4

Existing Guidance on Using Race and Ethnicity in Biomedical Research

Many partners in the biomedical research enterprise, including federal governmental agencies, professional societies, journals, and other institutions have published guidelines outlining changes they want to see in the use of race and ethnicity for clinical practice guidelines, clinical algorithms, clinical trial enrollment procedures, data reporting, and more. This chapter reviews some of these current guidelines and identifies gaps that remain. This existing guidance served the committee both in its deliberations and in developing its recommendations and their rationale, which are presented in Chapters 5 and 6. Before discussing specific guidance, it is worth noting some of the large gaps in guidance that exist. One area where the committee struggled to find guidance for the use of race and ethnicity is in earlier translational stages (T0 and T1) of biomedical research. As discussed in Chapter 3, race and ethnicity may be less relevant to the research questions at these early translational stages compared with later translational stages (T2–T4). However, there may be instances, particularly in T1, where human participants may be involved in research and guidance for how to collect and use race and ethnicity data would be beneficial. The committee also struggled to find specific guidance for the use of race and ethnicity for biobanks, which may hold health samples and health information for thousands of human samples.

GUIDANCE FOR USING POPULATION DESCRIPTORS IN GENETICS AND GENOMICS RESEARCH

The 2023 National Academies report Using Population Descriptors in Genetics and Genomics Research: A New Framework for an Evolving Field (“population descriptors” report), provided detailed guidance on the use of descriptors such as race, ethnicity, and ancestry for investigators conducting research using genetics or genomics data. Recommendations relevant to the broader biomedical community provided in that report are summarized in Box 4-1. Readers conducting genetics and genomics

research are encouraged, though, to read the population descriptors report for more detailed guidance.

As in this current report, the population descriptors report acknowledged that other parties in the broader genomics research ecosystem have critical roles in effecting change. Thus, the report provided specific guidance and recommendations for funding agencies, research institutions, research journals, and professional societies to assist in implementing the recommendations, establishing mechanisms of accountability, and creating an environment that will foster the recommended changes.

GUIDANCE FOR COLLECTING RACE AND ETHNICITY DATA IN CLINICAL TRIALS

Clinical trials are vital for testing new treatments aimed at reducing disease morbidity and mortality. Historically, trial populations did not represent the diversity of the general U.S. population, nor the diversity of the populations affected by the disease or condition under study. Clinical trials may encounter difficulties enrolling minoritized groups because trial recruitment may not be tailored to fit the needs of diverse groups. The underrepresentation of racial and ethnic minority populations has consequences, including limiting opportunities for equitable access to new therapies, threatening the efficiency of research, and limiting the generalizability of the results (NASEM, 2022).

Many populations remain underrepresented in trials for drugs approved by the U.S. Food and Drug Administration (FDA) (Martei et al., 2024), including Black, Hispanic, American Indian, and Alaska Native populations. As an example, Chen et al. (2021) conducted a review of clinical trials that led to the approval of 24 cardiometabolic treatments from 2006 to 2024 and found that only 2.9 percent of the 187,294 participants enrolled in these trials were Black (Chen et al., 2024; Martei et al., 2024). However, it is difficult to track the demographics of clinical trial participants in the United States. Although FDA guidance has encouraged the collection and reporting of participant age, sex, race, and ethnicity in clinical trials for many years (FDA, 2016), progress has been slow (Martei et al., 2024). For example, in a study that looked at more than 20,000 U.S.-based clinical trials, only 43 percent reported any race and ethnicity data (Turner et al., 2022). FDA “Drug Trial Snapshots” provides a helpful overview of the demographics of clinical trial participants in Phase III trials of approved drugs each year. However, this does not account for the many trials that never receive FDA approval nor earlier Phase I or Phase II trials. FDA Snapshots also does not track clinical trials for medical devices. Further, inconsistencies in reporting trial data to ClinicalTrials.gov make it difficult to examine race and ethnicity across a large number of clinical trials (NASEM, 2022).

Given the importance of enrolling diverse populations in clinical trials and research, a recent National Academies report examined strategies to improve the representation of underrepresented populations in clinical research and provided a series of recommendations to advance research with diverse populations (Box 4-2). Since the report was released, some of these recommendations have been implemented, including a

Congressional requirement that drug and device manufacturers must submit a diversity action plan to the FDA that details their plans to enroll a diverse population in the clinical trials. Although specific strategies to improve representation in clinical trials are outside of the scope for this committee, the committee wanted to highlight some of the findings of this report, which are critical for ensuring health equity and that all populations have the opportunity to contribute to clinical research.

In an effort to increase the diversity of clinical trial populations and broaden the eligibility criteria for clinical trial enrollment, the FDA has advised sponsors “to consider patients with co-morbid illnesses such as chronic kidney, heart, and liver disease, prior or concurrent malignancy, and extremes of weight” (Martei et al., 2024, p. 387; see also FDA, 2020). In another strategy to increase diversity, adaptive designs would start with a more narrowly defined population and then expand trial eligibility to a broader population, based on interim safety data (FDA, 2020). This guidance has important implications for the inclusion of participants in trials since many underrepresented minority populations have higher rates of multiple overlapping comorbidities, e.g., heart disease and chronic kidney disease.

FDA Diversity Action Plans

To improve representation in clinical trials, the FDA introduced guidance in 2022 for inclusion of diversity action plans in study design (FDA, 2022). In December 2022, an omnibus appropriations bill, the Consolidated Appropriations Act 2023 (P.L. 117-328), was passed, including the Food and Drug Omnibus Reform Act of 2022 (FDORA). This act includes “provisions intended to promote diversity in clinical trial enrollment, encourage the growth of decentralized clinical trials, and streamline clinical trials” (Peloquin, 2023). Under this act, the FDA has the legal authority to mandate sponsors of Phase III or other major drug trials to specify enrollment goals by age, sex, race, and ethnicity (Martei et al., 2024). This requirement includes a “rationale for these goals, informed by disease prevalence or incidence among various demographic groups, and an action plan for meeting these goals including demographic-specific outreach and enrollment strategies, inclusion and exclusion practices, and diversity training for study personnel” (Martei et al., 2024, p. 387).

However, there are many challenges to operationalizing FDORA. One shortcoming is the lack of an enforcement mechanism if sponsors fail to meet the intended plan for recruitment. Although the FDA could technically deny approval for a drug that does not meet recruitment targets, it is highly unlikely to delay approval and access for a safe and effective medication for this reason. Further, even after a drug is on the market, race and ethnicity data are not required for post-marketing studies and are rarely reported in postmarket databases, such as the Food and Drug Administration Event Reporting System (FAERS) (Muñoz et al., 2024).

