Risk factors and a predictive model for thyroid cancer in Korean women.
BACKGROUND
Thyroid cancer incidence in Korean women has increased radically and is the highest in all cancer types. However, the rate of cancer screening among women is very low.


OBJECTIVE
The aim of the study was to determine the risk factors for thyroid cancer and to develop a predictive model based on these risk factors.


METHODS
The study design comprised a literature review and a case-control study. To construct a predictive model, the participants selected were 260 female outpatients diagnosed with malignant neoplasm of thyroid gland who had undergone thyroid removal surgery. A total of 259 people for the control group were selected by adopting a 5-year age-matching method.


RESULTS
From the literature review, 6 categories of risk factors were identified. Nine variables, including occupation, live(d) in coastal region, family history of thyroid cancer, history of benign thyroid tumor, menopause status and weight gain, number of full-term deliveries, abortion, exercise intensity, and stress, remained as statistically significant risk factors in the stepwise regression model. Regarding the predictive power of the model, the area under the receiver operating characteristic curve was .79, accuracy was .77, sensitivity was .89, specificity was .65, positive predictive value was .72, and negative predictive value was .85.


CONCLUSIONS
The predictive power of the model was relatively good, so it can be used to identify individuals at high risk for thyroid cancer.


CONCLUSIONS
The predictive model can be used in promoting to participate in early cancer-screening tests. Thus, it will be possible to detect thyroid cancer in its earliest stage, diminish mortality, and improve quality of life.
Lee
Sun-Mi
SM
Authors' Affiliation: College of Nursing, The Catholic University of Korea, 505 Banpo-dong, Seocho-gu, Seoul, Korea 137-701. leesunmi@catholic.ac.kr
Kwak
Keun-Hae
KH
D000328:Adult; D016022:Case-Control Studies; D016009:Chi-Square Distribution; D004781:Environmental Exposure / Q000009:adverse effects / Q000032:analysis; D005260:Female; D006801:Humans; D015994:Incidence; D007723:Korea / Q000453:epidemiology; D008019:Life Style; D016015:Logistic Models; D008875:Middle Aged; D015999:Multivariate Analysis; D009765:Obesity / Q000150:complications; D017584:Reproductive History; D018570:Risk Assessment / Q000458:organization & administration*; D012307:Risk Factors; D012680:Sensitivity and Specificity; D013964:Thyroid Neoplasms / Q000453:epidemiology* / Q000209:etiology* / Q000517:prevention & control; D016387:Women's Health