Office Of Management And Budget Research Articles

Proposed Changes in Federally Mandated Race and Ethnic Classifications May Undermine Their Original Purpose

The Office of Management and Budget (OMB) is proposing an overhaul of “Statistical Policy Directive 15,” commonly referred to informally as OMB15 , the mandated method for collecting data on and classifying the race and ethnic status of the American population. Initiated in 1977, the standards for collecting data on race and ethnicity are mandated to be used by the Census Bureau and all federal agencies, and eventually all state, local, and private entities that receive federal funds, and/or come under civil rights compliance provisions designed to monitor disparities and correct discrimination. The classification was developed to monitor compliance with several civil rights laws affecting many sectors of American life that were passed in the 1960s and in later years. The widespread use and enormous implications of changes in how the United States collects race and ethnicity data mean that we should be very cautious about changes like the ones that have been proposed, and we think it would be wise to make these mandated data classifications both as simple as possible for the U.S. population to understand their meaning and to apply them in the many areas where they are to be used.

Abstract 410: Differences in Survivability of Out-of-Hospital Cardiac Arrest in Rural versus Urban Minnesota

Background: Out-of-Hospital Cardiac Arrest (OHCA) survival has improved in North America due to improved awareness, training, and bystander CPR. An important factor complicating OHCA survival is location on initial arrest, due to distance and access to emergency medical services (EMS). Aims: We sought to examine characteristics of OHCA encounters in Minnesota and determine underlying factors for differences in OHCA survival in rural versus urban areas. Methods: We examined data from the Cardiac Arrest Registry to Enhance Survival (CARES) registry between 2013-2021 in Minnesota. The analyses included encounters from 31 EMS services across the state. The Office of Management and Budget (OMB) county rural-urban classification schema was used to designate categories of rural (non-metropolitan counties of less than 50,000 population) versus urban (metropolitan counties with populations of 50,000 or more). Results: We identified 21,257 patient encounters, of which 3,524 (16.6%) of encounters were designated as rural and 17,733 (83.4%) as urban. In our cohort, patients were predominately male. Rural arrest patients tended to be older (mean age, 64.5 vs 60.6 years, p<0.01) and had a higher prevalence of heart disease (32.2% vs 26.9%, p<0.01). Rural arrests were more likely to be witnessed (57.1% vs 51.6%, p<0.01), receive bystander CPR (42.3% vs 35.2%, p<0.01), and have AED use prior to EMS arrival (55.7% vs 53.5%, p<0.01). The average time for EMS arrival at scene of arrest was longer among rural encounters (12.9 vs 10.6 minutes; p<0.01). Overall, survival-to-discharge was significantly lower among rural versus urban communities (9.3% vs 13.1%, p<0.01). Conclusion: In this analysis of the CARES dataset in Minnesota, there were significant differences in survival characteristics between rural vs urban OHCA patients. These findings warrant greater investment in rural EMS agencies and modifiable resuscitation practices to improve survival among rural communities.

Improving the Collection of National Health Data: the Case for the Middle Eastern and North African Checkbox for Communities in the USA.

The Office of Management and Budget (OMB) is reviewing current minimum standards for collecting race/ethnicity data on federal forms. This review is extremely important for Middle Eastern and North African (MENA) Americans, who have been overlooked and unable to receive federal funding for their communities for decades. MENA individuals are defined as "White," which is particularly concerning given that research continues to identify that their health and lived experiences differ from Whites. From January to April 2023, the OMB requested public comments on a separate MENA checkbox. The purpose of this research was to describe public comments regarding the addition of the MENA checkbox on the US Census and other federal forms. A public comment period outlining changes to the collection of race/ethnicity data on the US Census and other federal forms opened in January 2023. Public comments were reviewed to determine whether MENA was mentioned, whether comments supported a MENA checkbox, and whether comments mentioned acceptance for health-related reasons. There were 6700 comments reviewed. Most (73.88%) mentioned adding a MENA checkbox. Of those, 99.31% accepted adding the checkbox. Among the comments that accepted adding a MENA checkbox, 29.09% mentioned health, 44.75% mentioned linguistic/language services, and 44.75% mentioned education-related reasons. Overall, the comments reviewed demonstrated strong acceptance of the addition of a MENA checkbox on federal forms. These findings are encouraging, yet further review is needed to contribute to the OMB's final decision on whether to add the checkbox and uncover the health of this population.

