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Maternal anthropometry as a predictor of birth weight

机译:产妇人体测量学可预测出生体重

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OBJECTIVE: To determine whether maternal anthropometry predicted birth weight, and if so, to identify which cut-offs provided the best prediction of low birth weight (LBW) in a field situation. DESIGN: Community-based longitudinal study. SETTING: A rural union of Bhaluka Upazila, Mymensingh, located 110 km north-west of Dhaka, the capital of Bangladesh. PARTICIPANTS: A total of 1104 normotensive, non-smoking pregnant women who attended community nutrition centres were studied from first presentation at the centre until delivery of their child. RESULTS: Most of the pregnant mothers were between 20 and 34 years of age. Over one-third of the women were nulliparous, while 12.8% were multiparous (parity >/= 4). Most (93%) mothers registered between the 3rd and 5th month of pregnancy. The frequency of LBW ( < 2500 g) was 17%. Polynomial regression analyses showed that the best predictors of birth weight (based on adjusted R2 values) were in general weight at registration and weight at month 9, with adjusted R2 ranging from 2.5% to nearly 20%. Sequential regression analyses with height and weight showed that there was a significant effect of height after removing the weight variables, and adjusted R2 increased in all analyses. Weight and height at registration month continued to be the best predictors of LBW. Sensitivity and specificity curves were drawn for each registration month, body mass index and different weight gain groups, and using different weight and height combinations. The results showed that, for registration month 3-5, a combination of weight (
机译:目的:确定产妇人体测量学是否可以预测出生体重,如果是,则确定在现场情况下哪个临界值可以最好地预测低出生体重(LBW)。设计:基于社区的纵向研究。地点:Mymensingh的Bhaluka Upazila乡村联盟,位于孟加拉国首都达卡西北110公里处。参加者:从首次在社区营养中心就诊到分娩之前,总共对1104名参加社区营养中心的血压正常,不吸烟的孕妇进行了研究。结果:大多数孕妇的年龄在20至34岁之间。超过三分之一的妇女是未产妇,而有12.8%的妇女是多胎的(胎次> / = 4)。大多数(93%)母亲在怀孕的第三个月到第五个月之间进行了登记。 LBW(<2500 g)的频率为17%。多项式回归分析显示,出生体重(基于调整后的R2值)的最佳预测指标一般是登记时的体重和第9个月的体重,调整后的R2范围从2.5%到近20%。对身高和体重进行的顺序回归分析表明,去除体重变量后身高有显着影响,并且所有分析中调整后的R2均增加。登记月份的体重和身高继续是LBW的最佳预测指标。绘制每个登记月份,体重指数和不同体重增加组的敏感性和特异性曲线,并使用不同的体重和身高组合。结果表明,在登记的第3-5个月,体重(

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