Cross-sector Decision Landscape in Response to COVID-19: A Qualitative Analysis of North Carolina Decision-Makers

ContextThe COVID-19 pandemic response has demonstrated the interconnectedness of individuals, organizations, and other entities jointly contributing to the production of community health. This response has involved stakeholders from numerous sectors who have been faced with new decisions, objectives, and constraints. ObjectiveWe aimed to examine the cross-sector organizational decision landscape that formed in response to the COVID-19 pandemic. DesignWe applied a systems approach to the qualitative analysis of semi-structured interviews on the cross-sector, organizational response to the COVID-19 pandemic. We analyzed transcribed interviews using conventional content analysis to synthesize key themes. SettingSemi-structured interviews were conducted via secure, video-conferencing platform between October 2020 and January 2021. ParticipantsForty-four state and local decision-makers representing organizations from nine sectors in North Carolina participated. Main Outcome MeasuresWe defined the decision landscape as including decision-maker roles, key decisions, and inter-relationships involved in producing community health. ResultsDecision-maker roles were characterized by underlying tensions between balancing organizational mission with employee/community health and navigating organizational versus individual responsibility for reducing transmission. Key Decisions fell into several broad categories, including how to translate public health guidance into practice; when to institute, and subsequently loosen, public health restrictions; and how to address downstream social and economic impacts of public health restrictions. Lastly, given limited and changing information, as well as limited resources and expertise, the COVID-19 response required cross-sector collaboration, which was commonly coordinated by local health departments. ConclusionsBy documenting the local, cross-sector decision landscape that formed in response to COVID-19, we illuminate the impacts different organizations may have on information/misinformation, prevention behaviors, and, ultimately, health. Public health researchers and practitioners must understand, and work within, this complex decision landscape when responding to COVID-19 and future community health challenges.

Vaccinating Children Against COVID-19 is Essential Prior to the Removal of Non-Pharmaceutical Interventions

Objectives(s)To evaluate the joint impact of childhood vaccination rates and masking policies, in schools and workplaces, on community transmission and severe outcomes due to COVID-19. Study designWe utilized a stochastic, agent-based simulation of North Carolina, to evaluate the impact of 24 health policy decisions on overall incidence of disease, COVID-19 related hospitalization, and mortality from July 1, 2021-July 1, 2023. ResultsUniversal mask removal in schools in January 2022 could lead to a 38.1-47%, 27.6-36.2%, and 15.9-19.7% increase in cumulative infections for ages 5-9, 10-19, and the total population, respectively, depending on the rate of vaccination of children relative to the adult population. Additionally, without increased vaccination uptake in the adult population, a 25% increase in child vaccination uptake from 50% to 75% uptake and from 75% to 100% uptake relative to the adult population, leads to a 22% and 18% or 28% and 33% decrease in peak hospitalizations in 2022 across scenarios when masks are removed either January 1st or March 8th 2022, respectively. Increasing vaccination uptake for the entire eligible population can reduce peak hospitalizations in 2022 by an average of 89% and 92% across all masking scenarios compared to the scenarios where no children are vaccinated. Conclusion(s)High vaccination uptake among both children and adults is necessary to mitigate the increase in infections from mask removal in schools and workplaces.

Can Vaccine Prioritization Reduce Disparities in Covid-19 Burden for Historically Marginalized Populations?

1.ImportanceNationally stated goals for distributing SARS-CoV-2 vaccines included to reduce COVID-19 mortality, morbidity, and inequity using prioritization groups. However, the impact of these prioritization strategies is not well understood, particularly their effect on health inequity in COVID-19 burden for historically marginalized racial and ethnic populations. ObjectiveTo assess the impact of vaccination prioritization and operational strategies on disparities in COVID-19 burden among historically marginalized populations, and on mortality and morbidity by race and ethnicity. DesignWe use an agent-based simulation model of North Carolina to project SARS-CoV-2 infections and COVID-19-associated deaths (mortality), hospitalizations (morbidity), and cases over 18 months (7/1/2020-12/31/2021) with vaccine distribution beginning 12/13/2020 to frontline medical and people 75+, assuming initial uptake similar to influenza vaccine. We study two-stage subsequent prioritization including essential workers ("essential"), adults 65+ ("age"), adults with high-risk health conditions, HMPs, or people in low income tracts, with eligibility for the general population in the third stage. For age-essential and essential-age strategies, we also simulated maximal uptake (100% for HMP or 100% for everyone), and we allowed for distribution to susceptible-only people. ResultsPrioritizing Age then Essential had the largest impact on mortality (2.5% reduction from no prioritization); Essential then Age had the lowest morbidity and reduced infections (4.2% further than Age-Essential) without significantly impacting mortality. Under each prioritization scenario, the age-adjusted mortality burden for HMPs is higher (e.g., 33.3-34.1% higher for the Black population, 13.3%-17.0% for the Hispanic population) compared to the White population, and the gap grew under some prioritizations. In the Age-Essential strategy, the burden on HMPs decreases only when uptake is increased to 100% in HMPs. However, the Black population still had the highest mortality rate even with the Susceptible-Only distribution. Conclusions and RelevanceSimulation results show that prioritization strategies have differential impact on mortality, morbidity, and disparities overall and by race and ethnicity. If prioritization schemes were not paired with increased uptake in HMPs, disparities did not improve and could worsen. Although equity was one of the tenets of vaccine distribution, the vaccination strategies publicly outlined are insufficient to remove and may exacerbate disparities between racial and ethnic groups, thus targeted strategies are needed for the future.

Use of Modeling to Inform Decision-Making During the COVID-19 Pandemic: A Qualitative Study

BackgroundThe COVID-19 Pandemic has popularized computer-based decision-support models as a tool for decision-makers to manage their organizations. It is unclear how decision-makers have considered these models to inform COVID-19-related decisions. MethodsWe interviewed decision-makers from North Carolina across diverse organizational backgrounds to assess major decision-making processes during COVID-19, including the use of modeling as an input to inform decision-making. ResultsInterviewees were aware of models during COVID-19, with some depending upon multiple models. Models were used to compare trends in disease spread across localities, allocate scarce resources, and track disease spread within small geographic areas. Decision-makers desired models to project disease spread within subpopulations and estimate where local outbreaks could occur as well as estimate the outcomes of social distancing policies, including consequences beyond typical health-related outcomes. Challenges to the use of modeling included doubts that models could reflect nuances of human behavior, concerns about the quality of data used in models, and the limited amount of modeling at the local level. ConclusionsThroughout COVID-19, decision-makers perceived modeling as valuable for understanding disease spread within their communities and to inform organization decisions, yet there were variations in organizations ability and willingness to use models for these purposes.