Another set of challenges is the availability and source of the data used to inform the diversity action plans, limitations of the datasets used, timeliness of the data, and lack of data for rare diseases. Examples of currently available data that can inform diversity action plans include:

• Registries such as SEER (the National Cancer Institute’s Surveillance, Epidemiology, and End Results Program) or state cancer registries. The strengths are that these often have outcome data, but one limitation is that they lag and are not available in real time.
• Real-world data, such as data based on health care administrative (insurance) and pharmacy claims as well as data from electronic health records or electronic

• medical records. The large sample sizes available from these sources is a strength. However, these data also have many limitations—they are protected and limited to being collected at point of service, and they may be unstructured. Claims data often lack clinical outcomes/endpoints, and often people are lost to follow-up.
• Databases from companies such as Flatiron¹ have combined real-world data from multiple sources.

Sponsors of clinical trials are still seeking clarity to unanswered questions about diversity action plans. For example, if a sponsor recruits predominantly Black populations globally for a trial, how does that affect diversity action plans for drug approvals in the United States? Another challenge to reducing the burden of trials as stipulated in the diversity action plans is the absence of a national policy that provides safe harbor to the sponsors for the financial reimbursement of out-of-pocket expenses associated with participating in clinical trials (travel, lodging, lost wages, childcare/eldercare costs, etc.). There is also a tension between sponsors’ time pressures to bring drugs to market and operationalizing diversity action plans.

GUIDANCE FOR COMMUNITY AND PARTICIPANT ENGAGEMENT IN BIOMEDICAL RESEARCH

In recent years, there has been a growing recognition of the importance of collaborating with communities throughout the research process. Community engagement can be defined as “the process of working collaboratively with and through groups of people affiliated by geographic proximity, special interest, or similar situations to address issues affecting the well-being of those people” (CDC, 1997, p. 9). Moreover, as noted by Bergstrom and colleagues, community engagement is not simply a set of methods confined to a particular study but rather “a way of communication, decision making, and governance that gives community members the power to own the change they want to see, leading to equitable outcomes” (Bergstrom et al., 2012, p. 4).

Although an extensive review of best practice in community engagement is outside the scope of this report and merits further consideration, collaborative decision making throughout the research process is critical for improving the use of race and ethnicity in research studies, given how these concepts are interwoven with identity, social context, environmental exposures, and health. The health of individuals and communities is affected by socioeconomic, lifestyle, and behavioral factors as well as by the physical environment and the political and legal landscape (Hanson, 1988; IOM, 2003a,b). Community engagement has been an important strategy for achieving racial and ethnic representation in research (e.g., Beech and Heitman, 2024); the goal is to engage community members in the process of designing protocols, including procedures for collection and use of race and ethnicity data, that meet their needs and that account for the barriers they face to participation. Inclusive community involvement throughout the research process can also increase the relevance, quality, generalizability, and

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¹ https://flatiron.com/ (accessed October 16, 2024).

dissemination of health research (Hood et al., 2010). Community representatives and patient advocates can play a role in identifying and prioritizing research questions that are important to specific populations, provide insight and networks to aid dissemination of research results, and contribute to translating research findings into practice and policy (Hood et al., 2010). Racial and ethnic populations are not a cultural monolith, so including perspectives from a range of community members can increase research relevance to the broader community.

As part of their work, the committee sought to hear community perspectives on the use of race and ethnicity in biomedical research and hosted a moderated discussion with invited panelists. (See Appendix A for the agenda and session objectives.) Panelists discussed their experiences with the collection and use of race and ethnicity data during different phases of biomedical research and how to build trust and stronger partnerships between scientists and communities. The session emphasized the importance of partnering with communities to better address race- and ethnicity-related issues in biomedical research and highlighted several important themes. First, panelists highlighted that race and ethnicity are social constructs that are challenging to define but have meaning for people’s lives. The panelists emphasized the role of race and ethnicity for their own identities and the importance of seeing people like them and their communities in biomedical studies, further underscoring the importance of diverse representation in research. Moreover, when asked about their motivations for participating in studies, panelists named contributing data that could improve health among their communities as well as sharing information about their racial and ethnic identities for research purposes. For example, one panelist said, “[I was] following the footsteps of my mom and knowing that all of the different things that she had been involved [in] . . . was helping others in our community. My getting involved again [in clinical trials] was mainly because of the limited amount of participation from African American males particularly getting involved in research or practices in which the community as a whole could be better served” (Donald Adams, Jr., in remarks to the committee on March 14, 2024).

Second, panelists emphasized the importance of balancing qualitative methods with standardized measures of race and ethnicity to capture more granular information about an individual’s identity and about the heterogeneity of populations. Racial identity may be fluid and may shift as a result of life experiences and the social context individuals are in. Although most people use categorical descriptors to identify their race or ethnicity, these classifications are more complex and fluid for some individuals (Croll and Gerteis, 2019). Collecting additional information about racial and ethnic identities allows researchers to more accurately reflect the complexity of these classifications (Saperstein, 2012). However, panelists did note that we still need standardized approaches to asking about race and ethnicity in medical research settings to minimize misclassification, which can further exacerbate problems.

Lastly, understanding the narrative of what makes people who they are and what is important to communities can improve biomedical research, increase the quality of data about race and ethnicity, and illuminate unexpected and rewarding lines of inquiry. However, doing this work requires deep engagement with communities and research participants that cannot be episodic or transactional. As panelists noted, partnerships that are equitable support long-term, longitudinal relationships that are built on trust, and

future collaboration is predicated on continuous engagement and follow-through after the study. Building relationships with communities requires consistent engagement and an understanding of a community’s culture, viewpoints, and history (NASEM, 2022). These relationships take time to build but can be broken in an instant. Building and maintaining these relationships therefore requires an investment of resources and time that many academic researchers find challenging, given the limitations of grant budgets and timelines. Industry-sponsored research faces similar challenges of maintaining funding for community sites in the time between one study ending and another study beginning. Another challenge is that in order to get IRB approval, every party engaged in research, including staff from a community organization, must undergo the required human subjects protection training (Anderson et al., 2012). This may be challenging for those in community organizations to complete in a timely manner. Although outside of the scope of this report, these are real challenges for conducting and funding community engaged research that require attention.

