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Identifying the Factors Associated With Spatial Clustering of Incident HIV Infection Cases in High-Prevalence Regions: Quantitative Geospatial Study

文献类型：期刊论文

作者Zhu, Qiyu2,3; Jike, Chunnong4; Xu, Chengdong5; Liang, Shu6; Yu, Gang4; Yuan, Dan6; Mai, Hong1; Li, Yiping6; Xiao, Lin4; Wang, Ju4
刊名JMIR PUBLIC HEALTH AND SURVEILLANCE
出版日期2025
卷号11页码:e75291
关键词HIV disease transmission spatial analysis geographic mapping public health
ISSN号2369-2960
DOI10.2196/75291
产权排序4
文献子类Article
英文摘要Background: Incident HIV infection is a critical indicator of an ongoing epidemic, particularly in high-burden regions such as Liangshan Yi Autonomous Prefecture in China, where HIV prevalence exceeds 1% in 4 key counties(Butuo, Zhaojue, Meigu, and Yuexi). Identifying spatial clusters and drivers of recent infections is essential for implementing targeted interventions. Despite advancements in geospatial analyses of HIV prevalence, studiesidentifying drivers of incident HIV clustering remain limited, especially in low-resource settings. Objective: This study aims to identify spatial clusters of recent HIV infections and investigate potential driving factors in 4 key counties of the Liangshan Yi Autonomous Prefecture to inform targeted intervention strategies. Methods: From November 2017 to June 2018, we identified 246 (4.42%) recent HIV infection cases from 5555 newly diagnosed cases through expanded testing of the whole population in 4 key counties of Liangshan Yi Autonomous Prefecture. Recent infection cases were confirmed using limiting antigen avidity enzyme immunoassays or documented seroconversion within 6 months. The spatial distribution of incident HIV infection cases was analyzed using kernel density. Potential drivers, including population density, HIV prevalence, elevation, nighttime light index, urban proximity, and antiretroviral therapy (ART) coverage, were analyzed. The spatial lag regression model was used to identify factors associated with clustering of recent infection cases. The Geodetector q-statistic was used to quantify nonlinear interactive effects among these factors. Results: Significant spatial autocorrelation was observed in the distribution of recent HIV cases (Moran I=0.11; P<.01). Six spatial clusters were identified, and all were located near urban centers or major roads. Furthermore, 5 factors were identified by the spatial lag regression model as being significantly correlated with the clustering of recent HIV infection cases, including population density (beta=0.59; P<.001), HIV prevalence (beta=0.02; P<.001), distanceto local urban area (beta=-3.10; P=.01), SD of elevation (beta=-0.15; P=.02), and ART coverage rate (beta=183.80; P<.01). Geodetector analysis revealed strong interactive effects among these 5 factors, with population density and HIV prevalence exhibiting the largest interactive effect (q=0.69). Conclusions: This study reveals that besides HIV prevalence, urbanization-relatedfactors (population density and proximity to urban area) and transportation accessibility drive incident HIV clustering in Liangshan Yi Autonomous Prefecture. Paradoxically, higher ART coverage was associated with increased transmission, suggesting the need for integrated prevention strategies beyond ART expansion. Furthermore, the township-level geospatial approach provides a valuable model for pinpointing transmission hot spots and tailoring interventions in high-burden regions globally.
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WOS关键词LIANGSHAN PREFECTURE ; TRAVEL-TIME ; SICHUAN ; MALAWI ; TRANSPORTATION ; SURVEILLANCE ; HIV/AIDS ; PROVINCE ; TRENDS ; AREAS
WOS研究方向Public, Environmental & Occupational Health
语种英语
WOS记录号WOS:001575365100001
出版者JMIR PUBLICATIONS, INC
源URL[http://ir.igsnrr.ac.cn/handle/311030/216059]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Jin, Cong
作者单位1.First Peoples Hosp Liangshan Yi Autonomous Prefect, Xichang, Peoples R China
2.Chinese Ctr Dis Control & Prevent, Natl Ctr AIDS STD Control & Prevent, Natl Key Lab Intelligent Tracking & Forecasting In, 155 Changbai Rd, Beijing 102206, Peoples R China;
3.China Med Univ, Shenyang, Peoples R China;
4.Liangshan Prefecture Ctr Dis Control & Prevent, Xichang, Peoples R China;
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China;
6.Sichuan Prov Ctr Dis Control & Prevent, Chengdu, Peoples R China;
推荐引用方式
GB/T 7714		 Zhu, Qiyu,Jike, Chunnong,Xu, Chengdong,et al. Identifying the Factors Associated With Spatial Clustering of Incident HIV Infection Cases in High-Prevalence Regions: Quantitative Geospatial Study[J]. JMIR PUBLIC HEALTH AND SURVEILLANCE,2025,11:e75291.
APA Zhu, Qiyu.,Jike, Chunnong.,Xu, Chengdong.,Liang, Shu.,Yu, Gang.,...&Liu, Zhongfu.(2025).Identifying the Factors Associated With Spatial Clustering of Incident HIV Infection Cases in High-Prevalence Regions: Quantitative Geospatial Study.JMIR PUBLIC HEALTH AND SURVEILLANCE,11,e75291.
MLA Zhu, Qiyu,et al."Identifying the Factors Associated With Spatial Clustering of Incident HIV Infection Cases in High-Prevalence Regions: Quantitative Geospatial Study".JMIR PUBLIC HEALTH AND SURVEILLANCE 11(2025):e75291.

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来源：地理科学与资源研究所

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