Hadush Marta Yemane, Berhe Amanuel Hadgu, Medhanyie Araya Abrha
School of Medicine, Mekelle Univesity, College of Health Sciences, Mekelle, Ethiopia.
School of Public Health, Mekelle University, College of Health Sciences, Mekelle, Ethiopia.
BMC Pediatr. 2017 Apr 21;17(1):111. doi: 10.1186/s12887-017-0866-0.
Low birth weight (Birth weight < 2500 g) is a leading cause of prenatal and neonatal deaths. The early identification of Low birth weight (LBW) neonates is essential for any comprehensive initiative to improve their chance of survival. However, a large proportion of births in developing countries take place at home and birth weight statistics are not available. Therefore, there is a need to develop simple, inexpensive and practical methods to identify low birth weight (LBW) neonates soon after birth.
This is a hospital based cross sectional study. Four hundred twenty two (422) live born neonates were included and anthropometric measurements were carried out within 24 h of birth by three trained nurses. Birth weight was measured by digital scale. Head and chest circumference were measured by using non extendable measuring tape and foot length with hard transparent plastic ruler. Data was entered into SPSS version 20 for analysis. Characteristics of study participants were analyzed using descriptive statistics such as frequency and percentage for categorical data and mean and standard deviation for continuous data. Correlation with birth weight using Pearson's correlation coefficient and linear regression were used to identify the association between dependent and independent variables. Receiver operating characteristic (ROC) curve was used to evaluate accuracy of the anthropometric measurements to predict LBW.
The prevalence of low birth weight was found to be 27%. All anthropometric measurements had a positive correlation with birth weight, chest circumference attaining the highest correlation with birth weight (r = 0.85) and foot length had the weakest correlation (r = 0.74). Head circumference had the highest predictive value for birth weight (AUC = 0.93) followed by Chest circumference (AUC = 0.91). A cut off point of chest circumference 30.15 cm had 84.2% sensitivity, 85.4% specificity and diagnostic accuracy (P < 0.001). A cut off point of head circumference 33.25 had the highest positive predictive value (77%).
Chest circumference and head circumference were found to be better surrogate measurements to identify low birth weight neonates.
低出生体重（出生体重<2500克）是产前和新生儿死亡的主要原因。早期识别低出生体重（LBW）新生儿对于任何提高其生存几率的综合举措至关重要。然而，发展中国家很大一部分分娩是在家中进行的，且没有出生体重统计数据。因此，需要开发简单、廉价且实用的方法，以便在出生后不久就能识别出低出生体重（LBW）新生儿。
这是一项基于医院的横断面研究。纳入了422名活产新生儿，由三名经过培训的护士在出生后24小时内进行人体测量。出生体重用数字秤测量。头围和胸围用不可伸展的卷尺测量，足长用硬透明塑料尺测量。数据录入SPSS 20版进行分析。使用描述性统计分析研究参与者的特征，分类数据用频率和百分比表示，连续数据用均值和标准差表示。使用Pearson相关系数和线性回归分析与出生体重的相关性，以确定因变量和自变量之间的关联。受试者工作特征（ROC）曲线用于评估人体测量指标预测低出生体重的准确性。
发现低出生体重的患病率为27%。所有人体测量指标与出生体重均呈正相关，胸围与出生体重的相关性最高（r = 0.85），足长的相关性最弱（r = 0.74）。头围对出生体重的预测价值最高（AUC = 0.93），其次是胸围（AUC = 0.91）。胸围截断点为30.15厘米时，敏感性为84.2%，特异性为85.4%，诊断准确性（P < 0.001）。头围截断点为33.25时，阳性预测值最高（77%）。
发现胸围和头围是识别低出生体重新生儿更好的替代测量指标。
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