Factors associated with preterm birth in Tehran province using multilevel logistic regression model

How to Cite:Saman MaroufizadehRezao SamaniMahdi AminiMahdi SepidarkishFactors associated with preterm birth in Tehran province using multilevel logistic regression model.koomesh.18(1):e151168.

Abstract

Introduction: Preterm birth (PTB) is a major determinant of neonatal mortality and morbidity and has long-term adverse consequences for health. The aim of the study was to determine the rate of PTB, and identify factors associated with it. Materials and Methods: This cross-sectional study was conducted on 4419 pregnant women in Tehran province (Iran) from 6-21 July 2015. Data were collected by a researcher-made questionnaire through interview with mothers and review of their medical records. To identify factor associated with CS, two-level logistic regression model was used. Results: The PTB rate was 5.6% in this study. In univariate analysis, mother;#39s age, preeclampsia, Caesarian section, multiple pregnancies, and use of ART were significant factors of PTB. Moreover, multivariate analysis has shown a significant relationship between PTB and preeclampsia, multiple pregnancies and use of AR. In multivariate analysis, mother’s age had a positive impact on PTB, but this relationship was not statistically significant (p=0.051). The intra-class correlation (ICC) between hospital is 0.208 indicating approximately 21% of the total variation in the response variable accounted for by the hospital. Conclusion: According to the results, factors such as preeclampsia, multiple pregnancies and use of ART were associated with PTB. Therefore, it seems that these factors can be effective in determining the risk in neonates and providing factors in reducing mortality

Keywords

Multilevel logistic regression

رگرسیون لجستیک چندسطحی

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