Despite these challenges, the committee identified community partnership as a key strategy for implementing the report’s recommendations, especially in later stages of translational research (T2–T4), and for improving the use of race and ethnicity in future biomedical research. For those engaging in this work, how to conduct this engagement and where to begin will depend on the context of the study, the experience of the researcher and their institution working with the surrounding community, and what resources have already been built within the community. Although most academic medical centers have a long history of community service, a more comprehensive approach to community engagement in research would lead to more discoveries and healthier communities (Wilkins and Alberti, 2019). The following section outlines some strategies for doing this work and some examples of successful community engagement. (See Chapter 6 for more information on the role of community engagement in the committee’s recommendations.)

Operationalizing Community Engagement

From a pragmatic perspective, community engagement exists on an engagement continuum, ranging from outreach to shared leadership (CDC, 2011; see also Table 4-1). On one end of the engagement spectrum, communication is primarily unidirectional, with communities only informed about current research and results (Hood et al., 2010). In the middle of the spectrum, engagement transitions toward bidirectional communication, and communities are engaged in important, but limited, research processes, such as recruiting research participants (Hood et al., 2010). On the other end is community-based participatory research (CBPR), which consists of shared decision-making governance and leadership to facilitate equal involvement of community partners and researchers (Hood et al., 2010; Paskett et al., 2003). CBPR not only increases community understanding of important research topics but also enables researchers to understand and address community priorities and to implement culturally sensitive research approaches (NIMHD, 2024). Moving from left to right along this spectrum of engagement increases transparency and dialogue between community members and researchers and requires greater time, effort, and financial investment from study teams.

TABLE 4-1 Community Engagement Continuum: A Comparison of Levels of Engagement

^a See https://allofus.nih.gov/about/who-we-are/all-us-community-and-participant-advisory-boards (accessed November 20, 2024).

^b See https://ceal.nih.gov/ (accessed November 20, 2024).

^c See https://www.jacksonheartstudy.org/Community/Ways-to-Participate (accessed November 20, 2024).

^d See https://wai.wisc.edu/ (accessed November 20, 2024).

NOTES: See Appendix B for an extended version of this table. This table is a general guide, and there may be other considerations for specific populations, such as American Indians. See text for more information about best practices. HBCU = historically black colleges and universities.

SOURCE: Some content adapted from the CDC Principles of Community Engagement and New York City Department of Health and Mental Hygiene Community Engagement Framework and associated resources² (CDC, 2011; NYC Dept of Health and Mental Hygiene, 2017).

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² https://www.nyc.gov/site/doh/health/health-topics/race-to-justice.page (accessed July 10, 2024).

Successful Examples of Community Engagement

Developing relationships with communities means engaging with community members in meaningful and sustained ways throughout the research process. The Patient-Centered Outcomes Research Institute (PCORI) articulates this approach as its foundational expectations for research partnerships, which it requires from researchers who apply for funding.³ The expectations include team diversity and representation to mirror the community, early and ongoing engagement with partners (including decision making), dedicated funds for engagement and partner compensation, training and capacity building for the team, shared decision making with partners, and ongoing review and assessment of engagement. PCORI sees these as “building blocks for meaningful, effective, and sustainable engagement with patients, communities, and other partners in research.” The strategies are intentionally general because the elements of community engagement should be tailored to the specific context based on goals of the community, researchers, and project, which may mean taking into account community preferences, cultural beliefs, values, and historical background. PCORI offers a number of illustrative examples of its expectations in action.⁴

PCORI-funded studies are more likely to directly engage participants and include contributions from this engagement in research design decisions (Forsythe et al., 2019). Further, a review of PCORI-funded studies found that this community engagement is “important to patients and clinicians, recruitment and retention of study participants, data collection processes, interpretation of results, and dissemination.” PCORI also continues to monitor and evaluate their progress in advancing the National Priorities for Health, which will help researchers evaluate which aspects of PCORI’s approach are most effective for engaging community members in biomedical research.

A second example is the Jackson Heart Study of cardiovascular disease among African Americans in the Jackson, Mississippi, metropolitan area. It is a 20-year prospective community-based cohort study that successfully enrolled 5,306 African American participants by mobilizing community support and engagement through partnerships with over 100 churches, government agencies, and community-based and nonprofit organizations (Addison et al., 2021). The Community Outreach Center (CORC), which was created to maintain trust and community engagement throughout the study, utilized community health education activities, health promotion, and health prevention messages within the Jackson community. Due to the efforts of the CORC, the Jackson Heart Study recruited the largest cohort of African Americans to participate in a study on cardiovascular disease and maintained high retention, with 85 percent of participants returning from the first exam to the second exam. The study also developed an extensive media campaign that was channeled through trusted community leaders, which began at least one year prior to the inception of the study (Addison et al., 2021; Martei et al.,

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³ https://www.pcori.org/engagement/engagement-resources/engagement-research-pcoris-foundational-expectations-partnerships (accessed May 10, 2024).

⁴ https://www.pcori.org/engagement/foundational-expectations/in-action (accessed July 10, 2024).

2024). This large community-based cohort study now includes a biospecimen and data repository as well as a registry for the recruitment of Black participants into ancillary clinical trials (Martei et al., 2024). Furthermore, the approach could potentially be adapted to build cancer survivorship cohorts for management of long-term cardiovascular outcomes (Martei et al., 2024).

In another example, a pivotal cluster-randomized trial of blood pressure reduction used a nontraditional health care setting to develop and test a blood pressure control program for Black men. The study enrolled a total of 319 Black male participants with hypertension across 52 Black-owned barbershops (Victor et al., 2018). The intervention group saw a 95 percent retention rate, achieved by using “multiple blood pressure screenings, ID cards, follow-up calls at 3 months, culturally specific health sessions, and payments to offset the costs of generic drugs and transportation to pharmacies” (Martei et al., 2024, p. 388; Victor et al., 2018).⁵

Collaborating with Indian Tribes on Biomedical Research Studies

Working with American Indians and Alaska Natives (AIANs) on research studies requires additional considerations that are absent when working with other communities. Some of these are the result of the longstanding sovereignty of Indian Tribes within the boundaries of what is now the United States and their resulting governmental structures. This sovereign status is a defining feature of American Indian Tribes. Other reasons stem from the sharply different frames of reference of Western science and Indigenous knowledge.

