Spatio-Temporal Pattern and Socio-economic Influencing Factors of Tuberculosis Incidence in Guangdong Province: A Bayesian Spatiotemporal Analysis.

Hui Zhong WU; Xing LI; Jia Wen WANG; Rong Hua JIAN; Jian Xiong HU; Yi Jun HU; Yi Ting XU; Jianpeng XIAO; Ai Qiong JIN; Liang CHEN

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Spatio-Temporal Pattern and Socio-economic Influencing Factors of Tuberculosis Incidence in Guangdong Province: A Bayesian Spatiotemporal Analysis.

Author: Hui Zhong WU ¹ ; Xing LI ² ; Jia Wen WANG ¹ ; Rong Hua JIAN ³ ; Jian Xiong HU ² ; Yi Jun HU ³ ; Yi Ting XU ³ ; Jianpeng XIAO ² ; Ai Qiong JIN ⁴ ; Liang CHEN ⁵
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1. Center for Tuberculosis Control of Guangdong Province, Guangzhou, 510630, Guangdong, China.
2. Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, Guangdong, China.
3. School of Medicine, Jinan University, Guangzhou 510632, Guangdong, China.
4. Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, 510280, Guangdong, China.
5. Guangzhou Chest Hospital, Guangzhou, 510095, Guangdong, China.
Publication Type:Journal Article
Keywords: Bayesian; Social-economic factor; Spatio-temporal model; Tuberculosis
MeSH: Humans; China/epidemiology*; Incidence; Bayes Theorem; Spatio-Temporal Analysis; Tuberculosis/epidemiology*; Socioeconomic Factors
From: Biomedical and Environmental Sciences 2025;38(7):819-828
CountryChina
Language:English
Abstract: OBJECTIVE:To investigate the spatiotemporal patterns and socioeconomic factors influencing the incidence of tuberculosis (TB) in the Guangdong Province between 2010 and 2019.
METHOD:Spatial and temporal variations in TB incidence were mapped using heat maps and hierarchical clustering. Socioenvironmental influencing factors were evaluated using a Bayesian spatiotemporal conditional autoregressive (ST-CAR) model.
RESULTS:Annual incidence of TB in Guangdong decreased from 91.85/100,000 in 2010 to 53.06/100,000 in 2019. Spatial hotspots were found in northeastern Guangdong, particularly in Heyuan, Shanwei, and Shantou, while Shenzhen, Dongguan, and Foshan had the lowest rates in the Pearl River Delta. The ST-CAR model showed that the TB risk was lower with higher per capita Gross Domestic Product (GDP) [Relative Risk ( RR), 0.91; 95% Confidence Interval ( CI): 0.86-0.98], more the ratio of licensed physicians and physician ( RR, 0.94; 95% CI: 0.90-0.98), and higher per capita public expenditure ( RR, 0.94; 95% CI: 0.90-0.97), with a marginal effect of population density ( RR, 0.86; 95% CI: 0.86-1.00).
CONCLUSION:The incidence of TB in Guangdong varies spatially and temporally. Areas with poor economic conditions and insufficient healthcare resources are at an increased risk of TB infection. Strategies focusing on equitable health resource distribution and economic development are the key to TB control.