Clinical Epidemiology: Clinical Algorithms and the Legacy of Race-Based Correction: Historical Errors, Contemporary Revisions and Equity-Oriented Methodologies for Epidemiologists

July 2025

In a new study from the U.K., researchers Laura J. Horsfall, Paulina Bondaronek, Julia Ive, and Shoba Poduval bring the history and current issues surrounding race and clinical algorithms to the realm of epidemiology. In a comprehensive study, the authors explore the wide range of medical fields affected by this problem, the challenges inherent in the rise of machine learning and the necessity to avoid the replication of biased equations, the complexity of considering the impact of all uses or absences of race in clinical decision-making tools, and a framework for how epidemiologists can contribute to the movement to improve algorithmic fairness that has gained momentum in the United States as well as the U.K. The study includes detailed recommendations across research activities from design, measurement, the development of accurate variables that include social determinants and genetic factors, to bias detection, data processing, and transparency in every aspect of reporting and dissemination.