Clinical Integration of Community Health Workers to Reduce Health Inequities in Overburdened and Under-Resourced Populations.

The COVID-19 response has resulted in broader awareness of health inequities across the United States and their impact on overburdened and under-resourced communities. Investing in and more effectively integrating community health workers (CHWs) into health care delivery been prioritized in the COVID-19 response given the importance of trust and community connection to move people toward behavior change during times of uncertainty. CHWs serve as liaisons and connectors between patients, communities, and health/social care systems, providing culturally appropriate education and addressing complex social needs within the individual and community context. Given the pervasive health inequities that continue to persist despite decades of efforts to curb them, health care systems should reimagine current care delivery models to fully integrate CHWs into care teams. However, barriers exist to effectively deploying CHWs in health care systems. Through 20 years of experience developing, implementing, evaluating, and scaling CHW interventions, Sinai Urban Health Institute has learned valuable lessons in overcoming the common barriers to true and effective CHW integration. Organizations that approach CHW program implementation with a deliberate focus on recruitment and training and career pipelines/pathways, and adequately prepare their organization for CHWs will realize the benefits this unique workforce has to offer. Our experiences have demonstrated that if you hire the right people, train them effectively, and provide appropriate supervision, CHWs are transformative to health care delivery. We discuss our solutions in these areas within the context of integrating CHWs into our health care system to work with our most medically and socially complex patients.

Vulnerability to Hospitalizations Related to SARS Cov-2: A Neural Network Perspective (preprint)

In this paper, we put forth a novel perspective to measure the vulnerability of the population within the US to hospitalizations due to COVID-19. The current indices that are being used tend to separate the health perspective from that of the community and demographics. Besides, the Social, Community and Pandemic vulnerability indices use either a straight forward method of weighted averages or utilize Correlation and/or Bayesian approaches. We have focused on using a Deep Learning technique that tends to outperform other methods when the data is limited or has missing values.

Insights on COVID-19 From Community Health Worker State Leaders.

Community health workers (CHWs) leverage their trusting relationships with underresourced populations to promote health equity and social justice in their communities. Little is known about CHWs roles in addressing COVID-19 or how the pandemic may have affected CHWs' ability to interact with and support communities experiencing disparities. A focus group with CHW leaders from 7 states revealed 8 major themes: CHW identity, CHW resiliency, self-care, unintended positives outcomes of COVID-19, technology, resources, stressors, and consequences of COVID-19. Understanding the pandemic's impact on CHWs has implications for workforce development, training, and health policies.

MicrobeTrace: Retooling Molecular Epidemiology for Rapid Public Health Response (preprint)

Motivation Outbreak investigations use data from interviews, healthcare providers, laboratories and surveillance systems. However, integrated use of data from multiple sources requires a patchwork of software that present challenges in usability, interoperability, confidentiality, and cost. Rapid integration, visualization and analysis of data from multiple sources can guide effective public health interventions. Results We developed MicrobeTrace to facilitate rapid public health responses by overcoming barriers to data integration and exploration in molecular epidemiology. Using publicly available HIV sequences and other data, we demonstrate the analysis of viral genetic distance networks and introduce a novel approach to minimum spanning trees that simplifies results. We also illustrate the potential utility of MicrobeTrace in support of contact tracing by analyzing and displaying data from an outbreak of SARS-CoV-2 in South Korea in early 2020. Availability and Implementation MicrobeTrace is a web-based, client-side, JavaScript application ( https://microbetrace.cdc.gov ) that runs in Chromium-based browsers and remains fully-operational without an internet connection. MicrobeTrace is developed and actively maintained by the Centers for Disease Control and Prevention. The source code is available at https://github.com/cdcgov/microbetrace . Contact ells@cdc.gov