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Myung Hee Shin 3 Articles
Food, Nutrient, and the Risk of Breast Cancer.
Myung Hee Shin
Korean J Epidemiol. 2002;24(2):164-170.
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Abstract
Nutrients are the primary dietary component that are of interest in studying the diet-disease relationships because they give us more direct biological mechanisms. However, exploring the associations of the foods or food groups with disease occurrence could also give us an important information in the causal relationships between diet and diseases. When there are no specific nutrient that was shown to be related to a disease, a relationship between a certain food and the disease could provide a new hypothesis for the responsible components. If an association exists with both the overall intake of a nutrient and more than one food source of that nutrient, it is more likely that the association is causal. On the other hand, foods with similar nutrient components could have different effects on our body due to a complex interaction between the nutrients within the food. Epidemiologists should employ both approach (nutrient and food) in order to have an unbiased assessment for the association between diet and diseases. We will take an example of a cohort study which looked at the association between dairy products, calcium, and vitamin D and the risk of breast cancer.
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Korean summary
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The Menstrual, Reproductive, and Lactational Factors and the Risk of Breast Cancer in Korea: A Population Based Case-Control Study.
Myung Hee Shin, Jung Hyun Yang, Kuk Jin Choi, Yoon Ok Ahn
Korean J Epidemiol. 2000;22(1):68-68.
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Abstract
This study was conducted to assess the risk of breast cancer by menstrual, reproductive, and lactational factors in Korean women. A total of 210 women living in Seoul aged 30 to 60 with a first diagnosis of breast cancer from 2 teaching hospitals in Seoul, and 249 control subjects, randomly selected from the female pensioners of Korean Medical Insurance Company living in Seoul were interviewed. There was a increasing risk with the decrease of age at menarche, and the adjusted odds ratio(OR) of early menarche(age 14 years or less compared with over 17 years) was 1.61(95% Confidence Interval [CI]: 0.95-2.74). The longer the interval between menarche and regular menstruation, the less risk of breast cancer(OR=0.31, 95% CI: 0.16-0.60). The effect of these menarche factors were more prominent among premenopausal women. The adjusted OR for the second category(25-29 years old) of the age at first pregnancy and the age at the first full term pregnancy, compared to the reference category( 24 years old), were 0.52(95% CI: 0.30-0.92) and 0.58(95% CI: 0.32-1.06), respectively. The late age at the last full term pregnancy had significant protective effect (OR=0.29, 95% CI: 0.10-0.88), and the age itself was important rather than the interval between the first and last full term pregnancy. Factors related to breast feeding(BF) had protective effect consistently. Among parous women, in referent to no BF, women with 1 year of BF had adjusted OR of 0.34(95% CI: 0.17-0.68), while those with 1-3 years of BF had 0.35(95% CI: 0.18-0.67), 3-5 years had 0.29(95% CI: 0.12-0.74), and more than 5 years had 0.06(95% CI: 0.02-0.17). Age at menarche with regular cycle have stronger effect on especially pre-menopausal breast cancer among Korean women. Recent increase in breast cancer incidence in Korea would be explained by delayed age at first pregnancy or delivery, earlier age at menarche and last delivery, and decrease in brest feeding.
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Methodologic Considerations on the Cohort Study of Risk Factors of Stomach Cancer: On the Incompleteness of Case Ascertainment.
Moo Song Lee, Wee Chang Kang, Dong Hyun Kim, Jong Myun Bae, Myung Hee Shin, Young Jo Lee, Yoon Ok Ahn
Korean J Epidemiol. 1997;19(2):152-160.
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Abstract
BACKGROUND
AND PURPOSE: The authors conducted the study to evaluate the incompleteness of follow-up as well as the validity of the diagnostic code in the medical insurance databases in a cohort study. They also suggested several useful regression models for the analysis of such incomplete data.
METHODS
The subjects of Seoul Cohort(n=14,533) were followed up for three and a half years. Based on the chart reviews of the subjects who had the diagnostic code of gastric cancer in the medical insurance databases, forty-four cases of gastric cancer were idenfified, using cancer registry databases and death certificates as the secondary source. Regression coefficients and the associated p-values were estimated using the following six methods and the results were compared with each other. Method 1: The subjects with the diagnostic code in the medical insurance databases were considered as the cases of gastric cancer.
Method
2: The confirmed cases were considered as the cases of gastric cancer. Method 3: The cases were the subjects with the diagnositc code whose diagnosis was confirmed by medical chart reriew. Method 4: Ordinal logistic regression.
Method
5: Weighted logistic regression. Method 6: Polytomous logistic regression RESULTS: A total of 12,541 subjects were followed up excluding censored cases. One hundred and nine subjects were diagnosed with gastric cancer in the medical utilization databases: forty-three were probable cases whose dianosis was not confrimed by chart review, twenty-six were ruled out and 26 were confirmed cases. Another 14 cases were confirmed using the cancer registry and death certificates. Using the secondary sources, four another cases were confirmed and 44 cases were confirmed during follow-up. In method 1, past history of gastritis and gastric ulcer was significant risk factor whereas intake frequency of fresh vegetable, ice cream and coffee was associated with significantly decreased risk. In the second and the sixth method, green tea was a significant protective factor, whereas in methods 3-5, no significant variables were found.
CONCLUSIONS
Polytomous logistic regression was the preferred method in the cohort study using secondary sources of information for the follow-up, and it provided additional information for the risk factor identification, especially for the specificity of the risk factors.
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