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.