JMIR Publications

ABSTRACT

Background:

UNAIDS has set the "95-95-95" targets to ensure that 95% of all people living with HIV will know their HIV status, 95% of all people living with HIV will receive sustained Antiretroviral Therapy (ART), and 95% of all people receiving ART will achieve viral suppression (<1000 copies/mL). However, only a few countries have currently achieved these targets, posing challenges to the realization of UNAIDS goal to eliminate the global HIV/AIDS epidemic by 2030. While the Chinese government has implemented corresponding policies for HIV/AIDS prevention and control, it still faces the challenge of a large number of HIV/AIDS cases. Existing research has predominantly focused on the micro-level, with relatively limited exploration of the macro-level factors, such as the spatiotemporal distribution of HIV/AIDS in China and its association with socioeconomic factors.

Objective:

This study seeks to identify the impact of these factors on the spatiotemporal distribution of HIV/AIDS incidence in China, aiming to provide scientific recommendations for future policy development.

Methods:

This study employed ArcGIS 10.2 for spatial analysis, encompassing measures such as the imbalance index, geographic concentration index, spatial autocorrelation analysis (Moran's I), and hot spot analysis (Getis-Ord Gi*). These methods were used to unveil the spatiotemporal distribution characteristics of HIV/AIDS incidence, including both the number of cases and the incidence rate, in 31 provinces of China from 2009 to 2019. Geographical Detector was utilized for ecological detection, risk area detection, factor detection, and interaction detection. The analysis focused on nine selected socioeconomic indicators to further investigate the influence of socioeconomic factors on the incidence of HIV/AIDS in China.

Results:

The spatiotemporal distribution analysis of HIV/AIDS incidence in China from 2009 to 2019 revealed distinct patterns. The spatial distribution type of HIV/AIDS incidence in China was random in the years 2009-2010. However, from 2011 to 2019, the distribution pattern evolved towards a clustered arrangement, with the degree of clustering increasing each year. Notably, from 2012 onwards, there was a significant and rapid growth in the aggregation of cold and hot spot clusters of HIV/AIDS incidence in China, stabilizing only by the year 2016.An analysis of the impact of socioeconomic factors on the incidence of HIV/AIDS in China highlighted the Urbanization Rate and Expenditures on Urban Basic Medical Insurance Fund as the primary factors influencing the spatial distribution of HIV/AIDS incidence. Additionally, within the realm of social factors, indicators related to medical resources were found to exert a crucial influence on the incidence of HIV/AIDS.

Conclusions:

From 2009 to 2019, the incidence of HIV/AIDS in China was influenced by various socioeconomic factors. In the future, it is imperative to optimize the combination of different socioeconomic indicators based on the regional incidence patterns. This optimization will facilitate the formulation of corresponding policies to address the challenges posed by the HIV/AIDS epidemic.