Differences in Viewpoint

It is critical that researchers become aware and sensitive to the culture, belief systems, history, and priorities of the AIAN group with which they would like to work (Hiratsuka et al., 2012). Few non-native researchers possess such an awareness, especially a deep understanding of the continuing effect of American colonialism on the people they seek to include in their study. Emphasizing this point, a speaker shared with the committee, “If you want to study a native community, you will have to actually come to our world. You have to assimilate to us instead of the other way around. You have to leave your perspectives and beliefs at the door and be open to our perspectives and beliefs” (Audie Atole in remarks to the committee on March 14, 2024).

Biomedical researchers may find it challenging to understand non-Western scientific paradigms that undergird Indigenous knowledge and how to integrate that knowledge into a study. There is an entrenched Western mindset that Indigenous worldviews of studying natural phenomena and human health, which have developed over centuries (even millennia in some cases), are deficient and primitive (Beauchamp and Childress,

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⁵ https://www.cedars-sinai.org/newsroom/update-cedars-sinais-la-barbershop-study/ (accessed July 10, 2024).

2001; Deloria Jr., 1999; Jonsen et al., 1998). “Native peoples tend to see their cultures as encompassing systems of knowledge and understanding that are fundamental to the continuation of the Tribe itself. Any harm to culture is perceived as a direct harm to the ability of the Tribe to continue into the future” (Tsosie, 2007, p. 402). Traditional knowledge and stories are intellectual property and sources of a Tribe’s collective wealth passed down across generations (Cruikshank, 1992; Harding et al., 2012; Tsosie, 2002). As with any sovereign nation, a Tribal government has a responsibility to safeguard its Tribe’s cultural, intellectual, and spiritual inheritance along with the rights and resources of its people and place.

Consent from the Tribal Government

Biomedical researchers must understand that each Indian Tribal government is the only entity authorized to represent and make decisions on behalf of that Tribe. There are 574 distinct and federally recognized Tribes or Tribal entities afforded sovereignty within the United States today.⁶

Many Tribes have legally enforceable codes that set forth whether and how research will be done in their Nation, on their land, and with members of their Tribe (Carroll et al., 2022). For example, the Ho-Chunk Nation’s Tribal research code establishes an application and permitting process that is overseen by the Tribal IRB and is “designed to protect individuals, communities and the Nation itself from improper research procedures” (Ho-Chunk Nation, 2005, p. 2). The Ho-Chunk Nation’s code also protects the rights of individuals who are members of the Tribe; secures ownership of and provides protections for the Nation’s data; and ensures “appropriate Nation and community participation in the design and evaluation of research, and appropriate local opportunities in employment in all research projects permitted . . . within the Ho-Chunk community” (Ho-Chunk Nation, 2005, p. 2). Researchers who want to work with the Ho-Chunk Nation—or with many other Tribes—must apply for a permit either through the Tribe’s IRB or through the U.S. Indian Health Service (in addition to any academic or institutional IRB). A Tribal IRB considers potential adverse impacts to the Tribe, the Tribe’s government, and Tribal individuals and does so with knowledge and a viewpoint that an academic IRB may lack (Around Him et al., 2019); thus, the two review processes are complementary.

When Tribes do not have IRBs, there are still cultural and formal protocols that must be followed by researchers (Garba et al., 2023). Often it is a matter of getting in front of the Tribal council and head of the Tribe, who function as the de facto IRB—something that can take months. Sometimes an approval is also needed from a cultural head in addition to the governmental body. Therefore, before approaching Tribes about

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⁶ https://www.federalregister.gov/documents/2021/01/29/2021-01606/indian-entities-recognized-by-and-eligible-to-receive-services-from-the-united-states-bureau-of (accessed July 10, 2024).

permission to carry out reservation-based research, researchers should learn about the Tribe and its protocols, laws, codes, beliefs, and structures.

“[T]ribal needs may not accommodate research timelines, which are often too short (short-term or one-time federally funded initiatives) or too long (publication-heavy research without actual remedies for the community). It is rare that a federally funded initiative is timely and sustained, that the grant is received when needed by the Tribe, and that the Tribe is ready with adequate staff and processes in place” (Harding et al., 2012, p. 7).

With 78 percent of AIANs living off reservation, urban-based AIAN research has its own set of “protocols” that are distinct from reservation- or Nation-based research. There are several urban centers around the country that have some institutional body—whether formal or informal—where researchers can find out about procedures and protocols for seeking to carry out research, including what to avoid. These bodies may be able to assist in or have a mechanism for granting community assent and access (e.g., Metropolitan Urban Indian Directors,⁷ National Urban Indian Family Coalition,⁸ Seattle Indian Health Board⁹).

Data Sovereignty and Intellectual Property Rights

Researchers may face challenges centering on trust, data ownership, and sovereign rights. Although research is trending toward more open-source approaches, this may be at odds with the values and rights of Indigenous populations (Garrison and Carroll, 2024). Differences may include conceptions of how knowledge may be generated, used, and shared as well as who owns both the material and information collected for a study and the data, analyses, and disseminating documents generated from those collected materials. This necessitates that a material and data sharing agreement (MDSA) be crafted collaboratively and then agreed to and signed by all parties (Harding et al., 2012; see also Hayward et al., 2021). All parties will benefit, and trust will be built when material and data-sharing issues are discussed and resolved early in the study’s life cycle.

Here the complementary perspectives of academic and Tribal IRBs can be useful. An awareness of and sensitivity to past and ongoing abuses of Tribal material and information underscore why an MDSA is needed (Chadwick et al., 2019; Claw et al., 2021; Garrison and Carroll, 2023). Tribes may want the MDSA to include language that defines how Tribal information can be used in publications. They may want it to provide clarity about intellectual property rights in the context of studies involving participants who are members of sovereign nations. The MDSA will likely confirm that all materials and information remain the property of the Tribe and that they are not to be shared with a third party without Tribal permission or used for anything

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⁷ https://muidmn.org/committees (accessed October 16, 2024).

⁸ https://www.nuifc.org/ (accessed October 16, 2024).

⁹ https://www.sihb.org/ (accessed October 16, 2024).

other than permitted research. Furthermore, all materials will be returned to the Tribe at the conclusion of the study. Developed by the Indigenous Data Alliance,¹⁰ the CARE Principles for Indigenous Data Governance¹¹ can provide guidance for researchers engaging in this work. With a foundation of Indigenous worldviews, the CARE principles are collective benefit, authority to control, responsibility, and ethics (Carroll et al., 2022).

