Abstract
Birth defects (BDs) are a big public health issue in Guangxi, China. This study aimed to apply various spatial epidemiology approaches to detect the spatial trends and geographical cluster of BDs prevalence in Guangxi, and to explore the risk factors of BDs. BDs data were obtained from the Guangxi Birth Defects Monitoring Network (GXBDMN) between 2016 and 2020, perinatal infants (PIs) between 28 weeks of gestation and 7 days postnatal were monitored by the GXBDMN. The kriging interpolation, spatial autocorrelation, and spatial regression analyses were used to explore the spatial trends patterns, and risk factors of BDs. A total of 44146 PIs were born with BDs in Guangxi from 2016 to 2020. The overall prevalence of BDs was 121.71 per 10000 PIs [95% confidence intervals (CI): 120.58 to 122.84 per 10000 PIs]. The global spatial autocorrelation analysis showed a positive spatial autocorrelation in county-level prevalence of BDs, the local spatial autocorrelation analysis showed the major cluster types of BDs prevalence were High-High, Low-Low, and Low-High. 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 2020. 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. The findings of this study indicated that the spatial trends and geographical cluster patterns of county-level prevalence of BDs in Guangxi are very obvious, the BDs prevalence tends to high or low-value cluster together, the high BDs prevalence gradually shifts from Nanning and Liuzhou to Nanning over the years. Furthermore, higher education levels and an increase of family monthly income per capita of pregnant women, and pregnant women with PGDM or GDM increase the prevalence of BDs for PIs. 1Birth Defects Research Laboratory, Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, Nanning 530002, Guangxi, China. 2Birth Defects Research Laboratory, Birth Defects Prevention and Control Institute of Guangxi Zhuang Autonomous Region, Nanning 530002, Guangxi, China. 3Birth Defects Research Laboratory, Guangxi Key Laboratory of Reproductive Health and Birth Defect Prevention, Nanning 530002, Guangxi, China. 4Birth Defects Research Laboratory, Guangxi Key Laboratory of Birth Defects Research and Prevention, Nanning 530002, Guangxi, China. 5Birth Defects Research Laboratory, Guangxi Clinical Research Center for Fetal Diseases, Nanning 530002, Guangxi, China. 6Birth Defects Research Laboratory, Guangxi Clinical Research Center for Pediatric Diseases, Nanning 530002, Guangxi, China. 7These authors contributed equally: Zhenren Peng, Jie Wei and Xiuning Huang. email: heshengbiol@163.com; Wqf2024@163.com