TY  - EJOU
AU  - Shen, Yi 
AU  - Liu, Zeye 
AU  - Xie, Jing 
AU  - An, Xuanqi 
AU  - Jing, Zeyu 
AU  - Liao, Wenchuan 
AU  - Zhu, Yifan 
AU  - Jiang, Chenyu 
AU  - Zhou, Xingliang 
AU  - Huang, Xu 
AU  - Liu, Tianyu 
AU  - Liu, Jian 
AU  - Ji, Yuxi 
AU  - Yan, Yi 
AU  - Feng, Bei 
AU  - Liu, Yiwei 
AU  - Shi, Yi 
AU  - Sun, Yanjun 
AU  - Zhang, Hao 

TI  - Unsafe Sanitation and the Global Incidence of Congenital Heart Disease: A Spatial Correlation Analysis
T2  - Structural and Congenital Heart Disease

PY  - 2026
VL  - 21
IS  - 3
SN  - 3071-1738

AB  - <b>Background:</b> Congenital heart disease (CHD) is the most common congenital anomaly worldwide, yet the contribution of environmental factors to its global geographic variation remains incompletely understood. We aimed to systematically identify environmental factors associated with CHD incidence using an integrated framework combining machine learning and spatial epidemiology. <b>Methods:</b> Country-level data were obtained from the Global Burden of Disease (GBD) 2021 study. Boruta algorithm-based feature selection and random forest SHAP value ranking were applied to identify environmental factors associated with CHD incidence. Negative binomial regression was used to evaluate the associations between selected variables and CHD incidence. Spatial clustering was assessed using Global Moran’s I and Local Indicators of Spatial Association (LISA). The population attributable fraction (PAF) associated with unsafe sanitation was further estimated. <b>Results:</b> Boruta identified 28 candidate environmental variables associated with CHD incidence, among which unsafe sanitation consistently ranked as the most influential factor based on SHAP analysis. Negative binomial regression demonstrated a significant association between unsafe sanitation and CHD incidence. Spatial analyses revealed marked geographic concordance between regions with high CHD incidence and high unsafe sanitation exposure, particularly in Sub-Saharan Africa. The estimated PAF suggested that approximately 33% of the global CHD burden could theoretically be attributable to unsafe sanitation under the assumptions of the analytical model. <b>Conclusions:</b> Unsafe sanitation was consistently identified as the environmental factor most strongly associated with global CHD incidence across multiple analytical approaches. These findings provide ecological evidence supporting a potential relationship between sanitation conditions and the geographic distribution of CHD. However, further individual-level epidemiological and mechanistic studies are required to validate these findings and clarify the underlying biological mechanisms.
KW  - Congenital heart disease; unsafe sanitation; machine learning; spatial epidemiology; spatial autocorrelation; boruta algorithm; population attributable fraction

DO  - 10.32604/schd.2026.085942