Study of Neonatal Anthropometry as Screening Tool for Identifying Low Birth Weight Babies in Western Rajasthan

Background: Low birth weight (LBW) is linked to perinatal mortality and morbidity, growth, and cognitive developmental defects, along with a greater tendency to develop non – communicable diseases later in life. In rural settings, where health care facilities are not adequate, the birth weight of infant is not properly noted; weight is either not measured properly or tabulated accurately. This has necessitated the use of alternative indices in lieu of birth weight to reliably identify LBW babies, especially in settings where the availability of weighing scales is very limited. Aims and Objective: To determine the reliability of anthropometric parameters as a surrogate marker of weight in low birth weight babies. Materials and Methods: This hospital-based cross-sectional study was conducted in postnatal wards in Government Medical College Pali and attached Bangur hospital for 6 months duration from March 2024 to September 2024. All live birth healthy newborn babies, who were less than or equal to 48 hours of life and not admitted in NICU, were included in present study. All recruited babies were weighed and measured. Student’s t test, Pearson correlation coefficient, Scatter plot and Receiver operating characteristic curve analysis were used to statistically analyse the data. Results: Total 515 neonates were enrolled and evaluated. All neonatal anthropometric parameters had positive and statistically significant correlation with birth weight at p < 0.001. Amongst all parameters, highest correlation with birth weight was observed with mid upper arm circumference (MUAC) and least with foot length with r = 0.917 and r = 0.831 respectively. The best predictive regression model was formulated as birth weight (gm) = [531.58 * MUAC (cm)]- 1595.7. Cut off value of MUAC was found to be 7.4 cm to predict low birth weight newborns. As compared to individual neonatal anthropometric parameters, a combination of all four parameters (head circumference, mid-upper arm circumference, foot length and chest circumference) had highest significant correlation (r = 0.933) and multiple regression equation was formulated as birth weight = -4770.450 + {7.032 * HC (cm)} + {258.617 * MUAC (cm)} + {27.123 * FL(cm)} + {151.004 * CC(cm)}. Conclusion: Using combination of all four anthropometric parameters (head circumference, mid-upper arm circumference, foot length and chest circumference) followed by individual parameter MUAC of neonate within first 48 hours of life, we can identify low birth weight babies in rural areas where conventional weighing scale are not easily available. It is because combination of all parameters had the best predictive performance in detecting low birth weight babies. This is crucial for early and timely institution of life saving interventions or referring to higher centres.