Spatial Epidemiologic Analysis of Fetal Birth Defects in Guangxi, China - Peng Z; Huang X; Wei J; Chen B; Liang L; Feng B; Wei Q; He S (International Journal of General Medicine, 2025)
Zhenren Peng,1– 5,* Xiuning Huang,4,5,* Jie Wei,4,5,* Biyan Chen,4,5 Lifang Liang,4,5 Baoying Feng,4,5 Qiufen Wei,1– 5 Sheng He1– 5 1Birth Defects Research Laboratory, Guangxi Clinical Research Center for Birth Defects, Nanning, 530002, People’s Republic of China; 2Birth Defects Research Laboratory, Guangxi Key Laboratory of Reproductive Health and Birth Defect Prevention, Nanning, 530002, People’s Republic of China; 3Birth Defects Research Laboratory, Guangxi Clinical Research Center for Pediatric Diseases, Nanning, 530002, People’s Republic of China; 4Birth Defects Research Laboratory, Birth Defects Prevention and Control Institute of Guangxi Zhuang Autonomous Region, Nanning, 530002, People’s Republic of China; 5Birth Defects Research Laboratory, Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530002, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qiufen Wei, Birth Defects Research Laboratory, Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, No. 59 Xiangzhu Avenue, Nanning, Guangxi Zhuang Autonomous Region, 530002, People’s Republic of China, Email Wqf2024@163.com Sheng He, Birth Defects Research Laboratory, Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, No. 59 Xiangzhu Avenue, Nanning, Guangxi Zhuang Autonomous Region, 530002, People’s Republic of China, Email heshengbiol@163.comPurpose: To apply various spatial epidemiological approaches to detect spatial trends and geographical clusters of birth defects (BDs) prevalence in Guangxi, China, and to explore the risk factors for BDs.Methods: Between 2016 and 2022, the Guangxi Birth Defects Monitoring Network (GXBDMN) monitored a total of 4.57 million fetuses in this study. The BDs data for fetuses could be obtained from the GXBDMN. The kriging interpolation, spatial autocorrelation, and spatial regression analyses were used to explore the spatial trends patterns, and risk factors of BDs.Results: Between 2016 and 2022, 101,786 fetuses were diagnosed with BDs, resulting in an overall BDs prevalence of 222.68 [95% confidence intervals (CI): 221.33– 224.04] per 10,000 fetuses. The global spatial autocorrelation analysis showed a positive spatial autocorrelation in the prevalence of BDs at the county level. The local spatial autocorrelation analysis revealed that the primary clustering patterns of BDs prevalence were High–High and Low–Low. The local indicators of spatial association (LISA) cluster map and kriging interpolation analysis showed that the High–High cluster aggregation areas for the BDs prevalence were gradually shifted from Nanning and Liuzhou to Nanning from 2016 to 2022. The spatial lag model (SLM) results showed that the coefficients of education level (β=15.898, P=0.001), family monthly income per capita (β=0.010, P=0.005) and pre-gestational diabetes mellitus (PGDM)/gestational diabetes mellitus (GDM) (β=10.346, P=0.002) were statistically significant.Conclusion: The spatial trends and geographical cluster patterns of county-level prevalence of BDs in Guangxi are very obvious. Especially, the trend of high clustering in the prevalence of BDs is particularly evident. In addition, BDs are becoming more prevalent due to higher education levels, an increase in family monthly income per capita of pregnant women, and pregnant women with PGDM or GDM.Keywords: birth defects, fetuses, pregnant women, prevalence, spatiotemporal, spatial regression