Impact of Heterogeneity in Structural and Network Level Factors on HIV Treatment as Prevention in South Africa

 描述（由申请人提供）：结构和网络水平因素的作用越来越被认为是理解艾滋病毒感染传播的重要因素，因为研究人员发现，个人水平的特征不足以解释观察到的 艾滋病毒流行病。南非的艾滋病毒负担很重;据估计，15-49岁成年人的艾滋病毒感染率为18.8%，是世界上艾滋病毒感染者绝对人数最多的国家。最近的一项试验发现，早期艾滋病毒治疗可以防止进一步传播，目前的建议包括定期艾滋病毒检测和早期治疗。数学模型模拟了扩大“检测和治疗”计划的影响，以估计南非消除艾滋病毒的情况。这些模型主要假设那些被“测试和治疗”遗漏的个体是随机遗漏的，并且不包括结构因素或社区性行为的异质性。地区一级的结构性因素和艾滋病毒感染和测试历史之间的关系并没有一贯的特点，在全国代表性的调查南非人。同样，在南非，性行为的空间聚集性也没有得到很好的描述。了解艾滋病毒的结构和网络层面驱动因素的影响，对于有针对性地采取艾滋病毒预防和治疗干预措施至关重要。2012年，进行了第四次南非全国艾滋病毒行为和健康调查，这是一项对所有南非人进行的具有全国代表性的以家庭为基础的多阶段聚类抽样调查。SABSSM IV将是一个理想的研究，以了解结构和网络特征对南非艾滋病毒流行的影响，以及了解这些因素在艾滋病毒治疗作为预防干预措施的数学建模中的作用。我们将评估结构性因素（包括地区教育、收入、失业和移民）与个人艾滋病毒状况和艾滋病毒检测史之间的关联。接下来，我们将评估南非成年人性行为的空间聚集性。最后，我们将在南非的模拟社区中开发一个艾滋病毒传播的数学网络模型，该模型考虑到结构因素的差异性艾滋病毒检测， 在评估艾滋病毒治疗作为预防干预措施的影响时，按人口统计学特征和各社区性行为的异质性进行艾滋病毒治疗。这些目标可以通过评估结构和网络层面因素对艾滋病毒传播的影响，以及更好地理解不同的艾滋病毒检测、治疗和性行为对干预措施影响的作用，为艾滋病毒预防干预措施和政策提供信息。拟议的工作将：1）告知南非的艾滋病毒预防干预措施; 2）发展申请人在艾滋病毒传播，空间统计和多层次建模的数学建模方面的专业知识; 3）为进一步研究提供基础，特别是将拟议的数学模型扩展到博士后工作的其他人群和环境。

 DESCRIPTION (provided by applicant): The role of structural and network-level factors have increasingly been recognized to be important in understanding the spread of HIV infection, as researchers have found that individual-level characteristics are not sufficient to explain observed HIV epidemics. South Africa has a significant burden of HIV; with HIV prevalence estimated to be 18.8% among adults aged 15-49, and the largest absolute number of people living with HIV worldwide. A recent trial found that early HIV treatment prevents onward transmission, and current recommendations include both regular HIV testing and early treatment. Mathematical models have simulated the impact of scaling up a "test-and-treat" program to estimate the elimination of HIV in South Africa. These models primarily assume that individuals who are missed by "test-and-treat" are missed at random and do not incorporate heterogeneity in structural factors or in sexual behavior by community. The relationship between area-level structural factors and HIV infection and testing history has not been consistently characterized before in a nationally representative survey of South Africans. Similarly, spatial clustering of sexual behavior has not been well described in South Africa. Understanding the impact of structural and network-level drivers of HIV is important for targeting HIV prevention and treatment interventions. In 2012, the fourth South African National HIV Behavior and Health Survey (SABBSM IV), a nationally representative household-based multistage cluster sample survey of all South Africans, was conducted. SABSSM IV will be an ideal study to understand the impact of structural and network characteristics on the South African HIV epidemic as well as understanding the role of these factors in mathematical modeling of HIV treatment as prevention interventions. We will assess the association of structural factors (including area-level education, income, unemployment and migration) with individual-level HIV status and HIV testing history. We will next assess spatial clustering of sexual behavior among South African adults. Finally, we will develop a mathematical network model of HIV transmission in simulated communities in South Africa that takes into account differential HIV testing by structural factors, HIV treatment by demographic characteristics, and heterogeneity in sexual behavior across communities in assessing the impact of HIV treatment as prevention interventions. These aims can inform HIV prevention interventions and policy by assessing the impact of structural- and network-level factors on HIV transmission as well as better understanding of the role of differential HIV testing, treatment and sexual behavior on the impact of interventions. The proposed work will: 1) inform combination HIV prevention interventions in South Africa; 2) develop the applicant's expertise in mathematical modeling of HIV transmission, spatial statistics, and multilevel modeling; and 3) provide a basis for further research, particularly the expansion of the proposed mathematical model to additional populations and settings in post-doctoral work.