dbGaP Study Accession: phs002619

NIH Institute/Center: NCATS

RADx Data Program: RADx-UP

Release Date: 08/31/2023

DOI: 10.60773/ydsc-2z38

Updated Date: 04/17/2024

Study Description: Local health departments are employing widely varying approaches to address the dramatic health inequities associated with the COVID-19 pandemic. Learning collaboratives have been effective in other settings for bringing entities together to support joint problem solving and reduce variance in approach. These collaboratives built upon shared experiences to help their members understand not only what to do but how to do it, providing knowledge and skills that were critical to successful implementation of interventions. This study aimed to examine the effectiveness of a diverse, regional learning collaborative of health departments that rapidly adapt, test, and implement community-driven solutions for COVID testing disparities using a pragmatic, hybrid type II trial design. Based on preliminary data from a community-led approach developed in a severely affected Kansas county (Wyandotte), the project built Local Heath Equity Action Teams in 10 counties (4 urban and 6 rural) disproportionately affected by COVID and provided them with the training and resources needed to achieve COVID testing equity. These Local Health Equity Action Teams came together into a multi-jurisdictional learning collaborative or Community Partner Program (Aim 1) that can share best practices and collaborate on developing, testing and evaluating multi-component intervention packages for addressing COVID. Initial supported interventions included: community-sponsored pop-up testing events; home-based and worksite testing; community-informed communication strategies; and support packages for at-risk individuals and families receiving testing. Additional interventions developed or proposed by local community partners or identified through national RADx-UP efforts were continuously considered by the collaborative. Working with the local Action teams, a series of semi-structured interviews and surveys were conducted within each local community (Aim 2) to better understand common and subgroup (racial, ethnic, geographic, etc.) barriers to and facilitators of testing. Existing epidemiologic surveillance tools were leveraged to develop and track a series of county-level metrics of COVID testing inequities. Guided by the RE-AIM framework and the Consolidated Framework for Intervention Research, the effectiveness (Aim 3) of the collaborative in improving uptake of COVID testing in vulnerable communities was examined, as will the reach, adoption, implementation and maintenance of intervention components within each local community. This study not only advanced understanding of how to achieve COVID testing equity but also provided a framework for supporting underserved communities as they respond to future public health emergencies and COVID vaccination challenges.

Principal Investigator: Ellerbeck, Edward F

Has Data Files: Yes

Study Domain: Mobile Unit Testing; Self-Testing (At-Home or OTC); Community Outreach Programs; Testing Rate/Uptake

Data Collection Method: Survey; Interview or Focus Group; Antigen Testing Device

Keywords: COVID Outreach/Education; Testing Disparities

Study Design: Longitudinal Cohort

Multi-Center Study: FALSE

Data Types: Questionnaires/Surveys; Social; Behavioral

Study Start Date: 09/25/2020

Study End Date: 06/30/2022

Species: Human Data

Estimated Cohort Size: 3000

Study Population Focus: Rural Communities; Underserved/Vulnerable Population; Adults; Older Adults or Elderly

Publication URL: https://pubmed.ncbi.nlm.nih.gov/36446055/; https://pubmed.ncbi.nlm.nih.gov/36265091/

Acknowledgement Statement: This study was supported through funding, 3UL1TR002366-04S3, for the National Center for Advancing Translational Sciences (NCATS) as part of the RADx-UP program. Approved users should acknowledge the provision of data access by dbGaP for accession phs002619.v1.p1, and the NIH RADx Data Hub. Approved users should also acknowledge the specific version(s) of the dataset(s) obtained from the NIH RADx Data Hub.

Funding Opportunity Announcement (FOA) Number: PA-20-135

NIH Grant or Contract Number(s): 3UL1TR002366-04S3

Consent/Data Use Limitations: General Research Use