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Evaluating The Impact of Risk Factors on Birth Weight and Gestational Age: A Multilevel Joint Modeling Approach
Authors: payam Amini, Abbas Moghimbeigi, Farid Zayeri, Hossein Mahjub, Saman Maroufizadeh, Reza Omani Samani
Number of views: 323
Background: Abnormalities in birth weight and gestational age cause several adverse maternal and infant outcomes.
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 compared
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