Description
Introduction
Estimation
of gestational age is crucial for medical besides numerous public health
functions, including the assessment of
intrauterine growth curves and related tricky in populations, such as
delineating whether infants of a given low birth weight are either preterm or
growth retarded, the adjustment for prematurity when assessing gross motor
milestone attainment and determining at risk status for potential developmental
delay related to targeting populations in need of follow up and intervention
services (1).
Theoretically, gestational age (GA) denotes to
the length of time between conception and delivery; because the timing of
conception cannot be easily ascertained, GA is commonly estimated as the
difference between the first day of the last menstrual period (LMP) and the
delivery date. However, in low-resource settings GA estimation is difficult due
to late presentation for antenatal care, challenges of last menstrual period
(LMP) recall because of hormonal contraceptives usage or maternal diseases and
educational label of women, and
unavailability of ultrasonography (2,3).
Preterm
birth is a major cause of neonatal mortality, responsible for 28% of neonatal
deaths overall (4). According to study, one of the contributing factors to
neonatal mortality is duration of pregnancy (5). As prematurity is a leading
cause of neonatal death, early accurate estimation of gestational age is vital
for early identification of infants in need of specialized care. Thus,
estimation of accurate gestational age at birth and identification and prompt
care of preterm/premature babies provides us with an opportunity to not only
reduce neonatal mortality but also under-five mortality rate. Birth weight and
gestational age as calculated from last menstrual period have traditionally
been used as strong indicators of prematurity and neonatal death (6).
An estimated 1 million babies die globally
every year because of prematurity. According to the United Nations (UN)
mortality estimate in 2013, the neonatal mortality rate in Ethiopia was 28 per
1000 live births. Even though there is an achievement observed in the reduction
of neonatal mortality by 48%, still neonatal mortality is high. In 2017 alone,
an estimated 6.3 million children and young adolescents died, mostly from
preventable causes. Of all, about 2.5 million deaths occurred before
celebrating their 28th days. Among children and young adolescents,
the risk of dying was highest in the first month of life with average rate of
18 deaths per 1000 live births (7).
So, the
above problems specifies that their a need of another model development which
is new simple, cost effective, reliable, easy to use and uniform method for
estimation of gestational age especially in low income countries to facilitate
the early recognition and referral of premature infants, and the delivery of
potentially life-saving interventions.
Thus, alternative measurements of neonates at time of delivery have a
good correlation with gestational age in new-born. Foot length, hand length,
mid upper arm circumference, umbilical nipple distance, Intermamilary distance,
crown heel length and weight have been studied for their correlation with gestational
age. All of these neonatal parameters can be measured with simple and easily
available equipment ‘measuring tape’ and does not require any special training
for use. Therefore, the aim and
objective of this study were 1) to investigate the relationship between
gestational age and Birth weight, Head Circumference, Intermammary distance,
Umbilical nipple distance, Mid-upper arm- circumference, hand length, Foot
Length, and crown-heel length 2) to develop regression models to predict
gestational age using these neonatal anatomical anthropometric parameters 3) to
find the better parameter for gestational age assessment by calculating
regression equation of the best anthropometric parameter alone and/or in
combination in Dessie referral hospital delivered neonates.
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