Addressing COVID-19 Testing Disparities in Vulnerable Populations Using a Community JITAI (Just in Time Adaptive Intervention) Approach-A UTHealth CTSA Program

The novel coronavirus that causes COVID-19 has negatively impacted global health status and economies worldwide. In the U.S., vulnerable populations experience significant disparities in terms of both infection and mortality rates, especially in the State of Texas, where Hispanic, African American, and other groups have experienced substantial disparities in both incidence and mortality. For example, Hispanics comprise 29.4% of the state's population, but 40.1% of COVID-19 cases and 47.9% of confirmed fatalities. The communities prioritized in this study include populations with medical comorbidities and those experiencing homelessness, and underserved Hispanics and African Americans in three racially diverse regions: South Texas, Houston/Harris County, and Northeast Texas. All of these regions are experiencing crisis-level surges in COVID-19 cases and fatalities. Testing disparities in Texas are evident, but understudied, and rapid deployment and evaluation of prevention, diagnostic, and mitigation strategies is imperative. Building on the partnerships and resources of the UTHealth Center for Clinical and Translational Science (CCTS), the goal of this study was to partner with community and stakeholder colleagues to identify dynamic disease hotspots and testing deserts in racially diverse neighborhoods of the target regions and to use that information to inform the rapid adaptation and deployment of multilevel level just-in-time adaptive intervention strategies to reach vulnerable populations. This study accomplished the following aims: 1) Identify disparities and dynamics of SARS-CoV-2 testing and infections in three Texas regions by (i) estimating real-time testing availability and uptake patterns utilizing data from regional health departments and hospital records; (ii) estimating real-time disparities in incidence rate and positive test rate of SARS-CoV-2 infections and COVID-19, and (iii) identifying, characterizing and predicting dynamic disease hotspots and testing deserts in vulnerable communities utilizing spatial-temporal modeling approaches. 2) Use multilevel network modeling methods to examine the structural properties of interorganizational networks; and apply network optimization methods to identify influential organizations that can maximize system-level organizational performance to better coordinate local testing and service delivery needs. 3) Engage CCTS community networks to develop, implement and evaluate a targeted, adaptive multilevel intervention to increase reach, uptake, implementation, and sustainment of SARS-CoV-2 diagnostics among priority vulnerable populations. This study evaluated the reach, effectiveness and implementation of the Community level Just-In-Time Adaptive Interventions (Community JITAIs) to increase SARs-CoV-2 testing and reduce infections in identified high risk priority neighborhoods, and conducted an embedded study to evaluate the use of community health worker-delivered SARS-CoV-2 diagnostics. This work was continued in Phase II of the funding by examining the aims in light of the COVID-19 vaccine being available to the populations of interest.