Exploring, Predicting, and Intervening on Long-term Viral suppression Electronically (EPI-LoVE)

电子方式探索、预测和干预长期病毒抑制 (EPI-LoVE)

• 批准号: 10676683
• 负责人: Pamina Mae Gorbach
• 金额: $ 168.46万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-03 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10676683
• 关键词: Acquired Immunodeficiency Syndrome; Address; Adherence; Advertising; African American; Age; Age Years; Anti-Retroviral Agents; Articulation; Artificial Intelligence; Assessment tool; Back; Behavior; Behavioral; Biological; Biological Markers; Black American; Black Populations; Black race; Blood Specimen Collection; Blood specimen; California; Caring; Clinic; Clinical; Collaborations; Collection; Communication; Country; Data; Data Collection; Data Set; Disease; Disparity; Drug user; Enrollment; Ensure; Epidemic; Epidemiologic Methods; Epidemiology; Evidence based intervention; Foundations; Geography; HIV; Healthcare; Home; Individual; Injectable; Injecting drug user; Institution; Intervention; Laboratories; Longitudinal cohort; Longitudinal cohort study; Los Angeles; Love; Medical; Mental Health; Methods; Modeling; Movement; Nature; North Carolina; Outcome; Participant; Patients; Patterns of Care; Persons; Phase; Productivity; Provider; Public Health; Recording of previous events; Regimen; Reporting; Research; Risk; Risk Assessment; Risk Factors; Rural; Social Distance; Specimen; Surveys; Testing; Time; Treatment Efficacy; United States; Universities; Viral; Viral Epidemiology; Viral Load result; Vulnerable Populations; Youth; antiretroviral therapy; barrier to care; behavior change; care outcomes; care providers; cohort; contextual factors; digital; digital health; digital intervention; digital platform; evidence base; experience; experimental study; health inequalities; high risk; intersectionality; machine learning method; multiple data sources; novel; outreach; prospective; recruit; response; rural area; social; social media; social stigma; structural determinants; substance use; syndemic; tool; web site

In response to RFA-AI22-024 Limited Interaction Targeted Epidemiology: Viral Suppression (LITE-VS), we propose “Exploring, Predicting, and Intervening on Long-term Viral suppression Electronically (EPI-LoVE)” with a cross-cutting, interdisciplinary scientific team with strong expertise in longitudinal cohorts to enroll a large, digitally maintained cohort of people living with HIV (PLWH) in the US who are not adequately virally suppressed (>200 copies/mL), report prior gaps in HIV care engagement, or lack of sustained viral suppression (VS). The cohort will investigate trajectories of HIV care engagement and VS applying a theoretical approach and analytic strategies that recognize the cyclical nature of retention in HIV care and VS. We will examine the syndemics underlying these outcomes including substance use, mental health, and barriers to HIV care including stigma and rural isolation. Our strategy includes multiple recruitment methods such as a) social media recruitment in partnership with digital advertising companies employing unique tools, websites/apps and artificial intelligence (AI)-based models to optimize outreach/recruitment; and b) a partnership with AIDS Healthcare Foundation (AHF), the largest HIV medical provider in the country. We will enroll a diverse cohort of PLWH enriched with those living in rural areas, Black, under 25 years of age and experiencing substance use challenges. An evidence-based digital platform (HealthMpowerment, HMP) deployed in 10 studies, including other limited interaction targeted epidemiology (LITE) initiatives, will be used for data collection, real-time analysis and participant communication and will be supplemented with semi-annual blood specimens collected with a multi-pronged strategy (i.e. local laboratory, clinics, and home- based collection options) to facilitate participation. We will develop a sophisticated risk assessment tool to optimize when PLWH in our cohort are most at risk for VNS and deliver tailored, real-time intervention components based on their personalized needs at the time they need them most. Our investigative team from three leading institutions The University of California at Los Angeles and Irvine, and University of North Carolina, Chapel Hill has decades of experience with recruitment, engagement, and care of PLHW and large-scale longitudinal cohort studies, as well as long-standing expertise developing digital and AI-based tools in collaboration with HIV stakeholders. We capitalize upon productive partnerships and expertise to articulate the drivers of the ongoing HIV epidemic among the most vulnerable populations in the US and to identify the most effective, expeditious and scalable digital strategies to address this ongoing public health crisis.

响应RFA-AI22-024有限的相互作用针对性流行病学：病毒抑制 （Lite-VS），我们提出“探索，预测和干预长期病毒抑制 电子（Epi-love）”，具有横切，跨学科的科学团队 纵向队列中的专业知识，以招募大量的数字维护人群 在美国，艾滋病毒（PLWH）实际上未充分抑制（> 200份/毫升），请事先报告 艾滋病毒护理参与或缺乏持续病毒抑制（VS）的差距。队列将 调查使用理论方法的艾滋病毒护理参与和与VS的轨迹 认识到艾滋病毒护理中保留的周期性性质的分析策略。我们将 检查这些结果的基础集团，包括药物使用，心理健康和 艾滋病毒护理的障碍，包括污名和粗糙的隔离。我们的策略包括多次招聘 诸如a）与数字广告公司合作的社交媒体招聘之类的方法 采用独特的工具，网站/应用程序和人工智能（AI）模型来优化 外展/招聘； b）与最大的AIDS Healthcare Foundation（AHF）建立合作伙伴关系 该国的艾滋病毒医疗提供者。我们将招募一群与那些的PLWH队列 生活在黑色，25岁以下的黑色区域，并经历了物质使用挑战。 一个基于证据的数字平台（HealthMpower，HMP）部署了10项研究，包括 其他有限的相互作用针对性流行病学（LITE）计划将用于数据收集， 实时分析和参与者的沟通，并将补充半年度 采用多管策略收集的血液标本（即地方实验室，诊所和家庭 - 基于收集选项）以促进参与。我们将发展一个复杂的风险 评估工具以优化何时在我们的同类中何时处于VN的风险并交付 根据他们的个性化需求，量身定制的实时干预组件 最需要它们。我们来自加利福尼亚大学三个领先机构的调查团队 在洛杉矶和尔湾，以及北卡罗来纳大学，教堂山有数十年 在PLHW和大规模纵向队列的招聘，参与和护理方面的经验 研究以及长期的专业知识开发了基于数字和AI的工具进行协作 与艾滋病毒利益相关者。我们利用生产伙伴关系和专业知识来表达 在美国最脆弱的人群中，正在进行的艾滋病毒流行的驱动因素和 确定最有效，最迅速，可扩展的数字策略来解决这一持续的 公共卫生危机。