|Year : 2015 | Volume
| Issue : 2 | Page : 239-243
Study of maternal determinants influencing birth weight of newborn
Naziya Noor, Moolraj Kural, Tulika Joshi, Deepa Pandit, Anjali Patil
Department of Obstetrics and Gynaecology, Index Medical College and Research Centre, Indore, Madhya Pradesh, India
|Date of Web Publication||16-Dec-2015|
Department of Obstetrics and Gynaecology, Index Medical College and Research Centre, Indore, Madhya Pradesh
Source of Support: None, Conflict of Interest: None
Background: Birth weight is an important indicator of a child's vulnerability to the risk of childhood illness and chances of survival. The identification of factors contributing to low birth weight (LBW) is therefore of paramount importance. Objective: The objective was to investigate effects of maternal factors on birth weight of the baby. Materials and Methods: In a cross-sectional hospital-based study, data collection was done on 100 women for their age at the time of delivery, gestational age (GA), parity, height, weight, hemoglobin, blood sugar, blood pressure, sex of the delivered child, and weight of the child during the period from June to August 2014. Descriptive statistics, bivariate analysis, receiver operative characteristic (ROC) curve, and logistic regression analysis were used. Results: The proportion of LBW (<2500 g) was found to be 36.8% in the infants. Anemia was seen in 67% of the women. Bivariate analysis showed that GA (r = 0.68) was significantly associated with LBW (P < 0.05). ROC analysis revealed sensitivity (86% and 81%) and specificity (60% and 64%) for GA and parity, respectively. Furthermore, GA <37 and parity <2 found to increase risk of LBW by 8.9 and 4.5 times, respectively (P < 0.05). Maternal age, height, weight, and sex of the child had no statistical significant effect on determining the risk of LBW. Conclusion: GA and parity were found to be the important maternal parameters influencing the birth weight of the child.
Keywords: Low birth Weight, determinants, maternal risk factors
|How to cite this article:|
Noor N, Kural M, Joshi T, Pandit D, Patil A. Study of maternal determinants influencing birth weight of newborn
. Arch Med Health Sci 2015;3:239-43
|How to cite this URL:|
Noor N, Kural M, Joshi T, Pandit D, Patil A. Study of maternal determinants influencing birth weight of newborn
. Arch Med Health Sci [serial online] 2015 [cited 2019 Sep 16];3:239-43. Available from: http://www.amhsjournal.org/text.asp?2015/3/2/239/171912
| Introduction|| |
Birth weight is closely associated with the health and survival of the newborn. Low birth weight (LBW) (<2500 g) is the strongest determinant of infant morbidity and mortality in India. The World Health Organization estimated that 17% of the babies born worldwide are LBW, with marked differences between the incidence in developing (19%) and developed countries (7%).  As per national family health survey-3 (NFHS-3) report, proportions of LBW babies were slightly higher in rural (23%) than for urban population (19%) with regional disparity as low as 8% in Mizoram to 33% in Haryana and 23.4% in Madhya Pradesh (25% in Indore). 
Besides, biological factors such as gestational age (GA), maternal anthropometry, weight and height,  education,  parity of mother, sex of delivered child,  and lifestyle factors like dietary habits, tobacco or caffeine consumption  can also influence birth weight. Studies have also shown socioeconomic factors like maternal education and household income as important factors affecting birth weight.  Women with low education, poverty, and poor nutritional status are coexistent in rural part of India and therefore they are at increased risk of adverse reproductive outcomes including LBW and preterm birth.
It is generally assumed that prevention of LBW results in a corresponding reduction in perinatal mortality.  The identification of factors contributing to LBW is therefore of paramount importance. Although there is considerable work done on this topic in other areas in developing countries, the present work focuses on "kudel" block of Indore district, Madhya Pradesh. Therefore, the study intends to investigate the influence of parity, maternal anthropometry, and GA on the birth outcome of newborns.
| Materials and Methods|| |
Setting and design
The present cross-sectional study was undertaken at Index Medical College and Research Centre, Indore, Madhya Pradesh from June to August 2014. Data were collected for females residing in and around "kudel" block which comes under the rural area of Indore district, Madhya Pradesh. As the hospital is the only tertiary health care setup nearest to this block, the majority of the population used to visit this center. Therefore, the particular block has been selected for the study purpose. During this period, total of 100 women delivered in the hospital.
