Evidence map›Paper›PMID 38505761›Full record

ReviewHealth equity2024

The Importance of Community-Based and Community-Partnered COVID-19 Testing for Reducing Disparities Among African American Populations.

Chavon Hamilton-Burgess, Jannette Berkley-Patton, Jenifer Allsworth, Carole Bowe Thompson, Frank E Thompson, Tacia Burgin, Eric D Williams, Kathryn P Derose

Abstract readReview
In one paragraph

Review in Health equity, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Chavon Hamilton-BurgessDepartment of Health Promotion and Policy, University of Massachusetts Amherst, Amherst, Massachusetts, USA.
Jannette Berkley-PattonDepartment of Biomedical and Health Informatics, University of Missouri Kansas City (UMKC) School of Medicine, Kansas City, Missouri, USA.
Jenifer AllsworthDepartment of Biomedical and Health Informatics, University of Missouri Kansas City (UMKC) School of Medicine, Kansas City, Missouri, USA.
Carole Bowe ThompsonDepartment of Biomedical and Health Informatics, University of Missouri Kansas City (UMKC) School of Medicine, Kansas City, Missouri, USA.
Frank E ThompsonKansas City MO Health Department, Kansas City, Missouri, USA.
Tacia BurginDepartment of Psychology, University of Missouri Kansas City (UMKC), Kansas City, Missouri, USA.
Eric D WilliamsCalvary Temple Baptist Church, Calvary Community Outreach Network, Kansas City, Missouri, USA.
Kathryn P DeroseDepartment of Health Promotion and Policy, University of Massachusetts Amherst, Amherst, Massachusetts, USA.ORCID https://orcid.org/0000-0001-7742-6024

Funding

COVID-19 Testing and Linkage to Care with African American Church and Health Agency Partners R01DK124664 · NIDDK · UNIVERSITY OF MISSOURI KANSAS CITY · PI BERKLEY-PATTON, JANNETTE YVONNE · 2020 to 2024
$5.2M
Faithful Response II: COVID-19 Rapid Test-to-Treat with African American ChurchesU01MD018310 · NIMHD · UNIVERSITY OF MISSOURI KANSAS CITY · PI BERKLEY-PATTON, JANNETTE YVONNE · 2023 to 2023
$2.0M
NIDDK NIH HHS R01 DK124664NIMHD NIH HHS U01 MD018310
6 · The paper itself

Abstract

Background: Health inequalities in African American communities have been further exacerbated by COVID-19. Public health departments and other safety-net providers across the United States have partnered with community-based organizations to address barriers to COVID-19 testing in disproportionately impacted communities. This narrative review summarizes lessons learned from published examples of these community-based COVID-19 testing efforts. Methods: We searched online databases for peer-reviewed articles on community-based COVID-19 testing interventions in the United States aimed at increasing COVID-19 testing among African American populations. We abstracted information about each example and synthesized the primary lessons learned and key aspects that contributed to their success. Results: Seven examples of community-based COVID-19 testing aimed at increasing testing among African Americans and other underserved populations were identified and described, across various U.S. locations and involving multiple types of partners (1) St. Paul, MN (faith, health centers, Mayo Clinic); (2) Chicago, IL (university hospital and health centers); (3) NC (health centers, Community Advisory Board); (4) Baltimore, MD (hospitals, community clinic, mobile clinic); (5) Marion County, FL (health department and community partners); (6) New Orleans, LA (health department and health system); and (7) New York City, NY health and hospital system, mobile clinic). Discussion: Several key aspects of the COVID-19 testing models included the following: (1) close proximity of the testing site to affected communities and availability of walk-up and drive-through testing options; (2) partnerships between safety-net providers and broad community networks, which facilitated outreach and trust; (3) increased resources for safety-net providers; and (4) the use of data to identify areas of need and track impact. The merging of resources and relationships among well-equipped, safety-net providers and other health care institutions and culture-rich, community-centered organizations, to jointly address structural and systemic inequities, is key to cultivating health equity in the distribution of COVID-19 testing and other essential public health services.

Indexed as

African Americancommunity networksCOVID-19 testinghealth equity

Identifiers

PMID38505761
PMCPMC10949942

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.