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AFRICAN RESEARCH NEXUS

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Estimation of gestational age using neonatal anatomical anthropometric parameters in Dessie referral hospital, northeast Ethiopia

Risk Management and Healthcare Policy, Volume 13, Year 2020

Introduction: Estimation of gestational age is a key for the identification of a given low birth weight neonate is either preterm or growth retarded. Objective: To estimate gestational age from neonatal anatomical anthropometric parameters in Dessie Referral Hospital, Ethiopia. Methods: Institutional-based cross-sectional study design was employed in Dessie Referral Hospital from October 2019 to April 2020, with 424 consecutively live-born of 28–42 weeks of gestation. After considering the inclusion criteria, neonatal anthropometric parameters were measured within 3 days of birth. Foot length, hand length, mid-upper arm circumfer-ence, head circumference, crown-heel length, intermammary distance, umbilical nipple distance, and birth weight were measured and summarized using descriptive statistics, and the power of association was evaluated using correlation analysis. Regression equations of gestational age (GA) in completed weeks with anthropometric parameters were formulated using simple and multiple linear regression analysis. Results: Except for hand length, all other neonatal anthropometric measurements were positively correlated with GA in completed weeks at p< 0.05. Anthropometric parameters individually, mid-upper arm circumference (MUAC) and BW (birth weight) were correlated well with GA at correlation coefficient (r) of 0.406 and 0.334, respectively. Regression formula was formulated as GA (weeks) = 26.12+ [1.11×MUAC (cm)] and GA (Weeks) = 33.19 + [1.53×BW (kg)]. Multiple regression contributed correlation with GA and used for prediction of GA as GA (weeks) = 28.12 – [0.393×HL (cm)] + [1.07×BW (kg)] + [0.87×MUAC (cm)] (r= 0.458). Conclusion: The overall relative better correlation for prediction of GA, alone and in combination, is found by combined parameters (HL, MUAC, and BW). The relatively better individual anthropometric parameter for GA assessment is MUAC. Hence, using this neonatal parameter as a prediction of gestational age, the death of neonate due to preterm can be minimized.

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Citations: 9
Authors: 1
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Identifiers
Research Areas
Health System And Policy
Maternal And Child Health
Study Design
Cross Sectional Study
Study Approach
Quantitative
Study Locations
Ethiopia