Health Systems Need to Transform Data Collection to Advance Health Equity.

Health Systems Need to Transform Data Collection to Advance Health Equity.

Variations in how medical researchers report patient demographics: a retrospective analysis of published articles

The use of demographic variables in the medical literature has been a topic of much recent debate. Recent studies found that race and socioeconomic status (SES) are inconsistently reported. Best-practice use of sex and gender has been contentious. We aimed to characterise the state of medical demographic reporting in greater detail, especially regarding geography and specific terms used in articles. Original articles were included from issues of the New England Journal of Medicine (NEJM), JAMA, The Lancet, and the American Journal of Epidemiology (AJE) published from 1 January to 31 December 2020 (n=640). Articles without human participants, case reports, or with only aggregate data were excluded, leaving 594 articles. Use of age, sex, gender, race, ethnicity, and SES were coded, as well as corresponding author and participant geography. 99.0% of articles reported age. While 92.9% reported sex alone, only 4.7% used the term gender and 1.0% transgender. 47.8% of articles reported race and 29.6% reported ethnicity. Studies with U.S. corresponding authors or participants were significantly more likely to report race (72.9% and 73.7% respectively) or ethnicity (47.3% and 45.3% respectively) than those without (25.9% and 25.6% for race, 14.2% and 16.3% for ethnicity), p<0.01 for all. Of articles reporting race, 40.9% used only a Black-white binary; of those reporting ethnicity, 85.2% included two or fewer terms. Under 5.0% of all articles used Office of Management and Budget (OMB) categories. Across all articles, 33.0% reported SES, from 15.2% in NEJM to 80.2% in AJE. We found that while some factors (age, sex) are reported consistently, others (gender, race, ethnicity, SES) are not, despite recent attention. Authors often rely on binary or limited categories that inadequately capture human diversity. The presence of U.S. researchers or participants increased the reporting of race and/or ethnicity, highlighting wide variations that persist even as multinational collaborations become widespread. Researchers should reflect on their use of these terms, justify their decisions, and report results with care. None.

Disaggregating Racial and Ethnic Data: A Step Toward Diversity, Equity, and Inclusion

Disaggregating Racial and Ethnic Data: A Step Toward Diversity, Equity, and Inclusion

Redistributive Regulations and Deadweight Loss

AbstractRedistribution generates equity benefits and deadweight loss. A canonical result in economic theory holds that policymakers generally cannot escape the problem of deadweight loss by redistributing through non-tax rules. Nonetheless, the Office of Management and Budget (OMB) released a revised framework for regulatory analysis in November 2023 that encouraged federal agencies to consider distributional benefits – but not the deadweight loss of redistribution – when choosing which non-tax rules to promulgate. By omitting deadweight loss from distributional analysis, OMB’s framework will lead to inaccurate estimates of the welfare effects of regulatory changes and will leave agencies vulnerable to legal attack on the ground that they have ignored an important aspect of regulatory redistribution in their decision-making. This article illustrates how agencies can incorporate deadweight loss into distributional analysis and thereby place their redistributive rules on firmer economic and legal footing. It shows how the “elasticity of taxable income” approach – widely used by tax economists – can be modified to the regulatory context so that agencies can estimate the deadweight loss of redistributive regulations while incurring relatively modest additional analytical burdens. Explicitly incorporating deadweight loss into distributional analysis will provide the public with a more accurate view of the welfare consequences of regulatory actions and will make regulations more robust to legal challenges.

Data aggregation hides Pacific Islander health disparities

Meaningful measures of health disparities are needed to both motivate and monitor progress, but the accuracy of life expectancy estimates has been questioned, owing to misreporting of race and ethnicity and to small populations at the local level. In The Lancet, the GBD US Health Disparities Collaborators report their estimates of life expectancy in the USA, using two decades of death certificate data, with county-level corrections for race and ethnicity misclassification. The study1 used five race–ethnicity categories that align with the defunct 1977 Office of Management and Budget (OMB) standard (non-Latino and non-Hispanic White [White], non-Latino and non-Hispanic Black [Black], non-Latino and non-Hispanic American Indian or Alaska Native [AIAN], non-Latino and non-Hispanic Asian or Pacific Islander [API], and Latino or Hispanic [Latino]).

