Methods to Advance the HIV Prevention Research Agenda

推进艾滋病毒预防研究议程的方法

• 批准号: 9188055
• 负责人: VICTOR GERARD DEGRUTTOLA
• 金额: $ 40.86万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-12-01 至 2019-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9188055
• 关键词: AIDS prevention; Address; Affect; Age; Botswana; Cluster Analysis; Cluster randomized trial; Collection; Communities; Complex; Consent; Coupled; Data; Demographic Factors; Dependency; Development; Dropout; Epidemic; Excision; Failure; Gender; Generations; Goals; Grant; HIV; HIV Infections; Incidence; Infection; Instruction; Intervention; Investigation; Knowledge; Letters; Methods; Observational Study; Outcome; Participant; Policies; Population; Population Intervention; Prevention; Prevention Research; Prevention strategy; Preventive Intervention; Probability; Process; Property; Randomized; Research; Resources; Sex Behavior; Sexual Partners; Source; Statistical Methods; Structure; Testing; Time; Uncertainty; Work; analytical method; data integration; genetic linkage; genetic linkage analysis; insight; intervention effect; pre-exposure prophylaxis; research study; residence; response; scale up; self reported behavior; spatiotemporal; success; successful intervention; theories; transmission process; virus genetics

We develop new statistical methods for investigation and scale-up of HIV prevention interventions. Challenges arise from the complex dependencies that characterize data from HIV prevention research studies, reflecting the spread of HIV along the sexual contact networks we consider. The data generated from both randomized and observational studies include HIV incidence in different subpopulations, self-reported behavior regarding partner selection, and viral genetic sequences. Such data are likely to be incomplete because of failure to locate intended participants, denial of consent, and dropout. Furthermore even complete data would not allow networks to be fully characterized, or viral genetic linkage analyses to be conducted with certainty. Our methods are intended to make maximal use of incomplete information to estimate quantities that will be useful in guiding scale-up of successful interventions. These include not only the randomized effects of interventions, but also expected effects under policies of delivering them in ways that are likely to be used practice. Optimal scale-up requires knowledge of where and in which populations the interventions succeeded (fully or partially) and to identify factors, such as network features, that predict success. To investigate these questions, we extend methods developed in the first grant period for investigation of viral genetic linkage of incident and prevalent HIV infections. Such linkage provides information about the extent to which new infections arise from strains circulating within or across subpopulations defined by demographic factors, such as age, gender, and residence. It also provides insight into HIV transmission dynamics, such as identifying "bridge populations" that facilitate entry of HIV into new populations and the impact of interventions on such spread. During the first grant period, we focused on cross-sectional data collected in single villages; here we consider data collected across multiple villages over time that permit investigation of spatio-temporal HIV dynamics. Building on existing network theory and our own prior work, we also propose new methods to investigate how best to use prevention interventions to functionally fragment sexual contact networks and thereby impede epidemic spread. RELEVANCE (See instructions): Development of analytical methods is targeted to estimate quantities that will help guide policies regarding how best to target HIV prevention interventions, choose among them, and prioritize resources for their scale-up. We also describe application of new methods to data from HIV prevention research studies that may be incomplete because of denial of consent, dropout, or other mechanisms.

我们开发新的统计方法，用于调查和扩大艾滋病毒预防干预措施。 挑战来自艾滋病毒预防研究数据的复杂依赖性 研究，反映了艾滋病毒的传播沿着我们考虑的性接触网络。生成的数据 包括不同亚群的艾滋病毒发病率， 关于伴侣选择和病毒基因序列的自我报告行为。这些数据可能是 由于未能找到预期参与者、拒绝同意和退出而不完整。此外 即使是完整的数据也不能完全描述网络的特征，或者病毒遗传连锁分析， 要有把握地进行。我们的方法旨在最大限度地利用不完整的信息， 估计有助于指导扩大成功干预措施的数量。这不仅包括 干预措施的随机效应，以及在以不同方式提供干预措施的政策下的预期效应。 可能会被用于实践。最佳规模扩大需要了解在哪里和在哪些人群中 干预成功（全部或部分），并确定预测的因素，如网络特征， 成功为了研究这些问题，我们将第一个资助期开发的方法扩展到 调查艾滋病毒感染事件和流行病毒遗传连锁。这种联系提供了 关于新感染在多大程度上来自内部或跨部门传播的菌株的信息 由人口统计因素定义的亚群，如年龄、性别和居住地。它还提供了洞察力 艾滋病毒传播动态，如确定“桥梁人口”，促进艾滋病毒进入新的 人口和干预措施对这种传播的影响。在第一次拨款期间，我们专注于 在单个村庄收集的横截面数据;这里我们考虑跨多个村庄收集的数据 随着时间的推移，允许调查时空艾滋病毒动态。基于现有网络理论 和我们自己以前的工作，我们还提出了新的方法来研究如何最好地使用预防 * 采取干预措施，在功能上割裂性接触网络，从而阻止流行病的传播。 相关性（参见说明）： 制定分析方法的目标是估算数量，以帮助指导有关政策， 如何最好地针对艾滋病毒预防干预措施，选择其中，并优先考虑资源， 按比例放大。我们还描述了新方法在艾滋病预防研究数据中的应用， 可能由于拒绝同意、辍学或其他机制而不完整。