Evaluating The Impact of Risk Factors on Birth Weight and Gestational Age: A Multilevel Joint Modeling Approach

Background Abnormalities in birth weight and gestational age cause several adverse maternal and infant out- comes. Our study aims to determine the potential factors that affect birth weight and gestational age, and their association. Materials and Methods We conducted this cross-sectional study of 4415 pregnant women in Tehran, Iran, from July 6-21, 2015. Joint multilevel multiple logistic regression was used in the analysis with demographic and obstetrical variables at the first level, and the hospitals at the second level. Results We observed the following prevalence rates: preterm (5.5%), term (94%), and postterm (0.5%). Low birth weight (LBW) had a prevalence rate of 4.8%, whereas the prevalence rate for normal weight was 92.4, and 2.8% for macrosomia. Compared to term, older mother’s age [odds ratio (OR)=1.04, 95% confidence interval (CI): 1.02-1.07], preeclampsia (OR=4.14, 95% CI: 2.71-6.31), multiple pregnancy (OR=18.04, 95% CI: 9.75- 33.38), and use of assisted reproductive technology (ART) (OR=2.47, 95% CI: 1.64-33.73) were associated with preterm birth. Better socioeconomic status (SES) was responsible for decreased odds for postterm birth com- pared to term birth (OR=0.53, 95% CI: 0.37-0.74). Cases with higher maternal body mass index (BMI) were 1.02 times more likely for macrosomia (95% CI: 1.01-1.04), and male infant sex (OR=1.78, 95% CI: 1.21-2.60). LBW was related to multiparity (OR=0.59, 95% CI: 0.42-0.82), multiple pregnancy (OR=17.35, 95% CI: 9.73-30.94), and preeclampsia (OR=3.36, 95% CI: 2.15-5.24). Conclusion Maternal age, SES, preeclampsia, multiple pregnancy, ART, higher maternal BMI, parity, and male infant sex were determined to be predictive variables for birth weight and gestational age after taking into consideration their association by using a joint multilevel multiple logistic regression model

Abstract licence: CC BY-NC

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.