ORIGINAL RESEARCH article
Front. Public Health
Sec. Infectious Diseases: Epidemiology and Prevention
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1663473
A Multi-Method Spatial Analysis of Dysentery Incidence Determinants Across Chinese Provinces
Provisionally accepted
- 1Xiamen Haicang Hospital, Xiamen, China
- 2QianDongNanZhou Center for Disease Control and Prevention, QianDongNanZhou, China
- 3Honwing pharma(Guizhou)Company Limited, QianDongNanZhou, China
- 4Xingtai Center for Disease Control and Prevention, Xingtai, China
- 5Nanjing Lishui Dongping Street Health Center, Nanjing, China
- 6Community Health Service Center of Jiuxian Tongliang District, Chongqing, China
- 7Fuwai Central China Cardiovascular Hospital, Zhengzhou, China
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Introduction: Dysentery remains a significant notifiable Class B infectious disease in China, exhibiting distinct spatial variations in incidence patterns. This persistent geographical heterogeneity necessitates a systematic investigation into the underlying influencing factors to inform targeted prevention and control strategies. Methods: Our analytical approach incorporated Moran's I index for spatial autocorrelation analysis, multiple linear regression (MLR) for preliminary assessment, and advanced spatial regression models including spatial error model (SEM), geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR). The analysis incorporated socioeconomic, educational, healthcare, and demographic factors within a unified spatial framework. Results: The analysis revealed three key findings: (1) Significant spatial clustering of dysentery incidence with identified high-risk concentration in the Beijing-Tianjin region; (2) Superior performance of MGWR modeling in capturing spatial heterogeneity compared to conventional methods; (3) Distinct regional variations in dominant factors, with economic development most influential in western China, education factors predominant in northeastern areas, and healthcare resource availability showing strongest impact in the northeast but minimal effect in southern regions. Conclusions: The study demonstrates the value of multiscale spatial analysis in understanding geographical disease patterns, revealing that dysentery incidence in China is governed by different factors across regions.
Keywords: Dysentery Incidence, Influencing factors, multiple linear regression, Spatial error model, Geographically weighted regression, multiscale geographically weighted regression
Received: 10 Jul 2025; Accepted: 22 Sep 2025.
Copyright: © 2025 Hu, Yang, Ou, Li, Zhang, Xiao and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mingyang Yu, 2296991140@qq.com
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