Defining the Race and Ethnic Standards for Federal Statistics and Administrative Reporting

Race describes cultural, historical, and oppressive relationships in society. The use of race in biomedical and scientific studies has been a powerful tool that can reinforce and alter society’s current assumptions about race. Some of the historical uses of race include evidence for race-based medicine, biological inferiority, and genocide. These uses have all used race as a crude proxy for genetic makeup, rather than a biological expression of the social environment that infiltrates the health and well-being of every American. By defining race and its social and cultural impacts on identity and the human experience within research, the field of biomedicine will improve clarity and integrity in addressing historical, scientific, and clinical inequalities. Currently, the Office of Management and Budget (OMB) does not contain a definition of race and uses homogeneous ethnical categories when reporting population statistics. We propose that the definition of race be added in the collection of race data as a requirement of the OMB for nationally conducted research.

Impression Management in Public Sector Audit Proposals: Language and Fees

We adapt impression management theory to investigate: (1) whether governmental audit proposals include the values of ‘professionalism’ (i.e. independence and competence) and ‘commercialism’ (i.e. client cooperation, auditor trustworthiness, client satisfaction, commercial interests), and, (2) the relations of audit proposal language and contextual influences on proposed audit fees and proposal success. The results suggest that proposals accord with audit firms’ market strategies. Compared to other firms, proposals from small, non-specialist firms use more commercial language, while proposals from governmental specialist firms use more competence language. The results also suggest that the market for US public sector audits includes many low cost, low quality firms; specifically, the modal proposal (41.2%) includes high use of commercialism language and low proposed audit fees. We also find that audit firms propose higher audit fees in US states with a weak governmental internal auditing office, and, that greater use of commercial language correlates with higher rates of proposal success, but only in US states with weak governmental internal auditing functions. The finding of inter-relations between proposal language, the strength of governmental internal audit functions, and proposed fees suggests the value of considering multiple influences on the quality of the public sector financial controls. Acronyms : American Institute of Certified Public Accountants (AICPA) ; Certified Public Accountant (CPA) ; Corporate social responsibility (CSR) ; European Court of Auditors (ECA) ; European Union (EU) ; fixed effects (FE) ; General Accountability Office (GAO) ; Governmental Financial Officers Association (GFOA) ; Independent Regulatory Board for Auditors (IRBA) ; Institute of Internal Auditors (IIA) ; International Accounting Education Standards Board (IAESB) ; International Federation of Accountants (IFAC) ; International Organization of Supreme Audit Institutions (INTOSAI) ; National Association of State Auditors ; Comptrollers and Treasurers (NASACT) ; not-for-profit (NFP) entities ; natural language processing (NLP) ; Office of Management and Budget (OMB) ; Request for proposal (RFP) ; Two-stage least squares (2SLS) ; United Kingdom (UK) ; United States (US)

Scientist Civic Engagement and the US EPA’s “Strengthening Transparency in Regulatory Science” Rule

BACKGROUND AND AIM: For decades, vested interests, including tobacco and oil companies, have sought access to data used by observational epidemiologists, even when involving private health data. This has included attacks on environmental epidemiology, calling it “Secret Science”, in tandem with efforts to push legislation through the US Congress. History has shown that vested industries have abused such data access, and the legislation failed. More recently, the Trump Administration attempted a regulatory “end around” of Congress, via the so-called “Strengthening Transparency in Regulatory Science” rule. METHODS: The North American Chapter (NAC) Policy Committee participated in preparing and presenting written and oral testimony to the US EPA and Office of Management and Budget (OMB) during hearings and requested meetings. These submissions are online at our web page: https://isee-northamerica.github.io/isee-nac/policy.html. These records documented the many problems with the proposed regulation, including: increased potential for compromise of medical record confidentiality; a loss of intellectual property; imposition of a government unfunded mandate; likely abuse of the research data to inappropriately undermine science credibility; and, damage to future scientific research recruitment efforts. RESULTS:The ISEE NAC Policy Committee recognized the above noted dangers to scientific research, and especially environmental epidemiology and took action, via civic engagement with the government, to put in place a record of those problems for future consideration by the courts in likely later legal efforts to overturn the ill-conceived regulation. At the same time, these government meetings, along with those by other scientific societies, slowed the progress of this potential regulation. In addition, scientists worked with lawyers to prepare legal challenges to the regulation. Ultimately, these efforts succeeded in delaying the regulation long enough for it to be successfully revoked by the incoming Biden Administration. CONCLUSIONS:The ISEE NAC Policy Committee provides a means for our member scientists to successfully influence government decision-making through civic engagement. KEYWORDS: Policy, Research translation, Science communication, Transparency, Civic engagement

