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. 2025 Apr 15:20:101038.
doi: 10.1016/j.onehlt.2025.101038. eCollection 2025 Jun.

Spatio-temporal pattern and risk factors of HIV/AIDS prevalence in Zhejiang, China, from 2005 to 2022 using R-INLA

Yifan Tang  1   2 , Yifan Chen  3 , Jinglei Zheng  4 , Wei Cheng  4 , Yurong Jing  3 , Yushu Zhang  3 , Chengliang Chai  4 , Chengxiu Ling  1 , Ying Wang  3   5
Affiliations

Spatio-temporal pattern and risk factors of HIV/AIDS prevalence in Zhejiang, China, from 2005 to 2022 using R-INLA

Yifan Tang et al. One Health. .

Abstract

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.

Keywords: Bayesian spatio-temporal model; Conditional auto-regressive model; HIV and AIDS; Integrated nested Laplace approximation; Spatial autocorrelation analysis.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Administrative map of Zhejiang province. The fundamental geographic data of municipal boundary from the National Geomatics of China.
Fig. 2
Fig. 2
Temporal trend and spatial distribution of overall reported cases of HIV/AIDS across 89 counties during 2005–2022 in Zhejiang province.
Fig. 3
Fig. 3
Spatial distribution of HIV/AIDS SIRs per year in Zhejiang province from 2005 to 2022.
Fig. 4
Fig. 4
Local indicators of spatial association cluster map of the HIV/AIDS SIRs in Zhejiang province during 2005–2022.
Fig. 5
Fig. 5
Spatial distribution of the RR of HIV/AIDS in Zhejiang province from 2005 to 2022.
See this image and copyright information in PMC

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