Spatiotemporal dynamics and risk factors of pulmonary tuberculosis in a high-altitude Tibetan region of China from 2018 to 2022: A Bayesian modeling analysis
- 1School of Public Health (Shenzhen), Shenzhen Key Laboratory of Pathogenic Microbes and Biosafety, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- 2Department of Tuberculosis Control, Center for Disease Control and Prevention of Sichuan Province, China
- 3Guangdong Provincial Highly Pathogenic Microorganism Science Data Center, Guangzhou, China
Abstract
Background: Tuberculosis (TB) remains a major public health challenge in high-altitude, resource-limited regions of western China. Ganzi Tibetan Autonomous Prefecture, situated on the southeastern Tibetan Plateau, consistently reports the highest TB notification rates in Sichuan Province, yet fine-scale spatial heterogeneity and localized risk factors remain underexplored. This study aimed to quantify county-level spatiotemporal dynamics of TB and identify associated contextual and individual characteristics of reported TB cases from 2018 to 2022.
Methods: We conducted a retrospective analysis of 14,768 pulmonary TB cases reported in Ganzi Prefecture from 2018 to 2022. Bayesian hierarchical spatiotemporal model incorporating structured and unstructured spatial effects, a global temporal trend, and county-specific temporal deviations was implemented using the Integrated Nested Laplace Approximation (INLA) framework. County-level covariates were evaluated within a Poisson log-linear structure, while individual characteristics associated with residence in high-risk counties among reported TB cases were analyzed using multivariable logistic regression.
Results: The average annual TB notification rates was 251.2 per 100,000 population and increased over the study period. Persistent hotspots were identified in northern areas (e.g., Seda and Shiqu), while elevated risks were also observed in southern counties (e.g., Litang). Seasonal peaks occurred in spring and early autumn. At the ecological level, ultrahigh altitude (≥3500 m; RR = 2.52), higher natural population growth (RR = 1.87), and greater urbanization (RR = 2.03) were associated with increased TB notification rates, whereas greater cropland coverage was inversely associated (RR = 0.84). Among reported TB cases, herdsmen (aOR = 1.72) and children under 14 years (aOR = 2.12) were more likely to reside in high-risk counties.
Conclusions: Substantial spatial heterogeneity and dynamic temporal variation in TB risk were observed across counties in Ganzi Prefecture. The findings support geographically differentiated TB control strategies and highlight the importance of addressing contextual structural vulnerabilities in high-altitude settings.
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BibTeX
@article{lu2026spatiotemporal,
title = {{Spatiotemporal dynamics and risk factors of pulmonary tuberculosis in a high-altitude Tibetan region of China from 2018 to 2022: A Bayesian modeling analysis}},
author = {Yixiao Lu and Shuang Liu and Ting Li and Yifan Yu and Danxia Wang and Jia Lu and Chongguang Yang and Lan Xia},
journal = {Infectious Disease Modelling},
year = {2026},
month = sep,
doi = {10.1016/j.idm.2026.09.004},
url = {https://doi.org/10.1016/j.idm.2026.09.004},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Spatiotemporal dynamics and risk factors of pulmonary tuberculosis in a high-altitude Tibetan region of China from 2018 to 2022: A Bayesian modeling analysis
AU - Yixiao Lu
AU - Shuang Liu
AU - Ting Li
AU - Yifan Yu
AU - Danxia Wang
AU - Jia Lu
AU - Chongguang Yang
AU - Lan Xia
PY - 2026
DA - 2026/09/16
JO - Infectious Disease Modelling
DO - 10.1016/j.idm.2026.09.004
UR - https://doi.org/10.1016/j.idm.2026.09.004
AB - Background: Tuberculosis (TB) remains a major public health challenge in high-altitude, resource-limited regions of western China. Ganzi Tibetan Autonomous Prefecture, situated on the southeastern Tibetan Plateau, consistently reports the highest TB notification rates in Sichuan Province, yet fine-scale spatial heterogeneity and localized risk factors remain underexplored. This study aimed to quantify county-level spatiotemporal dynamics of TB and identify associated contextual and individual characteristics of reported TB cases from 2018 to 2022. Methods: We conducted a retrospective analysis of 14,768 pulmonary TB cases reported in Ganzi Prefecture from 2018 to 2022. Bayesian hierarchical spatiotemporal model incorporating structured and unstructured spatial effects, a global temporal trend, and county-specific temporal deviations was implemented using the Integrated Nested Laplace Approximation (INLA) framework. County-level covariates were evaluated within a Poisson log-linear structure, while individual characteristics associated with residence in high-risk counties among reported TB cases were analyzed using multivariable logistic regression. Results: The average annual TB notification rates was 251.2 per 100,000 population and increased over the study period. Persistent hotspots were identified in northern areas (e.g., Seda and Shiqu), while elevated risks were also observed in southern counties (e.g., Litang). Seasonal peaks occurred in spring and early autumn. At the ecological level, ultrahigh altitude (≥3500 m; RR = 2.52), higher natural population growth (RR = 1.87), and greater urbanization (RR = 2.03) were associated with increased TB notification rates, whereas greater cropland coverage was inversely associated (RR = 0.84). Among reported TB cases, herdsmen (aOR = 1.72) and children under 14 years (aOR = 2.12) were more likely to reside in high-risk counties. Conclusions: Substantial spatial heterogeneity and dynamic temporal variation in TB risk were observed across counties in Ganzi Prefecture. The findings support geographically differentiated TB control strategies and highlight the importance of addressing contextual structural vulnerabilities in high-altitude settings.
SN - 2468-2152
SN - 2468-0427
ER -