Which decisions affect cohort distribution in COVID-19 data analytics?

Data analytics has emerged as a crucial tool for understanding the multifaceted impacts of the COVID-19 pandemic. By collecting and analyzing extensive datasets, researchers have gained valuable insights into the virus's transmission, severity, and the effectiveness of public health measures. Yet, many contradictive and non-reproducible results have been published. The significance of cohort representativeness in this context cannot be overstated, as diverse cohorts provide a comprehensive understanding of how various demographic and clinical factors influence COVID-19 outcomes. This study investigates the impact of decision-making processes on cohort diversity, focusing on demographic categories including sex, race, and ethnicity. Results show that decisions made during data preprocessing and cohort construction increase variability in demographic distribution. Specifically, the difference in female representation varied by 0.77% to 2.68%, in Black race from 1.17% to 5.15%, and in Hispanic or Latino ethnicity from 5.84% to 8.21%. It highlights how arbitrary decisions can lead to varying data including changes to data distribution. Seemingly unrelated to demographics factors, including timing and provider selection, significantly influence patient distribution and outcomes, underscoring the necessity for informed, data-driven strategies. The findings emphasize the importance of strategic, evidence-based decision-making to enhance consistency, optimize resource utilization, and effectively serve diverse populations, ultimately contributing to more equitable health outcomes and informed public health policies.