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上海市低出生体重新生儿的风险预测 被引量:1

Risk prediction of low birth weight infants in Shanghai
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摘要 【目的】了解上海市早产和低出生体重的发生率变化趋势,探讨相关的生育行为危险因素并建立足月低出生体重的风险评估列线图。【方法】收集2010—2020年上海市出生登记数据,应用方差分析和χ^(2)检验,比较不同时段生育因素和儿童健康状况差异。根据是否早产或是否低出生体重,将新生儿分为4组,采用多分类logistic回归模型,估计在不同生育因素的影响下,早产或低出生体重组的发生风险,并计算OR值和95%CI。依据logistic模型建立足月低出生体重的风险评估列线图,用ROC曲线下面积评估模型效果。【结果】纳入的2089384名单胎新生儿中,足月低出生体重、早产正常体重和早产低出生体重的发生率分别为0.94%、2.48%和2.01%。2010—2020年期间,有40.00%的生育女性有过流产史,生育年龄≥40岁的产妇占比从1.05%上升至2.24%,父亲年龄≥40岁的占比从4.79%上升至7.48%,研究生及以上学历的产妇占比从4.81%上升至11.74%,早产低出生体重和足月低出生体重的发生风险均随时间呈上升趋势。多因素logistic回归分析显示,女婴发生早产低出生体重的风险低于男婴(OR=0.97,95%CI:0.95~0.98),发生足月产低出生体重的风险则高于男婴(OR=1.85,95%CI:1.80~1.90)。育龄夫妇学历越高,发生早产、低出生体重的风险越低。母亲生育年龄>30岁、父亲年龄>35岁、流产2次以上,新生儿发生早产低出生体重的风险呈上升趋势。母亲生育年龄<25岁或>35岁、父亲年龄>30岁、流产3次以上,足月新生儿的低出生体重风险增高。【结论】育龄夫妇选择过高或过低的生育年龄,均有可能增加早产、低出生体重的风险,须加强人群宣传,提倡适宜年龄生育。多次流产也与早产、低出生体重密切相关,宜普及避孕节育科学知识,降低不必要的人工流产。研究建立的风险评估列线图可直观展现不同因素水平下育龄夫妇发生足月低出生体重儿的风险,可用于备孕夫妇决策生育时机,了解风险水平。 [Objective]To investigate the risk factors of fertility behaviors with preterm birth and low birth weight,and to develop a nomogram model to predict the occurrence of low birth weight.[Methods]Birth registration information in Shanghai from 2010 to 2020 was collected,and ANOVA and Chi-square tests were used to compare the differences in reproductive behavior factors and newborn health status across time.The odds ratio(OR)value and 95%CI were calculated by a multi-classification logistic regression model to determine the association between reproductive behavior factors and preterm birth or low birth weight infants.A nomogram model was established based on logistic model and the area under the ROC curve was used to assess the effect of the model.[Results]This analysis included 2089384 live newborns.The incidence of full-term low birth weight,preterm normal weight and preterm low birth weight in Shanghai was 0.94%,2.48%and 2.01%,respectively.From 2010 to 2020,40.00%women had a history of abortion,the proportion of women who gave birth at age≥40 years old increased from 1.05%to 2.24%,the proportion of fathers aged≥40 years increased from 4.79%to 7.48%,and the proportion of women with postgraduate or above increased from 4.81%to 11.74%.The incidence of preterm low birth weight in Shanghai showed an increasing trend over time.Logistic regression analysis showed that the risk of preterm low birth weight was lower in female than in male infants(OR=0.97,95%CI:0.95‒0.98),and the risk of full-term low birth weight was higher than in male infants(OR=1.85,95%CI:1.80‒1.90).The risk of preterm birth and low birth weight was lower for couples of childbearing age with higher education.The risk of preterm low birth weight in newborns tended to increase with maternal age at childbirth>30 years,paternal age≥40 years,and the number of abortions>2 times.Mother<25 or>35 years,father aged 30‒34 years,and the number of abortions>3 times were the risk factors of full-term low birth weight infants.[Conclusion]Couples of childbearing age who choose to have children at too high or too low age may increase the risk of preterm birth or low birth weight,so it is necessary to strengthen population awareness and promote age-appropriate childbirth.Multiple abortions are also associated with preterm birth and low birth weight,and it is advisable to popularize the scientific knowledge of contraception and birth control to reduce unnecessary abortions.The nomogram in the study can visualize the risk of full-term and low birth weight infant at different levels of factors,which can assist couples preparing for pregnancy in making decisions about the timing of childbirth and understanding the level of risk.
作者 祝雅婷 虞慧婷 王春芳 王伟炳 付晨 ZHU Yating;YU Huiting;WANG Chunfang;WANG Weibing;FU Chen(School of Public Health,Fudan University,Shanghai 200032,China;Shanghai Municipal Center for Disease Control and Prevention,Shanghai 200336,China)
出处 《上海预防医学》 CAS 2023年第6期564-572,共9页 Shanghai Journal of Preventive Medicine
基金 国家自然科学基金(82003486) 上海市第五轮公共卫生体系建设三年行动计划重点学科建设项目(GWV-10.1-XK08)。
关键词 生育特征 早产 低出生体重 LOGISTIC回归 列线图模型 reproductive characteristics premature birth low birth weight logistics regression nomogram model
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