Temporal and Spatial Pattern of Pulmonary Tuberculosis in Gansu Province and Its Environmental Factors Detection and Analysis
As one of the major public health security issues, pulmonary tuberculosis had a global death rate of 1.6 million in 2021 alone, ranking 13th in the world, posing a great threat to society and families. Analyzing the temporal and spatial distribution and evolution trend of tuberculosis, discussing th...
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MDPI AG
2025-01-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/16/1/55 |
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| author | Haili Zhao Jun Wang Minghui Wu |
| author_facet | Haili Zhao Jun Wang Minghui Wu |
| author_sort | Haili Zhao |
| collection | DOAJ |
| description | As one of the major public health security issues, pulmonary tuberculosis had a global death rate of 1.6 million in 2021 alone, ranking 13th in the world, posing a great threat to society and families. Analyzing the temporal and spatial distribution and evolution trend of tuberculosis, discussing the exposure factors and studying the environmental background that affects the incidence can provide the basis for accurate prevention and control and promote the healthy and stable development of society. Based on the county scale, this study determined the high-incidence areas through hot spot analysis and selected nine districts and counties covering meteorological stations and air monitoring stations. The explanatory power of each factor to the incidence of pulmonary tuberculosis was analyzed by geographical detector, and the main influencing factors were explored. The results show that the following: (1) The number and incidence of pulmonary tuberculosis in Gansu Province declined from 2020 to 2022. (2) The influence of meteorological conditions such as temperature, precipitation and air pressure on pulmonary tuberculosis in different regions shows significant regional differences. Although the meteorological influence in adjacent regions shows certain convergence, the change in wind speed has no significant influence on the risk of pulmonary tuberculosis. (3) PM<sub>10</sub>, altitude, temperature, population density and GDP per capita have strong explanatory power to the incidence of tuberculosis, and the interaction between any two factors exceeds the effect of a single factor in explanatory power, showing the characteristics of two-factor enhancement and nonlinear enhancement. |
| format | Article |
| id | doaj-art-59f1a7eb20724d09ae62ac72664fd000 |
| institution | Kabale University |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-59f1a7eb20724d09ae62ac72664fd0002025-01-24T13:21:52ZengMDPI AGAtmosphere2073-44332025-01-011615510.3390/atmos16010055Temporal and Spatial Pattern of Pulmonary Tuberculosis in Gansu Province and Its Environmental Factors Detection and AnalysisHaili Zhao0Jun Wang1Minghui Wu2College of Geography and Environmental Sciences, Northwest Normal University of China, Lanzhou 730070, ChinaCollege of Geography and Environmental Sciences, Northwest Normal University of China, Lanzhou 730070, ChinaCollege of Geography and Environmental Sciences, Northwest Normal University of China, Lanzhou 730070, ChinaAs one of the major public health security issues, pulmonary tuberculosis had a global death rate of 1.6 million in 2021 alone, ranking 13th in the world, posing a great threat to society and families. Analyzing the temporal and spatial distribution and evolution trend of tuberculosis, discussing the exposure factors and studying the environmental background that affects the incidence can provide the basis for accurate prevention and control and promote the healthy and stable development of society. Based on the county scale, this study determined the high-incidence areas through hot spot analysis and selected nine districts and counties covering meteorological stations and air monitoring stations. The explanatory power of each factor to the incidence of pulmonary tuberculosis was analyzed by geographical detector, and the main influencing factors were explored. The results show that the following: (1) The number and incidence of pulmonary tuberculosis in Gansu Province declined from 2020 to 2022. (2) The influence of meteorological conditions such as temperature, precipitation and air pressure on pulmonary tuberculosis in different regions shows significant regional differences. Although the meteorological influence in adjacent regions shows certain convergence, the change in wind speed has no significant influence on the risk of pulmonary tuberculosis. (3) PM<sub>10</sub>, altitude, temperature, population density and GDP per capita have strong explanatory power to the incidence of tuberculosis, and the interaction between any two factors exceeds the effect of a single factor in explanatory power, showing the characteristics of two-factor enhancement and nonlinear enhancement.https://www.mdpi.com/2073-4433/16/1/55pulmonary tuberculosisinfluencing factorsgeographic detector |
| spellingShingle | Haili Zhao Jun Wang Minghui Wu Temporal and Spatial Pattern of Pulmonary Tuberculosis in Gansu Province and Its Environmental Factors Detection and Analysis Atmosphere pulmonary tuberculosis influencing factors geographic detector |
| title | Temporal and Spatial Pattern of Pulmonary Tuberculosis in Gansu Province and Its Environmental Factors Detection and Analysis |
| title_full | Temporal and Spatial Pattern of Pulmonary Tuberculosis in Gansu Province and Its Environmental Factors Detection and Analysis |
| title_fullStr | Temporal and Spatial Pattern of Pulmonary Tuberculosis in Gansu Province and Its Environmental Factors Detection and Analysis |
| title_full_unstemmed | Temporal and Spatial Pattern of Pulmonary Tuberculosis in Gansu Province and Its Environmental Factors Detection and Analysis |
| title_short | Temporal and Spatial Pattern of Pulmonary Tuberculosis in Gansu Province and Its Environmental Factors Detection and Analysis |
| title_sort | temporal and spatial pattern of pulmonary tuberculosis in gansu province and its environmental factors detection and analysis |
| topic | pulmonary tuberculosis influencing factors geographic detector |
| url | https://www.mdpi.com/2073-4433/16/1/55 |
| work_keys_str_mv | AT hailizhao temporalandspatialpatternofpulmonarytuberculosisingansuprovinceanditsenvironmentalfactorsdetectionandanalysis AT junwang temporalandspatialpatternofpulmonarytuberculosisingansuprovinceanditsenvironmentalfactorsdetectionandanalysis AT minghuiwu temporalandspatialpatternofpulmonarytuberculosisingansuprovinceanditsenvironmentalfactorsdetectionandanalysis |