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The Factors Associated with the Abnormal Eating Behavior in University Hospital's Nurses.
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Young Geon Ji, Su Jin Kim, Hyung Gon Kang
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Korean J Epidemiol. 2005;27(1):108-117.
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Abstract
OBJECTIVES
To compare eating behavior according to the shift work and to find the factors associated with the abnormal eating behavior in university hospital's nurses.METHODS For this assessment, a cross-sectional study was conducted with 300 university hospital's nurses. Nurses were questioned with self-reported questionnaire forms. After excluding cases with incomplete data, 276 cases are enrolled in the study. To find the factors associated with the abnormal eating behavior, we conducted multiple logistic regression analysis.RESULTS The proportions of shift work nurses were 63.77%(176 cases). The proportions of abnormal eating behavior among shift work nurses were 22.73%(40 cases), but only 6.00%(6 cases) among non-shift work nurses had the abnormal eating behavior. Considering the shift work period, the proportions of abnormal eating behavior were 31.25%, 22.92% and 17.50% in case of shift work period were less than 1 year, 1~3 years and more than 3 years, respectively.
The abnormal eating behavior was associated with having shift work, doing exercise, more weekly working hours, being on a diet and having perception of overweight. But age and body mass index were not influenced the abnormal eating behavior.CONCLUSIONS In this study, we found that the factors associated with the abnormal eating behavior are the shift work, exercise, weekly working hours, perception of overweight and diet.
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A Case-Control Study on Risk Factors of CHD: Vegetable consumption and risk for CHD in Korean men.
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Kyung Won Oh, Il Suh, Kang Hee lee, Chung Mo Nam, Suk Il Kim, Hyung Gon Kang, Sun Ha Jee, Seung Yun Cho, Won Heum Shim
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Korean J Epidemiol. 1998;20(2):234-245.
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The purpose of this study was to investigate the association of vegetable consumption with the incidence of CHD in Korean men by a case-control study. The case group consisted of 108 patients with EKG-confirmed myocardial infarct or angiographically-confirmed(>or=50% stenosis) angina pectoris admitted to a university teaching hospital in Seoul, Korea.
Controls were 142 age-matched patients admitted to the departments of ophthalmology and orthopedic surgery at the same hospital. Dietary intake was assessed by a nutritionist using a semiquantitative food frequency method, while body mass index (BMI), tobacco use, and past history of cardiovascular disease were determined during an interview and examination. The consumption of vegetables was classified by the average frequency of intake(less than 3 times/week, 3~4 times/week, 5~6 times/week, more than once/day). The percentage of subjects who consumed vegetables less than 3 times per week was 29.6% for cases and 17.6% for controls; while men who consumed vegetavle more than once per day were 16.7% for cases and 32.4% for controls. After the adjustment for age, body mass index, and tobacco use, the odds ratio of men who consumed vegetables at least once per day was 0.38(95% confidence interval, 0.18-0.85) compared with men who consumed vegetables less than three times per week. These results suggested that in a population with a relatively low fat intake, consumption of vegetables at least once per day may reduce the risk of CHD in men.
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A Study on Tracking by Using Random Coefficient Model.
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Hyung Gon Kang, Byung Soo Kim, Chung Mo Nam
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Korean J Epidemiol. 1997;19(1):58-66.
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There are many epidemiologic studies to find the tracking phenomenon. In repeated measurement data, a population is said to have tracking phenomenon with respect to particular chracteristic if, for each individual, the relative rank of observed value maintained over time. Understanding tracking phenomena in epidemiologic study is quite important. If the risk factors of chronic disease have tracking phenomenon, early detection of these risk factors can lead to the possibility of early treatment. In this study, we defined tracking as relative maintenance and proposed new measure of tracking by using random coefficient model. We compared our measure with McMahan's using simulation study. And we applied ours to real data. We may conclude that our new measure of tracking is adequate in explaining and dectecting the tracking phenomenon.
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A Study on Misclassification Arising from Random Error in Exposure Measurement.
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Il Suh, Chung Mo Nam, Hyung Gon Kang
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Korean J Epidemiol. 1996;18(1):108-118.
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There are many epidemiologic studies to find the relationship between disease occurrence and categorized exposure variables which are measured in continuous scales.
Recently, it has been found that the differential misclassification can arise when exposure variables are observed with measurement errors and categorized for the analysis. Even though the differential misclassification leads to serious misclassification bias, there is no theoretical attempt to correct the misclassification bias occuring in these circumstances. In this paper, we propose a new statistical method to reduce the misclassification bias due to dichotomizing continuous exposure variables. Since the exposure values are more likely to be misclassified when the true exposure values are close to the cutoff point, the method proposed here gives smaller weights in these case and more weights when these values are far from cutoff point.
Simulation studies are performed to compare the bias and the power of the proposed method compared to other methods. Main results are as follows: 1. The proposed method produces the smaller bias and the higher power than the simple method which modifies misclassified data using sensitivity and specificity of exposure misclassification. 2. When the standard deviation of the measurement error are moderately large, the bias and the power of the proposed estimate are somewhat better than those of the modified estimate which excluding the misclassified observations in the analysis. In conclusion, the method proposed here is found to be useful in epidemiologic studies when continuous exposure variables are obtained with measurement error and categorized in the analysis.
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