Data standards for race and ethnicity need to reflect and evolve with the scientific advances around the measurement and mechanisms by which race and ethnicity can affect health [17,18]. The stated goals of the OMB standards are to harmonize the collection and presentation of population race and ethnicity information across federal databases and to facilitate comparisons and analyses [16]. To achieve the objectives of precision medicine, ECHORN data on those who selected multiple categories, such as the white Hispanic, black Hispanic, black Asian, black East Indian, or Caribbean Hispanic individuals, could be aggregated with other data sources with defined granular race and ethnicity data on this population. For example, the US Census Bureau has developed granular data collection standards on race and ethnicity, quantifying the growth of these multiracial and multiethnic populations. The State of Massachusetts, along with other states, has for several decades mandated the collection of more granular race and ethnicity information, with over 30 categories on many data collection forms, including birth certificates and clinical data [19-21]. However, it is not specified how multiracial and multiethnic populations’ granular data can be harmonized to facilitate comparisons and analyses across any datasets collected using different standards. Current research collaborations, such as Observational Health Data Sciences and Informatics, that are focused on facilitating harmonization and sharing data by using standard ontologies such as Systematized Nomenclature of Medicine - Clinical Terms and Logical Observation Identifiers Names and Codes, have no agreed-upon standard for granular race and ethnicity data or a mechanism to map across data standards [22].

Standards have not been refined or expanded adequately to accommodate studies collecting granular race and ethnicity data on mixed or multiracial individuals [23,24], and these imprecise standards have unmeasured effects on health. Current standards may contribute to health disparities by providing insights on persons privileged by these data structures and by further marginalizing persons whose racial and ethnic identity is obscured in suboptimal data standards for classification and harmonization. While the OMB standards clearly state that “the racial and ethnic categories set forth in the standards should not be interpreted as being primarily biological or genetic in reference,” [16] current standards used in health sciences continue to support the conflation of the social categories of race and ethnicity with the biologic and genetic categories in day-to-day practice. This conflation has powerful implications for population-level biomedical discoveries and clinical care and treatment [25]. For example, inherent inequities in data standards are reflected in the continued use of monoracial guidelines as the standard of care in clinical practice, in spite of knowledge that suggests the fallacies of this framework [26,27]. It also has implications for population genetics and the translation of discoveries. These social constructs are used to develop standards for genetic population norms, wherein outlier data are discarded and study findings are associated with a singular racial group, influencing the development of biologic treatments directed at mutations that are predominantly in a particular racial group [28,29]. However, there is no biological origin for much of what we define as health inequities [30]. At the individual level, understanding how contextual factors, such as racism and discrimination, affect how a multiracial and multiethnic person understands and addresses their own health risks is significantly limited because of the lack of data and data standards that organize and share granular data in a viable way [31]. Importantly, multiracial and multiethnic populations are only one example of diverse subgroups affected by this lack of evolved standards; we risk the loss of rich information on the individuals who select more than one category for their race and ethnicity.

Several incremental changes can be instituted to improve these data standards on race and ethnicity. In addition to standardizing the definitions and collection of more granular race and ethnicity data across research studies, as advocated by numerous prior researchers [10-13], the standards need to be developed and adopted by funding agencies, such as the NIH, so that researchers from different groups who are collecting granular data can share and aggregate data [32,33]. These comprehensive data standards need to specify how to systematically categorize self-identified granular race and ethnicity data for those who have selected 2 or more categories. The standards can provide mechanisms to map race and ethnicity data collection standards to other standards over time, such as the Centers for Disease Control and Prevention Race and Ethnicity Code Set, Version 1.0, or the United States Census Bureau [32-34]. As data are collected and shared between regional and national entities, this mapping between standards will enable the aggregation of smaller data about underrepresented populations across studies and global contexts [32,35,36] and will provide a systematic method for individuals who have collected granular data to organize and map their data to less-evolved data collection standards.

Comprehensive data standards for race and ethnicity throughout the life course of health science research studies are critical to identifying and achieving heath equity for populations differentially affected by discrimination [23,24,37]. Partnerships with relevant stakeholders in the development and mapping of standards are essential and will likely increase availability of meaningful and usable data [38]. Multidisciplinary research teams that focus on health equity (eg, informaticists, data scientists, information scientists, geneticists, sociologists, and public health researchers) need to adapt standards and advance the theory and systems for organizing, storing, and analyzing complex data. These standards are urgently needed to ensure the sustained value of data collected about diverse populations and their health. With the status quo, we will continue to privilege one group’s data over another’s, lack meaningful and useable information of diverse racial and ethnic population groups, and likely further perpetuate health inequities.