Background: In Nepal, more than 90% of the deliveries take place at home where birth weight is often not recorded. In developing countries, low birth weight (LBW, <2500 grams) accounts for 60-80% of neonatal deaths. Early identification and referral of LBW babies for extra essential newborn care is vital in preventing neonatal deaths. Studies carried out in different populations have suggested that the use of newborn anthropometric surrogates of birth weight may be a simple and reliable method to identify LBW babies in a home setting. However, a reliable anthropometric surrogate to identify LBW babies and its cut-off point is not known for Nepalese newborns.
Methods: A cross-sectional study was carried out in Western Regional Hospital, Pokhara between April and June, 2006. All consecutive full-term, singleton, live born babies were included. To ensure reliability and avoid inter-observer bias one of the investigators weighed all the newborns and carried out anthropometric measurements within 24 hours after birth. Circumferences of head, chest, mid-upper arm, thigh and calf were measured according to standard techniques. Non-parametric receiver operating characteristic (ROC) curve analyses were carried out using bootstrap to calculate 95% confidence intervals of areas under the curve (AUC). The cut-points with lowest total misclassification rate were chosen to identify LBW babies.
Results: Out of 400 newborns studied, 204 (51%) were males and 196 (49%) were females. The mean birth weight was 3029 +/- 438 grams and 34 (8.5%) newborns were LBW. By ROC-AUC analyses, head circumference (AUC = 0.89, 95% CI 0.85 to 0.93) and chest circumference (AUC = 0.86, 95% CI 0.80 to 0.91) were identified as the optimal surrogate indicators of LBW babies. The optimal cut-points for head circumference and chest circumference to identify LBW newborns were > or = 33.5 cm and > or = 30.8 cm respectively.
Conclusion: Head and chest circumferences were the best anthropometric surrogates of LBW among Nepalese newborns. Further studies are needed in the field to cross-validate our results.