The interplay between environment and sexual health: Investigating the association of climate change and HIV in vulnerable populations of sub-Saharan

艾滋病毒和气候变化都是长期的不利因素，加剧了贫困、性别不平等和对儿童的剥削。此外，被边缘化的个人，如少女、年轻妇女、移民、性和性别少数群体，以及资源有限的个人往往受到的影响最大。最后，这两种现象都很难解决，因为存在政治参与问题以及大规模和空间上的不均匀分布。艾滋病毒和气候变化在不同层面影响着地球仪的某些区域，撒哈拉以南非洲是受这两种现象影响最严重的区域。随着各国努力实现2020年12月制定的联合国艾滋病规划署95-95-95新目标，气候变化相关事件可能危及疫情控制。因此，这可以推动环境、人类行为、脆弱性和艾滋病毒传播、发病率和严重性之间的协同关系。关于环境与艾滋病毒之间关系的统计证据非常有限。目前只有四项研究关注这种关系。所有这些研究都证实，环境变化可能对艾滋病毒发病率产生负面影响。然而，这些研究并没有全面概述与气候变化和艾滋病毒有关的所有潜在变量。他们也只关注一种天气事件（如温暖的天气，干旱或暴雨），主要关注区域数据，只关注成年人。气候变化与艾滋病毒严重程度之间的联系仍然完全没有得到探讨。该项目的主要目的是通过创建和分析一系列定量模型来确定潜在的关系，并了解气候变化与艾滋病毒之间的复杂途径，以发现哪些介质对这种关系影响最大。第二个目的是确定在家庭和区域一级，弱势个人（如贫困者、妇女、儿童）是否比其他人口更容易受到气候变化对艾滋病毒的影响。根据提出的目标，将调查一些研究问题：1）气候变化如何影响撒哈拉以南非洲的艾滋病毒血清转换率？2)气候变化对撒哈拉以南非洲脆弱人群艾滋病毒血清转化的影响是否加剧？3)气候变化如何影响撒哈拉以南非洲艾滋病毒阳性患者的艾滋病毒严重程度？4)气候变化对撒哈拉以南非洲脆弱人群中艾滋病毒阳性患者艾滋病毒严重程度的影响是否加剧？为了回答研究问题，将使用PHIA数据集。这些调查是对撒哈拉以南非洲13个国家艾滋病毒发病率的全国代表性加权调查，可外推到国家一级的病例。它们是迄今为止撒哈拉以南非洲艾滋病毒新感染人数最多的地区。在13个国家中，有143，853个家庭的数据可用。在这些数据集中，有368，373人具有HIV状态生物标志物。环境数据将来自多个来源：月降雨量将从气候灾害组“红色降水”中提取，温度数据将从气候数据库哥白尼数据中提取。将进行贝叶斯多级建模，以评估条件间接影响（适度中介分析）。数据分析方法将包括两个步骤。第一步将通过一些中介因素（如粮食不安全、饮用水不足、生活条件差、获得保健的机会有限、性暴力、性交易、药物滥用和危险性行为），估计环境变量与艾滋病毒血清状况/严重程度之间的间接关联（研究问题1和3）。第二步将涉及将先前命名的参数与在单独的不同模型中添加的主持人（性别，年龄，教育程度）相结合（研究问题，2&4）

HIV and climate change are both long-term adversities which exacerbate poverty, gender inequalities and exploitation of children. Additionally, marginalised individuals such as teenage girls, young women, migrants, sexual and gender minorities, as well as individuals with limited resources are often affected the most. Finally, both phenomena are difficult to tackle due to issues with political engagement and a large and spatially heterogenous distribution. HIV and climate change affect certain regions of the globe at different levels, with sub-Saharan Africa being the region the most heavily affected by both phenomena. As countries strive to achieve new UNAIDS 95-95-95 goals established in December 2020, climate change-related events could jeopardise epidemic control. This can hence drive the synergistic relationship between environment, human behaviours, vulnerabilities and HIV transmission, incidence and severity. There is very limited statistical evidence on the associations between environment and HIV. There are currently only four existing studies which looked at this relationship. All those studies confirm that environmental changes might have a negative impact on HIV incidence. However, those studies do not provide a comprehensive overview of all the potential variables associated with climate change and HIV. They also focus on only one weather event (such as warm weather, drought or heavy rainfall), mostly focus on regional data and only on adults. The links between climate change and HIV severity remain completely unexplored. The main aim of this project is to identify a potential relationship and understand the complex pathways between climate change and HIV by creating and analysing a range of quantitative model to discover which mediators have the greatest impact on that relationship. The secondary aim is to decide whether vulnerable individuals (such as deprived individuals, women, children) suffer from the impact of climate change on HIV more than the rest of the population at household and regional levels. Based on the proposed aims, a number of research questions will be investigated:1) How does climate change impact HIV seroconversion rates in sub-Saharan Africa?2) Is the impact of climate change on HIV seroconversion exacerbated in vulnerable populations in sub-Saharan Africa?3) How does climate change impact HIV severity in HIV-positive patients in sub-Saharan Africa?4) Is the impact of climate change on HIV severity in HIV-positive patients exacerbated in vulnerable populations in sub-Saharan Africa?To answer the research questions, PHIA datasets will be used. They are nationally representative, weighted surveys of HIV incidence in 13 sub-Saharan African countries which can be extrapolated to cases on the national level. They contain the largest numbers of new HIV infections in sub-Saharan Africa to date. Across 13 countries, data from 143,853 households is available. In the datasets, there are 368,373 individuals with HIV status biomarker available. Environmental data will be used from a number of sources: monthly rainfall will be extracted from Climate Hazards group InfraRed Precipitation with Station data and temperature data will be extracted from The Climate Data Store Copernicus data.Bayesian multilevel modelling will be conducted to assess conditional indirect effects (moderated mediation analysis). The data analytical approach will consist of two steps. The first step will estimate the indirect association between the environmental variables and HIV serostatus/severity (Research Questions 1 and 3) through a number of mediators (such as food insecurity, inadequate drinking water, inadequate living conditions, limited healthcare access, sexual violence, transactional sex, drug abuse and risky sexual behaviours). The second step will involve integrating previously named parameters with the addition of the moderators (gender, age, education) in separate distinct models (Research Qs, 2&4)