Indian Pediatr Suppl 2009;46: S59-S62
Predictors of Birthweight A Cross
CJ Anitha, MKC Nair, K Rajamohanan, SM Nair, KT Shenoy
and M Narendranathan
From Clinical Epidemiology Research and Training Centre
(CERTC), Medical College, Thiruvananthapuram, Kerala, India.
Correspondence to: Dr Anitha CJ, Department of Pediatrics
and Neonatology, Singleton Hospital,
University of Wales, UK.
This study was conducted to find out the
anthropometric, biosocial and obstetric predictors of birthweight in
Kerala. The study sample consisted of 599 consecutive liveborn babies
delivered at SAT Hospital, Medical College, Thiruvananthapuram and their
mothers in November 2001. Details of maternal history, anthropometry,
and biosocial and demographic factors were recorded. Birthweight was
primary outcome variable. Multivariate analysis revealed that the
biologically acceptable predictors of birthweight of a baby in our
population are maternal height (P<0.001), parity (P<0.001)
gestational age (P<0.001), pregnancy induced hypertension(P=0.05)
and history of low birthweight in the previous pregnancy(P=0.05).
Keywords: India, Low birthweight, maternal height.
The weight of an infant at birth is an
important indicator of health and nutrition prior to and during pregnancy
and a powerful predictor of infant survival, growth and development. Low
birthweight (LBW) babies (<2500 g) contribute to majority (65-70%) of the
neonatal mortality(1,2). It has also been implicated in the childhood
onset of adult diseases including type II diabetes mellitus, hypertension,
coronary heart disease, chronic respiratory disease and even some forms of
cancer-Barker hypothesis(3). Thus, it is important to determine predictors
of low birthweight to facilitate appropriate preventive measures and
Birthweight is said to be determined by
sociodemographic, biological, genetic, fetal and obstetric factors. A
study from India, showed that birthweight is linearly correlated with
maternal height and pre-pregnant weight(4). Another study demonstrated
that socioeconomic status, anemia, antenatal care and pre-pregnant height
and weight have the highest population attributable risk for low birth
In those parts of the country where, provision of
adequate natal care and antenatal care including prevention of anemia have
already been taken care of, the next priority would be community
intervention programs for adolescent future mothers to improve the
pre-pregnant height and weight, which is now feasible under the National
Rural Health Mission (NRHM). This would necessitate local data on
modifiable predictors of birthweight. We conducted this study to find out
the anthropometric, biosocial and obstetric predictors of birthweight in
Kerala, with the specific hypothesis that maternal height is a predictor
of birthweight in our population.
The study sample consisted of 599 consecutive live born
babies delivered in one month at SAT Hospital, Medical College,
Thiruvananthapuram and their mothers. Those who died in the labor room or
were born with obvious external congenital anomalies or had clinical
evidence of intrauterine infections, were excluded. The main tool used was
a pre-piloted structured questionnaire probing specific details of
biosocial, demographic and obstetric factors, developed after discussions
with colleagues from obstetrics, pediatrics and social sciences faculty.
Clinical interview of each mother was done separately, 24-72 hours
post-partum in the postnatal ward. Computed gestational age was expressed
in completed weeks. Maternal height was taken using an anthropometric rod
calibrated every day and maternal weight was taken using a single balance
calibrated every day. Pre-pregnancy weight was calculated from the present
weight, using the method referred to by Morse, et al.(6).
Weight of the baby was taken soon after delivery using
an electronic weighing machine standardized to the nearest 5 g and
calibrated daily for accuracy. Birthweight (as a continuous variable) was
considered as the primary outcome measure. Simple linear regression was
employed for univariate analysis. Multiple linear regression was used
taking birthweight as the dependant variable and maternal height and other
parameters as the independent variables. P<0.05 was considered
Complete data of 599 mother infant pairs who entered
into the study were available for analysis. Of these, there were 325 boys
and 264 girls. There were 466 full term appropriate for gestational age
(AGA) babies, 89 babies with term intrauterine growth retardation (IUGR)
and 44 preterm babies below 37 week gestation. Weight ranged from 950 g to
4150g [mean (SD); 2783.4 (492.37) g]. Both the birthweight and maternal
height were normally distributed.
