Endawkie Abel, Miheretu Birhan Asmame, Yalew Anteneh, Nyasulu Peter S, Worku Getahun, Asaminew Ashebir, Hailu Bayuh Asmamaw
Department of Epidemiology and Biostatistics, School of Public Health, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia.
Department of Geography and Environmental Studies, Wollo University, Dessie, Ethiopia.
BMC Res Notes. 2024 Dec 23;17(1):379. doi: 10.1186/s13104-024-07053-7.
HIV/AIDS remains a major public health challenge, in Sub-Saharan Africa (SSA). In 2020, 16% of people living with HIV did not know their HIV status in SSA. Understanding the geospatial distribution of HIV infection, awareness status, and transmission knowledge is crucial for designing effective prevention and control strategies to end the HIV/AIDS pandemic by 2030. However, to the best of our literature searching the evidence of geospatial analysis and a machine learning algorithm, specifically a decision tree to decide on a Sustainability Development Goal (SDG), and to establish a clear pathway of HIV awareness status and HIV infection rates in each region of SSA is limited. Therefore, this study aims to determine HIV Infection, awareness status, and transmission knowledge among Adults in SSA using a machine learning approach and geospatial analysis.
The study used demographic and health survey data from 2009 to 2019. Machine learning algorithms and geospatial analysis techniques were employed to determine HIV infection, awareness of HIV status, and HIV transmission knowledge.
The overall prevalence of HIV infection among adults in SSA from 2009 to 2019 is 4.96%. The machine learning algorithm (decision tree) indicates that infected individuals are unaware of their HIV infection, about half of them do not have HIV transmission knowledge, and more of them were found in Southern SSA. The spatial hotspots show that high HIV prevalence, low levels of HIV status awareness, and adequate transmission knowledge are specifically located in the Southern and some Eastern SSA.
The machine learning algorithm (decision tree) revealed that the risk of HIV infection is high among individuals who are unaware of their HIV status and lack knowledge about HIV transmission in Southern and eastern parts of Sub-Saharan Africa. The spatial analysis revealed the high-risk areas of HIV infection with low HIV status awareness and HIV transmission knowledge were located in Southern and some Eastern SSA countries. Therefore public health strategies should focus on educating individuals about the importance of knowing their HIV status, transmission knowledge, and ensuring accessible testing options in these affected regions to address the observed spatial disparities in HIV infection, HIV status awareness, and HIV transmission knowledge to achieve the 2030 Sustainable Development Goal of ending the HIV/AIDS epidemic in Africa.
在撒哈拉以南非洲地区,艾滋病毒/艾滋病仍然是一项重大的公共卫生挑战。2020年,撒哈拉以南非洲地区16%的艾滋病毒感染者不知道自己的感染状况。了解艾滋病毒感染的地理空间分布、知晓状况和传播知识,对于制定有效的预防和控制策略以在2030年前终结艾滋病毒/艾滋病大流行至关重要。然而,就我们文献检索的最佳情况而言,关于地理空间分析和机器学习算法,特别是用于确定可持续发展目标的决策树,以及建立撒哈拉以南非洲每个地区艾滋病毒知晓状况和艾滋病毒感染率的清晰路径的证据有限。因此,本研究旨在使用机器学习方法和地理空间分析来确定撒哈拉以南非洲成年人中的艾滋病毒感染情况、知晓状况和传播知识。
该研究使用了2009年至2019年的人口与健康调查数据。采用机器学习算法和地理空间分析技术来确定艾滋病毒感染情况、艾滋病毒感染状况知晓率和艾滋病毒传播知识。
2009年至2019年期间,撒哈拉以南非洲成年人中艾滋病毒感染的总体患病率为4.96%。机器学习算法(决策树)表明,受感染个体不知道自己感染了艾滋病毒,其中约一半人没有艾滋病毒传播知识,且更多此类个体出现在撒哈拉以南非洲南部地区。空间热点显示,艾滋病毒高流行率、低艾滋病毒感染状况知晓率和足够的传播知识具体集中在撒哈拉以南非洲南部地区和部分东部地区。
机器学习算法(决策树)显示,在撒哈拉以南非洲南部和东部地区,不知道自己艾滋病毒感染状况且缺乏艾滋病毒传播知识的个体感染艾滋病毒的风险很高。空间分析表明,艾滋病毒感染高风险地区,艾滋病毒感染状况知晓率低且艾滋病毒传播知识匮乏,位于撒哈拉以南非洲南部和部分东部国家。因此,公共卫生策略应侧重于教育个人了解知晓自己艾滋病毒感染状况和传播知识的重要性,并确保在这些受影响地区提供可及的检测选择,以解决在艾滋病毒感染、艾滋病毒感染状况知晓率和艾滋病毒传播知识方面观察到的空间差异,从而实现到2030年在非洲终结艾滋病毒/艾滋病流行的可持续发展目标。