ArticleJMIR public health and surveillance2025
Identifying the Factors Associated With Spatial Clustering of Incident HIV Infection Cases in High-Prevalence Regions: Quantitative Geospatial Study.
Article in JMIR public health and surveillance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Molecular transmission network and spatial analysis of HIV/AIDS in Hainan, China: a population-based study.Frontiers in microbiology · 2026Article
- A county-level HIV prevention gap index in the US Deep South using publicly available proxy indicators.Frontiers in public health · 2026Article
- Study on the spatial-temporal evolution and influencing factors of AIDS incidence in China from 2006 to 2022.BMC infectious diseases · 2025Article
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24 authors.
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Abstract
backgroundIncident 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, studies identifying drivers of incident HIV clustering remain limited, especially in low-resource settings.
objectiveThis 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.
methodsFrom 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.
resultsSignificant 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 (β=0.59; P<.001), HIV prevalence (β=0.02; P<.001), distance to local urban area (β=-3.10; P=.01), SD of elevation (β=-0.15; P=.02), and ART coverage rate (β=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).
conclusionsThis study reveals that besides HIV prevalence, urbanization-related factors (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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