Analysis of influencing factors of atmospheric pollution on pulmonary tuberculosis incidence in China from 2014 to 2020
- 1Xizang University
Abstract
Objective: To investigate the spatiotemporal heterogeneity in the associations between ambient air pollutants (PM 2.5, SO ₂ , NO ₂ , CO, O ₃ ) and pulmonary tuberculosis (TB) incidence in China from 2014 to 2020, and to provide evidence for region-specific prevention and control strategies.
Methods: Based on panel data from 31 provincial-level regions, spatial autocorrelation analysis, spatiotemporal scanning, and a Geographically and Temporally Weighted Regression (GTWR) model were employed. An exposure-window robustness analysis using 1- to 5-year moving averages was further conducted.
Results: The overall TB incidence declined, yet high-burden clustering remained significant in Xinjiang, Xizang, and Qinghai (RR = 3.56). The GTWR model showed good fit ( R ² = 0.889). PM 2.5 coefficients exhibited an “east-negative, west-positive” gradient; SO ₂ , NO ₂ , and CO showed synergistic positive associations in Northeast China; O ₃ was negatively associated in most provinces and attenuated as the exposure window lengthened, suggesting predominantly short-term associations; and GDP coefficients reversed from positive in the east to negative in the west. The moving-average analysis confirmed that these spatial patterns were temporally robust.
Conclusion: The associations between air pollution and pulmonary TB exhibit marked spatiotemporal non-stationarity, supporting region-specific prevention and control strategies prioritizing high-burden western regions. As an ecological study, this investigation identifies associations rather than causal relationships.
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BibTeX
@article{shi2026analysis,
title = {{Analysis of influencing factors of atmospheric pollution on pulmonary tuberculosis incidence in China from 2014 to 2020}},
author = {Haibo Shi and Xinyu Guo and Yangzong Ciren},
journal = {Frontiers in Public Health},
year = {2026},
month = oct,
doi = {10.3389/fpubh.2026.1951817},
url = {https://www.frontiersin.org/articles/10.3389/fpubh.2026.1951817/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Analysis of influencing factors of atmospheric pollution on pulmonary tuberculosis incidence in China from 2014 to 2020
AU - Haibo Shi
AU - Xinyu Guo
AU - Yangzong Ciren
PY - 2026
DA - 2026/10/14
JO - Frontiers in Public Health
DO - 10.3389/fpubh.2026.1951817
UR - https://www.frontiersin.org/articles/10.3389/fpubh.2026.1951817/full
AB - Objective: To investigate the spatiotemporal heterogeneity in the associations between ambient air pollutants (PM 2.5, SO ₂ , NO ₂ , CO, O ₃ ) and pulmonary tuberculosis (TB) incidence in China from 2014 to 2020, and to provide evidence for region-specific prevention and control strategies. Methods: Based on panel data from 31 provincial-level regions, spatial autocorrelation analysis, spatiotemporal scanning, and a Geographically and Temporally Weighted Regression (GTWR) model were employed. An exposure-window robustness analysis using 1- to 5-year moving averages was further conducted. Results: The overall TB incidence declined, yet high-burden clustering remained significant in Xinjiang, Xizang, and Qinghai (RR = 3.56). The GTWR model showed good fit ( R ² = 0.889). PM 2.5 coefficients exhibited an “east-negative, west-positive” gradient; SO ₂ , NO ₂ , and CO showed synergistic positive associations in Northeast China; O ₃ was negatively associated in most provinces and attenuated as the exposure window lengthened, suggesting predominantly short-term associations; and GDP coefficients reversed from positive in the east to negative in the west. The moving-average analysis confirmed that these spatial patterns were temporally robust. Conclusion: The associations between air pollution and pulmonary TB exhibit marked spatiotemporal non-stationarity, supporting region-specific prevention and control strategies prioritizing high-burden western regions. As an ecological study, this investigation identifies associations rather than causal relationships.
SN - 2296-2565
ER -