& objectives: Birth
weight is the most important factor that affects infant and child mortality.
The main objective of this study to learn the main factors contributing to LBW
of babies from mother.
Methods: This one year study was conducted in a
cohort of pregnant women to study the proportion of low birth weight babies and
to find the maternal factors affecting the birth weight of new-born’s.
Information regarding obstetric history, present pregnancy, socio demographic
variables, medical shortcoming as sugar and hypertension, heart rate and
respiratory rate was recorded for pregnant women. The data of babies for these
women was recorded for weight within 24 hours of delivery, age of baby when
heart rate and respiratory rate where recorded. The data of 95 women was
Results: The birth weight was available for 460births
with obstetric history of their mothers. Out of 460 births 56.95% births were C-section
remaining were vaginal. The overall prevalence of low birth weight was 32.61%
and preterm baby was 13.04%. The overall birth weight was found to be 2.60±0.42with
95% confidence interval of 2.56-2.64.
Conclusion: The results show that the obstetric
history and age of mother contribute to low birth weight in babies. Also it was
found that if women is hypertensive or has random blood sugar the birth weight
of baby can be low. The abortion is one covariate in the study which is factor
in low birth weight.
LBW, heart rate, preterm, obstetric history, HRV, LBW factors
approximately 20 million infants are born with low birth weight (LBW < 2500 g) annually 1. Approximately 95% of LBW infants are born in developing countries. The two main reasons for LBW are preterm birth (< 37 weeks) and intrauterine growth restriction (IUGR). Birth weight is the single most important criterion for determining the neonatal and infant survival. Low Birth Weight (LBW) is a sensitive indicator of the socio-economic conditions and indirectly measures the health of the mother and the child. Babies with a birth weight of less than 2500 g irrespective of the period of their gestation are termed as Low Birth Weight (LBW) babies 2. By international agreement, LBW has been defined as a birth weight of less than 2500 grams, with the measurement being taken preferably within the first hour of life, before significant postnatal weight loss has occurred 3. It contributes substantially to neonatal, infant, and childhood mortality and morbidity 4.Across the world, neonatal mortality is 20 times more likely for LBW babies compared to NBW babies (>2.5?kg) 5. It is now a
well-recognized fact that birth weight is not only a critical determinant of
child survival, growth, and development, but also a valuable indicator of
maternal health, nutrition, and quality of life 6.
In study conducted in 2016,it was concluded that unadjusted
prevalence of LBW in India was reported
21.5% and adjusted prevalence of low birth weight in India was obtained 27.1%.
Between 1990 and 2015, more children in age group
0-5 died in India than anywhere in the world. Despite a 62% reduction in child
mortality over these years, the number stands at 1.3 million every year. In
India, of all infants who died before they completed 29 days post-birth, 48.1%
suffered from LBW and premature birth, according to the Causes of Death
Statistics, 2010-13 report by the census office. This figure was 35.9% for
children under one year of age, and 29.8% for those in the 0-4 age group.
In 2013, as many as 22
million newborns–an estimated 16% of babies born globally–had
LBW, according to the UNICEF.In terms of regional variations, South
Asia had the highest incidence of LBW, with 28% newborns weighing less
than 2.5 kg. This region also had the highest percentage of infants (66%) not
weighed at birth. Sub-Saharan Africa’s incidence of LBW among newborns is
estimated to be 13%; and 54% newborns are not weighed at birth.At 28%, India
had the third highest percentage of LBW newborns, behind only Mauritania (35%),
Pakistan and Yemen (32% each). Except for Pakistan, India performed worse than
all its South Asian neighbours. UNICEF, however, has cautioned that the data
maybe inaccurate because of under-reporting.
There are an estimated 15 million preterm births across the world each
year, according to latest available data released by the WHO in 2012. Over 60% of preterm births occur in
Africa and South Asia. At 3.5 million, India accounted for the most
preterm births in the world, followed by China (1.17 million) and Nigeria (0.77
Material and Methods:
material included the chest lids for recording the clinical data using MP20
monitor (Philips). We used iXtrend software to record the data from the monitor
into computer for using data later for analysis. The database of patients was
maintained in excel with different variable tags. The data was tabulated
according to various maternal factors included in study. The data was analysed
using statistical software STATA 13.0 (MP) for windows. In order to test the
association chi-square and regression were applied. Approval was granted prior
to the commencement of data collection by team.Overall; the study population
included 460 women and newborn. The written consent was taken from the mother
of the newborn for collecting data of baby and the obstetric history of mother.All
babies were enrolled in study without any exclusion criteria. Currently,babies
are being followed up on phone calls and check-up is being scheduled. The
schedule was to record the information regarding mothers and the birth weight
and information of the infants born at hospital. The information was collected
in neonatal unit.
