Big Data Digital Outreach and Epidemiology Methods for HIV Care among Communities of Color

有色人种社区艾滋病毒护理的大数据数字推广和流行病学方法

• 批准号: 10619830
• 负责人: Sean Young
• 金额: $ 81.16万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-24 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10619830
• 关键词: AIDS/HIV problem; Address; Adherence; Adopted; Advertising; Affect; African American; Area; Big Data; Black Populations; Black race; COVID-19 pandemic; COVID-19 risk; Caring; Case Study; Centers for Disease Control and Prevention (U.S.); Clinic; Cohort Studies; Color; Communication; Communities; County; Crime; Data; Data Sources; Devices; Dropout; Enrollment; Epidemic; Epidemiologic Methods; Epidemiology; Ethics; European Union; Facebook; Future; Geography; HIV; Health; Health Insurance Portability and Accountability Act; Home; Individual; Intervention; Interview; Knowledge; Latinx; Latinx population; Laws; Learning; Machine Learning; Maps; Marketing; Mediator of activation protein; Mental Health; Methods; Modeling; Movement; Neighborhoods; Oranges; Outcome Measure; Participant; Pattern; Persons; Prevention; Privacy; Process; Public Health; Research; Research Personnel; Risk; Sampling; Services; Structure; Surveys; Target Populations; Technology; Time; United States; Visualization; Washington; aged; barrier to care; base; care outcomes; cohort; data de-identification; data quality; design; digital; digital communication; experience; follow-up; handheld mobile device; high risk; implementation science; improved; innovation; insight; mobility aid; multilevel analysis; novel; outreach; people of color; public-private partnership; recruit; scale up; socioeconomics; success; tool

Abstract One of the most significant problems in the field of HIV deals with addressing the low rates of HIV care among individuals living with HIV/AIDS, especially among Black/African American and Latinx communities. This application seeks to study a novel way to address that problem by adopting and applying a cutting-edge “big data outreach” approach being used to increase consumer engagement by top technologies companies. This approach has recently been replacing other digital outreach methods largely for privacy reasons-- to conform to stringent European Union privacy laws-- as it involves de-identified data. The digital outreach method being proposed is already being applied in health (but not yet HIV) settings. During the COVID-19 pandemic, our team and others (including the CDC) studied and found success applying these methods for targeted digital recruitment and outreach to those at high-risk for COVID-19. As a result of the COVID-19 pandemic and its effect on use of digital/remote tools, these approaches will soon be applied to HIV to assist in targeting and engaging hard-to reach individuals in HIV research and care. Importantly, the proposed methods allow access to large-scale passively-collected, opt-in, community and mobility (GPS pings) data, which have been shown to add rich and granular data to improve health surveillance and interventions. This application seeks to use these novel digital outreach methods to identify and enroll individuals living with HIV/AIDS from communities of color who are at high-risk for being out of care, and analyze their mobility and community data to identify the key geographic contexts that impact HIV care engagement. We are conducting this effort for, and in partnership with, 2 Ending the HIV Epidemic (EHE) jurisdictions (Washington D.C. and Orange County health departments) and key participant stakeholders to gain their insights on needs, implementation (including ethical concerns), and potential future scale-up of this approach to improve surveillance and intervention efforts. Specifically, we seek to 1) Identify individuals of color living with HIV/AIDS within EHE regions who are at high-risk for being out of care, 2) Using GPS mobility, community (e.g., local crime), and HIV care data, identify the key geographic contexts that impact HIV care engagement, and 3) In partnership with the Washington D.C. and Orange County health departments, explore a case study of the ongoing barriers and facilitators of this approach at the individual, interpersonal, and structural levels. This 1-year cohort study will be focused on identifying people living within an EHE region who have been hard-to-reach for HIV care in order to converge with EHE outcome measures. To our knowledge, this is the first study to apply these novel “big data outreach” methods to HIV, will enroll the largest cohort to date with GPS mobility, community, and other HIV care contextual data, and the first HIV study to passively collect mobility data, which helps to increase data quality and reduce dropout rates compared to previous studies.

抽象的 艾滋病毒领域中最重要的问题之一就是解决艾滋病毒护理率低的率 在患有艾滋病毒/艾滋病的人中，特别是在黑人/非裔美国人和拉丁裔社区中。 该申请通过采用和应用尖端来研究一种新的方法来解决该问题 “大数据推广”方法用于增加顶级技术公司的消费者参与度。 最近出于隐私原因，这种方法最近一直在替换其他数字外展方法 - 符合严格的欧盟隐私法，因为它涉及去识别的数据。 提出的数字外展方法已经在健康（但尚未艾滋病毒）的环境中应用。 在COVID-19大流行期间 这些方法是针对性数字招聘和与高风险的Covid-19的方法。后果 COVID-19大流行及其对数字/远程工具使用的影响，这些方法将很快应用于 艾滋病毒以帮助和努力吸引个人参与艾滋病毒研究和护理。 重要的是，所提出的方法允许访问大规模的被动收集，选择加入，社区和 移动性（GPS PINGS）数据已被证明添加了丰富而颗粒状的数据以改善健康状况 监视和干预措施。该应用程序旨在使用这些新颖的数字外展方法来识别 并注册来自艾滋病毒/艾滋病的人，来自有色人种的社区，他们因失业而高风险， 并分析其流动性和社区数据，以确定影响艾滋病毒护理的关键地理环境 订婚。我们正在为艾滋病毒流行（EHE）努力并与2结束2结束的努力。 司法管辖区（华盛顿特区和奥兰治县卫生部门）和主要参与者的利益相关者 获得他们对需求，实施（包括道德问题）的见解以及对此的潜在扩大规模 改善监视和干预工作的方法。具体来说，我们寻求1）确定 艾滋病毒/艾滋病的颜色在EHE地区居住，他们因失业而高风险，2）使用GPS机动性， 社区（例如当地犯罪）和艾滋病毒护理数据，确定影响艾滋病毒护理的关键地理环境 参与，3）与华盛顿特区和奥兰治县卫生部门合作探索 对个人，人际关系和该方法的持续障碍和促进者的案例研究 结构水平。 这项为期1年的队列研究将重点放在确定生活在EHE地区的人们 为了融合EHE成果指标，难以应对艾滋病毒护理。据我们所知，这是 首次将这些新颖的“大数据推广”方法应用于艾滋病毒的研究将纳入迄今为止最大的队列 GPS移动性，社区和其他艾滋病毒护理上下文数据，以及第一个被动收集的HIV研究 与以前的研究相比，移动性数据有助于提高数据质量并降低辍学率。