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社区层面低出生体重的预测因素。

Predictors of low birthweight at the community level.

作者信息

Ngare D K, Neumann C

机构信息

Department of Behavioural Sciences, Moi University, Eldoret, Kenya.

出版信息

East Afr Med J. 1998 May;75(5):296-9.

PMID:9747002
Abstract

The outcome of pregnancy was studied in 148 women over a two year period in a rural area of Kenya as part of a prospective longitudinal study whose main objective was to study the functional effects of mild to moderate malnutrition. Data were collected on maternal anthropometric variables monthly, haemoglobin levels were determined by blood samples taken every six months, food intake was based on two days each month of actual weight and recall. Each woman's past reproductive history was established at the beginning of the study. Birth weight was taken and recorded within seventy two hours of delivery. Discriminant analysis was used to identify predictors of low birthweight. The analysis was based on 123 cases who had complete data on all the variables used in the equation. Of those included in the analysis, 14 women (11%) delivered low birthweight babies and 109 had normal birthweight babies. Results of the discriminant analysis showed that mid upper arm circumference (MUAC), body mass index (BMI), Blood haemoglobin levels (HB) and socioeconomic status (SES), are the best predictors of low birthweight. Ranked in order of relative contribution to birthweight they are BMI, HB, MUAC and SES. Low birthweight prevalence was determined as being 11.2 per cent. Eighty per cent of all known cases were correctly classified using the four variables. As a screening tool for low birthweight this model with four variables has 93% sensitivity, 78.4% specificity, 35.13% positive predictive value and 98.98% negative predictive value. The results suggest that it is possible to identify women at high risk for delivering low birthweight babies at the community level.

摘要

作为一项前瞻性纵向研究的一部分,在肯尼亚农村地区对148名妇女进行了为期两年的妊娠结局研究,该研究的主要目的是研究轻度至中度营养不良的功能影响。每月收集孕产妇人体测量变量数据,每六个月采集血样测定血红蛋白水平,食物摄入量基于每月两天的实际称重和回忆记录。在研究开始时确定了每位妇女过去的生育史。在分娩后72小时内测量并记录出生体重。采用判别分析来确定低出生体重的预测因素。该分析基于123例在方程中使用的所有变量都有完整数据的病例。在纳入分析的病例中,14名妇女(11%)分娩出低出生体重儿,109名有正常出生体重儿。判别分析结果表明,上臂中部周长(MUAC)、体重指数(BMI)、血血红蛋白水平(HB)和社会经济地位(SES)是低出生体重的最佳预测因素。按对出生体重的相对贡献顺序排列,它们依次为BMI、HB、MUAC和SES。低出生体重患病率确定为11.2%。使用这四个变量对所有已知病例的80%进行了正确分类。作为低出生体重的筛查工具,这个包含四个变量的模型具有93%的灵敏度、78.4%的特异度、35.13%的阳性预测值和98.98%的阴性预测值。结果表明,在社区层面有可能识别出分娩低出生体重儿风险较高的妇女。

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