Spatial heterogeneity and its influencing factors of cardiometabolic multimorbidity in a natural community population: a study based on Lingwu city, rural Northwest China

作者：Wei Gong, Yuxin Zhao, Jianping Shi, Siyu Ma, Xiaoxiao Hu, Manya Ma, Xiaotian Li, Jinlong Shi, Jianjun Yang · 发表于：BMC Public Health · 年份：2025 · DOI：10.1186/s12889-025-24483-5 · 被引用次数：6 · 研究领域：Chronic Disease Management Strategies、Cardiovascular Health and Risk Factors、Primary Care and Health Outcomes

OBJECTIVE: Cardiometabolic multimorbidity (CMM) significantly contributes to the economic burden in China, particularly in rural areas. This study aimed to analyze the spatiotemporal distribution of CMM and identify its primary influencing factors in different townships in Lingwu City, Ningxia, to inform public health policies in Northwest China. METHODS: The standardized prevalence of CMM was investigated using data from Cardiovascular Disease High-Risk Group Early Screening and Comprehensive Intervention Program (2017-2022) conducted in Lingwu City, Ningxia. We applied spatial autocorrelation, cluster analysis, and spatiotemporal scanning to explore the spatiotemporal distribution characteristics of CMM and identify high-risk clusters. Four machine learning algorithms, logistic regression (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) were developed using 15 major cardiovascular disease influence factors. The performance of these models was evaluated based on accuracy, precision, recall, and AUC to determine their applicability across different townships in Lingwu City. The optimal model was selected for further analysis using interpretable machine learning algorithms (SHAP analysis) to identify common and key influence factors influencing CMM prevalence across townships. RESULTS: Among the 11,353 participants, 1,334 individuals (11.8%, 95% CI: 11.2-12.4%) were diagnosed with CMM, with significant variations in influence fact...