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

结构和网络层面因素的异质性对南非艾滋病毒治疗预防的影响

• 批准号: 9063200
• 负责人: Kathryn Risher
• 金额: $ 4.36万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9063200
• 关键词: AIDS prevention; Accounting; Adult; Age; Area; Behavior; Biological; Biology; Censuses; Characteristics; Collection; Communities; Complex; Country; Cross-Sectional Studies; Data; Demographic Factors; Early treatment; Education; Educational Background; Epidemic; HIV; HIV Infections; HIV risk; Health Surveys; Heterogeneity; High Prevalence; Household; Human immunodeficiency virus test; Income; Individual; Infection; Intervention; Life; Literature; Modeling; Municipalities; Needs Assessment; Play; Policies; Population; Postdoctoral Fellow; Prevalence; Prevention; Preventive Intervention; Probability Samples; Publishing; Recommendation; Recording of previous events; Reporting; Research; Research Personnel; Respondent; Role; Sampling; Sex Behavior; South Africa; South African; Surveys; Testing; Unemployment; Work; aged; antiretroviral therapy; base; condoms; consistent condom use; demographics; health care availability; mathematical model; migration; multilevel analysis; network models; prevent; programs; public health relevance; scale up; sex; statistics; success; transmission process

 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.

 描述(申请人提供)：结构性和网络层面的因素在理解艾滋病毒感染传播方面的作用日益被认识到是重要的，因为研究人员发现，个人层面的特征不足以解释观察到的 艾滋病毒流行。南非的艾滋病毒负担很重，15-49岁成年人中艾滋病毒感染率估计为18.8%，是全世界艾滋病毒携带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“测试和治疗”计划的影响，以估计在南非消除艾滋病毒的可能性。这些模型主要假设，被“测试和治疗”错过的个体是随机错过的，并且没有在结构性因素或社区的性行为中纳入异质性。区域一级的结构因素与艾滋病毒感染和检测历史之间的关系在以前对南非人进行的具有全国代表性的调查中并没有得到一致的描述。同样，在南非，性行为的空间聚集也没有得到很好的描述。了解艾滋病毒的结构性和网络级驱动因素的影响对于有针对性地开展艾滋病毒预防和治疗干预措施非常重要。2012年，进行了第四次南非全国艾滋病毒行为与健康调查(SABBSM IV)，这是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整群抽样调查。SABSSM IV将是一项理想的研究，以了解结构和网络特征对南非艾滋病毒流行的影响，并了解这些因素在艾滋病毒治疗作为预防干预措施的数学模型中的作用。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历史的关系。接下来，我们将评估南非成年人性行为的空间聚集性。最后，我们将开发一个在南非模拟社区中传播艾滋病毒的数学网络模型，该模型考虑了按结构因素进行的不同艾滋病毒检测， 在评估艾滋病毒治疗作为预防干预措施的影响时，通过人口统计特征和跨社区性行为的异质性来评估艾滋病毒治疗。这些目标可以通过评估结构性和网络级因素对艾滋病毒传播的影响以及更好地了解不同的艾滋病毒检测、治疗和性行为对干预措施的影响的作用，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)为南非的艾滋病毒预防综合干预措施提供信息；2)发展申请人在艾滋病毒传播的数学建模、空间统计和多水平建模方面的专业知识；以及3)为进一步的研究，特别是将拟议的数学模型扩展到更多的人口和博士后工作环境提供基础。