Harmonizing Race and Ethnicity Data to Facilitate Data Analysis of Pooled Environmental Health Studies

BACKGROUND AND AIM: Race and ethnicity are social constructs that function as necessary tools in environmental health research, helping to identify underserved populations. Despite much discussion of more inclusive ways for individuals to self-identify, there is insufficient consideration for organizing these diverse subgroups for use as data across multiple studies, facilitating comparisons. The HHEAR (Human Health Exposure Analysis Resource) Data Repository houses deidentified data of environmental and exposome studies. Data are harmonized to a common vocabulary to facilitate data pooling. Most HHEAR studies use variations of the current Office of Management and Budget (OMB) standards to place groups of people into categories. These different categorizations can hinder harmonization, as some studies separate race and ethnicity using a two-question format while others construct a combined format. Few incorporate country or region of origin. Additionally, OMB standards are extended to studies that take place outside the United States, which may not have originally used these categorizations. METHODS: We conducted a review of published literature, standard ontologies used by research collaborations, and national reviews from countries outside the United States to review the harmonization of race and ethnicity and identify methods to facilitate data pooling in the HHEAR repository. RESULTS:As there was no clear consistency in mappings across literature, we created our own broad data standards to accommodate the diverse definitions incorporating race and ethnicity on both national and international levels. We encourage more standardized reporting in future studies in order to facilitate harmonization. CONCLUSIONS:Large, diverse datasets allow for investigations with larger sample sizes and greater exposure variability. By improving and standardizing methodology for data harmonization, environmental health studies can promote social justice by informing policy to allocate time and resources to at-risk communities and, in turn, reduce health disparities. KEYWORDS: Dictionary Mapping, Knowledge Modeling, Data Standards, Harmonization, Race, Ethnicity

What’s Your Street Race? The Urgency of Critical Race Theory and Intersectionality as Lenses for Revising the U.S. Office of Management and Budget Guidelines, Census and Administrative Data in Latinx Communities and Beyond

What’s your street race? If you were walking down the street what race do you think strangers would automatically assume you are based on what you look like? What is the universe of data and conceptual gaps that complicate or prevent rigorous data collection and analysis for advancing racial justice? Using Latinx communities in the U.S. as an example, we argue that scholars, researchers, practitioners and communities across traditional academic, sectoral and disciplinary boundaries can advance liberation by engaging the ontologies, epistemologies and conceptual guideposts of critical race theory and intersectionality in knowledge production for equity-use. This means not flattening the difference between race (master social status and relational positionality in a racially stratified society based on the social meanings ascribed to a conglomeration of one’s physical characteristics, including skin color, facial features and hair texture) and origin (ethnicity, cultural background, nationality or ancestry). We discuss the urgency of revising the U.S. Office of Management and Budget (OMB) standards, as well as the Census and other administrative data to include separate questions on self-identified race (mark all that apply) and street race (mark only one). We imagine street race as a rigorous “gold standard” for identifying and rectifying racialized structural inequities.

Enhancing Language Services to Native American Children: A Look From the Inside

Enhancing Language Services to Native American Children: A Look From the Inside

Implementation of the Federal Performance Framework under Presidents Obama and Trump: A Comparative Analysis of Agency Strategic Plans

Strategic plans are a foundational element of the Federal Performance Framework introduced by the U.S. Office of Management and Budget (OMB) in 2012 to encourage agency implementation of the GPRA Modernization Act of 2010. This study compared federal Chief Financial Officer (CFO) agency strategic plans written between Fiscal Years (FY) 2012 and 2019 under Presidents Obama and Trump, respectively. A component analysis of selected agency strategic plans revealed that CFO agencies adhered to a rational plan design before and after the presidential transition. But under President Obama, agency plans adhered more closely to the design criteria in OMB implementation guidance. A decrease was observed in the number of strategic goals and strategic objectives in agency strategic plans written under President Trump. Under both Presidents, CFO agencies pursued a mix of strategic objectives including a larger percentage that were externally-oriented, mission-related outcomes or objectives in both administrations. A higher percentage of strategic objective statements in all of the CFO agency plans written under President Trump were more externally-oriented and included more mission-related outcomes, when compared to agency plans written under President Obama.