Informed Consent

Entry into a Tribal nation or an Indigenous community requires time on site and introduction to key members relevant to the envisioned study well before the research is set to begin. Once a relationship with the Tribe or community is established, researchers should consider partnering with or have on the research team one or two trusted members of the Tribe or community. Someone who “speaks the language” of both Indigenous and Western science can facilitate collaborations that build mutual trust and respect (Hatch et al., 2023). It is important for researchers to communicate the risks, consequences, and benefits of consenting to participating in a research study, by offering participants clear and concise descriptions of the purpose of the research, the use and storage of the information collected, and any potential issues of anonymity (Harding et al., 2012). There will likely need to be confidentiality agreements “signed by university research personnel who have access to project material and data. These forms are held by Tribal researchers under secured conditions so that they know who has access to the data and for what purpose” (Harding et al., 2012, p. 9).

Strategies for Success

Partnering with Tribal nations entails time and commitment to overcome unexpected barriers or roadblocks, including potential geographic and logistical challenges that are unique to each Tribe (Jones et al., 2019; Laveaux and Christopher, 2009). Each Tribe has distinct protocols for research approvals, and researchers often underestimate the time required to meet them. While perhaps time-consuming, Tribal approval processes allow time for cultural immersion, demonstrating trustworthiness and commitment, and developing relationships (Jones et al., 2019). Essential to productive collaborations with Tribal nations, relationship building benefits from cultural sensitivity, humility, and openness to personal growth (Jones et al., 2019). Taken a step further, understanding Tribes’ priorities could also result in changes to the research questions or study design.

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¹⁰ https://www.gida-global.org/whoweare (accessed October 16, 2024).

¹¹ https://www.gida-global.org/care (accessed October 16, 2024).

Some research studies have used a version of CBPR (Laveaux and Christopher, 2009) or coproduction, and the outcomes benefited equitably both the Indian Tribe and the academic researchers (Woodbury et al., 2019). Some examples are:

• The Center for Braiding Indigenous Knowledges and Science,¹² which is not biomedical research, but has been funded by the National Science Foundation at $30 million for 5 years.¹³
• The Center for Indigenous Health Research and Policy,¹⁴ which focuses on cardiovascular and related confounding diseases, like diabetes and obesity, and behavioral and environmental changes to reduce their prevalence, especially by expanding access to healthy foods and tapping into traditional cultural practices.
• The Confederated Tribes of the Umatilla Indian Reservation and Oregon State University collaborated to address Tribal exposures to polycyclic aromatic hydrocarbons and assess ways to improve community health. As part of the process, they collaborated on the formation of a model material and data sharing agreement and a confidentiality agreement (Harding et al., 2012).

Other resources, including extramural grant opportunities, can be found through parts of the federal government that focus on biomedical research with AIAN populations, such as the NIH Tribal Health Research Office, NIH Native American Research Centers for Health, HHS Indian Health Service, HHS Office of Minority Health, and Environmental Protection Agency’s Science to Achieve Results (STAR) tribal environmental health research program.

GUIDANCE ON THE COLLECTION OF RACE AND ETHNICITY INFORMATION IN ELECTRONIC HEALTH RECORDS

As noted in Chapter 3, electronic health records (EHRs) are now the standard method for collecting and retrieving patient and patient-related information. EHR systems have become extremely sophisticated, and multiple commercial systems are in use by health care systems across the United States and the globe. EHR data are now used in biomedical research studies and in the training and validation of AI algorithms. Yet despite repeated calls for standardizing EHR data collection on race and ethnicity, such standardization requirements do not exist (IOM, 2003b; NASEM, 2024). A 2018 report by AHRQ said, “Given variations in locally relevant populations, no single national set of additional ethnicity categories is best for all entities that collect these data” and recommended tailoring the more granular ethnicity groups based on locally relevant categories chosen from a national standard set of options (AHRQ, 2018). Since then, some have explored the use of

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¹² https://www.umass.edu/gateway/research/indigenous-knowledges (accessed October 16, 2024).

¹³ https://www.umass.edu/news/article/umass-amherst-partnering-indigenous-communities-launch-30m-nsf-center-braiding (accessed October 16, 2024).

¹⁴ https://medicine.okstate.edu/cihrp/ (accessed October 16, 2024).

imputation models to expand the granularity in ethnic and racial categories for Hispanic, Asian, Native Hawaiian, and Pacific Islander populations (NASEM, 2024). It should be noted that the revised U.S. Office of Management and Budget (OMB) standards now recommend collecting more granular data as well (OMB, 2024).

The U.S. Office of the National Coordinator for Health Information Technology is building standardized data elements that are being evaluated ahead of becoming required elements in EHR systems. EHR systems are currently required to support not only the OMB categories for race and ethnicity but also the more granular CDC Race and Ethnicity Code Set representations of race, ethnicity, and Tribal affiliation (NASEM, 2024).¹⁵

GUIDANCE FOR RACE AND ETHNICITY IN CLINICAL PRACTICE GUIDELINES

Several organizations, such as the U.S. Preventive Services Task Force (USPSTF), AHRQ, and the American Academy of Pediatrics (AAP), have issued guidance on the use of race and ethnicity in clinical practice guidelines. The USPSTF creates evidence-based clinical practice guidelines to guide the delivery of clinical preventive services. In 2021, the USPSTF acknowledged that systemic racism is a root cause of health disparities in preventive care and made a commitment to promote antiracism and health equity in its preventative care recommendations (Davidson et al., 2021). Its recommendations and next steps include the following:

1. “Consider race as a social, not a biological construct
2. Promote racial and ethnic diversity in membership and leadership and foster a culture of inclusivity
3. Commission a report to understand how systemic racism undermines the benefits of evidence-based clinical preventive services
4. Iteratively update methods to overcome health inequities experienced by populations affected by systemic racism
5. Communicate gaps created by systemic racism in all dissemination efforts
6. Collaborate with its partners and experts to reduce the influence of systemic racism on health” (Davidson et al., 2021, p. 2406; Doubeni et al., 2021).

In December 2023, AHRQ developed a health equity framework for incorporating a health equity lens that spans the entirety of the USPSTF recommendation-making process (J. Lin et al., 2023, 2024). The report provides an equity framework for each phase of the USPSTF guideline development: “(1) topic nomination, selection, and prioritization, (2) development of the work plan, (3) evidence review, (4) evidence deliberation, (5) development of the recommendation statement, and (6) dissemination of recommendation(s)” (J. Lin et al., 2023, p. 5).

