Neonatal Foot Length: An Alternative Predictor of Low Birth Weight Babies in Rural India-Juniper Publishers
Juniper Publishers-Journal of Pediatrics
Abstract
Background: Birth weight is an
important parameter and a determinant factor regarding perinatal
morbidity and mortality. However in rural area of developing countries,
weighing facility may not be available for all home deliveries, where an
alternative parameter like foot length may be considered in place of
birth weight.
Objective: The present study was
undertaken to find out the best simple anthropometric parameter for
identifying low birth weight (LBW) babies.
Study design: Hospital-based cross-sectional study.
Participants: Newborn babies born in AVBRH hospital, Sawangi (Meghe), Wardha.
Methods: All Consecutive
full-term, Single ton, live born babies were included and anthropometric
measurements carried out within 48 hours after birth.
Results: Out of 520 newborn
babies, there were 267 male and 253 female babies. Foot length (FL)
attained the highest correlation with birth weight (r = 0.715) while mid
arm circumference (MAC) attained the lowest (r = 0.355). FL had the
highest coefficient of determination (r2 value= 0.511). Receiver
operating curve (ROC) analysis was done to identify the optimal cut-off
points of these anthropometric measures separately for LBW babies. The
best discrimination of LBW, as detected by Area under curve (AUC), was
obtained by FL (AUC = 0.909, 95% CI 0.0133- 0.93538) followed by length
(AUC = 0.89, 95% CI 0.87642-0.92969). Length of 49cm, head circumference
(HC) of 33cm, MAC of 9.5cm, and chest circumference (CC) of 30cm and FL
of 8cm were the corresponding cut-off values with the best combination
of sensitivity and specificity for identifying LBW babies.
Conclusion: FL appears to be
better indicators for picking up LBW babies. These parameter can be used
at community level by health workers for early detection of LBW babies.
Keywords: Low birth weight; Foot length; Length; Midarm circumference.
Abbreviations: LBW: Low Birth
Weight; Fl: Foot Length; Mac: Mid Arm Circumference; Roc: Receiver
Operating Curve; AUC: Area Under Curve; HC: Head Circumference; CC:
Chest Circumference; SPSS: Statistical Package For Social Sciences; Roc:
Receiver Operating Characteristic; P: Probability; TC: Thigh
Circumference; CFC: Calf Circumference
Introduction
Anthropometry is the measurement of physical
dimensions of the human body at different ages. When assessing
intrauterine growth, the anthropometric parameters in neonate at birth
are considered to be of great value. Comparison of these measurements
with standards measurements provides a reliable and simple method of
identifying the neonates that deviates from the normal [1]. The physical
growth of a newborn is evaluated by comparing body measurements such as
weight, length and HC, with standards established in Western countries
[2-5]. Birth weight has been accepted as the reliable index of the
health status of the community and is an indicator of neonatal morbidity
and mortality [2]. Babies with birth weight of less than 2500gm is
called as low birth weight babies. They are more susceptible to
infection and they do not grow to their full potential of physical and
leads to high-infant morbidity and mortality. The perinatal mortality in
LBW babies is eight times higher than that in infants weighing more
than 2500gms [6]. However, in our country where most of the births take
place at home, measuring accurate birth weight is a big problem due to
unavailability of weighing scale and trained personnel. So, other
authors have used different surrogate anthropometric measurements from
different parts of our country to predict LBW babies [2,3]. Hence it is
imperative to identify
the newborns with LBW and to give them adequate and needed
care instantly for their survival. The proportion of LBW infants is
particularly high in south- Asia, especially India, where between
20-40% of babies have LBW [4-7]. Thus the present study was
conducted to find out the predictor of LBW by measuring the foot
length and other anthropometric parameter in neonates.
