Detecting Spatiotemporal Patterns of Newly Diagnosed HIV Infection in the China-Myanmar Border Region, 2010 to 2022:...

Geospatial analysis plays an essential role in informing targeted human immunodeficiency virus (HIV) intervention. The Dai-Jingpo Autonomous Prefecture of Dehong (hereinafter referred to as Dehong), located along the China-Myanmar border in the Yunnan province, has been heavily impacted by HIV infection. Given the complex local epidemic context, particularly frequent cross-border population movement, there is an urgent need to apply spatiotemporal analytical approaches to guiding resource allocation. Existing evidence has demonstrated the substantial spatial variations of newly diagnosed HIV infection this region. However, these spatiotemporal variations have not been fully explored at a finer geographic and temporal resolution.

This study aims to comprehensively investigate the spatiotemporal variations of newly diagnosed HIV infection at a finer scale in this border region to inform targeted interventions.

Data on newly diagnosed HIV cases at the township level in Dehong were collected from 2010 to 2022. The rate of newly diagnosis HIV cases was calculated annually. GeoDetector q statistics were performed to assess the spatially stratified heterogeneity of the rate of newly diagnosed HIV cases. The Bayesian space-time hierarchical model was applied to detect the spatiotemporal patterns of newly diagnosed HIV infection across the region.

A total of 5045 newly diagnosed HIV cases were identified in Dehong from 2010 to 2022. The rate of newly diagnosed HIV cases decreased from 57.1 cases per 100,000 population in 2010 to 13.3 cases per 100,000 population in 2022, a decrease of 76.7% over the past 13 years. The overall temporal relative risk decreased from 2.11 (95% CI 1.84-2.41) in 2010 to 0.48 (95% CI 0.40-0.56) in 2022. There was substantial spatiotemporal heterogeneity in the risk of newly diagnosed HIV infection, with townships near the China-Myanmar border having a higher spatial relative risk. Notable spatially stratified heterogeneity in the rate of newly diagnosed HIV cases when stratified by the distance of townships to the China-Myanmar border was observed (q=0.27; P=.004). Among the 51 townships in Dehong, 22 (43.1%) hotspots and 22 (43.1%) coldspots were identified. Notably, in comparison to the overall declining temporal trend, 2 hotspots and 4 coldspots exhibited a slower declining trend, suggesting that these regions may require additional intervention efforts.

This study comprehensively estimated the spatiotemporal risk of newly diagnosed HIV infection across Dehong, revealing high-risk areas concentrated near the China-Myanmar border. Priority should be given to implementing targeted interventions to control cross-border HIV transmission, including the establishment of cross-border HIV control mechanisms, as well as the strengthening of management measures for cross-border populations. Furthermore, this study offers methodological insights into the use of routine surveillance data and Bayesian spatiotemporal modeling to better understand HIV transmission dynamics at finer geographic scales and to support precision-oriented HIV prevention services.