The AAP released a policy statement in June 2022 to tackle the eradication of race-based medicine as a component of a larger effort to deconstruct the structural and

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¹⁵ https://www.healthit.gov/isa/representing-patient-race-and-ethnicity (accessed May 12, 2024).

systemic injustices responsible for racial health disparities (Wright et al., 2022). The following recommendations were adopted:

• “The AAP will critically examine all policies and practice guidelines for the presence of race-based approaches in their development and deconstruct, revise, and retire, if necessary, all policies and practice guidelines that include race assignment as a part of clinical decision-making.
• The AAP will critically examine all policies and guidelines currently under development as well as consideration of all such future documents to ensure the exclusion of race assignment as part of clinical decision-making.
• The AAP will leverage the ‘Words Matter’ document to ensure that all authors, editors, presenters, media spokespersons, and other content contributors recognize race as a social construct only and desist from any use, or its reference, as a biological proxy” (p. 5-6).

Application of the AAP framework to a clinical pathways library at Boston Children’s Hospital established several recommended practices, including: (1) “define race, ethnicity, and ancestry rigorously; (2) assess the most likely mechanisms underlying epidemiologic associations; (3) consider whether inclusion of the term is likely to mitigate or exacerbate existing inequities; and (4) exercise caution when applying population-level data to individual patient encounters” (Rosen et al., 2023, p. 1-2). This process, and those framed by the USPSTF and AHRQ, could serve as a useful model for other organizations looking to reevaluate how race and ethnicity are incorporated into clinical pathways.

GUIDANCE FOR RACE AND ETHNICITY IN CLINICAL ALGORITHMS

Despite the potential of clinical algorithms to exacerbate existing health disparities, there are very few studies that evaluate the disparity effects of using clinical algorithms. In addition, some algorithms are proprietary, or created by private companies such as EHR vendors or payers, and even less is known about their development and impact on racial and ethnic disparities. One of the few existing resources that tackles this subject in a comprehensive manner is a recent report by AHRQ that reviewed the literature to examine the effect of 18 statistical algorithms on racial and ethnic differences in access to care, quality of care, and health outcomes (Tipton et al., 2023). The review found that algorithms sometimes perpetuate or exacerbate disparities and at other times reduce racial and ethnic disparities. Rarely do the algorithms have no discernable effect on disparities. An important conclusion from the AHRQ report is that the quality of the evidence was found to be weak in terms of rigorous study designs (Powe, 2024). Most studies suggesting deleterious effects were designed as simulations, modeling what might happen when an algorithm is used in care. Such studies are susceptible to biases. Few used randomized clinical trials or pre-post implementation designs to examine outcomes.

Tipton and coauthors (2023) also reviewed the literature for mitigation strategies used to address potential race-based harms from algorithms and identified six types of mitigation strategies, which are summarized in the AHRQ report:

• removing a race or ethnicity input variable from the algorithm
• replacing race or another input variable with a different measure
• adding an input variable
• recalibrating the algorithm with a more representative patient population
• stratifying algorithms to assess patients of different racial and ethnic groups separately
• using different statistical techniques within algorithms

The authors concluded, “It is unclear from the current evidence base if certain types of strategies are generally more effective than others” (Tipton et al., 2023, p. 30). Employing mitigation strategies to influence health outcomes can have mixed results, improving one outcome while worsening another. For example, most studies showed that omitting race from the estimated kidney function test (eGFR) led to an increase in diagnoses of chronic and severe renal disease in Black patients (Delgado et al., 2022). Thus, the removal of race and ethnicity values from this algorithm can result in earlier interventions to treat chronic kidney disease and increase the referral of Black patients for kidney transplants. However, omitting race from eGFR may also exacerbate disparities related to clinical trial eligibility or cancer therapy access because Black patients are classified as having more severe kidney disease (Tipton et al., 2023; see also Figure 3 in Siddique et al., 2024).

In a report summarizing a meeting sponsored by the Doris Duke Foundation and others in June 2023, the Council of Medical Specialty Societies also observed that the consequences of using race as a variable in research studies can vary considerably and can be beneficial (e.g., if race is included in an intentional, well-considered effort to reduce inequities); neutral/have no impact; or harmful (e.g., if including race perpetuates disparities or the misconception of innate biological differences between racial groups) (CMSS, 2023). According to the report, the “path to improving health equity will likely differ for each algorithm” (CMSS, 2023, p. 4). The best option for a particular algorithm may be to exclude race altogether; to replace race with an alternative variable, such as measures of social determinants of health; to stop using the algorithm and replace it with one that promotes equity; or to continue to include race as it is currently being used (CMSS, 2023). For this reason, there is likely not a one-size-fits-all solution to the persistent problems surrounding the use of clinical algorithms that incorporate race and ethnicity data.

GUIDANCE FOR RACE AND ETHNICITY IN CLINICAL AI ALGORITHMS

Bias in AI Algorithms

Sources of artificial intelligence (AI) bias can appear throughout the developmental life cycle of AI algorithms (Cary Jr. et al., 2024; see also Box 4-3). To address racial and ethnic bias in the development and use of health care algorithms, AHRQ and the

National Institute on Minority Health and Health Disparities convened an expert panel that developed guiding principles to mitigate or eliminate the impact of bias at each phase of the algorithm life cycle (Figure 4-1; Chin et al., 2023).

The AI Program in the FDA’s Center for Devices and Radiological Health has identified major regulatory science gaps and challenges in clinical AI algorithms. One of the focus areas of program activity is “to develop methods to measure and quantify algorithmic bias, reduce performance difference among subpopulations, and ensure generalizability” (FDA, 2023). Presumably, this will include consideration of the use of race and ethnicity as input variables and assessment of performance on racial and ethnic subpopulations.

Reporting Guidelines and Repositories for AI Algorithms

As research in health AI algorithms has progressed, reporting guidelines have been developed to assure scientific validity, clarity of presented results, reproducibility, and adherence to ethical principles. Some of these guidelines (Box 4-4), such as SPIRIT-AI and CONSORT-AI, are extensions for AI of existing guidelines that were developed for biomedical research studies. More are under development, such as STARD-AI,

TRIPOD-AI, and PROBAST-AI. Of the currently published guidelines, commentary on the use of race and ethnicity is limited. Only FUTURE-AI calls for the evaluation of AI across ethnic categories, though not across racial categories.

