2.18 (1.63-2.92) and 3.75 (2.27-6.18), compared with men without proteinuria in 1990 and 1992 examinations. CONCLUSIONS: Our results showed that dipstick proteinuria is associated with cardiovascular mortality in Korean men. Dipstick proteinuria could be a predictor for cardiovascular mortality.
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Summary
English Abstracts
- Estimation of a Nationwide Statistics of Hernia Operation Applying Data Mining Technique to the National Health Insurance Database.
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Sunghong Kang, Seok Kyung Seo, Yeong Ja Yang, Aekyung Lee, Jong Myon Bae
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J Prev Med Public Health. 2006;39(5):433-437.
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Abstract
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- OBJECTIVES
The aim of this study is to develop a methodology for estimating a nationwide statistic for hernia operations with using the claim database of the Korea Health Insurance Cooperation (KHIC). METHODS: According to the insurance claim procedures, the claim database was divided into the electronic data interchange database (EDI_DB) and the sheet database (Paper_DB). Although the EDI_DB has operation and management codes showing the facts and kinds of operations, the Paper_DB doesnjt. Using the hernia matched management code in the EDI_DB, the cases of hernia surgery were extracted. For drawing the potential cases from the Paper_DB, which doesnjt have the code, the predictive model was developed using the data mining technique called SEMMA. The claim sheets of the cases that showed a predictive probability of an operation over the threshold, as was decided by the ROC curve, were identified in order to get the positive predictive value as an index of usefulness for the predictive model. RESULTS: Of the claim databases in 2004, 14,386 cases had hernia related management codes with using the EDI system. For fitting the models with applying the data mining technique, logistic regression was chosen rather than the neural network method or the decision tree method. From the Paper_DB, 1,019 cases were extracted as potential cases. Direct review of the sheets of the extracted cases showed that the positive predictive value was 95.3%. CONCLUSIONS: The results suggested that applying the data mining technique to the claim database in the KHIC for estimating the nationwide surgical statistics would be useful from the aspect of execution and cost-effectiveness.
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Summary
- Developing the Predictive Model for the Group at High Risk for Colon Cancer.
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Ae Kyoung Lee, Sang Yi Lee, Il Soo Park, Su Young Kim, Tae Ho Yoon, Baek Geun Jeong
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J Prev Med Public Health. 2006;39(5):438-446.
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Abstract
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- OBJECTIVES
We developed the predictive model for the incidence of colon cancer by utilizing the health screening data of the National Health Insurance in Korea. We also explored the characteristics of the high risk group for colon cancer. METHODS: The predictive model was used to determine those people who have a high risk for colon cancer within 2 years of their NHI health screening, and we excluded the people who had already been treated for cancer or who were cancer patient. The study population is the insured of the NHI, aged 40 or over and they had undergone health screening from the year 2000 to 2004, according to NHI health screening formula. We performed logistic regression analysis and used SAS Enterprise Miner 4.1. RESULTS: This study shows that there exists a higher rate of colon cancer in males than females. Also, for the population in their 60s, the incidence rate of colon cancer is much higher by 5.36 times than that for those people in their 40s. Amongst the behavioral factors, heavy drinking is the most important determinant of the colon cancer incidence (7.39 times in males and 21.51 times in females). CONCLUSIONS: Our study confirms that the major influencing factors for the incidence of colon cancer are drinking, lack of exercise, a medical history of colon polypus and a family history of colon cancer. As a result, we can choose the group that is at a high risk for colon cancer and provide customized medical information and selective management services according to their characteristics.
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Summary
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