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Sue K Park 4 Articles
Reliability and Data Integration of Duplicated Test Results Using Two Bioelectrical Impedence Analysis Machines in the Korean Genome and Epidemiology Study.
Boyoung Park, Jae Jeong Yang, Ji Hyun Yang, Jimin Kim, Lisa Y Cho, Daehee Kang, Chol Shin, Young Seoub Hong, Bo Youl Choi, Sung Soo Kim, Man Suck Park, Sue K Park
J Prev Med Public Health. 2010;43(6):479-485.
DOI: https://doi.org/10.3961/jpmph.2010.43.6.479
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AbstractAbstract PDF
OBJECTIVES
The Korean Genome and Epidemiology Study (KoGES), a multicenter-based multi-cohort study, has collected information on body composition using two different bioelectrical impedence analysis (BIA) machines. The aim of the study was to evaluate the possibility of whether the test values measured from different BIA machines can be integrated through statistical adjustment algorithm under excellent inter-rater reliability. METHODS: We selected two centers to measure inter-rater reliability of the two BIA machines. We set up the two machines side by side and measured subjects' body compositions between October 2007 and December 2007. Duplicated test values of 848 subjects were collected. Pearson and intra-class correlation coefficients for inter-rater reliability were estimated using results from the two machines. To detect the feasibility for data integration, we constructed statistical compensation models using linear regression models with residual analysis and R-square values. RESULTS: All correlation coefficients indicated excellent reliability except mineral mass. However, models using only duplicated body composition values for data integration were not feasible due to relatively low R2 values of 0.8 for mineral mass and target weight. To integrate body composition data, models adjusted for four empirical variables that were age, sex, weight and height were most ideal (all R2>0.9). CONCLUSIONS: The test values measured with the two BIA machines in the KoGES have excellent reliability for the nine body composition values. Based on reliability, values can be integrated through algorithmic statistical adjustment using regression equations that includes age, sex, weight, and height.
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  • Nutritional Consequences and Management After Gastrectomy
    Jae-Moon Bae
    Hanyang Medical Reviews.2011; 31(4): 254.     CrossRef
Cigarette Smoking and Mortality in the Korean Multi-center Cancer Cohort (KMCC) Study.
Eun Ha Lee, Sue K Park, Kwang Pil Ko, In Seong Cho, Soung Hoon Chang, Hai Rim Shin, Daehee Kang, Keun Young Yoo
J Prev Med Public Health. 2010;43(2):151-158.
DOI: https://doi.org/10.3961/jpmph.2010.43.2.151
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  • 30 Crossref
AbstractAbstract PDF
OBJECTIVES
The aim of this study was to evaluate the association between cigarette smoking and total mortality, cancer mortality and other disease mortalities in Korean adults. METHODS: A total of 14 161 subjects of the Korean Multi-center Cancer Cohort who were over 40 years of age and who were cancer-free at baseline enrollment reported their lifestyle factors, including the smoking status. The median follow-up time was 6.6 years. During the follow-up period from 1993 to 2005, we identified 1159 cases of mortality, including 260 cancer mortality cases with a total of 91 987 person-years, by the national death certificate. Cox proportional hazard regression model was used to estimate the hazard ratio (HR) of cigarette smoking for total mortality, cancer mortality and disease-specific mortality, as adjusted for age, gender, the geographic area and year of enrollment, the alcohol consumption status, the education level and the body mass index (BMI). RESULTS: Cigarette smoking was significantly associated with an increased risk of total mortality, all-cancer mortality and lung cancer mortality (p-trend, <0.01, <0.01, <0.01, respectively). Compared to non-smoking, current smokers were at a higher risk for mortality [HR (95% CI)=1.3 (1.1-1.5) for total mortality; HR (95% CI)=1.6 (1.1-2.2) for all-cancer mortality; HR (95% CI)=3.9 (1.9-7.7) for lung cancer mortality]. CONCLUSIONS: This study's results suggest that cigarette smoking might be associated with total mortality, all-cancer mortality and especially lung cancer mortality among Korean adults.
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    Journal of Korean Academy of Oral Health.2017; 41(2): 122.     CrossRef
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    Epidemiology and Health.2017; 39: e2017026.     CrossRef
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    The Journal of School Nursing.2017; 33(5): 355.     CrossRef
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    Journal of the Korea Academia-Industrial cooperation Society.2015; 16(3): 2079.     CrossRef
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  • Attributable fraction of tobacco smoking on cancer using population-based nationwide cancer incidence and mortality data in Korea
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    Sung Suk Chung, Kyoung Hwa Joung
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Erratum.
Sue K Park, Ji Yeob Choi
J Prev Med Public Health. 2010;43(1):96-97.
DOI: https://doi.org/10.3961/jpmph.2010.43.1.96
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AbstractAbstract PDF
There was an error in the numbering of the references in page 375-376: Sue K. Park, Ji-Yeob Choi. Risk Assessment and Pharmacogenomics in Molecular and Genomic Epidemiology. J Prev Med Public Health 2009; 42(6): 371-6.
Summary
Risk Assessment and Pharmacogenetics in Molecular and Genomic Epidemiology.
Sue K Park, Ji Yeob Choi
J Prev Med Public Health. 2009;42(6):371-376.
DOI: https://doi.org/10.3961/jpmph.2009.42.6.371
  • 4,979 View
  • 59 Download
  • 3 Crossref
AbstractAbstract PDF
In this article, we reviewed the literature on risk assessment (RA) models with and without molecular genomic markers and the current utility of the markers in the pharmacogenetic field. Epidemiological risk assessment is applied using statistical models and equations established from current scientific knowledge of risk and disease. Several papers have reported that traditional RA tools have significant limitations in decision-making in management strategies for individuals as predictions of diseases and disease progression are inaccurate. Recently, the model added information on the genetic susceptibility factors that are expected to be most responsible for differences in individual risk. On the continuum of health care, from diagnosis to treatment, pharmacogenetics has been developed based on the accumulated knowledge of human genomic variation involving drug distribution and metabolism and the target of action, which has the potential to facilitate personalized medicine that can avoid therapeutic failure and serious side effects. There are many challenges for the applicability of genomic information in a clinical setting. Current uses of genetic markers for managing drug therapy and issues in the development of a valid biomarker in pharmacogenetics are discussed.
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JPMPH : Journal of Preventive Medicine and Public Health