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Payam Amini 2 Articles
Factors Associated with Cesarean Section in Tehran, Iran using Multilevel Logistic Regression Model
Payam Amini, Maryam Mohammadi, Reza Omani-Samani, Amir Almasi-Hashiani, Saman Maroufizadeh
Osong Public Health Res Perspect. 2018;9(2):86-92.   Published online April 30, 2018
DOI: https://doi.org/10.24171/j.phrp.2018.9.2.08
  • 3,461 View
  • 58 Download
  • 8 Citations
AbstractAbstract PDF
Objectives

Over the past few decades, the prevalence of cesarean sections (CS) have risen dramatically worldwide, particularly in Iran. The aim of this study was to determine the prevalence of CS in Tehran, and to examine the associated risk factors.

Methods

A cross-sectional study of 4,308 pregnant women with singleton live-births in Tehran, Iran, between July 6–21, 2015 was performed. Multilevel logistic regression analysis was performed using demographic and obstetrical variables at the first level, and hospitals as a variable at the second level.

Results

The incidence of CS was 72.0%. Multivariate analysis showed a significant relationship between CS and the mother’s age, socioeconomic status, body mass index, parity, type of pregnancy, preeclampsia, infant height, and baby’s head circumference. The intra-class correlation using the second level variable, the hospital was 0.292, indicating approximately 29.2% of the total variation in the response variable accounted for by the hospital.

Conclusion

The incidence of CS was substantially higher than other countries. Therefore, educational and psychological interventions are necessary to reduce CS rates amongst pregnant Iranian women.

Prevalence and Determinants of Preterm Birth in Tehran, Iran: A Comparison between Logistic Regression and Decision Tree Methods
Payam Amini, Saman Maroufizadeh, Reza Omani Samani, Omid Hamidi, Mahdi Sepidarkish
Osong Public Health Res Perspect. 2017;8(3):195-200.   Published online June 30, 2017
DOI: https://doi.org/10.24171/j.phrp.2017.8.3.06
  • 2,780 View
  • 29 Download
  • 7 Citations
AbstractAbstract PDF
Objectives

Preterm birth (PTB) is a leading cause of neonatal death and the second biggest cause of death in children under five years of age. The objective of this study was to determine the prevalence of PTB and its associated factors using logistic regression and decision tree classification methods.

Methods

This cross-sectional study was conducted on 4,415 pregnant women in Tehran, Iran, from July 6–21, 2015. Data were collected by a researcher-developed questionnaire through interviews with mothers and review of their medical records. To evaluate the accuracy of the logistic regression and decision tree methods, several indices such as sensitivity, specificity, and the area under the curve were used.

Results

The PTB rate was 5.5% in this study. The logistic regression outperformed the decision tree for the classification of PTB based on risk factors. Logistic regression showed that multiple pregnancies, mothers with preeclampsia, and those who conceived with assisted reproductive technology had an increased risk for PTB (p < 0.05).

Conclusion

Identifying and training mothers at risk as well as improving prenatal care may reduce the PTB rate. We also recommend that statisticians utilize the logistic regression model for the classification of risk groups for PTB.


PHRP : Osong Public Health and Research Perspectives