Community Organizations, Local Health Equity Action Teams, and a Learning Collaborative to Address COVID-19 Disparities in Urban and Rural Communities.

Am J Public Health

Edward F. Ellerbeck, Vicki L. Collie-Akers, Sarah Landry, Sara Obermeier, Mariana Ramirez, Daniel L. Kurz, and Stacy McCrea-Robertson are with the Department of Population Health, University of Kansas Medical Center, Kansas City. Elizabeth Ablah and Allison Honn are with the Department of Population Health, University of Kansas Medical Center, Wichita. Yvonnes Chen is with the School of Journalism and Mass Communication, University of Kansas, Lawrence. Ian R. Knight is a community member, Phoenix, AZ. Crystal Y. Lumpkins is with the Department of Communication, Huntsman Cancer Institute, University of Utah, Salt Lake City. Mary Ricketts is with Turning Point Training and Consultation, Overland Park, KS. Tony Carter is with Salem Missionary Baptist Church, Kansas City, KS. Ullyses Wright is a community member, Overland Park, KS. Christal Watson is with Kansas City Kansas Schools Foundation, Kansas City, KS. Sarah Finocchario-Kessler, Joseph LeMaster, Erin Corriveau, and K. Allen Greiner are with the Department of Family Medicine, University of Kansas Medical Center, Kansas City. Broderick Crawford was with the NBC Community Development Corporation, Kansas City, KS. Jianghua He is with the Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City.

Published: November 2024

Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) Kansas worked with 10 Kansas counties from November 2020 through June 2022 to form local health equity action teams (LHEATs), develop COVID-19 testing strategies, foster communication about COVID-19, and share best practices through a learning collaborative. Participating counties documented 693 distinct COVID-19 testing and 178 communication activities. Although the intervention was not associated with changes in the proportion of positive COVID-19 tests, LHEATs in the learning collaborative implemented new testing strategies and responded to emerging COVID-19 challenges. (. 2024;114(11):1202-1206. https://doi.org/10.2105/AJPH.2024.307771).

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11447774PMC
http://dx.doi.org/10.2105/AJPH.2024.307771DOI Listing

Publication Analysis

Top Keywords

learning collaborative
12
local health
8
health equity
8
equity action
8
action teams
8
covid-19 testing
8
testing strategies
8
covid-19
6
community organizations
4
organizations local
4

Similar Publications

As the global economy expands, waterway transportation has become increasingly crucial to the logistics sector. This growth presents both significant challenges and opportunities for enhancing the accuracy of ship detection and tracking through the application of artificial intelligence. This article introduces a multi-object tracking system designed for unmanned aerial vehicles (UAVs), utilizing the YOLOv7 and Deep SORT algorithms for detection and tracking, respectively.

View Article and Find Full Text PDF

Global Use, Adaptation, and Sharing of Massive Open Online Courses for Emergency Health on the OpenWHO Platform: Survey Study.

J Med Internet Res

January 2025

Learning and Capacity Development Unit, Health Emergencies Programme, World Health Organization, Geneva, Switzerland.

Background: The COVID-19 pandemic demonstrated the global need for accessible content to rapidly train health care workers during health emergencies. The massive open access online course (MOOC) format is a broadly embraced strategy for widespread dissemination of trainings. Yet, barriers associated with technology access, language, and cultural context limit the use of MOOCs, particularly in lower-resource communities.

View Article and Find Full Text PDF

The Impact of Artificial Intelligence and Machine Learning in Organ Retrieval and Transplantation: A Comprehensive Review.

Curr Res Transl Med

January 2025

Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, United Kingdom.

This narrative review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in organ retrieval and transplantation. AI and ML technologies enhance donor-recipient matching by integrating and analyzing complex datasets encompassing clinical, genetic, and demographic information, leading to more precise organ allocation and improved transplant success rates. In surgical planning, AI-driven image analysis automates organ segmentation, identifies critical anatomical features, and predicts surgical outcomes, aiding pre-operative planning and reducing intraoperative risks.

View Article and Find Full Text PDF

Background: Clinically amyopathic dermatomyositis (CADM) is a rare subtype of idiopathic inflammatory myositis often linked with the presence of autoantibodies targeting melanoma differentiation-associated protein 5 (MDA5). Patients with CADM are at increased risk of developing rapidly progressing interstitial lung disease, which significantly increases both morbidity and mortality compared to other forms of inflammatory myopathies. While there is no standardized treatment regimen, current therapeutic strategies are generally focused on combination immunosuppressive therapies.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!