Abstract:
Background: Women’s health is important for a healthy pregnancyoutcome. It influences the health of the newborn from neonatal through their adulthood. Present investigation is designed to study the influence of some maternal-variables/socio-demographicprofile on the gestational-period, birth-weight and neonatal-health.
Methods: This is a prospective cross sectional study. Participants are ninety five low-birth-weight (LBW) singleton-babies (male-45) and their mothers (non-diabetic) from Medical College, Eastern India. Evaluations are performed of socio-demographic profiles, body-mass index (BMI), maternal blood-glucose, haemoglobin,
neonatal APGAR score (A=Appearance, P=Pulse, G=Grimace, A=Activity, R=Respiration) and anthropometric-data. Statistical package SPSS-17 is employed for one way ANOVA and Tukey’s post-hoc-test. Student-‘t’ test were performed for continuous variables and Pearson’s χ2 test for categorical-variables. The correlation and multiple-regression-analysis were employed for continuous-dependent-variables. Results: It is observed from the ANOVA result that the birthorder, mother’s-education, socioeconomic-status are significantly related to APGAR score and some neonatal parameters. Maternal BMI directly correlates to the birth weight and some neonatal parameters except APGAR score. The study suggests a direct association between APGAR score and haemoglobin (Hb). The Hb level was found to be significantly and inversely correlated with the maternal BMI (r=-0.204; P<0.05). These findings are supported by correlation, and regression-analysis (R2=0.497, F=45.46, P<0.001). The underprivileged mothers are more anemic and they deliver larger number of very-preterm baby. The multiple regression analysis suggests that some independent predictors like maternal weight, BMI, and gestational periods are associated with neonatal biometric data. Conclusions: Present data suggest that maternal education, BMI and socioeconomic-status significantly predict the pregnancy outcome. Though, most of the statistical analysis supports the present prediction, the disapproval by some analysis like logisticregression necessitates more sample study. Early interventions of maternal and neonatal health may enable us to predict any possible disease outcome which will help for the effective therapeutic measure.
