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: 10709902
• 负责人: Sean Young
• 金额: $ 52.83万
• 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/10709902
• 关键词: Address; Adherence; Adopted; Advertising; Affect; African American; Area; Big Data; Black Populations; Black race; COVID-19 pandemic; COVID-19 risk; Caring; Case Study; Clinic; Cohort Studies; Color; Communication; Communities; County; Crime; Data; Data Sources; Devices; District of Columbia; Dropout; Enrollment; Epidemic; Epidemiologic Methods; Epidemiology; Ethics; European Union; Facebook; Future; Geography; HIV; HIV/AIDS; Health; Health Insurance Portability and Accountability Act; Individual; Intervention; Interview; Knowledge; Latinx; Latinx population; Laws; Learning; Machine Learning; Maps; Marketing; Mediator; Mental Health; Methods; Modeling; Movement; Neighborhoods; Oranges; Outcome Measure; Participant; Pattern; Persons; Policy Maker; Prevention; Privacy; Process; Public Health; Research; Research Personnel; Risk; Sampling; Services; Structure; Surveys; Target Populations; Technology; Time; United States; Visualization; aged; barrier to care; care outcomes; cohort; data de-identification; data quality; data reduction; design; digital; digital communication; experience; follow-up; handheld mobile device; high risk; implementation science; improved; innovation; insight; mobility aid; novel; outreach; participant enrollment; people of color; public-private partnership; recruit; scale up; socioeconomics; stay-at-home order; 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 艾滋病毒领域最重要的问题之一是解决艾滋病毒护理率低的问题 艾滋病毒/艾滋病感染者，特别是黑人/非裔美国人和拉丁裔社区。 该应用程序旨在研究一种通过采用和应用尖端技术来解决该问题的新方法 顶级科技公司正在使用“大数据推广”方法来提高消费者的参与度。 这种方法最近已经取代了其他数字推广方法，主要是出于隐私原因—— 符合欧盟隐私法——因为它涉及去识别化的数据。 所提议的数字推广方法已经应用于健康（但尚未应用于艾滋病毒）环境中。 在 COVID-19 大流行期间，我们的团队和其他人（包括 CDC）研究并发现成功应用 这些方法用于针对 COVID-19 高风险人群进行有针对性的数字招聘和外展。 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 HIV 协助针对难以接触到的个体并让其参与 HIV 研究和护理。 重要的是，所提出的方法允许访问大规模被动收集、选择加入、社区和 移动性（GPS ping）数据，已被证明可以添加丰富且精细的数据来改善健康 该应用程序旨在使用这些新颖的数字外展方法来识别。 并招募来自有色人种社区的艾滋病毒/艾滋病感染者，这些人处于失去护理的高风险中， 并分析他们的流动性和社区数据，以确定影响艾滋病毒护理的关键地理环境 我们正在与 2 终结艾滋病毒流行 (EHE) 合作开展这项工作。 司法管辖区（华盛顿特区和奥兰治县卫生部门）和主要参与者利益相关者 了解他们对需求、实施（包括道德问题）以及未来可能扩大规模的见解 具体来说，我们力求 1) 识别以下人员。 生活在 EHE 地区的艾滋病毒/艾滋病患者，他们处于失去护理的高风险中，2) 使用 GPS 移动性， 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，以及 3) 与华盛顿特区和奥兰治县卫生部门合作，探索 对这种方法在个人、人际和社会中持续存在的障碍和促进因素的案例研究 结构层面。 这项为期 1 年的队列研究将重点确定居住在 EHE 地区的人们 据我们所知，艾滋病毒护理很难与 EHE 结果措施相一致。 第一项将这些新颖的“大数据推广”方法应用于艾滋病毒的研究将招募迄今为止最大的队列 GPS 移动性、社区和其他 HIV 护理背景数据，以及第一个被动收集的 HIV 研究 流动性数据，与之前的研究相比，这有助于提高数据质量并降低辍学率。