Advanced analysis of depression tendency in China: an investigation of environmental and social factors based on geographical and temporal weighted regression
The spatiotemporal distribution of depressive tendencies across China from 2011 to 2022 was investigated using the Baidu Depression Search Index (BDSI). We examined key influencing natural factors, such as water pollution, air pollution, and deforestation, along with economic indicators, such as gr...
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| Language: | English |
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PAGEPress Publications
2025-07-01
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| Series: | Geospatial Health |
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| Online Access: | https://www.geospatialhealth.net/gh/article/view/1385 |
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| author | Yanhong Xu Zhilin Hong Huimei Lin Xiaofeng Huang |
| author_facet | Yanhong Xu Zhilin Hong Huimei Lin Xiaofeng Huang |
| author_sort | Yanhong Xu |
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The spatiotemporal distribution of depressive tendencies across China from 2011 to 2022 was investigated using the Baidu Depression Search Index (BDSI). We examined key influencing natural factors, such as water pollution, air pollution, and deforestation, along with economic indicators, such as gross domestic product per capita, disposable income per capita, and health professionals per 10,000 population. Geographical and Temporal Weighted Regression (GTWR) was applied to capture the spatiotemporal heterogeneity of the BDSI determinants. The results revealed significant regional disparities, with the China’s eastern region consistently exhibiting the highest values reflecting heightened mental health concerns, while the western region were found to have the lowest. The BDSI trends followed different trajectories, all of which peaked in 2019 before a sharp decline in 2020. Water pollution transitioned from negative to positive influence in the East, while deforestation exhibited regionally variable effects. Air pollution, peaking in 2019 and 2022, demonstrated the highest impact variability. The economic indicators showed complex regional and temporal patterns underscoring the need for tailored interventions. Together, these findings provided critical insights into the intricate interplay between environmental, economic, and healthcare factors in shaping mental health that highlighted the necessity of region-specific policies to mitigate depressive tendencies and enhance public mental well-being. These research results offer targeted recommendations for regionally adaptive mental health strategies across China.
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| format | Article |
| id | doaj-art-d7142eb71a964155b5db3935c421dec6 |
| institution | Kabale University |
| issn | 1827-1987 1970-7096 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | PAGEPress Publications |
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| series | Geospatial Health |
| spelling | doaj-art-d7142eb71a964155b5db3935c421dec62025-08-20T03:42:00ZengPAGEPress PublicationsGeospatial Health1827-19871970-70962025-07-0120210.4081/gh.2025.1385Advanced analysis of depression tendency in China: an investigation of environmental and social factors based on geographical and temporal weighted regressionYanhong Xu0Zhilin Hong1Huimei Lin2Xiaofeng Huang3Laboratory Department, Quanzhou Third Hospital, QuanzhouCenter of Clinical Laboratory, Second Affiliated Hospital of Fujian Medical University, QuanzhouDepartment of Pathology, Haicang Hospital, Haicang District, XiamenLaboratory Department, Quanzhou Third Hospital, Quanzhou The spatiotemporal distribution of depressive tendencies across China from 2011 to 2022 was investigated using the Baidu Depression Search Index (BDSI). We examined key influencing natural factors, such as water pollution, air pollution, and deforestation, along with economic indicators, such as gross domestic product per capita, disposable income per capita, and health professionals per 10,000 population. Geographical and Temporal Weighted Regression (GTWR) was applied to capture the spatiotemporal heterogeneity of the BDSI determinants. The results revealed significant regional disparities, with the China’s eastern region consistently exhibiting the highest values reflecting heightened mental health concerns, while the western region were found to have the lowest. The BDSI trends followed different trajectories, all of which peaked in 2019 before a sharp decline in 2020. Water pollution transitioned from negative to positive influence in the East, while deforestation exhibited regionally variable effects. Air pollution, peaking in 2019 and 2022, demonstrated the highest impact variability. The economic indicators showed complex regional and temporal patterns underscoring the need for tailored interventions. Together, these findings provided critical insights into the intricate interplay between environmental, economic, and healthcare factors in shaping mental health that highlighted the necessity of region-specific policies to mitigate depressive tendencies and enhance public mental well-being. These research results offer targeted recommendations for regionally adaptive mental health strategies across China. https://www.geospatialhealth.net/gh/article/view/1385Baidu indexdepression tendencygeographic and time weighted regressionnatural environmenteconomysocial environment |
| spellingShingle | Yanhong Xu Zhilin Hong Huimei Lin Xiaofeng Huang Advanced analysis of depression tendency in China: an investigation of environmental and social factors based on geographical and temporal weighted regression Geospatial Health Baidu index depression tendency geographic and time weighted regression natural environment economy social environment |
| title | Advanced analysis of depression tendency in China: an investigation of environmental and social factors based on geographical and temporal weighted regression |
| title_full | Advanced analysis of depression tendency in China: an investigation of environmental and social factors based on geographical and temporal weighted regression |
| title_fullStr | Advanced analysis of depression tendency in China: an investigation of environmental and social factors based on geographical and temporal weighted regression |
| title_full_unstemmed | Advanced analysis of depression tendency in China: an investigation of environmental and social factors based on geographical and temporal weighted regression |
| title_short | Advanced analysis of depression tendency in China: an investigation of environmental and social factors based on geographical and temporal weighted regression |
| title_sort | advanced analysis of depression tendency in china an investigation of environmental and social factors based on geographical and temporal weighted regression |
| topic | Baidu index depression tendency geographic and time weighted regression natural environment economy social environment |
| url | https://www.geospatialhealth.net/gh/article/view/1385 |
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