西部二级城市女性乳腺癌发病风险相关因素分析及风险预测模型的建立

Research on Relevant Factors of Female's Breast Cancer and Establishment of Risk Factors Prediction Model in Secondary Cities of The West

目的探讨西部二级城市女性乳腺癌发病风险的相关因素并建立风险预测模型,以筛查高危人群,为乳腺癌的一、二级预防提供依据。方法对随机(随机数字表法)抽取的1 700例(其中门诊就诊者1 020例,体检者680例)女性进行问卷调查,其中62例经病理学检查证实为乳腺癌。以X线影像及问卷调查为基础,根据X线片上的乳腺密度分为高和低密度组,就乳腺密度、年龄、体质量指数、乳腺癌家族史、社会经济地位、生活方式和生殖生育情况与乳腺癌的关系进行单因素及多因素分析,并拟合相关危险因素建立乳腺癌风险预测模型。结果单因素分析结果显示,乳腺癌的相关危险因素有年龄(P=0.006)、体质量指数(P=0.007)、初潮年龄(P=0.039)、职业(P=0.001)、长期居住地(P=0.000)、文化程度(P=0.001)、相比前1年的健康状况(P=0.046)、初产年龄(P=0.014)、是否绝经(P=0.003)及绝经年龄(P=0.006);多因素分析(logistic回归分析)结果显示,乳腺癌的相关危险因素有年龄(P=0.003)、初产年龄(P=0.000)、职业(P=0.010)及长期居住地(P=0.000),其保护因素是初潮年龄(P=0.000)。根据多因素分析结果初步建立的所调查地区的乳腺癌风险预测模型为y=-5.557+0.042x1-0.375x2+1.206x3+0.509x4+2.135x5,拟合系数(R square)=0.170,能反应出实际情况的17%。结论通过流行病学调查并结合相关风险因素分析建立的乳腺癌风险预测模型,能对今后的临床工作起到一定的指导作用,但该模型尚需进行大规模人群的验证研究。

Objective To explore the risk factors of female＇s breast cancer in secondary cities of the west and establish a risk prediction model to identify high-risk groups, and provide the basis for the primary and secondary prevention of breast cancer. Methods Random sampling （method of random digits table） 1 700 women in secondary cities of the west （including 1 020 outpatient cases and 680 physical examination cases） were routinely accept the questionnaire survey. Sixty-two patients were confirmed breast cancer with pathologically. Based on the X-image of the mammary gland patients and questionnaire survey to put mammographic density which classificated into high- and low-density groups. Therelationships between the mammographie density, age, body mass index （BMI）, family history of breast cancer, socioeconomic status （SES）, lifestyle, reproductive fertility situation, and breast cancer were analyzed, then a risk prediction model of breast cancer which fitting related risk factors was established. Results Univariate analysis showed that risk factors for breast cancer were age （P=0.006）, BMI （P=0. 007）, age at menarche （P=0.039）, occupation （P=0.001）, domicile place （P=0.000）, educational level （P=-0. 001）, health status compared to the previous year （P=0.046）, age at first birth （P=-0.014）, whether menopause （P=-0. 003）, and age at menopause （P=0.006）. The unconditional logistic regression analysis showed that the significant risk factors were age （P=-0. 003）, age at first birth （P=0.000）, occupation （P=-0.010）, and domicile place （P=0. 000）, and the protective factor was age at menarche （P=0.000）. The initially established risk prediction model in the region which fitting related risk factors was y=-5.557＋0. 042x1-0.375x2＋1.206x3＋0.509x4＋ 2.135x5. The fitting coefficient （R square）=0. 170, it could reflect 17% of the actual situation. Conclusions The breast cancer risk prediction model which established by using related risk factors analysis and epidemiological investigation could guide the future clinical work, but there is still need the validation studies of large populations for the model.