越南胡志明市手足口病的空间分布格局

Thi-Quynh Nguyen, Thi-Bich-Thuy Luong, Thi-Thuy Ngo, Thi-Yen Mai
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摘要

背景:手足口病(手足口病)最常由肠病毒71型(EV-A71)或柯萨奇病毒A16型(CV-A16)引起,婴幼儿发病风险最大。描述手足口病的空间模式可以帮助制定和更好地针对干预措施。本研究的目的是确定2023年前8个月越南胡志明市手足口病的空间格局。方法:采用全球Moran’s I统计量、Moran’s I散点图、局部Moran’s I统计量和箱线图研究手足口病的空间分布规律。将确定手足口病病例和感染率的空间模式,包括空间聚集(高-高和低-低)和空间异常值(低-高和高-低)。结果:3个高-高空间集群主要分布在城市西部的平潭、平清、平富等区域。这些高-高空间聚集区分别为289、283和281例/10万居民,属于全市手足口病感染率最高的地区。中心城区1区和5区存在2个低-低空间聚集区,手足口病感染率分别为190例/10万人和209例/10万人。结论:本研究结果支持箱形图、局部和全局Moran’s I统计量以及Moran’s I散点图在手足口病空间聚类和空间异常值识别中的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatial Patterns Of Hand-Foot And Mouth Disease In Ho Chi Minh City, Vietnam
Background: Hand-Foot-and-Mouth Disease (HFMD) is most frequently caused by Enterovirus71 (EV-A71) or Coxsackie virus A16 (CV-A16), infants and young children are at greatest risk. Describing the spatial patterns of HFMD can help develop and better target interventions. The objective of this study is to identify spatial patterns of HFMD in the first 8-months of 2023 in Ho Chi Minh city, Vietnam. Methods: The global Moran’s I statistic, Moran’s I scatterplot and local Moran’s I statistic and Boxplot will be applied to study spatial patterns of HFMD. Spatial patterns including spatial clusters (high-high and low-low) and spatial outliers (low-high and high-low) will be identified for HFMD cases and HFMD infection rates. Results: three high-high spatial clusters were mainly distributed in districts in the western region of the city such as Binh Tan, Binh Chanh, and Tan Phu. These high-high spatial clusters belonged to districts having the highest rates of HFMD infections in the city with their corresponding rates of 289, 283 and 281 cases/100,000 inhabitants, respectively. On the other hand, two low-low spatial clusters were detected in Districts 1 and 5 in the city center with their HFMD infection rates of 190 and 209 cases/100,000 inhabitants, respectively. Conclusions : findings in this study support the usefulness of boxplots, local and global Moran’s I statistics, and Moran’s I scatterplots in the identification of spatial clusters and spatial outliers of HFMD.
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