Simple linear regression taking birthweight as the
dependant variable and each of the study variables as the independent
variables revealed that maternal height, weight, BMI, gestational age in
weeks, parity, history of low birthweight or infertility, pregnancy
induced hypertension, and antepartum hemorrhage contributed significantly
to the birthweight. Sex of the baby, education of mother and father, per
capita income, passive smoking, previous abortion, antenatal care,
maternal age and anemia were not found to be related to birthweight on
univariate analysis. Variables found significant by simple linear
regression on univariate analysis were entered into the multiple linear
regression analysis model in step-wise manner. The final model for the
prediction of birth weight consisted of 5 variables; height of the mother
(P<0.001), gestational age in weeks (P<0.001), parity (P<0.001),
presence of pregnancy induced hypertension (P<0.005) and history of
low birthweight (P<0.01) (Table I).
Predictors of Birthweight on Multivariate Analysis
|Pregnancy induced hypertension
|History of LBW
The model shows that an increase in maternal height of
1 cm contributes to an increase of 13g in birthweight. The prolongation of
1 week of gestation was associated with an increase of 142g in
birthweight. Nullipara were likely to have a baby with a birth weight 148g
less, compared to multiparous women. History of pregnancy induced
hypertension was associated with a decrease of 94g in the birthweight and
the history of low birthweight baby in the past was associated with a
decrease of 169g in birth weight. The final model had R2
of 0.342 (adjusted 0.336) meaning that 34% of the variance in birthweight
is explained by these predictors.
We also compared independent variables by individual
t-statistic, which indicated the relative magnitude of unique
contribution of each variable to the overall variability in birthweight.
In our model, gestational age in weeks is the largest contributor to the
explained variation in birthweight.
The study has shown that biologically acceptable
predictors of birthweight of a baby operating in our population are;
maternal height (anthropometric variable), parity (biosocial variable),
gestational age, pregnancy induced hypertension, and history of low
birthweight in the previous pregnancy (obstetric variables). Maternal
height as a significant predictor of birthweight has been well supported
in the literature from all over the world as well as from community
studies in India(4,5). Alam DS, et al.(7) from rural Bangladesh
have also studied the influence of maternal anthropometry on birthweight
and concluded that it had a positive association with the maternal height.
Though height of the mother is ultimately influenced by genetic factors,
it can be modified during the pre-adolescent and adolescent periods of
rapid growth spurt.
We have included in the study all variables known to be
biologically related to the outcome but we have not been able to include
the variables like physical activity and psychological support during
pregnancy due to logistic reasons. Perceived prenatal social support have
been reported as a predictor of infant birthweight(8). In the final model
we have included only height, gestational age, parity, pregnancy induced
hypertension and history of LBW in the previous pregnancy, as this model
was found to be the best (R2=0.342)
for using maternal height as a predictor for birthweight. No additional
advantage was obtained by including maternal weight and BMI, which were
found to be independently significant in univariate analysis.
In a prospective hospital based study Nahum, et al.(9)
reported that the significant predictors of birthweight are maternal
height, gestational age, parity, third trimester maternal weight gain rate
and fetal gender. These prospectively measurable variables explained 33%
of the variance in the birthweight and predicted birthweight to within
10.8%. The other important variable found signi-ficant in our study was
gestational age in weeks. It is very well known that the birthweight is a
product of gestational age and intrauterine growth.
Dr Noel Narayanan, Dr Valsama Chacko, Leena ML, and
Contributors: MKCN was involved in designing the
study and preparation of the manuscript and will act as guarantor. CJA was
involved in designing the study and data collection, KR and SMN were
involved in analysis of data and KTS and MN supervised the data
Competing interests: None stated. The findings of
the trial are those of the authors and do not necessarily represent the
views of the funding agency.
What This Study Adds?
Predictors of birthweight in Kerala include
maternal height, parity, gestational age, pregnancy induced
hypertension, and history of low birthweight in the previous
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