MATERNAL FACTORS ASSOCIATED WITH LOW BIRTH WEIGHT AMONG THE DELIVERIES IN A SIR SUNDER LAL HOSPITAL, BHU, VARANASI

Abstract

Abstract: Birth weight is very important indicator of the health and viability of a newborn infant. It is a significant factor of newborn growth and survival. Globally, it is estimated that there are 20 million of infants who are born with low birth weight, it depends on many maternal factors such as maternal age, gestations, antenatal care, education level, weight gain, parity, sex of child, and body mass index. Logistic regression is a statistical model for analyzing a dataset in which one or more independent variables that determine an outcome. The main objective of this paper is to identify the predictors of low birth weight through binary logistic regression model. Methods: A hospital based cross sectional study was conducted in Obstetrics and Gynecology postnatal ward of Sir Sunder Lal hospital, BHU, Varanasi from 14th June 2015 to 15 January 2017. Altogether 500 respondents were taken and respondents were Mother who had delivered the newborns in SSL hospital. A spring type weighing machine scale was used to measure the birth weight of babies and birth weight was taken after the birth within the 24 hours. Result: A total of 517 births occurred during the study period among which 39.07% were low birth weight and 60.93% were normal birth weight. Low birth weight neonates mean birth weight was found to be 1.93 kg and overall the mean in birth weight was 2.97 kg. Chi square test to find out the risk factor associated with the low birth weight which shows that maternal education, initial weight of mother, weight gain of mother, gestation, sex of child, pregnancy complication, body mass index, antenatal care are statistically significant with low birth weight. The fitted binary Logistic regression model shows that use of iron and calcium supplements has the highest odd ratio compared to the other factors. Conclusion: This study suggest that there were several factors finding to affect the birth weight which are education level, maternal weight, weight gain, body mass index, antenatal care, sex of neonates, pregnancy complication, gestation age and use of iron calcium supplements.