Spatio-temporal pattern and risk factors of HIV/AIDS prevalence in Zhejiang, China, from 2005 to 2022 using R-INLA DOI Creative Commons
Yifan Tang, Yifan Chen, Jia Zheng

et al.

One Health, Journal Year: 2025, Volume and Issue: unknown, P. 101038 - 101038

Published: April 1, 2025

The number of reported HIV/AIDS cases in the Zhejiang province, China, has increased drastically. However, spatial disparity and temporal trends risk at fine level remain unclear. We analyzed prevalence Zhejiang, China to develop targeted prevention strategies health resources. This study included 56,699 patients province from 2005 2022. Data were obtained Database National Comprehensive Response Information Management System. Spatial autocorrelation analysis was conducted using GeoDa 1.22, factors influencing identified through a Bayesian hierarchical Poisson regression model with fast-computing R-INLA approach. Cases decreased coastal inland areas, while standardized incidence ratio (SIR) relative (RR) showed an overall increase. Key RR average diagnosed age (ADA), healthcare technical personnel per thousand people (HTP), male proportion (MP), GDP capita (GDP), population density (PD), disposable income (DPI), teachers (TTP). by 1.011, 0.989, 1.010, 0.997, 0.932, 0.990, 0.830 unit increase ADA, HTP, MP, GDP, PD, DPI, TTP, respectively. TTP negatively associated high-prevalence regions but positively low-prevalence regions. DPI negative association most not significant upper-middle-prevalence areas. varies significantly across China. High-prevalence require education rapid testing, areas need improved infrastructure.

Language: Английский

Spatio-temporal pattern and risk factors of HIV/AIDS prevalence in Zhejiang, China, from 2005 to 2022 using R-INLA DOI Creative Commons
Yifan Tang, Yifan Chen, Jia Zheng

et al.

One Health, Journal Year: 2025, Volume and Issue: unknown, P. 101038 - 101038

Published: April 1, 2025

The number of reported HIV/AIDS cases in the Zhejiang province, China, has increased drastically. However, spatial disparity and temporal trends risk at fine level remain unclear. We analyzed prevalence Zhejiang, China to develop targeted prevention strategies health resources. This study included 56,699 patients province from 2005 2022. Data were obtained Database National Comprehensive Response Information Management System. Spatial autocorrelation analysis was conducted using GeoDa 1.22, factors influencing identified through a Bayesian hierarchical Poisson regression model with fast-computing R-INLA approach. Cases decreased coastal inland areas, while standardized incidence ratio (SIR) relative (RR) showed an overall increase. Key RR average diagnosed age (ADA), healthcare technical personnel per thousand people (HTP), male proportion (MP), GDP capita (GDP), population density (PD), disposable income (DPI), teachers (TTP). by 1.011, 0.989, 1.010, 0.997, 0.932, 0.990, 0.830 unit increase ADA, HTP, MP, GDP, PD, DPI, TTP, respectively. TTP negatively associated high-prevalence regions but positively low-prevalence regions. DPI negative association most not significant upper-middle-prevalence areas. varies significantly across China. High-prevalence require education rapid testing, areas need improved infrastructure.

Language: Английский

Citations

0