All these women were included in the study after obtaining written informed consent. The data on sociodemographic characteristics and maternal risk factors were collected using predesigned and pretested protocol. The sociodemographic variables were age at the time of delivery, economic status, and the maternal characteristics were height, weight, parity, hemoglobin (Hb), blood sugar, and blood pressure. A standard scale measured maternal anthropometry. Infant weight was measured with the salter scale (UNICEF). The GA was calculated from the last menstrual period in completed weeks of gestation. The outcome of pregnancy was recorded in terms of birth weight of the newborns.
In the present study of the total number recruited, only one baby was diagnosed as fetal growth restriction antenatally (small for date) however that has been excluded while doing analysis, so data have not been presented separately.
An analysis was performed using SPSS software for Windows (version 11.0, 2001, SPSS Inc., Chicago, IL, USA). All the variables were tested for normality by the Kolmogorov-Smirnov test prior to statistical comparisons. Receiver operating characteristics (ROCs) curves and logistic regression were used for the analyses. Adjusted odds ratios (ORs) were calculated in the regression of the outcome events. P < 0.05 was considered to be significant. ROCs curves were carried out to predict LBW, specificity and sensitivity tests were performed with the age of mother, parity and gestational weeks. In this context, sensitivity is the ability to detect an LBW baby while specificity is the ability to detect a normal birth weight baby.  A good predictor is one which has a high sensitivity and high specificity.
| Results|| |
Maternal characteristics such as age, weight and height, blood pressure, Hb, and blood sugar are presented in [Table 1]. Mean maternal age of mother at the time of delivery was 23.9 ± 4.0 years (range: 18-40 years). Around 17.5% women were below the age of 20 years and 5.1% were above 35 years. Mean height and weight was 152.9 ± 6.3 cm and 46.7 ± 8.2 kg, respectively. Nearly, 62% of the women were multiparous were as remaining 38% were primiparous.
Average Hb was observed to be 10.2 ± 1.8, majority of females (67%) were anemic, Hb <11 g/dl (Indian Council of Medical Research). Random blood sugar was seen to be 88.3 ± 13.5 g/dl; almost all of the females had normal sugar level. None of them were observed to be hyperglycemic. Blood pressure was normal in maximum number of females, except for 13% in whom blood pressure was on higher side [Table 1].
Gestational age ranged between 28 and 42 complete gestational weeks, 26.8% of infants were delivered before 37 completed gestational weeks (preterm infants). Birth weight of the newborns ranged with 0.85-4.2 kg. More than one-third (36.8%) of the babies were LBW (LBW <2.5 kg). Of the total newborn, 59% were males and 41% were females [Table 2].
Type of delivery was vaginal (normal delivery) in case of 52.1% of the women and in remaining 47.9% women operational deliveries (cesarian section) were performed.
Bivariate analysis showed a strong association between GA and baby weight (r = 0.684; P < 0.001) however, no such associations were observed with maternal age and anthropometry. Studies have shown that early adolescent pregnancies have resulted in LBW, therefore; despite no significance of maternal age with LBW, ROC analysis was performed to confirm the sensitivity and specificity in determining LBW.
Maternal anthropometry showed no significant relationship with baby weight. Regarding socioeconomic status, since the majority of the families belonged to a lower socioeconomic status, no associations were derived (data not presented).
The discriminative power of the maternal characteristics to estimate the risk for LBW was assessed by the area under the curve (AUC). As shown in [Table 3], only parity and GA had statistically significant discriminative ability to distinguish between normal and LBW infants (P < 0.05). Sensitivity and specificity of the defined cut-off points are shown in [Table 3]. Age of the mother did not show any significant relationship with the birth weight of the baby (P > 0.1). The underlying reason could be less number of females (17.5%) below the age of 20 years.
|Table 3: ROC analysis of maternal characteristics in the estimation of the risk for LBW|
Click here to view
On the contrary, apart from ROC analysis, results also highlight the fact that as parity increases the weight of the newborn was increasing. Across parity one, two, three, and four, the mean respective baby weights were (2.5 ± 0.1), (2.7 ± 0.07), (3.0 ± 0.1), and (3.2 ± 0.1) although significant increase was not observed.
