OBJECTIVE: To evaluate the factors associated with low birth weight (LBW) and to formulate a scale to predict the probability of having a LBW infant. METHODS: This hospital based case-control study was conducted in a tertiary care university hospital in North India. The study included 250 LBW neonates and 250 neonates with birth weight ≥2,500 g. Data were collected by interviewing mothers using pre-designed structured questionnaire and from hospital records. RESULTS: Factors significantly associated with LBW were inadequate weight gain by the mother during pregnancy (<8.9 kg), inadequate proteins in diet (<47 g/d), previous preterm baby, previous LBW baby, anemic mother and passive smoking. The prediction model made on these six variables has a sensitivity of 71.6 %, specificity 67.0 %, positive LR 2.17 and negative LR of 0.42 for a cut-off score of ≥29.25. On validation, it has a sensitivity of 72 % and specificity of 64 %. CONCLUSIONS: It is possible to predict LBW using a prediction model based on significant risk factors associated with LBW.
OBJECTIVE: To evaluate the factors associated with low birth weight (LBW) and to formulate a scale to predict the probability of having a LBW infant. METHODS: This hospital based case-control study was conducted in a tertiary care university hospital in North India. The study included 250 LBW neonates and 250 neonates with birth weight ≥2,500 g. Data were collected by interviewing mothers using pre-designed structured questionnaire and from hospital records. RESULTS: Factors significantly associated with LBW were inadequate weight gain by the mother during pregnancy (<8.9 kg), inadequate proteins in diet (<47 g/d), previous preterm baby, previous LBW baby, anemic mother and passive smoking. The prediction model made on these six variables has a sensitivity of 71.6 %, specificity 67.0 %, positive LR 2.17 and negative LR of 0.42 for a cut-off score of ≥29.25. On validation, it has a sensitivity of 72 % and specificity of 64 %. CONCLUSIONS: It is possible to predict LBW using a prediction model based on significant risk factors associated with LBW.
Authors: Hamid Y Hassen; Seifu H Gebreyesus; Bilal S Endris; Meselech A Roro; Jean-Pierre Van Geertruyden Journal: J Clin Med Date: 2020-05-23 Impact factor: 4.241
Authors: Ana C M G Figueiredo; Isaac S Gomes-Filho; Roberta B Silva; Priscilla P S Pereira; Fabiana A F Da Mata; Amanda O Lyrio; Elivan S Souza; Simone S Cruz; Mauricio G Pereira Journal: Nutrients Date: 2018-05-12 Impact factor: 5.717