Study Design: The data was recorded for ANC patients, selected
randomly. The patients were observed for different clinical signs of
hypertension, blood sugar, Haemoglobin etc. There was no exclusion criteria the
patients parameter were recorded. The mothers whose data was collected the same
mothers child data was recorded. The mother information included: age, obstetric
history, period ofgestation, blood group, hypertension, eclampsia and other
indications. The child information included:sex, weight, gestational age, feed,
heart rate, respiratory rate, length, head circumference,Apgar score. The birth
weight was recorded within first 24 hours of the birth. The present study was
conducted in Department of paediatrics, Acharya Vinobha Bhave Rural Hospital,
Sawangi (M) Wardha from June 2015 to October 2016.The research reported in the
paper was approved by the Research Ethics Committee of Datta Meghe Institute of
mean birth weight was found to be 2.60±0.42 with 95% confidence interval of 2.56-2.64.
Out of total 460 newborns 150 i.e. 32.61% newborns were weighing less than 2.50 kg and 95%
CI for the prevalence of low birth weight 2.12-2.20. The population comprised
of 50.65% of males and 49.35% of females.
Table I Distribution of Birth
Birth Weight in gms
less than 1500
total number of singleton births at AVBRH in 2015 was 240 which are 52.17% of
all babies during that period. The number of multiple births is 33 i.e. 7.17%
of total babies.
Table II Distribution of
Multiple and Singleton Pregnancy
Table III Distribution of
Multiple And Singleton Births
Distribution of Birth weight against Mothers Age
Age in years (N)
Birth Weight in gms
<1500 1501-2000 2001-2499 2500 2500-3000 3001-3500 3501-4000 <2.5 >2.5
p<0.00 (0.009-0.032) 21-25 (276) 3 21 67 50 102 28 5 91 185 26-30 (128) 0 16 25 17 51 18 1 41 87 31-35 (25) 0 1 5 2 10 6 1 6 19 36-40 (6) 0 1 1 1 2 0 1 2 4 regress baby_weight mothers_age Scatter Plot for the regression line There were 150 LBW babies born at AVBRH in 2015, 10 babies (6.66% of LBW babies)were born from mothers of the age between 16- 20 years old, 91 babies (60.67% of LBWbabies) were born from mothers of 21-25years old, 41 babies (27.33% of LBW babies)were born from mothers of 26-30 years ofage, 6 babies (4%) from mothers of31-35 years old and 2 babies (1.33%) frommothers above 35 years of age.The large numbers of LBW babies were bornfrom mothers of 21-25 years of age. Table V Distribution of Baby Weight Against Obstetric History Birth Weight in gms Total Births Singleton Pregnancy (p=0.023) Multiple Pregnancy (p=0.003) Abortions (p=0.614) Still Births (p<0.00) Inference less than 1500 3 0 3 3 0 p<0.02 (0.006-0.88) p*<0.0002 (2.28-2.665) 1501-2000 43 16 27 14 18 2001-2499 104 43 61 8 4 2500 74 27 47 6 6 2501-3000 174 64 110 9 5 3001-3500 54 11 43 6 6 3501-4000 8 2 6 0 1 regress baby_weight obs_gravida regress baby_weight obs_gravida obs_primi obs_abortion obs_lives 14 mothers from 460 mothers suffered from hypertension. The mean birth weight of babies whose mother was suffering from hypertension is 2.77 with confidence interval of 2.48-3.07. 446 mothers were normal. The mean birth weigh of babies is 2.59 with confidence interval of 2.55-2.63. Discussion: LBW infants have a greater risk of life as they are likely to die during their infancy, especially in the neonatal period 8-9. Thus, birth weight has long been the subject of clinical and epidemiological investigations and a target for public health intervention. The main objective of study was to find the factors affecting low birth weight of babies due to mother. The incidence of LBW was low with high i.e. 32.61% as compared to incidence of LBW in 2013 i.e. 28%. 10. Existence of setup to monitor the birth weight in this region seems plausible explanation to monitor low birth weight. Female babies were having higher risk of LBW than male babies which is supported by the studies 11-12. In a number of previous studies, including the present one, it was observed that mothers age 11,13 and obstetric history10,14 were significantly associated with LBW. In this study it can be seen that LBW is highest in mothers with age between 21 to 25. This can be an area of research why the mother who is mature can result in LBW babies. The main reason for this can be the obstetric history of the mothers. They can have high abortions whicj may lead to low birth weight after. From the above discussion it is seen that mother age and obstetric history are important factors which lead to low birth weight in this region. Thus, there is an urgent need to carry out further such studies among other groups in central India considering their biosocial diversity, for understanding the problems of LBW at the population level which may help to formulate an effective maternal and child health care programme in this region. Acknowledgement: The present study was funded by GCC. I am indebt to DMIMS deemed university for providing me opportunity to link with research. I am also grateful to authority of Acharya Vinobha Bhave Rural Hospital for allowing us to collect data from their hospital. I would like to offer my sincere thanks to Mr. Roshan Umate, for the constant efforts and work in collecting clinical data and Dr. Anuraj Shankar for the innovative though that lead to this research.