Material and Methods
This study was carried out in the Pediatric department,
AVBRH hospital, Sawangi (Meghe), Wardha. AVBRH hospital being
a tertiary care hospital situated in a rural area and all types of
deliveries take place here. It was a hospital based cross-sectional
study. The study was done on 520 live born neonates who were
born during the month of Jan 2013 to July 2013. All newborn
infants were term babies (gestational age 37-42 weeks) included
in the study. Babies of mothers with risk factors, premature, and
malformed babies were all excluded. Equipments used during
the study were of flexible, non-stretchable measuring tapes,
electronic weighing machine, and infant meter. Nude weight of
the baby was taken in a beam balance electronic measuring scale.
Length were recorded to the nearest of 0.1cm on an infant meter
with baby supine, knees fully extended and soles of the feet held
firmly against the foot board and head touching fixed board. Head
circumference (HC) was measured by putting the measuring
tape interiorly at glabellas and posterior along with the most
prominent point. Chest circumference (CC) was measured at the
level of lipoid cartilage by measuring tape to the nearest of 0.1 cm.
Mid-arm circumference (MAC) was measured midway between
acromion process and olecranon process of left arm to the nearest
of 0.1 cm by measuring tape. Foot length was measured from an
imaginary line tangential to the posterior prominence of the heel
to the tip of the longest toe (the first or the second toe). Babies left
foot was used to maintain standard during study period. All the
measurements were recorded by trained social worker within 48
hour of birth.
Statistical Analysis
Data were entered in Microsoft excel and analyzed using the
Statistical Package for Social Sciences (SPSS) software version 17.
The anthropometric measures of newborn babies are presented
as mean and standard deviation. Correlation and linear regression
analyses were done to examine linear relationship between
two continuous variables. To define the cut-off point which best
discriminates between low birth weight and normal birth weight,
the value which yielded the highest accuracy, or percentage of
correct classification was determined. Sensitivity, specificity,
likelihood ratio for positive test (LR+) and Likelihood ratio
for negative test(LR-) were calculated at all cut-points for any
anthropometric measurement. Receiver operating characteristic
(ROC) curves were used to evaluate the accuracy of different
anthropometric measurements to predict LBW. Probability (p)
value less than 0.05 was considered statistically significant.
Results
Out of 520 neonates, there were 267 male and 253 female
babies. Means and standard deviation of anthropometric variables
are shown in (Table 1). It was observed that weight, length, HC,
CC, MAC and FL were higher in male babies than in female babies
but not statistically significant. Table 2 shows the correlation
coefficient between birth weight and anthropometric variables
of the neonates, where birth weight significantly correlated
(p<0.000) with other anthropometric variables i.e. FL, length,
HC, CC and MAC. Foot Length attained the highest correlation
with birth weight (r = 0.715) while MAC attained the lowest (r =
0.355). Also, FL had the highest coefficient of determination (R2
value= 0.511). This implies that FL has the highest proportion
(51.1%) of variation in weight. Table 3 demonstrated the best
discrimination of LBW, as detected by ROC- area under curve
(AUC), was obtained by FL (AUC = 0.909, 95% CI 0.0133- 0.93538)
followed by length (AUC = 0.89, 95% CI 0.87642-0.92969). Length
of 49cm, HC of 33cm, MAC of 9.5cm, CC of 30cm and FL of 8cm
were the corresponding cut-off values with the best combination
of sensitivity and specificity for identifying LBW babies as shown
in (Table 4). Also, the superiority of FL over other anthropometric
indicators in the identification of LBW with 84.85% sensitivity
and 85.94% specificity.
Discussion
Anthropometry is an effective and frequently performed
child health and nutrition screening procedure. Birth weight
data indicate the important role of geographic location as an
environmental factor on fetal growth. LBW babies detection in
rural community is of highest priority to provide effective minimal
perinatal care to decrease mortality. Also, there is a constant
search for a simple and inexpensive method for screening such
newborns. Number of studies has been done to find out the
suitable alternative parameter for predicting the birth weight
of the newborn. Many of the anthropometric indices have been
proposed such as HC, MAC, CC, thigh circumference (TC) and
calf circumference (CFC). The present study was conducted to
find the best surrogate parameters, which could be used by birth
attendants in rural areas and health workers at community level,
to identify LBW babies.