Two repositories for clinical practice guidelines are an extensive free repository maintained by the ECRI Guidelines Trust¹⁶ and the repository maintained by Guideline Central.¹⁷ An online database of clinical statistical algorithms with race-based guidelines, medications with race-based guidelines, and medical devices with differential racial performance is available (Clinical Algorithms with Race, 2023). The FDA also maintains a database of AI and machine learning-enabled medical devices,¹⁸ including approved AI algorithms.¹⁹

GUIDANCE ON THE REPORTING OF RACE AND ETHNICITY DATA IN PUBLICATIONS

Publication in medical and science journals is an important avenue through which the use of race and ethnicity in research can be improved. The language used and the ways race and ethnicity are discussed reflect the care with which the concepts were considered throughout the research process. Clear descriptions of how and why race and ethnicity were used in a study promote transparency, openness, and replicability—principles of sound science introduced in Chapter 2. While the inclusion of race and ethnicity in health research has increased over time, these terms are almost never clearly defined (Martinez, 2023a). Less than 50 percent of articles across disciplines and only approximately 30 percent of medical and epidemiological publications had any justification regarding their use of race or ethnicity (Martinez, 2023a, 2023b). There is a lack of standardized practice around the use of race as a study variable and inconsistency in how researchers conceptualize and report on race in biomedical publications (Boyd et al., 2020).

There have been many attempts to establish guidelines directed toward researchers, journal editors, and reviewers to standardize practices for discussing the use of race and ethnicity data. In a recent article, nine biomedical journal editors provided guidance for using race, ethnicity, and geographic origin as proxies for genetic ancestry groups when publishing study results (Feero et al., 2024). Although focused on genetics, the authors encouraged researchers throughout the biomedical community to embrace these best practices, which would help to ensure scientific accuracy and the interpretability of published research across biomedical research disciplines. Among existing reporting guidelines, a few common themes have emerged, including:

• Explain why race and ethnicity are being used as study variables.
• Define race and ethnicity as they are used in the study.

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¹⁶ https://guidelines.ecri.org/ (accessed August 27, 2024).

¹⁷ https://www.guidelinecentral.com/guidelines/ (accessed August 27, 2024).

¹⁸ https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices (accessed August 27, 2024).

¹⁹ https://medicalfuturist.com/fda-approved-ai-based-algorithms/ (accessed August 27, 2024).

• Describe how race and ethnicity data were collected.
• Avoid using race or ethnicity as explanatory variables for observed health disparities.
• Use clear, consistent, and inclusive language in reporting on race and ethnicity.

It is important that researchers have a clear understanding of the objectives of the study they are developing, select the most appropriate variables needed to carry out these objectives, and use them consistently (Lu et al., 2022). According to the International Committee of Medical Journal Editors (ICMJE) recommendations, “Authors should define how they determined race or ethnicity and justify their relevance” and if they choose to not collect these data, they should explain their reasonings behind this decision (ICMJE, 2023, p. 7). The Journal of the American Medical Association (JAMA) states that if “race and ethnicity categories were collected for a study, the reasons that these were assessed should be described in the Methods section” (Flanagin et al., 2021, p. 623). Moreover, researchers should note if the collection of these data was a requirement by a funding agency (Flanagin et al., 2021). Employing race and ethnicity as study variables without any clear reasoning for doing so implies that these categories are a “primary, natural, and neutral means of grouping humans” (Bhopal, 1997, p. 1751; see also Kaplan and Bennett, 2003), a practice that should be avoided.

When race and ethnicity are used as study variables, researchers will need to define these terms as they are used in their study (Boyd et al., 2020). In their guidance on race and ethnicity reporting, JAMA offers some discussion of definitions that could be of use to researchers as a starting point (Flanagin et al., 2021). To avoid conflating race with ethnicity, JAMA suggests that researchers use “race and ethnicity” as opposed to “race/ethnicity” in their publications (Flanagin et al., 2021). Journals and funding agencies can encourage researchers to carefully appraise their use of these terms by setting requirements for the inclusion of precise definitions of these constructs in their submissions (Lu et al., 2022) and ensuring clear descriptions in the methods sections of articles (Feero et al., 2024).

Researchers will need to describe in the methods section how race and ethnicity data were collected. If self-report was used to assign participants into racial or ethnic categories, researchers should specify if they were given a fixed set of options to choose from or if the questions were open-ended (Kaplan and Bennett, 2003). Some journals consider self-identification with an option to write-in the desired response or select multiple categories to be the gold standard for collecting this type of data. When assignments to racial or ethnic categories were made by research or health care personnel, it should be specified how these categories were attributed (AHA, 2021). Ultimately, researchers should do their best to collect data that capture as much of the multidimensional nature of an individual’s identity as possible with the tools at their disposal (NHGRI, 2016; Roth, 2016). In doing so, they are better equipped to develop and execute studies that are more rigorous and transparent.

Other guidelines advise against treating race and ethnicity as explanatory variables for health disparities found between different groups. Rather, their use in health disparities research is appropriate “as [a way] . . . of examining the underlying sociocultural reasons for these disparities” (Rivara and Finberg, 2001, p. 119). An association

between membership in a racial or ethnic group and a higher incidence of a disease does not in itself establish causality (Kaplan and Bennett, 2003). Instead of using race and ethnicity as explanations for the existence of disparities in health, investigating other relevant factors, including racism and discrimination, socioeconomic status, social class, environmental exposures, and educational attainment, will likely provide more meaningful results (Bonham et al., 2018; Kaplan and Bennett, 2003; NHGRI, 2016). Several journals have advised that the collection of race and ethnicity data is best done in conjunction with all relevant social and structural factors that influence the research question (AHA/ASA, 2021; Flanagin et al., 2021). Explicitly using terms like “structural racism” and “racial equity” in the study could be another important way to highlight the impact of racism on health outcomes and avoid misusing race and ethnicity data (Flanagin et al., 2021). Boyd and colleagues (2020) suggest that researchers name racism as a key determinant of health, identify what type of racism (interpersonal, institutional, internalized) is relevant, consider the mechanism by which it is operating, and examine additional forms of oppression that may exacerbate racism’s effects (Boyd et al., 2020). By considering the multitude of social and environmental forces that impact the health of individuals and populations, researchers can avoid reinforcing the existence of a biological basis of race and get a clearer understanding of what drives differences in health outcomes between groups.

