OBJECTIVE: Since 1986, the reference of birth weight for gestational age has not been updated. The aim of this study was to set up Chinese neonatal network to investigate the current situation of birth weight in China, especially preterm birth weight, to develop the new reference for birth weight for gestational age and birth weight curve. METHOD: A nationwide neonatology network was established in China. This survey was carried out in 63 hospitals of 23 provinces, municipalities and autonomous regions. We continuously collected the information of live births in participating hospitals during the study period of 2011-2014. Data describing birth weight and gestational age were collected prospectively. Newborn's birth weight was measured by electronic scale within 2 hours after birth when baby was undressed. The evaluation of gestational age was based on the combination of mother's last menstrual period, ultrasound in first trimester and gestational age estimation by gestational age scoring system. STATISTICAL ANALYSIS: the growth curve was drawn by using LMSP method, which was conducted in GAMLSS 1.9-4 software package in R software 2.11.1. RESULT: A total of 159 334 newborn infants were enrolled in this study. There were 84 447 male and 74 907 female. The mean birth weight was (3 232 ± 555) g, the mean birth weight of male newborn was (3 271 ± 576) g, the mean weight of female newborn was (3 188 ± 528) g. The test of the variables' distribution suggested that the distribution of gestational age and birth weight did not fit the normal distribution, the optimal distribution for them was BCT distribution. The Q-Q plot test and worm plot test suggested that this curve fitted the distribution optimally. The male and female neonatal birth weight curve was developed using the same method. CONCLUSION: Using GAMLSS method to establish nationwide neonatal birth weight curve, and the first time to update the birth weight reference in recent 28 years.
OBJECTIVE: Since 1986, the reference of birth weight for gestational age has not been updated. The aim of this study was to set up Chinese neonatal network to investigate the current situation of birth weight in China, especially preterm birth weight, to develop the new reference for birth weight for gestational age and birth weight curve. METHOD: A nationwide neonatology network was established in China. This survey was carried out in 63 hospitals of 23 provinces, municipalities and autonomous regions. We continuously collected the information of live births in participating hospitals during the study period of 2011-2014. Data describing birth weight and gestational age were collected prospectively. Newborn's birth weight was measured by electronic scale within 2 hours after birth when baby was undressed. The evaluation of gestational age was based on the combination of mother's last menstrual period, ultrasound in first trimester and gestational age estimation by gestational age scoring system. STATISTICAL ANALYSIS: the growth curve was drawn by using LMSP method, which was conducted in GAMLSS 1.9-4 software package in R software 2.11.1. RESULT: A total of 159 334 newborn infants were enrolled in this study. There were 84 447 male and 74 907 female. The mean birth weight was (3 232 ± 555) g, the mean birth weight of male newborn was (3 271 ± 576) g, the mean weight of female newborn was (3 188 ± 528) g. The test of the variables' distribution suggested that the distribution of gestational age and birth weight did not fit the normal distribution, the optimal distribution for them was BCT distribution. The Q-Q plot test and worm plot test suggested that this curve fitted the distribution optimally. The male and female neonatal birth weight curve was developed using the same method. CONCLUSION: Using GAMLSS method to establish nationwide neonatal birth weight curve, and the first time to update the birth weight reference in recent 28 years.
Authors: Chang Chen; Jin Wen Zhang; Hong Wei Xia; Hui Xin Zhang; Ana Pilar Betran; Lin Zhang; Xiao Lin Hua; Li Ping Feng; Dan Chen; Kang Sun; Chun Ming Guo; Hong Bo Qi; Tao Duan; Jun Zhang Journal: Am J Public Health Date: 2019-09-19 Impact factor: 9.308