Rethinking ethnicity data for precision health

Abstract

Ethnic inequalities in health and healthcare are well documented and the focus of research and quality improvement. Ethnicity is “the social group a person belongs to, and either identifies with or is identified with by others, as a result of a mix of cultural and other factors, including language, diet, religion, and geographical and ancestral origins.” One approach that has the potential to reduce health inequalities is precision medicine, which tailors diagnosis and treatment through analysis of genomic, environmental, and lifestyle data. Increasing amounts of data from healthcare records, research, pathology, imaging, and wearables are raising the potential of precision medicine. However, current UK ethnicity data relies on broad categories that may generalise across generations, communities, and locations, which may potentially exacerbate health inequalities. To avoid this, precision health links precision medicine with public health through socioeconomic, behavioural, environmental, and cultural factors in individual and population level interventions. Successful efforts to reduce inequalities will require action to improve ethnicity data; enhance public trust and standardise ethnicity data categories; and integrate data on social determinants of health.