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利用地理空间映射和空间回归模型分析孟加拉国空气传播疾病的时空模式及相关气象因素

Spatio-temporal pattern and associate meteorological factors of airborne diseases in Bangladesh using geospatial mapping and spatial regression model.

作者信息

Chowdhury Arman Hossain, Rahman Md Siddikur

机构信息

Department of Statistics Begum Rokeya University, Rangpur Rangpur Bangladesh.

出版信息

Health Sci Rep. 2024 Jun 19;7(6):e2176. doi: 10.1002/hsr2.2176. eCollection 2024 Jun.

Abstract

BACKGROUND AND AIMS

Airborne diseases due to climate change pose significant public health challenges in Bangladesh. Little was known about the spatio-temporal pattern of airborne diseases at the district level in the country. Therefore, this study aimed to investigate the spatio-temporal pattern and associated meteorological factors of airborne diseases in Bangladesh using exploratory analysis and spatial regression models.

METHODS

This study used district-level reported cases of airborne diseases (meningococcal, measles, mumps, influenza, tuberculosis, and encephalitis) and meteorological data (temperature, relative humidity, wind speed, and precipitation) from 2017 to 2020. Geospatial mapping and spatial error regression models were utilized to analyze the data.

RESULTS

From 2017 to 2020, a total of 315 meningococcal, 5159 measles, 1341 mumps, 346 influenza, 4664 tuberculosis, and 229 encephalitis cases were reported in Bangladesh. Among airborne diseases, measles demonstrated the highest prevalence, featuring a higher incidence rate in the coastal Bangladeshi districts of Lakshmipur, Patuakhali, and Cox's Bazar, as well as in Maulvibazar and Bandarban districts from 2017 to 2020. In contrast, tuberculosis (TB) emerged as the second most prevalent disease, with a higher incidence rate observed in districts such as Khagrachhari, Rajshahi, Tangail, Bogra, and Sherpur. The spatial error regression model revealed that among climate variables, mean ( = 9.56, standard error [SE]: 3.48) and maximum temperature ( = 1.19, SE: 0.40) were significant risk factors for airborne diseases in Bangladesh. Maximum temperature positively influenced measles ( = 2.74, SE: 1.39), whereas mean temperature positively influenced both meningococcal ( = 5.57, SE: 2.50) and mumps ( = 11.99, SE: 3.13) diseases.

CONCLUSION

The findings from the study provide insights for planning early warning, prevention, and control strategies to combat airborne diseases in Bangladesh and similar endemic countries. Preventive measures and enhanced monitoring should be taken in some high-risk districts for airborne diseases in the country.

摘要

背景与目的

气候变化导致的空气传播疾病给孟加拉国带来了重大的公共卫生挑战。该国地区层面空气传播疾病的时空模式鲜为人知。因此,本研究旨在通过探索性分析和空间回归模型,调查孟加拉国空气传播疾病的时空模式及相关气象因素。

方法

本研究使用了2017年至2020年地区层面报告的空气传播疾病(脑膜炎球菌病、麻疹、腮腺炎、流感、结核病和脑炎)病例以及气象数据(温度、相对湿度、风速和降水量)。利用地理空间映射和空间误差回归模型对数据进行分析。

结果

2017年至2020年,孟加拉国共报告了315例脑膜炎球菌病、5159例麻疹、1341例腮腺炎、346例流感、4664例结核病和229例脑炎病例。在空气传播疾病中,麻疹的患病率最高,2017年至2020年期间,孟加拉国沿海地区的拉克希米布尔、帕图阿卡利和科克斯巴扎尔以及毛尔维巴扎尔和班达班地区的发病率较高。相比之下,结核病成为第二大流行疾病,在卡格拉乔里、拉杰沙希、唐盖尔、博格拉和舍尔布尔等地区观察到较高的发病率。空间误差回归模型显示,在气候变量中,平均温度(=9.56,标准误差[SE]:3.48)和最高温度(=1.19,SE:0.40)是孟加拉国空气传播疾病的重要风险因素。最高温度对麻疹有正向影响(=2.74,SE:1.39),而平均温度对脑膜炎球菌病(=5.57,SE:2.50)和腮腺炎(=11.99,SE:3.13)均有正向影响。

结论

该研究结果为孟加拉国及类似流行国家制定空气传播疾病的预警、预防和控制策略提供了见解。该国一些空气传播疾病高风险地区应采取预防措施并加强监测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84fe/11186039/2919b5608bd6/HSR2-7-e2176-g003.jpg

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