# Investigating the effectiveness of COVID-19 testing choices, community engagement, and culturally-embedded mHealth literacy delivery in a medically-underserved, community-based sample

> **NIH NIH R01** · UNIVERSITY OF CHICAGO · 2021 · $1,609,765

## Abstract

Since the first U.S. cases were identified in Washington on January 20th and soon thereafter in Chicago
on January 24th, COVID-19 has rapidly emerged as the most prevalent and deadly respiratory infection within
the State of Illinois, with 220,178 total confirmed cases and 7,880 (3.6%) deaths. The UIC Hospital and Health
Care System (UI Health), in collaboration with its 14 partnering Federally Qualified Mile Square Heath Center
practice sites (FQHCs) (where this project will be implemented), saw over 4,000 of these cases between March
and June, 2020 (6), noting the disproportionately higher rates if COVID-19 in Latinx people (47.1% with 32.7%
of deaths) and in people identifying as African American or Black (28.9% with 43.1% of deaths). Of 48,111 cases
reported in Cook County alone, 7,231 (15.0%) were hospitalized with 2,234 (4.6%) in an intensive care unit.
These COVID-19-related morbidity and mortality disparities are accompanied by numerous other health
disparities. Many of our FQHCs lack the staffing and revenue to test on-site and provide extensive follow-up
outpatient care for the large influxes of symptomatic patients concerned about COVID-19. These challenges
have likely resulted in increased COVID-19-related complications and deaths that might have been prevented
with clearer and more accurate public messaging about COVID-19, wider access to testing, earlier diagnosis,
and an increased perception that care is confidential, accessible, and self-driven. This project will employ
sentinel and community-based epidemiological surveillance and participatory research methods to evaluate
whether a person-centered, rapid COVID-19 testing intervention (at-home swab and send, or on-site point of
care testing), coupled with a novel mHealth COVID-19 Literacy and Outreach Suite of apps and videos, will
serve to increase the acceptance, access, reach, uptake, and impact of COVID-19 testing at UI Health and in the
14 FQHC practice sites and their corresponding catchment areas. We will also analyze the social, ethical,
economic, and behavioral drivers and consequences of our outreach and testing approaches according to the
degree to which participants contribute to the co-creation of study-related messaging. Finally, we will leverage
our existing infrastructures to expand testing uptake and analyze (by PCR) viral load at infection
onset/exposure and following onset/exposure to determine viral dynamics and identify individuals at early and
late stages of infection. PhenX measures will be used to test intervention effects on testing and retesting
uptake, time-to-diagnosis, COVID-19 knowledge, healthcare access and seeking, disclosure, medical adherence,
and practice of home self-isolation, quarantine, and other infection control behaviors. All data acquisition,
collection and curation approaches will be informed by the CDCC, including AboutML-informed consent
approaches and with the other RADxUP studies and relevant federal agencies.

## Key facts

- **NIH application ID:** 10258548
- **Project number:** 3R01ES028615-06S1
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Ayman Al-Hendy
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,609,765
- **Award type:** 3
- **Project period:** 2020-11-11 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10258548

## Citation

> US National Institutes of Health, RePORTER application 10258548, Investigating the effectiveness of COVID-19 testing choices, community engagement, and culturally-embedded mHealth literacy delivery in a medically-underserved, community-based sample (3R01ES028615-06S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10258548. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*
