中国妇幼健康研究2012，Vol.23Issue(5)：557-559,595,4.DOI:10.3969/j.issn.1673-5293.2012.05.001
早产危险因素的Logistic回归及分类树分析
Logistic regression analysis and classification tree analysis of risk factors for preterm birth
摘要
Abstract
Objective To explore the risk factors of preterm birth in Beijing by using method of classification tree and Logistic regression model. Methods A 1:1 case-control study was performed in five maternal and child health hospitals in Beijing. Data of 1 323 preterm birth and 1 323 controls was analyzed by classification tree and Logistic regression analysis. Results Logistic regression model showed that having a balanced diet ( OR = 0. 509 ), taking prenatal care ( OR = 0. 233 ) and living in towns and cities ( OR = 0. 555 ) were the protective factors of preterm birth, while less education ( OR = 1. 674 ), negative life events ( OR = 6. 086 ), sexual activity ( OR = 1. 704 ), placenta previa ( OR = 11. 834 ), gestational diabetes mellitus ( OR = 3. 170 ), hypertensive disorder complicating pregnancy ( OR = 5. 024 ), history of preterm birth ( OR = 17. 574 ) and premature rupture of membrane ( PROM ) ( OR =4. 083 ) were risk factors. Five factors were selected by classification tree set up with chi-square automatic interaction detection ( CHAID ), and PROM was the most important factor, including hypertensive disorder complicating pregnancy, non-balanced diet, without prenatal care and less education. Conclusion Regular prenatal care should be taken to detect high-risk pregnancy early. Pregnant women should avoid various bad stimulations, keep good mood and prevent PROM actively. The Logistic regression model can provide quantitative interpretation of impact of variables, and the classification tree model can show the complex interactions among variables. The two data processing methods can be combined for epidemiological study.关键词
早产/危险因素/分类树模型/Logistic回归Key words
preterm birth/risk factor/classification tree model/Logistic regression分类
医药卫生引用本文复制引用
高素红,邢艳梅,赵金凤,赵书燕,张运平,刘晓红,王佳楣,顾岳山,张久越,周霞,李庆霞,张欣荣..早产危险因素的Logistic回归及分类树分析[J].中国妇幼健康研究,2012,23(5):557-559,595,4.基金项目
联合国儿童基金会资助项目(YH601-11-8) （YH601-11-8）
北京市科委资助项目(Z090507017709014) （Z090507017709014）
