Leveraging artificial intelligence and social innovation to reduce disparities in COVID-19 testing among African Americans

利用人工智能和社会创新减少非裔美国人之间的 COVID-19 检测差异

基本信息

批准号：
10845418
负责人：
Tiarney D Ritchwood
金额：
$ 53.37万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-11-01 至 2024-10-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10845418
关键词：
Address Advertising Affect African American population American Artificial Intelligence Attitude Behavior Behavioral Belief COVID-19 COVID-19 disparity COVID-19 pandemic COVID-19 risk COVID-19 susceptibility COVID-19 testing Communication Communities Community Outreach Computers Data Disease Emotions Ethics Evaluation Event Exposure to Fatigue Future Goals Government Guidelines Health Health Disparities Research Healthcare Human Human immunodeficiency virus test Image Imagery Individual Industry Inequity Infrastructure Intervention Lead Measures Medical Misinformation Modeling Morbidity - disease rate Motivation Movement National Institute on Minority Health and Health Disparities Outcome Perception Persuasive Communication Population Public Health RADx Underserved Populations Randomized Recommendation Research Research Methodology Running Science Scientist Speech Technology Testing Time Trust Uncertainty Variant Voice Voting Vulnerable Populations Work animation behavior test breakthrough infection community based participatory research community burden community engagement community partnership community setting deep learning design disparity reduction effectiveness evaluation experience experimental study health communication human centered design improved innovation insight literacy low health literacy medical vulnerability member minority health minority health disparity mortality new technology pandemic disease pandemic response protective behavior public health research racism response social social vulnerability testing uptake therapy development uptake vaccine-induced immunity

项目摘要

Abstract Pandemic fatigue—a phenomenon characterized by a demotivation to follow recommended protective behaviors that emerges over time and is affected by one’s emotions, experiences and perceptions—threatens our ability to end the COVID-19 pandemic. Waning vaccine-induced immunity, breakthrough infections, new variants, and uncertainty all contribute to pandemic fatigue. These ongoing challenges highlight the importance of sustaining COVID-19 mitigation strategies, including COVID-19 testing, over the long run to achieve pandemic control. While pandemic fatigue is an expected and natural response to a prolonged public health crisis, it compromises our ability to keep members of underserved and medically and/or socially vulnerable populations safe, including African Americans. Given that complete eradication or elimination are not feasible, scientists and public health officials are focused on control measures to make COVID-19 endemic. To achieve endemic status, we must identify and address barriers to COVID-19 testing within vulnerable populations, including pandemic fatigue. Moreover, we must advance communication science interventions that enable us to determine how variations in the presentation of messages targeting perceived risk for COVID-19 can be leveraged to increase motivation for COVID-19 testing behaviors, and employ effective communication strategies to mitigate the impact of exposure to misinformation on testing acceptance and uptake. Guided by the Capability Opportunity Motivation—Behavior and Minority Health and Health Disparities Research Frameworks, this study will leverage participatory research methods, artificial intelligence, and infrastructure from ongoing community-engaged COVID-19 mitigation research to: 1) Host a design-a-thon to develop deep learning computer animations capable of conveying the importance of COVID-19 testing and promoting its uptake in community settings among African Americans in NC. 2) Determine whether a deep learning computer animation intervention (vs a control) improves COVID-19 testing uptake using a 1:1 randomized experiment. Study results will identify effective COVID-19 testing promotion messages for African Americans with the potential for generalization to other key populations.

抽象的流行病疲劳——一种以缺乏遵循推荐的保护行为的动力为特征的现象随着时间的推移而出现，并受到一个人的情绪、经历和看法的影响——威胁我们的能力结束因疫苗引起的免疫力减弱、突破性感染、新变种和新冠病毒大流行。不确定性都会导致大流行疲劳。这些持续的挑战凸显了维持下去的重要性。从长远来看，COVID-19 缓解策略（包括 COVID-19 测试）可实现大流行控制。虽然大流行疲劳是对长期公共卫生危机的预期和自然反应，但它会损害我们有能力保证服务不足以及医疗和/或社会弱势群体成员的安全，包括鉴于完全根除或消除非洲裔美国人是不可行的，科学家和公共卫生部门。官员们的重点是采取控制措施，使 COVID-19 成为流行病。为了实现流行病状态，我们必须这样做。识别并解决弱势群体中进行 COVID-19 检测的障碍，包括流行病疲劳。此外，我们必须推进传播科学干预措施，使我们能够确定针对已知的 COVID-19 风险的信息的呈现可以用来增加积极性 COVID-19 测试行为，并采用有效的沟通策略来减轻暴露的影响以能力机会动机——行为为指导的关于测试接受和采用的错误信息。和少数族裔健康和健康差异研究框架，本研究将利用参与性研究来自社区持续参与的 COVID-19 缓解措施的方法、人工智能和基础设施研究目的： 1) 举办设计马拉松，开发能够传达信息的深度学习计算机动画 COVID-19 检测的重要性以及促进其在非裔美国人社区环境中的采用 NC。2) 确定深度学习计算机动画干预（相对于对照）是否可以改善 COVID-19 使用 1:1 随机实验进行测试，研究结果将确定有效的 COVID-19 测试。针对非裔美国人的促销信息有可能推广到其他关键人群。