Predicting Diabetic Retinopathy from Risk Factor Data and Digital Retinal Images

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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.