Race and ethnicity reporting for clinical trials in ClinicalTrials.gov and publications

Inclusion and subsequent reporting of minority participants in clinical trials are critical for ensuring external validity and detecting differences among subgroups, however reports suggest that ongoing gaps persist. ClinicalTrials.gov began requiring the reporting of race/ethnicity information (if collected) during results submission for trials in April 2017. For this study, we downloaded and compared trial race/ethnicity information from ClinicalTrials.gov submitted before (N = 3540) and after (N = 3542) the requirement date. We found that 42.0% of pre-requirement trials compared to 91.4% of post-requirement trials reported race/ethnicity information in ClinicalTrials.gov; 8.6% of post-requirement trials indicated race/ethnicity information was not collected. Use of NIH/U.S. Office of Management and Budget (OMB) classification categories was slightly higher in the post-requirement (77.1%) compared to pre-requirement (72.8%) samples. Additionally, we examined two 10% random samples of post-requirement trials – one with customized race/ethnicity reporting in ClinicalTrials.gov and the other with corresponding results publications available in PubMed. In the first random sample, 95.9% of customized categories included race information and 52.7% included ethnicity information. In the other random sample, 33.1% had a corresponding results publication, of which 62.4% reported race/ethnicity information in the publication. Among trials without published race/ethnicity information, 90.0% reported race/ethnicity information on ClinicalTrials.gov. This analysis demonstrates that the requirement has advanced public availability of information on the inclusion of minorities in research, but that further work remains to systematically ensure collection and complete reporting of race/ethnicity information.

Abstract PO-258: Cancer risk factors and screening across various U.S. rural classification codes: Cancer disparities in the context of rurality

Abstract Incidence and mortality of many preventable cancers are higher in rural compared to urban U.S. regions. However, prior research is limited by methodological inconsistencies in defining rurality, and has largely focused on a rural-urban dichotomy which ignores potential variability of cancer risk factors and outcomes within rural and urban settings. These limitations hamper data interpretation and may lead to inequitable distribution of resources to address disparities. In this study, we describe cancer risk factors and screening across various U.S. rural-urban classification codes to inform our understanding of cancer disparities across the rural to urban continuum. We examined the prevalence and trends in cancer risk factors and screening across the following rural-urban classification codes for counties and county-equivalents in 2013; U.S. Office of Management and Budget (OMB), National Center for Health Statistics (NCHS), U.S. Department of Agriculture, Economic Research Service’s rural-urban continuum codes (RUCC), and Urban Influence Codes (UIC). County-level data on demographics (age, race, education, income, poverty level, insurance status), and cancer risk factors (obesity, physical inactivity, alcohol consumption), including cancer screening (colorectal, cervical and breast) were obtained from the U.S. Census Bureau, Behavioral Risk Factor Surveillance System, and National Health Interview Survey from 2008-2013. Overall, rural counties’ populations were more likely to be older, white, and uninsured with lower educational attainment and household income than urban counties (all ptrend&amp;lt;0.001). Across all classification codes, the prevalence of smoking, obesity, physical inactivity, and binge alcohol use increased (all ptrend&amp;lt;0.03), while colorectal, cervical and breast cancer screening decreased (all ptrend&amp;lt;0.001) with increasing rurality. Variation in the prevalence of cancer risk factors and screening was also evident within rural regions. For example, cervical cancer screening ranged from 67.6% to 74.7%, breast cancer screening from 65.4% to 68.2%, and colorectal cancer screening from 52.8% to 57.2% across rural counties. Our findings suggest that rural cancer disparities persist across multiple rural-urban classification codes, with marked variation in cancer risk factors and screening apparent within rural regions. Thus, focusing on a rural-urban dichotomy may fail to identify subpopulations of rural residents at greater risk for cancer and cancer-related mortality. As such, the variation in cancer disparities within rural regions, including persistent racial/ethnic and socioeconomic differences, should be considered in future cancer control research to inform the development of targeted policies and strategies to address rural cancer disparities. Citation Format: Kelly A. Hirko, Huiwen Xu, Laura Q. Rogers, Michelle Y. Martin, Siddhartha Roy, Kimberly M. Kelly, Shannon M. Christy, Kimlin T. Ashing, Jean C. Yi, Marquita W. Lewis-Thames, Cathy D. Meade, Qian Lu, Clement K. Gwede, Julianna Nemeth, Rachel M. Ceballos, Usha Menon, Katie Cueva, Karen Yeary, Lisa Klesges, Monica L. Baskin, Kassandra I. Alcaraz, Sabrina Ford. Cancer risk factors and screening across various U.S. rural classification codes: Cancer disparities in the context of rurality [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr PO-258.

