# Bridging the evidence-to-practice gap: Evaluating practice facilitation as a strategy to accelerate translation of a systems-level adherence intervention into safety net practices

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2022 · $480,412

## Abstract

ABSTRACT
COVID-19 has shed light on the significant and long-standing disparities in underserved communities. Current
data still show hospitalization rates among Black and Latinx individuals in the United States are 4 times greater
than that of Whites. The Rapid Acceleration of Diagnostics for Underserved Populations (RADx-UP) initiative
supports supplements to individual NIH awards to identify the determinants of COVID-19 testing among
underserved populations. For this proposal, we will leverage the infrastructure of a NIMHD-funded project in the
Family Health Centers (FHCs) of NYU Langone Health, a network of federally qualified health centers in NYC
that serves over 125,000 low-income and racially and ethnically diverse patients. In the current application, we
propose a three-phase community-engaged study that will employ a multipronged, sequential mixed methods
design (i.e., one methodology builds on the findings of the other) to gain a comprehensive understanding of the
multilevel factors that drive uptake of testing (and future vaccination) for COVID-19 of Black and Latinx patients
(primary outcome), and participation in follow-up care offered by safety-net health systems. Phase 1 will consist
of three steps: In step 1, we will leverage a well-characterized electronic health record database (~75% Black
and Latinx) to examine differences in the individual-level factors associated with receiving a positive versus
negative PCR test for COVID-19 among 400 Black and Latinx patients who receive care at the FHCs. We will
also capture the community- and structural-level determinants of testing in this sample using validated self-report
measures (e.g., NIH PhenX Tool Kit). In step 2, we will compare these multilevel factors across three patient
groups: Group 1- patients who tested positive and received follow-up care and/or services; Group 2- patients
who tested positive but did not receive follow-up care and/or services; and Group 3- patients who were eligible
for testing (based on symptoms and probable exposure), but did not get tested. In step 3, we will employ
predictive modeling to correctly identify patients at high-risk (group 3). In Phase 2, we will combine data from
the previous phase with qualitative data (i.e., ethnographic observations, document analyses, and focus groups
with FHC staff, providers, administrators, patients and community members) to capture organizational (e.g., FHC
staff/provider attitudes and communications with patients, organizational culture) and ethical issues (e.g., data
transparency and privacy) to shed light on important social, cultural, and contextual factors associated with
uptake of COVID-19 testing and potential vaccine. Finally, in Phase 3, in collaboration with our Community
Oversight Task Force, we will integrate Phase 1 and 2 data to refine, test, and disseminate tailored toolkits and
ethical governance guidelines (e.g. clinical trials transparency and data privacy). These toolkits will be designed
to in...

## Key facts

- **NIH application ID:** 10323161
- **Project number:** 3R01MD013769-04S1
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** OLUGBENGA G. OGEDEGBE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $480,412
- **Award type:** 3
- **Project period:** 2019-04-09 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10323161

## Citation

> US National Institutes of Health, RePORTER application 10323161, Bridging the evidence-to-practice gap: Evaluating practice facilitation as a strategy to accelerate translation of a systems-level adherence intervention into safety net practices (3R01MD013769-04S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10323161. Licensed CC0.

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