TY - JOUR
T1 - Spatio-temporal pattern and risk factors of HIV/AIDS prevalence in Zhejiang, China, from 2005 to 2022 using R-INLA
AU - Tang, Yifan
AU - Chen, Yifan
AU - Zheng, Jinglei
AU - Cheng, Wei
AU - Jing, Yurong
AU - Zhang, Yushu
AU - Chai, Chengliang
AU - Ling, Chengxiu
AU - Wang, Ying
N1 - Publisher Copyright:
© 2025 The Authors
PY - 2025/6
Y1 - 2025/6
N2 - Background: The number of reported HIV/AIDS cases in the Zhejiang province, China, has increased drastically. However, spatial disparity and temporal trends in HIV/AIDS risk at the fine level remain unclear. We analyzed HIV/AIDS prevalence in Zhejiang, China to develop targeted HIV/AIDS prevention strategies and health resources. Methods: This study included 56,699 HIV/AIDS patients reported in the Zhejiang province from 2005 to 2022. Data were obtained from the Zhejiang province Database of the National HIV/AIDS Comprehensive Response Information Management System. Spatial autocorrelation analysis was conducted using GeoDa 1.22, and factors influencing HIV/AIDS cases were identified through a Bayesian hierarchical Poisson regression model with the fast-computing R-INLA approach. Results: Cases decreased from coastal to inland areas, while the standardized incidence ratio (SIR) and relative risk (RR) showed an overall increase. Key factors influencing RR included average diagnosed age (ADA), healthcare technical personnel per thousand people (HTP), male proportion (MP), GDP per capita (GDP), population density (PD), per capita disposable income (DPI), teachers per thousand people (TTP). The RR increased by 1.011, 0.989, 1.010, 0.997, 0.932, 0.990, and 0.830 per unit increase in ADA, HTP, MP, GDP, PD, DPI, and TTP, respectively. TTP was negatively associated with RR in high-prevalence regions but positively associated in low-prevalence regions. DPI showed a negative association in most regions but was not significant in upper-middle-prevalence areas. Conclusion: HIV/AIDS risk varies significantly across the Zhejiang province, China. High-prevalence regions require targeted health education and rapid testing, while low-prevalence areas need improved healthcare infrastructure.
AB - Background: The number of reported HIV/AIDS cases in the Zhejiang province, China, has increased drastically. However, spatial disparity and temporal trends in HIV/AIDS risk at the fine level remain unclear. We analyzed HIV/AIDS prevalence in Zhejiang, China to develop targeted HIV/AIDS prevention strategies and health resources. Methods: This study included 56,699 HIV/AIDS patients reported in the Zhejiang province from 2005 to 2022. Data were obtained from the Zhejiang province Database of the National HIV/AIDS Comprehensive Response Information Management System. Spatial autocorrelation analysis was conducted using GeoDa 1.22, and factors influencing HIV/AIDS cases were identified through a Bayesian hierarchical Poisson regression model with the fast-computing R-INLA approach. Results: Cases decreased from coastal to inland areas, while the standardized incidence ratio (SIR) and relative risk (RR) showed an overall increase. Key factors influencing RR included average diagnosed age (ADA), healthcare technical personnel per thousand people (HTP), male proportion (MP), GDP per capita (GDP), population density (PD), per capita disposable income (DPI), teachers per thousand people (TTP). The RR increased by 1.011, 0.989, 1.010, 0.997, 0.932, 0.990, and 0.830 per unit increase in ADA, HTP, MP, GDP, PD, DPI, and TTP, respectively. TTP was negatively associated with RR in high-prevalence regions but positively associated in low-prevalence regions. DPI showed a negative association in most regions but was not significant in upper-middle-prevalence areas. Conclusion: HIV/AIDS risk varies significantly across the Zhejiang province, China. High-prevalence regions require targeted health education and rapid testing, while low-prevalence areas need improved healthcare infrastructure.
KW - Bayesian spatio-temporal model
KW - Conditional auto-regressive model
KW - HIV and AIDS
KW - Integrated nested Laplace approximation
KW - Spatial autocorrelation analysis
UR - http://www.scopus.com/inward/record.url?scp=105002866905&partnerID=8YFLogxK
U2 - 10.1016/j.onehlt.2025.101038
DO - 10.1016/j.onehlt.2025.101038
M3 - Article
AN - SCOPUS:105002866905
SN - 2352-7714
VL - 20
JO - One Health
JF - One Health
M1 - 101038
ER -