A Method to Improve Availability and Quality of Patient Race Data in an Electronic Health Record System.

Although federal regulations mandate documentation of structured race data according to Office of Management and Budget (OMB) categories in electronic health record (EHR) systems, many institutions have reported gaps in EHR race data that hinder secondary use for population-level research focused on underserved populations. When evaluating race data available for research purposes, we found our institution's enterprise EHR contained structured race data for only 51% (1.6 million) of patients. We seek to improve the availability and quality of structured race data available to researchers by integrating values from multiple local sources. To address the deficiency in race data availability, we implemented a method to supplement OMB race values from four local sources-inpatient EHR, inpatient billing, natural language processing, and coded clinical observations. We evaluated this method by measuring race data availability and data quality with respect to completeness, concordance, and plausibility. The supplementation method improved race data availability in the enterprise EHR up to 10% for some minority groups and 4% overall. We identified structured OMB race values for more than 142,000 patients, nearly a third of whom were from racial minority groups. Our data quality evaluation indicated that the supplemented race values improved completeness in the enterprise EHR, originated from sources in agreement with the enterprise EHR, and were unbiased to the enterprise EHR. Implementation of this method can successfully increase OMB race data availability, potentially enhancing accrual of patients from underserved populations to research studies.

Metropolitan Reclassification and the Urbanization of Rural America.

We highlight the paradoxical implications of decadal reclassification of U.S. counties (and America's population) from nonmetropolitan to metropolitan status between 1960 and 2017. Using data from the U.S. Census Bureau, we show that the reclassification of U.S. counties has been a significant engine of metropolitan growth and nonmetropolitan decline. Over the study period, 753-or nearly 25% of all nonmetropolitan counties-were redefined by the Office of Management and Budget (OMB) as metropolitan, shifting nearly 70 million residents from nonmetropolitan to metropolitan America by 2017. All the growth since 1970 in the metropolitan share of the U.S. population came from reclassification rather than endogenous growth in existing metropolitan areas. Reclassification of nonmetropolitan counties also had implications for drawing appropriate inferences about rural poverty, population aging, education, and economic growth. The paradox is that these many nonmetropolitan "winners"-those experiencing population and economic growth-have, over successive decades, left behind many nonmetropolitan counties with limited prospects for growth. Our study provides cautionary lessons regarding the commonplace narrative of widespread rural decline and economic malaise but also highlights the interdependent demographic fates of metropolitan and nonmetropolitan counties.

Information Loss in Harmonizing Granular Race and Ethnicity Data: Descriptive Study of Standards.

BackgroundData standards for race and ethnicity have significant implications for health equity research.ObjectiveWe aim to describe a challenge encountered when working with a multiple–race and ethnicity assessment in the Eastern Caribbean Health Outcomes Research Network (ECHORN), a research collaborative of Barbados, Puerto Rico, Trinidad and Tobago, and the US Virgin Islands.MethodsWe examined the data standards guiding harmonization of race and ethnicity data for multiracial and multiethnic populations, using the Office of Management and Budget (OMB) Statistical Policy Directive No. 15.ResultsOf 1211 participants in the ECHORN cohort study, 901 (74.40%) selected 1 racial category. Of those that selected 1 category, 13.0% (117/901) selected Caribbean; 6.4% (58/901), Puerto Rican or Boricua; and 13.5% (122/901), the mixed or multiracial category. A total of 17.84% (216/1211) of participants selected 2 or more categories, with 15.19% (184/1211) selecting 2 categories and 2.64% (32/1211) selecting 3 or more categories. With aggregation of ECHORN data into OMB categories, 27.91% (338/1211) of the participants can be placed in the “more than one race” category.ConclusionsThis analysis exposes the fundamental informatics challenges that current race and ethnicity data standards present to meaningful collection, organization, and dissemination of granular data about subgroup populations in diverse and marginalized communities. Current standards should reflect the science of measuring race and ethnicity and the need for multidisciplinary teams to improve evolving standards throughout the data life cycle.