Using the cut-off points from ROC analyses, the influence of maternal characteristics on birth weight was further investigated using logistic regression analysis by calculation of the OR for LBW. Obviously, if the measurements of maternal characteristics are below the cut-off point, there is a trend to increase the risk of LBW. Accordingly, preterm babies (GA <37) have 8.9 times (confidence interval [CI]: 2.7-20.8) (P < 0.05), higher risk of LBW as compared GA >37. If parity is <2 then 4.5 times higher chance (CI: 0.7-20.6) of LBW babies as against parity >2 (P < 0.05).
| Discussion|| |
In the present study of the 100 women, 36.8% delivered LBW babies, that is, baby weight <2500 g. This was very high in comparison with NFHS-3 data where the reported LBW was 23% in rural areas in India.  Other studies from Indian subcontinent also have documented almost similar percentage of LBW, 30.3% in Deshmukh et al. study,  Velankar  reported the incidence as high as 45.2%. Negi et al.  observed the incidence to be around 23.8% whereas; Trivedi and Mavalankar  and Kamaladoss et al.  reported 20.37% and 24.6% LBW, respectively, in their studies. Despite various efforts done to improve maternal and child health in our country, the prevalence of LBW is still on the higher side. The mean birth weight in the present study was 2.7 ± 0.6 kg which was on par with Solanki et al.  and low as compared to the study conducted by Negi et al. and Ramankutty et al. ,
The incidence of LBW was high in mothers of age 20 years or less as reported in various studies. , NFHS-3 also confirms that the proportion of births with a LBW is lesser among children born to older women (age at birth ≥20 years). These findings indicate prevention of teenage pregnancy to avoid LBW. However, in the present study, bivariate analysis did not show any significant relationship between the two. Though literature suggests association between maternal age and LBW, after applying multivariate analysis maternal age was found to be insignificant in some of the studies. 
Studies have reported a lower risk of LBW in case of males  however; the present study did not see any such difference. The incidence of LBW was largely similar in both males and females.
Maternal education is one of the important factors affecting baby weight. Majority of the women in the present study had studied not more than class fifth (data not presented), therefore, we could not establish any association between maternal education and LBW. The duration of maternal education was found to be insignificant with the risk for LBW in Solanki et al. study.  On the other hand, Karim and Mascie-Taylor  found that birth weight increases with higher maternal education.
Study results showed a strong association between GA and LBW, ROC analysis revealed 86% sensitivity and 60% specificity with AUC 0.78 for GA in determining the risk of LBW. Furthermore, logistic regression analysis confirmed that the risk of LBW to be 8 times higher if GA is <37 weeks. These findings are consistent with other studies reported by Negi et al. and Deshmukh et al. , In contrast to present study, Velankar,  found an insignificant association of preterm with the LBW.
The relationship between height of mother and birth weight was found to be insignificant in the present study which is on par with results reported by Amin et al.  On the contrary, Kramer  and Trivedi and Mavalankar  reported a significant association between maternal height and LBW. The relationship between gestational weight and LBW was also insignificant in this study and is in line with several other studies where no association between gestational weight and LBW was observed. ,
Nonetheless, women's anthropometry, health status, and birth order are one of the major factors affecting birth weight,  a large body of literature has shown that birth weight increases with birth order ,. Hirve and Ganatra  in India found 1.3 times higher relative risk for LBW in primipara and in Africa, Lawoyin et al.  found that first-born babies had a 3.1-fold higher mortality risk. In our study, primi-parity was associated with an increased relative risk for LBW with 4.5 times (P < 0.05). High sensitivity (81%) and specificity (64%) were also observed in predicting the risk of LBW.
Thus, overall results conclude that GA and parity were the major risk factors influencing the birth weight of the baby. With increase in parity, the birth weight of the baby improves, however that does not necessarily mean one needs to have more children. GA <37 weeks has significant more chances for LBW babies. There are numerous reasons for low GA such as lack of adequate nutrition, low body mass index, high blood pressure, maternal anthropometry and age, and anemia. Hence, the study implies that women need to be careful of all these above factors so as to avoid LBW babies.
