ArticleGeoHealth2026
Analysis of Spatiotemporal Risks of Environmental Driving Factors of Respiratory Diseases.
Article in GeoHealth, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Respiratory diseases pose a serious threat to the health of residents in China and cause enormous socio-economic losses. Jiangsu has a total of over 27 million vulnerable people in two groups: children under 15 years old and the elderly over 65 years old. Therefore, studies on the spatio-temporal risks of respiratory diseases and their influencing factors in Jiangsu Province are of particular importance. Based on the Distributed Lag Nonlinear Model (DLNM) and the Geographically and Temporally Weighted Regression Model (GTWR), this study took the hospitalized cases of respiratory diseases in Jiangsu Province from 2020 to 2023 as research samples. It deeply explored the complex temporal relationships and spatial differences between environmental driving factors and the HRRD (hospitalization rate of respiratory diseases). It quantified the nonlinear relationships and lag effects of environmental driving factors on the HRRD. Additionally, it conducted a quantitative analysis of the specific impacts of each factor on the hospitalization rate under different spatio-temporal conditions. The research results show that there are significant differences in the lag effects of extreme meteorological factors, air pollutants, and comprehensive meteorological factors on respiratory diseases among different age groups. Similarly, the impacts of these factors on the HRRD also exhibit different spatial heterogeneities. This research reveals the spatio-temporal distribution patterns and driving mechanisms of respiratory diseases, offering a scientific foundation for the development of regionalized and differentiated prevention and control strategies.
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Registered trials
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