A number of journals provide style-based guidance on terminology, capitalization, and the use of inclusive language when describing racial and ethnic groups in biomedical publications. A few examples follow. Authors are advised to use language that is “neutral, precise, and respectful” when describing research participants and discouraged from using terminology that may be stigmatizing (ICMJE, 2023, p. 17). The term “White” is acceptable while “Caucasian” is not, because the latter is rooted in “racist taxonomies used to justify slavery” (Popejoy, 2021, p. 463). Descriptors like “Non-White” and “Other” homogenize populations or groups of study participants, thus obscuring meaningful data and reducing the power and validity of a research study. An exception to using “Non-White” or “Other” may be when it was an explicit preselected category in an existing database or research instrument (Flanagin et al., 2021). Grouping together different populations that are unrelated except for their being “non-White” can contribute to the assumption that whiteness is the reference category. It is preferable to identify the smaller population groups, whenever possible (AHA, 2021). Capitalizing the names of racial and ethnic groups and avoiding the use of pejorative terms like brown and yellow are recommended (Flanagin et al., 2021).

Remaining Challenges with Race and Ethnicity Reporting Guidelines

Despite the concordance among journals on many aspects of reporting on race and ethnicity in biomedical research studies, several challenges remain. One obstacle is the difficulty of maintaining and tracking adherence to these guidelines. Since the majority of the policies on race and ethnicity have been published in recent years, there is not yet much information on adherence to updated guidance documents (Martinez, 2023a). Notably, guidelines developed by the ICMJE and JAMA in 2004 have largely not been

met (Martinez, 2023a). An examination of reporting of race and ethnicity in three JAMA network journals before and after the development of the 2021 AMA Manual of Style found improvement in the reporting of measures, coding, and style-based policies but did not explore the impact of the guidance on how race and ethnicity are defined in publications and the justifications for their use (Flanagin et al., 2023). It was also unclear in their analysis whether the noted improvements in reporting occurred prior to submission, during peer review, or during the editing process following acceptance of the manuscript (Flanagin et al., 2023). Thus, in order for the biomedical research community to benefit from these guidelines and publish studies that employ race and ethnicity categories in a thoughtful manner, there need to be mechanisms in place to enforce these guidelines and a formalized way of measuring their impact.

A second challenge is the lack of standardization across journals and disciplines concerning the reporting of race and ethnicity in biomedical research studies (Lu et al., 2022). The distinctions between race and ethnicity as descriptors along with recommendations on when to use each term have not yet been consistently agreed upon (Lu et al., 2022). A scoping review of publications that offer guidance on the use of population descriptors like race and ethnicity in genomics research found that many of the guidance documents had definitions of “race” that differed from one another along the social–biological axis and offered conflicting opinions on how strictly these descriptors should be defined (Mauro et al., 2022). The extent of this widespread disagreement demonstrates that a lack of standardized definitions for these terms results in considerable confusion on their use (Mauro et al., 2022). The biomedical research community could benefit from ensuring that reporting guidelines for race and ethnicity in publications are clearly defined and enforced in a systematic manner (see Chapter 6 for recommendations).

Another limitation of publishing guidelines focused on race and ethnicity is the potential to inadvertently obfuscate the impact of racism on health disparities (see Chapter 3, section “Health Disparities and the Study of Racism”). One way that this can be observed is in how frequently terms like race and ethnicity appear in publications, but not terms like structural or systemic racism. A PubMed database search conducted in 2020 turned up a mere 86 articles that included both race as a term and structural or institutional racism (Boyd et al., 2020). Furthermore, research on health disparities often posits the role of “environmental” or “social” factors without explicitly mentioning how racism is the underlying mechanism by which these factors have biological consequences (Boyd et al., 2020). This framing devalues racism as a social and political force that has a resounding influence on health and health care. In addition, without careful consideration of racism as a relevant contributor to health outcomes, researchers run the risk of incorrectly attributing health disparities to innate biological or genetic characteristics, thereby stigmatizing and alienating populations that are already marginalized within the biomedical research enterprise.

This section makes clear that useful guidance on reporting race and ethnicity data exists; however, gaps in implementation remain. Moreover, the time of publication is late in the research process, and reporting guidelines often focus on using the “right” language or terms. Although those are important, many issues related to the conceptualization and use of race and ethnicity occur earlier in a study.

CHAPTER SUMMARY AND CONCLUSIONS

The committee set out to use the research process cycle introduced in Chapter 3 as a framework for analyzing existing guidance on the use of race and ethnicity in biomedical research. It quickly became apparent that there is a host of best practice information on reporting standards. There have also been efforts to provide guidance to improve representation in clinical trials, promote community engagement, standardize EHR data collection, and reexamine the use of race and ethnicity in clinical tools. But guidance is lacking for other stages in the research process, notably for earlier steps such as conception, leaving open the possibility of misuse.

Conclusion 4-1: There are gaps in guidance on the use of race and ethnicity in research studies, especially early in the research process. Some helpful guidance for recruitment and data collection exists, yet improvements and accountability are needed. Existing publishing guidelines are useful, but the biomedical research community could benefit from further standardization for reporting race and ethnicity in publications and enforcement in a systematic manner. In addition, guidance is needed on study design, engagement and partnership with communities and other invested parties, use of legacy data, data analysis, and interpretation of results.

Some existing recommendations attempt to provide direction for considering race and ethnicity earlier in study design, suggesting, for example, the need to provide clear rationale for the use of race and ethnicity. However, these guidelines tend to lack detailed standards illustrating the kind of rationale required, allowing researchers to justify their approach without strong scientific reasoning. Furthermore, these guidelines can be difficult to enforce at the time of publication because publishing and disseminating results are downstream of many key points of decision making in the research process. These earlier junctures in study design and execution can be pivotal intervention points for improving the use of race and ethnicity in biomedical research.

Also based on their analysis of the existing guidance and the gaps identified, the committee provides the following conclusions:

Conclusion 4-2: Conducting biomedical research with American Indian or Alaska Native Tribes is distinct from working with other populations. Enrolled Tribal members have both a racial identity and citizenship within one of the 574 federally recognized Tribes. As Tribes are sovereign nations, soliciting entrance to the community may require demonstrated understanding of their history as survivors of relocation, reservation, assimilation, and termination federal policy. Members may be guarded toward participating in research. It is critical to recognize requirements for working with Tribal nations, including distinct IRB processes, data sovereignty, and approval for dissemination of results.

Conclusion 4-3: There is a critical gap in guidance regarding the use of race and ethnicity in all phases of the development of clinical algorithms and biomedical

AI models, including in problem formulation, data selection, algorithm development, deployment, and monitoring. There are inadequate reporting, regulatory, and legal standards for the use of race and ethnicity in clinical prediction models and other clinical decision tools. Additional investment in creating repositories and specific standards for assessment across racial and ethnic groups as appropriate could help close these gaps and promote transparency in clinical algorithms and biomedical AI models.

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