背景:联合国艾滋病毒/艾滋病联合规划署(UNAIDS)设定了“95-95-95”目标,以确保95%的艾滋病毒感染者知道自己的艾滋病毒状况。95%的艾滋病毒感染者将接受持续的抗逆转录病毒治疗(ART),所有接受ART的人中有95%将实现病毒抑制(<1000拷贝/mL)。然而,目前很少有国家实现这些目标,对实现联合国艾滋病规划署到2030年消除全球艾滋病毒/艾滋病流行的目标构成挑战。中国政府已经实施了相应的艾滋病防治政策,它仍然面临着大量艾滋病毒/艾滋病病例的挑战。现有的研究主要集中在对中国某一特定地区或人群的研究上,对中国HIV/AIDS时空分布及其与社会经济因素的关系进行宏观分析的研究相对有限。
目的:本研究旨在确定这些因素对中国HIV/AIDS发病率时空分布的影响。旨在为今后的政策制定提供科学的建议。
方法:本研究采用ArcGIS10.2(Esri)进行空间分析,包括不平衡指数等衡量标准,地理集中指数,空间自相关分析(MoranI),和热点分析(Getis-OrdGi*)。利用这些方法揭示了2009-2019年中国31个省份HIV/AIDS发病的时空分布特征。地理探测器用于生态探测,风险区域检测,因素检测,和交互检测。分析重点选取9项社会经济指标,进一步探讨社会经济因素对我国艾滋病发病率的影响。
结果:2009-2019年中国HIV/AIDS发病的时空分布分析显示出不同的规律。2009-2010年中国HIV/AIDS发病率的空间分布类型是随机的。然而,从2011年到2019年,分布格局朝着集群排列的方向发展,随着聚类程度的逐年增加。值得注意的是,从2012年起,在中国,HIV/AIDS发病率的冷热聚集显著快速增长,只有到2016年才能稳定下来。通过对社会经济因素对我国艾滋病发病率的影响分析,突出了“城镇化率”和“城镇基本医疗保险基金支出”是影响艾滋病发病率空间分布的主要因素。此外,在社会因素中,与医疗资源相关的指标对艾滋病毒/艾滋病的发病率产生了至关重要的影响。
结论:2009-2019年,中国HIV/AIDS发病率受多种社会经济因素影响。在未来,必须根据区域发病率模式优化组合不同的社会经济指标。这种优化将有助于制定相应的政策,以应对艾滋病毒/艾滋病流行病带来的挑战。
BACKGROUND: The Joint United Nations Program on HIV/AIDS (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, few countries have currently achieved these targets, posing challenges to the realization of the UNAIDS goal to eliminate the global HIV/AIDS epidemic by 2030. The Chinese government has implemented corresponding policies for HIV/AIDS prevention and control; however, it still faces the challenge of a large number of HIV/AIDS cases. Existing research predominantly focuses on the study of a particular region or population in
China, and there is relatively limited research on the macro-level analysis of the spatiotemporal distribution of HIV/AIDS across
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 (Esri) for spatial analysis, encompassing measures such as the imbalance index, geographical concentration index, spatial autocorrelation analysis (Moran I), and hot spot analysis (Getis-Ord Gi*). These methods were used to unveil the spatiotemporal distribution characteristics of HIV/AIDS incidence in 31 provinces of China from 2009 to 2019. Geographical Detector was used for ecological detection, risk area detection, factor detection, and interaction detection. The analysis focused on 9 selected socioeconomic indicators to further investigate the influence of socioeconomic factors on HIV/AIDS incidence 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 2009-2010. However, from 2011 to 2019, the distribution pattern evolved toward 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 HIV/AIDS incidence in China highlighted the \"urbanization rate\" and \"urban basic medical insurance fund expenditure\" as the primary factors influencing the spatial distribution of HIV/AIDS incidence. Additionally, among social factors, indicators related to medical resources exerted a crucial influence on HIV/AIDS incidence.
CONCLUSIONS: From 2009 to 2019, HIV/AIDS incidence in China was influenced by various socioeconomic factors. In the future, it is imperative to optimize the combination of different socioeconomic indicators based on regional incidence patterns. This optimization will facilitate the formulation of corresponding policies to address the challenges posed by the HIV/AIDS epidemic.