Previous studies have shown that male babies are larger than
the female babies [8,9]. Similar finding was present in our study
group. Taksande A et al. [10] reported that HC appear to be better
indicators for picking up <2500 g babies. MAC is easier to record
and its effective use in community situation, by paramedical
workers has been shown by earlier works [11]. Ramaiya et al.
[12] reported the percentage of LBW were 18.8% with an arm
circumference below 9.5 cm. Sacher et al. [13] reported that MAC
can be used as a measurement to predict the birth weight of the
newborn during the first few days as these do not change over this
period. MAC is the better indicator in picking up less than 2000
g birth weight babies. A positive correlation existed between
MAC and birth weight and a MAC of 8.7 cm predicts birth weight
of 2500gm and definitely excludes newborns with birth weight
less than 2000gm [14]. In our study, mid arm circumference is a
less reliable parameter and less degree of correlation (0.35) with
birth weight for identification of LBW babies with a cut off value
of 9.5 cm in neonates. The best correlation between birth weight
and surrogate parameter to identify LBW babies was shown by FL
(0.71) followed by length (0.769), then HC (0.63), CC (0.590) and
lastly MAC (0.355).
Bhargava et al. [15] found the highest degree of correlation
of 0.86 between birth weight and CC and a cut off ≤ 30 cm.
Verma et al. [16] had found the highest degree of correlation of
0.93 in males and 0.92 in female, thus they found CC to be most
sensitive in estimation of LBW babies, by developing multiple
linear regression – equations for predicting birth weight from
CC. Whereas Sreeramareddy et al. [17] in their study found a correlation coefficient of 0.86 and Etio Goto et al. [18] found a
coefficient correlation of 0.95 between CC and birth weight with a
cut off value of 30.8 cm and 31.25 cm respectively. For determining
LBW babies < 2.5kg the cut off limits or values were formed using
regressions equation. The cut off value for FL, length, HC, CC and
MAC were 8 cm, 49 cm, 33 cm, 30cms and 9.5 cm respectively
babies. Many studies have been conducted in the past to determine
the best surrogate parameters to determine birth weight.
Foot length is being considered as an important parameter
for detection of birth weight and identification of high risk babies.
This alternative measurement should be easy to be conducted
even by inexperienced health care staff and should have a very
little intra and inter observer variability. It is one such parameter
which can be measured easily in neonates without disturbing the
baby. Kakrani V et al. [19] reported the highest sensitivity was FL
(92.8%) for detecting LBW less than 2000gms followed by MAC
(89.5%). Many studies have reported positive correlation between
FL and other indices of body [20-25]. Mathur et al. [24] had cut
off FL <7.2cm, Mukherjee et al. [25] 7.9 cm , Joshi G et al. [26] had
8.2cm whereas in our case it was 8cm for predicting LBW babies.
Joshi G et al. [26] found FL had highest correlation with birth
weight (r=0.96), followed by HC (r=0.88), CC(r=0.82), CFC(r=0.76)
and length (r=0.65). Elizabeth et al. [27] also reported the highest
correlation of FL with birth weight (r=0.97) like us.
Some studies have recommended that FL, HC, MAC and HC
may be used as anthropometric surrogates to identify LBW
babies [28-30]. Therefore we considered all these anthropometric
measurement. Thus the result of this study shows FL are among
the best surrogate parameters to identify LBW babies which can
be used at community level by health workers in rural areas.
Conclusion
In conclusion, the present study shows the significant
correlation of birth weight with other anthropometric parameters.
FL was both sensitive and specific for identifying LBW babies.
Thus amongst all the parameters studied FL can be used as an
alternative to birth weight as an indicator for detection of LBW
babies. Thus these measurements can be easily used even in
rural areas by health workers to predict the birth weight where
weighing facilities for newborns is not available.
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