Analysis on influencing factors of HBV intrauterine transmission based on integration of decision tree model and logistic regression model.
10.3760/cma.j.cn112338-20210630-00511
- Author:
Wen Xin CHEN
1
;
Cong JIN
1
;
Ting WANG
1
;
Yan Di LI
1
;
Shu Ying FENG
2
;
Bo WANG
2
;
Yong Liang FENG
1
;
Su Ping WANG
1
Author Information
1. Department of Epidemiology, Center of Clinical Epidemiology and Evidence Based Medicine, School of Public Health, Shanxi Medical University, Taiyuan 030001, China.
2. Department of Obstetrics and Gynaecology, The Third People Hospital of Taiyuan, Taiyuan 030001, China.
- Publication Type:Journal Article
- MeSH:
DNA, Viral/genetics*;
Decision Trees;
Female;
Hepatitis B Surface Antigens;
Hepatitis B e Antigens;
Hepatitis B virus/genetics*;
Humans;
Infant, Newborn;
Infectious Disease Transmission, Vertical;
Logistic Models;
Mothers;
Pregnancy;
Pregnancy Complications, Infectious/epidemiology*
- From:
Chinese Journal of Epidemiology
2022;43(1):85-91
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To investigate the influencing factors of HBV intrauterine transmission and their interaction effects by integrating logistic regression model and Chi-squared automatic interaction detector (CHAID) decision tree model. Methods: A total of 689 pairs of HBsAg-positive mothers and their neonates in the obstetrics department of the Third People's Hospital of Taiyuan from 2007 to 2013 were enrolled, and the basic information of mothers and their neonates were obtained by questionnaire survey and medical record review, such as the general demographic characteristics, gestational week and delivery mode. HBV DNA and HBV serological markers of the mothers and newborns were detected by fluorescence quantitative PCR and electrochemiluminescence immunoassay respectively. The CHAID decision tree model and unconditional logistic regression analysis were used to explore the factors influencing HBV intrauterine transmission in neonates of HBsAg-positive mothers. Results: Among the 689 neonates, the incidence of HBV intrauterine transmission was 11.47% (79/689). After adjusted for confounding factors, the first and second logistic multivariate analysis showed that cesarean delivery was a protective factor for HBV intrauterine transmission (OR=0.25, 95%CI: 0.14-0.43; OR=0.27, 95%CI: 0.15-0.46); both models indicated that maternal HBeAg positivity and HBV DNA load ≥2×105 IU/ml before delivery were risk factors of HBV intrauterine transmission (OR=3.89, 95%CI: 2.32-6.51; OR=3.48, 95%CI: 2.12-5.71), respectively. The CHAID decision tree model screened three significant factors influencing HBV intrauterine transmission, the most significant one was maternal HBeAg status, followed by delivery mode and maternal HBV DNA load. There were interactions between maternal HBeAg status and delivery modes, as well as delivery mode and maternal HBV DNA load before delivery. The rate of HBV intrauterine transmission in newborns of HBeAg-positive mothers by vaginal delivery increased from 19.08% to 29.37%; among HBeAg-positive mothers with HBV DNA ≥2×105 IU/ml, the rate of HBV intrauterine transmission increased to 33.33% in the newborns by vaginal delivery. Conclusions: Maternal HBeAg positivity,maternal HBV DNA ≥2×105 IU/ml and vaginal delivery could be risk factors for HBV intrauterine transmission in newborns. Interaction effects were found between maternal HBeAg positivity and vaginal delivery, as well as vaginal delivery and high maternal HBV DNA load. Logistic regression model and the CHAID decision tree model can be used in conjunction to identify the high-risk populations and develop preventive strategies accurately.
