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.

回应 RFA-AI22-024 有限互动针对性流行病学：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）”，拥有一支跨领域、跨学科的科学团队， 纵向队列方面的专业知识，可招募大量经过数字化维护的患有此类疾病的人群 美国未充分病毒抑制的 HIV (PLWH)（>200 拷贝/mL），事先报告 HIV 护理参与度方面的差距，或缺乏持续的病毒抑制 (VS)。 应用理论方法研究艾滋病毒护理参与和 VS 的轨迹 认识到艾滋病毒护理和 VS 保留的周期性的分析策略。 检查这些结果背后的综合症，包括物质使用、心理健康和 艾滋病毒护理的障碍包括耻辱和农村隔离。我们的策略包括多重招募。 方法，例如 a) 与数字广告公司合作的社交媒体招聘 采用独特的工具、网站/应用程序和基于人工智能 (AI) 的模型来优化 外展/招聘；以及 b) 与最大的艾滋病医疗保健基金会 (AHF) 建立合作伙伴关系 我们将在该国招募一批多元化的艾滋病毒感染者和艾滋病患者。 居住在农村地区、年龄在 25 岁以下且面临药物使用挑战的黑人。 在 10 项研究中部署了基于证据的数字平台（HealthMpowerment，HMP），包括 其他有限互动目标流行病学 (LITE) 举措将用于数据收集， 实时分析和参与者交流，并将以半年度为补充 通过多管齐下的策略（即当地实验室、诊所和家庭）采集血液样本 基于收集选项）以促进参与。 当我们队列中的 PLWH 面临 VNS 风险最大时，可优化评估工具并提供 根据他们当时的个性化需求量身定制实时干预组件 我们最需要他们的调查团队来自三个领先的机构：加州大学。 在洛杉矶和欧文以及北卡罗来纳大学教堂山分校拥有数十年的 具有招募、参与和护理 PLHW 和大规模纵向队列的经验 研究以及长期合作开发的基于数字和人工智能的专业工具 我们利用富有成效的伙伴关系和专业知识来阐明艾滋病毒利益相关者的利益。 美国最脆弱人群中艾滋病毒持续流行的驱动因素 确定最有效、最迅速和可扩展的数字战略来解决这一持续存在的问题 公共卫生危机。