Predicting Diabetic Retinopathy from Risk Factor Data and Digital Retinal Images

根据危险因素数据和数字视网膜图像预测糖尿病视网膜病变

• 批准号: 10258973
• 负责人: Lauren Daskivich
• 金额: $ 40万
• 依托单位: CHARLES R. DREW UNIVERSITY OF MED & SCI
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-11-17 至 2022-05-17
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10258973
• 关键词: Address; Affect; African American; Behavioral; COVID testing; COVID-19; COVID-19 testing; COVID-19 vaccine; California; Caring; Cessation of life; Characteristics; Clinical; Communities; Community Health; Community Health Aides; Community Health Services; Contracts; County; Data; Databases; Diabetic Retinopathy; Environment; Ethnic Origin; Feedback; Food; Fright; Funding; Future; Gender; Glean; Goals; Grant; Health Services; Health Status; Health education; Health system; Healthcare; Healthcare Systems; Housing; Insurance Coverage; Intention; Internet; Intervention; Interview; Knowledge; Language; Latinx; Linguistics; Los Angeles; Machine Learning; Marital Status; Measures; Medicine; Mental Health; Methods; Morbidity - disease rate; Patients; Pattern; PhenX Toolkit; Population; Procedures; Public Health; Race; Randomized; Readiness; Religion; Research; Resources; Risk Factors; SARS-CoV-2 positive; Science; Self Efficacy; Shapes; Site; System; Testing; Time; Training; Translating; Transportation; Trust; United States; United States National Institutes of Health; Universities; Ursidae Family; Vaccines; Weighing patient; base; comorbidity; design; digital; economic need; health care availability; health care settings; improved; individual patient; innovation; insight; literacy; machine learning method; medically underserved; multidisciplinary; outreach; pandemic disease; patient population; prevent; retinal imaging; safety net; skills; social health determinants; socioeconomics; telehealth; testing access; testing uptake; unsupervised learning; uptake; vaccine acceptance; virtual intervention; virtual patient

African American and Latinx communities nationally and in California not only bear a disproportionate burden of COVID-19 positive cases and deaths but are also not taking part in COVID-19 testing for a wide range of understudied reasons. This can have profound implications in safety net health care settings where vulnerable patients, who are in need of clinical procedures to prevent significant morbidity, are refusing such potentially lifesaving procedures because of fear of COVID-19 testing and/or contracting COVID-19. The Los Angeles County Department of Health Services (LACDHS) is the second largest publicly operated county safety net health care system in the United States, serving more than 750,000 patients annually. Timely access to health care in this under-resourced, high-need setting has been an ongoing challenge for its majority Latinx and African American patients. With the current pandemic, COVID-19 testing for patients has become an essential first step in the provision of critical procedural care. However, the range of reasons why patients refuse COVID-19 testing is little understood. To this end, we propose to explore the obstacles to COVID-19 pre-procedural testing and provide COVID-19 specific training to LACDHS Community Health Workers (CHWs) from these same communities to effectively address: a) the primary goal of increasing COVID-19 testing for individual patients, and the secondary goals of b) facilitating needed procedural care in a timely manner for the safety net health system, and c) developing a sustained public health presence in these communities to build trust and preparedness for critical COVID-19 related future needs. Trained CHWs can help to more effectively overcome obstacles to COVID-19 testing, including historical barriers of mistrust, provide COVID-19 health education, help address social determinants of health and help facilitate technological literacy to improve patient access to testing and care in a telehealth environment. The proposal uses a multidisciplinary, mixed-methods approach including unsupervised machine learning and qualitative interviews to systematically explore barriers and facilitators to COVID-19 testing among vulnerable safety net patients. We will then train clinically based, ethnically/linguistically matched CHWs to implement a hypothesis-driven intervention consisting of six group classes and six personalized patient encounters with African American and Latinx safety net patients. This study has the following specific aims: Aim 1- Utilize machine learning methods to assess whether there are characteristics that define African American and Latinx safety-net patients who engage in or refuse COVID-19 testing; Aim 2 - Conduct in-depth interviews with African American and Latinx patients who either declined or accepted COVID testing to explore contextual, behavioral, and attitudinal factors shaping patient circumstances and concerns; Aim 3 - Develop, implement, and pre-test a CHW intervention with the information from Aims 1 and 2, utilizing a randomized control design among African American and Latinx safety net patients to assess the effect of the CHW hypothesis-driven intervention on trust, self-efficacy, and intent to participate in COVID-19 testing.

全国和加州的非裔美国人和拉丁裔社区不仅承受着COVID-19阳性病例和死亡的不成比例的负担，而且由于各种未得到充分研究的原因，他们也没有参加COVID-19检测。这可能对安全网医疗保健环境产生深远影响，因为需要临床程序以防止重大发病率的脆弱患者由于担心COVID-19检测和/或感染COVID-19而拒绝这种可能挽救生命的程序。洛杉矶县卫生服务部（LACDHS）是美国第二大公共运营的县安全网医疗保健系统，每年为75万多名患者提供服务。对于大多数拉丁裔和非裔美国患者来说，在这种资源不足、需求高的环境中及时获得医疗保健一直是一个持续的挑战。在目前的疫情下，为患者进行COVID-19检测已成为提供关键程序护理的重要第一步。然而，人们对患者拒绝COVID-19检测的一系列原因知之甚少。为此，我们建议探索COVID-19术前检测的障碍，并为来自这些社区的LACDHS社区卫生工作者（CHW）提供COVID-19特定培训，以有效解决：a）增加对个体患者的COVID-19检测的主要目标，以及B）为安全网卫生系统及时提供所需的程序性护理的次要目标，以及c）在这些社区建立持续的公共卫生存在，以建立信任，并为COVID-19相关的未来需求做好准备。经过培训的社区卫生工作者可以帮助更有效地克服COVID-19检测的障碍，包括不信任的历史障碍，提供COVID-19健康教育，帮助解决健康的社会决定因素，并帮助促进技术扫盲，以改善患者在远程医疗环境中获得检测和护理的机会。该提案使用多学科、混合方法，包括无监督机器学习和定性访谈，系统地探索脆弱安全网患者中COVID-19检测的障碍和促进因素。然后，我们将培训基于临床、种族/语言匹配的CHW，以实施假设驱动的干预措施，包括六个小组课程和与非裔美国人和拉丁裔安全网患者的六次个性化患者接触。这项研究有以下具体目标：目标1-利用机器学习方法评估是否存在定义参与或拒绝COVID-19检测的非裔美国人和拉丁美洲人安全网患者的特征;目标2 -对拒绝或接受COVID检测的非洲裔美国人和拉丁裔患者进行深入访谈，以探索其背景、行为、和态度因素塑造病人的情况和关注;目标3 -利用目标1和2中的信息，制定、实施和预先测试CHW干预措施，在非裔美国人和Latinx安全网患者中采用随机对照设计，以评估CHW假设驱动的干预对信任的影响，自我效能感和参与COVID-19检测的意愿。