# Harnessing Technological Innovation and Community-Engaged Implementation Science to Optimize COVID-19 Testing for Women and Children in Underserved Communities > **NIH NIH P42** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2020 · $3,759,967 ## Abstract ABSTRACT San Ysidro sits on the US-Mexico border and has a linguistically and ethnically diverse (91% Latinx) population, which is impacted by significant economic and health disparities. 33% of household incomes are less than $29K/yr, and there is a high rate of the comorbidities linked to poor COVID-19 outcomes. The San Ysidro Port of Entry is one of the largest international border crossing facilities in the world, with an estimated 50,000 vehicles and 25,000 pedestrians crossing into the US each day. A young and culturally diverse community of 27K residents, San Ysidro has the largest number of pre- and middle school children in San Diego. Physically removed from the major testing centers in San Diego, the response to the pandemic in San Ysidro has been hampered by a shortage of COVID-19 testing coupled with long test result turnaround times. The San Ysidro community has been disproportionately impacted by COVID-19, with the highest incidence of COVID-19 cases in San Diego County. Although children with COVID-19 infection generally have good outcomes, closures of public schools and associated activities have deprived many children of important educational and nutritional resources. Fear of infection has resulted in marked drops in prenatal and pediatric visits and lower childhood immunization rates. Since regular prenatal and pediatric care is associated with optimal pregnancy and child health outcomes, ensuring the safety of clinics is a high priority. San Ysidro Health is a federally qualified health center and the largest healthcare delivery system serving San Ysidro residents, including the uninsured and underinsured. The recommended schedule of prenatal and pediatric visits (for surveillance of maternal and fetal well-being, monitoring of childhood development, and provision of immunizations) provides an excellent opportunity to engage families in COVID-19 testing within an otherwise hard-to-reach population. At UC San Diego, scientists and engineers have developed a new high-throughput FDA authorized robotic testing workflow that provides an inexpensive, accurate, and rapid detection of COVID-19 infections. As part of the UC San Diego Superfund Research Center (SRC), we have in place extensive Community Outreach and Engagement with several South San Diego regions and have established strong partnerships with these communities, including those at San Ysidro. We will rely on a mixed methods, multi-level community-engaged approach to gather information on the barriers and facilitators of the delivery and uptake of COVID-19 testing at the individual, organizational and community levels. In partnership with our diverse Community and Scientific Advisory Board, we will use co-creation and appreciative inquiry approaches to inform the development, implementation and evaluation of a set of contextually relevant strategies that will accelerate the broad delivery and uptake of COVID- 19 testing among pregnant women and children and scaling across ... ## Key facts - **NIH application ID:** 10233717 - **Project number:** 3P42ES010337-19S2 - **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO - **Principal Investigator:** Robert H Tukey - **Activity code:** P42 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $3,759,967 - **Award type:** 3 - **Project period:** 2000-07-01 → 2023-03-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10233717 ## Citation > US National Institutes of Health, RePORTER application 10233717, Harnessing Technological Innovation and Community-Engaged Implementation Science to Optimize COVID-19 Testing for Women and Children in Underserved Communities (3P42ES010337-19S2). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10233717. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*