摘要
目的了解天津市微信虚拟社区居民癌症防治核心知识知晓情况及影响因素,为今后肿瘤防治提供科学依据。方法于2017年11月17日18:00至2017年11月22日24:00,通过天津市疾病预防控制中心开发的“普及健康生活”公众号,发布匿名网络电子问卷对天津市18岁及以上常住居民进行调查.问卷内容包括一般人口学特征(性别、年龄、户口所在地、民族、文化程度和职业等)及《中国癌症防治三年行动计划(2015-2017年)》提出的癌症防治核心知识知晓率问卷中的核心知识问题:采用SPSS22.0软件进行x^2检验、趋势x^2检验、非条件logistic回归和分类树模型,采用通过受试者工作特征(ROC)曲线下面积对logistic回归模型和分类树模型进行预测概率的效果评价。结果本研究共回收微信虚拟社区天津市居民的有效问卷8290份,癌症防治核心知识知晓率为59.42%,不同性别、年龄、城乡、文化程度和职业调查对象的癌症防治核心知识知晓比例差异均有统计学意义(P<0.05)。多因素logistic回归模型结果显示,调整了其他因素后,女性、低年龄组、户口属于市区、文化程度高和职业为机关、事业单位是癌症防治知识知晓的有利因素,均有统计学意义(P<0.05)。分类树模型知晓分类正确百分比为90.9%.筛选出文化程度(重要性为0.043,标准化重要性为100%)、职业(重要性为0.020,标准化重要性为48.7%)、城乡(重要性为0.011,标准化重要性为24.7%)和年龄(重要性为0.001,标准化重要性为3.2%)为癌症防治知识知晓的重要影响因素,并存在交互作用。多因素非条件logistic回归模型预测概率的ROC曲线下面积为0.738,小于分类树模型预测概率的ROC曲线下面积(0.748),差异有统计学意义(P<0.05)。结论天津市微信虚拟社区居民癌症防治知识的知晓情况不平衡,文化程度、职业、城乡和年龄等因素独立或交互影响人群对癌症防治知识的知晓
Objective To understand the current status of awareness of cancer prevention knowledge and risk factors in WeChat virtual community in Tianjin,and to provide the scientific basis for preventing and treating cancers. Methods From 18:00 of17 th in November of 2017 to 24:00 of 22 th in November of 2017,the online questionnaire investigation for adult residents(≥18 years old) was established in WeChat virtual community based on WeChat official platform. The questionnaire included the general demographic information(gender,age,registered permanent address,nationality,education level and occupation) and the core knowledge of cancer prevention and control proposed by 《Three-Year Action Plan for Cancer Prevention and Control in China(2015-2017)》. The used software was SPSS 22.0. χ^2 test,non-conditional logistic regression and classification tree models were used to analyze the data. The prediction of probability of logistic regression model and classification tree model were evaluated by the area under receiver operating characteristic curve(ROC). Results The awareness rate of cancer prevention knowledge was59.42%. The awareness proportions in different gender,age,urban-rural areas,education level and occupation were significantly different(P <0.05). Multivariate logistic regression analysis showed that after adjusting other factors,female,lower age group,urban registration,the higher education level,occupation(government organization and institution) may be protective factors for the awareness of cancer prevention knowledge,P<0.05. The correct percentage of classification tree model awareness classification was 90.9%,the education level(importance:0.043,standardized importance:100%),occupation(importance:0.020,standardized importance:48.7%),urban-rural areas(importance:0.011,standardized importance:24.7%) and age(importance:0.001,standardized importance:3.2%) were important influencing factors of knowledge of cancer prevention and treatment with interaction effect. The areas under the ROC curve of the prediction
作者
王德征
王冲
张爽
沈文达
潘怡
沈成凤
罗莎
马洁
王卓
李昌昆
郑文龙
江国虹
WANG De-zheng;WANG Chong;ZHANG Shuang;SHEN Wen-da;PAN Yi;SHEN Cheng-feng;LUO Sha;MA Jie;WANG Zhuo;LI Chang-kun;ZHENG Wen-long;JIANG Guo-hong(Tianjin Centers for Diseases Control and Prevention,Tianjin 300011,China)
出处
《中国慢性病预防与控制》
CAS
北大核心
2018年第12期910-915,共6页
Chinese Journal of Prevention and Control of Chronic Diseases
基金
大气重污染成因与治理攻关项目(DQGG0404)
关键词
虚拟社区
癌症防治
知晓率
影响因素
分类树
Virtual community
Cancer prevention and treatment
Awareness rate
Influence factors
Classification tree