The influence of the other maternal characteristics like anthropometry, blood pressure, maternal age, nutrition, infections, genetic makeup, twin pregnancies etc., remained insignificant may be because of small sample size.
| References|| |
Ferreirea A, Harikumar P. Maternal determinants of birth weight. Indian J Community Med 1991;16:106-9.
International Institute for Population Sciences. Macro International. National Family Health Survey (NFHS-3), 2005-06. vol. I. India: IIPS; 2007.
Voigt M, Heineck G, Hesse V. The relationship between maternal characteristics, birth weight and pre-term delivery: Evidence from Germany at the end of the 20 th
century. Econ Hum Biol 2004;2:265-80.
Nordentoft M, Lou HC, Hansen D, Nim J, Pryds O, Rubin P, et al.
Intrauterine growth retardation and premature delivery: The influence of maternal smoking and psychosocial factors. Am J Public Health 1996;86:347-54.
Wasunna A, Mohammed K. Low birthweight babies: Socio-demographic and obstetric characteristics of adolescent mothers at Kenyatta National Hospital, Nairobi. East Afr Med J 2002;79:543-6.
Report of Working Group on Anemia Prophylaxis Programme, Government of India, Ministry of Health; 1989.
Nahar S, Mascie-Taylor CG, Begum HA. Maternal anthropometry as a predictor of birth weight. Public Health Nutr 2007;10:965-70.
Deshmukh JS, Motghare DD, Zodpey SP, Wadhva SK. Low birth weight and associated maternal factors in an urban area. Indian Pediatr 1998;35:33-6.
Velankar DH. Maternal factors contributing to low birth weight babies in an urban slum community of greater Mumbai. Bombay Hosp J 2009;51:26-35.
Negi KS, Kandpal SD, Kukreti M. Epidemiological factors affecting low birth weight. JK Sci J Med Educ Res 2006;8:31-4.
Trivedi CR, Mavalankar DV. Epidemiology of low birth weight in Ahmedabad. Indian J Pediatr 1986;53:795-800.
Kamaladoss T, Abel R, Sampathkumar V. Epidemiological co-relates of low birth weight in rural Tamil Nadu. Indian J Pediatr 1992;59:299-304.
Solanki N, Kavishwar A, Chaudhari V, Chhasatiya N. The effect of maternal anthropometric characteristics and social factors on birth weight of child in small town hospital of dandevi block of Navsari district. Int J Med Sci Public Health 2012;1:32-7.
Ramankutty P, Tikreeti RA, Rasaam KW, Al-Thamery DM, Yacoub AA, Mahmood DA. A study on birth weight of Iraqi children. J Trop Pediatr 1983;29:5-10.
Jawarkar AK, Lokare PO, Dore S. Study of socio-demographic and maternal determinants influencing birth-weight. J MGIMS 2012;17:28-33.
Karim E, Mascie-Taylor CG. The association between birthweight, sociodemographic variables and maternal anthropometry in an urban sample from Dhaka, Bangladesh. Ann Hum Biol 1997;24:387-401.
Amin N, Abel R, Sampathkumar V. Maternal risk factors associated with low birth weight. Indian J Pediatr 1993;60:269-74.
Kramer MS. Determinants of low birth weight: Methodological assessment and meta-analysis. Bull World Health Organ 1987;65:663-737.
Anand K, Garg BS. A study of factors affecting low birth weight. Indian J Community Med 2000;25:57-61.
Celik Y, Younis MZ. Effects of antenatal care services on birthweight: importance of model specification and empirical procedure used in estimating the marginal productivity of health inputs. J Med Syst 2007;31:197-204.
Maruoka K, Yagi M, Akazawa K, Kinukawa N, Ueda K, Nose Y. Risk factors for low birthweight in Japanese infants. Acta Paediatr 1998;87:304-9.
Seidman DS, Ever-Hadani P, Stevenson DK, Slater PE, Harlap S, Gale R. Birth order and birth weight reexamined. Obstet Gynecol 1988;72:158-62.
Hirve SS, Ganatra BR. Determinants of low birth weight: A community based prospective cohort study. Indian Pediatr 1994;31:1221-5.
Lawoyin TO. Infant and maternal deaths in rural south west Nigeria: A prospective study. Afr J Med Med Sci 2007;36:235-41.
[Table 1], [Table 2], [Table 3]