Geospatial Health Research Group, College of Medicine and Public Health, Ubon Ratchathani University, Ubonratchathani 34190, Thailand.
Department of Geology, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.
Int J Environ Res Public Health. 2024 May 13;21(5):614. doi: 10.3390/ijerph21050614.
Melioidosis is an endemic infectious disease caused by bacteria, which contaminates soil and water. To better understand the environmental changes that have contributed to melioidosis outbreaks, this study used spatiotemporal analyses to clarify the distribution pattern of melioidosis and the relationship between melioidosis morbidity rate and local environmental indicators (land surface temperature, normalised difference vegetation index, normalised difference water index) and rainfall. A retrospective study was conducted from January 2013 to December 2022, covering data from 219 sub-districts in Northeast Thailand, with each exhibiting a varying morbidity rate of melioidosis on a monthly basis. Spatial autocorrelation was determined using local Moran's , and the relationship between the melioidosis morbidity rate and the environmental indicators was evaluated using a geographically weighted Poisson regression. The results revealed clustered spatiotemporal patterns of melioidosis morbidity rate across sub-districts, with hotspots predominantly observed in the northern region. Furthermore, we observed a range of coefficients for the environmental indicators, varying from negative to positive, which provided insights into their relative contributions to melioidosis in each local area and month. These findings highlight the presence of spatial heterogeneity driven by environmental indicators and underscore the importance of public health offices implementing targeted monitoring and surveillance strategies for melioidosis in different locations.
类鼻疽病是一种由细菌引起的地方性传染病,污染土壤和水。为了更好地了解导致类鼻疽病爆发的环境变化,本研究使用时空分析来阐明类鼻疽病的分布模式以及类鼻疽病发病率与当地环境指标(地表温度、归一化差异植被指数、归一化差异水指数)和降雨量之间的关系。本研究进行了一项回顾性研究,时间范围为 2013 年 1 月至 2022 年 12 月,涵盖了泰国东北部 219 个分区的数据,每个分区每月都有不同的类鼻疽病发病率。使用局部 Moran's 来确定空间自相关,使用地理加权泊松回归评估类鼻疽病发病率与环境指标之间的关系。结果显示,类鼻疽病发病率在分区之间存在聚集的时空模式,热点主要出现在北部地区。此外,我们观察到环境指标的系数范围从负到正不等,这为了解它们在每个局部地区和月份对类鼻疽病的相对贡献提供了线索。这些发现突出了由环境指标驱动的空间异质性的存在,并强调了公共卫生部门在不同地点实施针对类鼻疽病的有针对性监测和监测策略的重要性。