基于R语言的邹城市慢型克山病地理分布和空间回归分析

Geographical distribution and spatial regression analysis of chronic Keshan disease based on R language in Zoucheng city

摘要

目的 利用R语言对邹城市县级区域内的克山病地理分布进行研究,以实现地理分布可视化和空间回归。方法 2019—2020年济宁市疾病预防控制中心对邹城市慢性克山病病程≥1年的患者进行深入调查,我们以该调查数据为依托,研究邹城市慢性克山病的地理分布,并介绍在R语言环境下疾病地理分布可视化和空间回归的实现过程。结果 2019—2020年邹城市16个乡镇(街道)共发现慢性克山病79例。通过R语言实现了邹城市慢性克山病的地理分布可视化;空间回归模型分析了慢性克山病的发病与当地社会人口经济数据、环境的关系。莫兰检验显示Moran′s指数I=-0.19,P=0.50,表明不存在空间自相关。结论 R语言可以实现地理分布可视化和空间回归研究。邹城市慢性克山病的地理分布不存在空间聚集性,目前呈平稳散发态势。

Abstract

Objective R language was used to study the geographic distribution of Keshan disease at the county level of Zoucheng city,to realize the visualization of geographic distribution and spatial regression. Methods From 2019 to 2020,Jining Center for Disease Control and Prevention conducted an in-depth survey on patients with chronic Keshan disease whose duration was ≥1 year in Zoucheng city.Based on the survey data,we studied the geographical distribution of chronic Keshan disease in Zoucheng city,and introduced the realization process of visualization and spatial regression of geographical distribution of disease in R language environment. Results From 2019 to 2020,79 cases of chronic Keshan disease were detected in 16 towns in Zoucheng city.The visualization of the geographical distribution of chronic Keshan disease in Zoucheng city was realized by using R language.The spatial regression model analyzed the relationship between the incidence of chronic Keshan disease and the local social population economic data and environment.The Moran test showed that the value of Moran′s index I=-0.19,P=0.50,indicating no spatial autocorrelation. Conclusion R language can realize the visualization of geographic distribution and spatial regression research.There is no spatial aggregation in the geographical distribution of chronic Keshan disease in Zoucheng city.

关键词

R语言 / 克山病 / 地理分布 / 空间回归 / 莫兰指数

Key words

R language / Keshan disease / Geographical distribution / Spatial regression / Moran index

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基金资助

山东省医药卫生科技发展计划项目(2019WS367)