Barriers to Meeting Community Needs in New York State during COVID-19: Results From a Pilot Survey of Nongovernmental Population and Community Health Professionals

Capacity building and training help empower the community and population health organizations to partner with local health departments and collaboratively design multisectoral interventions that account for the complexity of public health and health promotion challenges in the era of COVID-19 and beyond. Ideally, training programs should be informed by an understanding of the needs and priorities of the professionals for whom they are intended. This brief report focuses on the results of a pilot online survey conducted as part of a larger pilot study by the New York State Association of County Health Officials and the Region 2 Public Health Training Center among population and community health professionals (n = 27) from four counties in New York State during the COVID-19 pandemic. Survey participants included a diverse group of staff members from various large and small nonprofit organizations, federally qualified health centers, academic institutions, hospitals, and insurers. Survey findings provide preliminary insights into the extent to which these organizations have been involved in the COVID-19 response in partnership with LHDs, barriers they faced in responding to the needs of the populations they serve and adjusting their work routines/operations to COVID-19 guidelines, and their top emerging organizational and training needs. Lessons learned from conducting an online survey during a public health emergency and implications for future training interventions for population and community health professionals are also discussed within the context of promoting multisectoral collaboration with local health departments, solving complex public health problems, and advancing health equity.

Supporting Health Equity Through Data-Driven Decision-Making: A Local Health Department Response to COVID-19

COVID-19 highlights preexisting inequities that affect health outcomes and access to care for Black and Brown Americans. The Marion County Public Health Department in Indiana sought to address inequities in COVID-19 testing by using surveillance data to place community testing sites in areas with the highest incidence of disease. Testing site demographic data indicated that targeted testing reached populations with the highest disease burden, suggesting that local health departments can effectively use surveillance data as a tool to address inequities. (Am J Public Health. 2021;111(S3):S197–S200. https://doi.org/10.2105/AJPH.2021.306421 )

A Statistical Model to Assess Risk for Supporting COVID-19 Quarantine Decisions

In Germany, local health departments are responsible for surveillance of the current pandemic situation. One of their major tasks is to monitor infected persons. For instance, the direct contacts of infectious persons at group meetings have to be traced and potentially quarantined. Such quarantine requirements may be revoked, when all contact persons obtain a negative polymerase chain reaction (PCR) test result. However, contact tracing and testing is time-consuming, costly and not always feasible. In this work, we present a statistical model for the probability that no transmission of COVID-19 occurred given an arbitrary number of negative test results among contact persons. Hereby, the time-dependent sensitivity and specificity of the PCR test are taken into account. We employ a parametric Bayesian model which combines an adaptable Beta-Binomial prior and two likelihood components in a novel fashion. This is illustrated for group events in German school classes. The first evaluation on a real-world dataset showed that our approach can support important quarantine decisions with the goal to achieve a better balance between necessary containment of the pandemic and preservation of social and economic life. Future work will focus on further refinement and evaluation of quarantine decisions based on our statistical model.

COVID-19 Surveillance for Local Decision Making

Objective Data-informed decision making is valued among school districts, but challenges remain for local health departments to provide data, especially during a pandemic. We describe the rapid planning and deployment of a school-based COVID-19 surveillance system in a metropolitan US county. Methods In 2020, we used several data sources to construct disease- and school-based indicators for COVID-19 surveillance in Franklin County, an urban county in central Ohio. We collected, processed, analyzed, and visualized data in the COVID-19 Analytics and Targeted Surveillance System for Schools (CATS). CATS included web-based applications (public and secure versions), automated alerts, and weekly reports for the general public and decision makers, including school administrators, school boards, and local health departments. Results We deployed a pilot version of CATS in less than 2 months (August–September 2020) and added 21 school districts in central Ohio (15 in Franklin County and 6 outside the county) into CATS during the subsequent months. Public-facing web-based applications provided parents and students with local information for data-informed decision making. We created an algorithm to enable local health departments to precisely identify school districts and school buildings at high risk of an outbreak and active SARS-CoV-2 transmission in school settings. Practice Implications Piloting a surveillance system with diverse school districts helps scale up to other districts. Leveraging past relationships and identifying emerging partner needs were critical to rapid and sustainable collaboration. Valuing diverse skill sets is key to rapid deployment of proactive and innovative public health practices during a global pandemic.

Motivations, barriers, and communication recommendations for promoting face coverings during the COVID-19 pandemic: Survey findings from a diverse sample

State and local health departments have been tasked with promoting the use of face coverings to decrease the spread of COVID-19 in their respective communities. However, little is known about motivations and barriers to wearing face coverings in the context of COVID-19 prevention, particularly among communities of color who are at an increased risk of serious illness from the disease. The purpose of this study was to identify common motivations and barriers to face covering use, as well as explore perceptions of messages encouraging the use of face coverings among a racially and ethnically diverse sample. A survey was distributed electronically to North Carolina (NC) residents through NC Department of Health and Human Services listservs in July 2020. Participants self-categorized as Latino/a (33.5%), Black (39.1%), or white or another race (27.5%). The most commonly endorsed motivations for wearing face coverings were to avoid spreading COVID-19 (77%), as well as to protect people who are vulnerable (76%) and one’s community (72%). Being uncomfortable (40%) was the most commonly endorsed barrier. Messages that included a clear request (ex. please wear a face covering) and a direct benefit (ex. keep community safe) were more commonly endorsed than those that did not. Commonly endorsed motivations, behaviors, and messages differed by race and ethnicity. Increased attention to message content, message structure, and access to information and resources may aid local officials in increasing consistent use of face coverings.