ArticleOne health (Amsterdam, Netherlands)2025
Spatio-temporal pattern and risk factors of HIV/AIDS prevalence in Zhejiang, China, from 2005 to 2022 using R-INLA.
Article in One health (Amsterdam, Netherlands), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Cost-effectiveness of concurrently available oral and long-acting injectable pre-exposure prophylaxis for preventing HIV infections among men who have sex with men in eastern China: A modelling study.BMC public health · 2026Article
- Spatiotemporal heterogeneity and socioeconomic determinants of syphilis incidence in Xining, Northwest China: a geospatial analysis.BMC infectious diseases · 2026Article
- Risk Factors Associated With Tuberculosis Diagnostic Delay in the Jiangsu Province, China (2011-2021): Spatiotemporal Database Analysis Study.JMIR public health and surveillance · 2026Article
- Graph-aware spatio-temporal attention for forecasting HIV/AIDS case counts in public health surveillance.Frontiers in public health · 2026Article
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9 authors.
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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.
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