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Saman Maroufizadeh 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
  • 3,792 View
  • 58 Download
  • 9 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.

Citations

Citations to this article as recorded by  
  • The double burden of maternal overweight and short stature and the likelihood of cesarean deliveries in South Asia: An analysis of national datasets from Bangladesh, India, Maldives, Nepal, and Pakistan
    Mosiur Rahman, Syed Emdadul Haque, Md. Jahirul Islam, Nguyen Huu Chau, Izzeldin Fadl Adam, Md. Nuruzzaman Haque
    Birth.2022; 49(4): 661.     CrossRef
  • Geospatial analysis of cesarean section in Iran (2016–2020): exploring clustered patterns and measuring spatial interactions of available health services
    Alireza Mohammadi, Elahe Pishgar, Zahra Salari, Behzad Kiani
    BMC Pregnancy and Childbirth.2022;[Epub]     CrossRef
  • Factors associated with cesarean delivery in Bangladesh: A multilevel modeling
    Md. Akhtarul Islam, Mst. Tanmin Nahar, Md. Ashfikur Rahman, Sutapa Dey Barna, S.M. Farhad Ibn Anik
    Sexual & Reproductive Healthcare.2022; 34: 100792.     CrossRef
  • The Birth Satisfaction Scale-Revised Indicator (BSS-RI): a validation study in Iranian mothers
    Reza Omani-Samani, Caroline J. Hollins Martin, Colin R. Martin, Saman Maroufizadeh, Azadeh Ghaheri, Behnaz Navid
    The Journal of Maternal-Fetal & Neonatal Medicine.2021; 34(11): 1827.     CrossRef
  • The effect of familiarization with preoperative care on anxiety and vital signs in the patient’s cesarean section: A randomized controlled trial
    Mehrnush Mostafayi, Behzad Imani, Shirdel Zandi, Faeze Jongi
    European Journal of Midwifery.2021; 5(June): 1.     CrossRef
  • Dynamic prediction of liver cirrhosis risk in chronic hepatitis B patients using longitudinal clinical data
    Ying Wang, Xiang-Yong Li, Li-Li Wu, Xiao-Yan Zheng, Yu Deng, Meng-Jie Li, Xu You, Yu-Tian Chong, Yuan-Tao Hao
    European Journal of Gastroenterology & Hepatology.2020; 32(1): 120.     CrossRef
  • Factors Contributing to Iranian Pregnant Women’s Tendency to Choice Cesarean Section
    Soraya Nouraei Motlagh, Zahra Asadi-piri, Razyeh Bajoulvand, Fatemeh Seyed Mohseni, Katayoun Bakhtiar, Mehdi Birjandi, Maryam Mansouri
    Medical - Surgical Nursing Journal.2020;[Epub]     CrossRef
  • Trends and correlates of cesarean section rates over two decades in Nepal
    Aliza K. C. Bhandari, Bibha Dhungel, Mahbubur Rahman
    BMC Pregnancy and Childbirth.2020;[Epub]     CrossRef
  • Symptoms of Discomfort and Problems Associated with Mode of Delivery During the Puerperium: An Observational Study
    Martínez-Galiano, Delgado-Rodríguez, Rodríguez-Almagro, Hernández-Martínez
    International Journal of Environmental Research an.2019; 16(22): 4564.     CrossRef
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
  • 3,032 View
  • 30 Download
  • 8 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.

Citations

Citations to this article as recorded by  
  • Associations Among Multimorbid Conditions in Hospitalized Middle-aged and Older Adults in China: Statistical Analysis of Medical Records
    Yan Zhang, Chao Chen, Lingfeng Huang, Gang Liu, Tingyu Lian, Mingjuan Yin, Zhiguang Zhao, Jian Xu, Ruoling Chen, Yingbin Fu, Dongmei Liang, Jinmei Zeng, Jindong Ni
    JMIR Public Health and Surveillance.2022; 8(11): e38182.     CrossRef
  • Iranian midwives’ awareness and performance of respectful maternity care during labor and childbirth
    Simin Haghdoost, Fatemeh Abdi, Azam Amirian
    European Journal of Midwifery.2021; 5(December): 1.     CrossRef
  • A diagnostic profile on the PartoSure test
    Safoura Rouholamin, Maryam Razavi, Mahroo Rezaeinejad, Mahdi Sepidarkish
    Expert Review of Molecular Diagnostics.2020; 20(12): 1163.     CrossRef
  • Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis
    Herdiantri Sufriyana, Atina Husnayain, Ya-Lin Chen, Chao-Yang Kuo, Onkar Singh, Tso-Yang Yeh, Yu-Wei Wu, Emily Chia-Yu Su
    JMIR Medical Informatics.2020; 8(11): e16503.     CrossRef
  • Analysis of Spontaneous Preterm Labor and Birth and Its Major Causes Using Artificial Neural Network
    Yun-Sook Kim
    Journal of Korean Medical Science.2019;[Epub]     CrossRef
  • A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models
    Evangelia Christodoulou, Jie Ma, Gary S. Collins, Ewout W. Steyerberg, Jan Y. Verbakel, Ben Van Calster
    Journal of Clinical Epidemiology.2019; 110: 12.     CrossRef
  • Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province
    Guo Li, Xiaorong Zhou, Jianbing Liu, Yuanqi Chen, Hengtao Zhang, Yanyan Chen, Jianhua Liu, Hongbo Jiang, Junjing Yang, Shaofa Nie, Michael French
    PLOS Neglected Tropical Diseases.2018; 12(2): e0006262.     CrossRef
  • Algorithm on age partitioning for estimation of reference intervals using clinical laboratory database exemplified with plasma creatinine
    Xiaoxia Peng, Yaqi Lv, Guoshuang Feng, Yaguang Peng, Qiliang Li, Wenqi Song, Xin Ni
    Clinical Chemistry and Laboratory Medicine (CCLM).2018; 56(9): 1514.     CrossRef

PHRP : Osong Public Health and Research Perspectives