The COVID-19 pandemic has had a disproportionate impact on certain racial and ethnic groups, resulting in significant health outcome disparities. The National COVID Cohort Collaborative (N3C) provides a valuable resource for exploring these disparities through big data analytics. This study belongs to a broader work that examines decisions made during data processing and their impact on the analyses performed. Central to our analysis is the introduction of the Continuous Inpatient Encounter (CIE) concept—a novel method we propose for aggregating inpatient visits. By utilizing big data analytics, we aim to identify potential disparities in CIE rates among different racial groups. The results of this study are critical for enhancing the equity of data-driven decision-making in healthcare and for addressing the racial disparities observed in COVID-19 outcomes.

An inherent challenge arising in any dataset containing information of space and/or time is uncertainty due to various sources of imprecision. Integrating the impact of the uncertainty is a paramount when estimating the reliability (confidence) of any query result from the underlying input data. To deal with uncertainty, solutions have been proposed independently in the geo-science and the data-science research community. This interdisciplinary tutorial bridges the gap between the two communities by providing a comprehensive overview of the different challenges involved in dealing with uncertain geo-spatial data, by surveying solutions from both research communities, and by identifying similarities, synergies and open research problems.