Novel Evaluation Methods for Multi-Level/Combination HIV Prevention Interventions

多层次/组合艾滋病预防干预措施的新评估方法

• 批准号: 8409933
• 负责人: Edwin Duncan Charlebois
• 金额: $ 15.75万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-11 至 2014-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8409933
• 关键词: AIDS prevention; AIDS/HIV problem; Biological Markers; Caring; Centers for Disease Control and Prevention (U.S.); Characteristics; Cities; Clinic; Communities; Computational Biology; Continuity of Patient Care; Data; Data Set; Data Sources; Development; Developmental Process; Effectiveness; Elements; Equation; Ethnic Origin; Evaluation; Evaluation Research; Geographic Locations; HIV; HIV diagnosis; HIV-1; Health Policy; Human immunodeficiency virus test; Hybrids; Individual; Intervention; Intervention Studies; Measures; Mediating; Methods; Modeling; Outcome; Pathway Analysis; Pathway interactions; Pattern; Performance; Policy Maker; Population; Prevention; Prevention Research; Prevention approach; Preventive Intervention; Probability; Process; Process Measure; Proxy; Public Health; Race; Relative (related person); Research; Research Methodology; Research Project Grants; Resource Allocation; Resources; Risk; San Francisco; Science; Sensitivity and Specificity; Simulate; Structure; Testing; Time; Translational Research; Variant; Viral; Viral Load result; Viral load measurement; antiretroviral therapy; base; behavior change; cohort; evaluation/testing; evidence base; follow-up; innovation; method development; model design; novel; outreach; programs; response; surveillance data; theories; tool; transmission process; trend

DESCRIPTION (provided by applicant): The increasing call for community-level evaluation of "combination HIV prevention" (packages of our best evidence-based HIV prevention interventions) has brought to the forefront the need to develop novel methods of both evaluating these combinations of interventions in aggregate and estimating the relative contributions of individual components of the combined intervention. However, such comprehensive methods do not exist. The recent development of the concept of Community Viral Load (CVL), presents a significant opportunity to evaluate T2 translational research (bedside-to-community) at the community level. CVL is an aggregate biologic measure of HIV-1 viral load from surveillance data that serves as a population-level marker of treatment mediated HIV virologic suppression and HIV transmission risk. We propose to develop novel research evaluation methods to assess the relative contribution of intervention components within a combination intervention by bringing together two developing innovations: 1) computational biology modeling of community viral load, and 2) multi-level process pathway analysis of program inputs, process measures of intervention components, and observed changes in CVL. The specific aims are: Aim 1 - Computational Biology Modeling of Community Viral Load (CVL): In Aim 1 we will develop and test a computational biology model of CVL for San Francisco based on existing surveillance and clinic data. Aim 2 - Multi-Level Process Path Analysis Methods Development: In Aim 2 we will develop methods for the evaluation of change in CVL in response to multi-level/combination HIV prevention interventions by combining data obtained from CVL modeling in Aim 1 and hypothetical process measures of intervention components that could be observed in a combination HIV prevention intervention. We will utilize Structured Equation Modeling (SEM) and path analysis on simulated CVL data sets to estimate the relative contribution of intervention elements and estimate the sensitivity and specificity of competing analytic approaches. Aim 3 - Empirical Data Testing: In Aim 3 we will beta-test the best candidate process path model identified in Aim 2 using empirical data from a community-level combination HIV prevention intervention in San Francisco and estimate the performance of the methods. Significance: The proposed development of these urgently needed methods for evaluation of combination HIV prevention interventions has the promise of informing the science of community-level intervention assessment and will establish the feasibility of combining innovative CVL measurement with SEM process pathway analysis. Public Health Significance: Ultimately, the development of these methods to evaluate multi- level/combination prevention interventions at the community level will provide public health policy makers with an important tool to assess the community level effectiveness of these potentially high impact interventions and to assess which components merit implementation resource allocation. PUBLIC HEALTH RELEVANCE: This research will develop new methods to evaluate community-level "combination HIV prevention" (packages of our best evidence-based HIV prevention interventions). These methods will provide public health policy makers with an important tool to assess the community level effectiveness of these potentially high impact combination HIV prevention interventions and to assess which components merit implementation and to help allocate prevention resources.

描述（由申请人提供）：越来越多的人呼吁对社区层面的“综合艾滋病预防”（我们最好的循证艾滋病预防干预措施的一揽子方案）进行评估，这将开发新方法的必要性提上了前台，既要评估这些综合干预措施的组合，又要估计综合干预措施中各个组成部分的相对贡献。然而，这种综合的方法并不存在。社区病毒载量（CVL）概念的最新发展为在社区水平上评估T2转化研究（床边到社区）提供了一个重要的机会。CVL是来自监测数据的HIV-1病毒载量的综合生物学测量，可作为治疗介导的HIV病毒学抑制和HIV传播风险的人群水平标记。我们建议开发新的研究评估方法，通过结合两个正在发展的创新来评估组合干预中干预成分的相对贡献：1)社区病毒载量的计算生物学建模；2)程序输入、干预成分的过程测量和观察到的CVL变化的多层次过程途径分析。具体目标是：目标1 -社区病毒载量（CVL）的计算生物学建模：在目标1中，我们将基于现有的监测和临床数据开发和测试旧金山CVL的计算生物学模型。目标2 -多层次过程路径分析方法开发：在目标2中，我们将通过结合目标1中从CVL建模中获得的数据和在联合艾滋病毒预防干预中可以观察到的干预成分的假设过程措施，开发评估CVL响应多层次/联合艾滋病毒预防干预的变化的方法。我们将利用结构化方程建模（SEM）和模拟CVL数据集的路径分析来估计干预元素的相对贡献，并估计竞争分析方法的敏感性和特异性。目标3 -经验数据测试：在目标3中，我们将使用来自旧金山社区一级联合艾滋病毒预防干预的经验数据，对目标2中确定的最佳候选过程路径模型进行beta测试，并估计方法的性能。意义：这些迫切需要的HIV预防联合干预措施评估方法的发展有望为社区层面的干预评估提供科学依据，并将建立创新CVL测量与SEM过程路径分析相结合的可行性。公共卫生意义：最终，这些评估社区层面多层次/联合预防干预措施的方法的发展将为公共卫生政策制定者提供一个重要的工具，以评估这些潜在的高影响干预措施在社区层面的有效性，并评估哪些组成部分值得实施资源分配。