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Moving Forward: Data Infrastructure Needs in Harnessing Data for Research in Structural Racism

HARNESSING DATA FOR RESEARCH ON STRUCTURAL RACISM

Marjory Givens (associate director of the University of Wisconsin Population Health Institute and co-director of County Health Rankings & Roadmaps) emphasized that data are needed for measuring and model-

ing structural racism, as well as for identifying and measuring the mechanisms that link racism to population health and well-being over time. She described power—which can be visible, hidden, or invisible—as a fundamental driver of health and equity (Lukes, 2005). Influencing the invisible face of power are data and narrative, which reveal “value-based meta-stories” about how the world functions and affect public consciousness (i.e., societal responsibility and possibility).

Givens indicated that many scholars have discussed the value of thinking critically about measurement, as well as about how the power of narrative and data can be used to advance change. She underscored the goal to harness data to “ensure that public infrastructure and social systems nurture collective well-being and help create the conditions where everyone has what they need to thrive.” For example, Givens and colleagues (2021) highlight the value of critical race praxis, which asks structural racism researchers to consider their research inquiries and applicable disciplinary knowledge in light of the following question: Who decides what matters and what is measurable? The researchers document the power maintained by those who determine what can be measured, how to invest in data infrastructure, and who can access the data; they note that seemingly objective choices about methodology have important implications both for the research and for future policy.

Reflecting on how existing data could be leveraged to strengthen structural racism research, Givens explained that theory-based methodological approaches offer historical and geographical context and engage both qualitative and quantitative methods that portray the systemic features of structural racism (Hardeman et al., 2022). Such methodological approaches are particularly important within a data system that is structurally racist, she continued. For example, the tax code is only one of many systems that reinforce the racial wealth divide over the life course and across generations—credit scoring, lending, Medicaid expansion, and higher education are others. According to Dorothy Brown (2021), “tax policies ignore the day-to-day reality of most black Americans, who are still playing catchup in a system that deliberately excluded them for many years.” To illustrate the magnitude of this racial wealth divide, Givens pointed out that according to the 2019 Survey of Consumer Finances the median household wealth in the United States in 2019 was $24,100 for Black Americans and $188,200 for White Americans, a disparity that has persisted for decades.

Givens underscored that repeated practices and policies create, contribute to, and maintain this racial wealth divide; as wealth is passed from generation to generation, people are born into different levels of opportunity. Although many U.S. households build wealth through home ownership, Black households are at a disadvantage owing to discriminatory housing and lending practices, for example. Furthermore, community wealth is key

to funding public goods and community infrastructure such as schools and parks. In other words, she continued, racism and the racial wealth divide have tangible implications, such as geographic differences in school funding—the most significant deficits are in the Southern Black Belt, a region with a long history of structural racism that continues to affect community resources.

Givens asserted that addressing this structural racism requires both data and political will.

She accentuated that data have been and will continue to be “politicized and weaponized”; for instance, the U.S. Census, which is essential for public health, has been “structurally flawed and racist from [its] origins” (Krieger, 2019) and has been used as a political instrument (e.g., the attempt to exclude unauthorized immigrants from the 2020 Census counts for representation). Therefore, she stated that data to examine the mechanisms that link racism to population health and well-being over time are essential.

Givens also explained that conceptual frameworks can shape “how we make sense of the world: what we measure, how, and why.” For example, population health researchers have begun to develop graphic representations of health and its many drivers. Such health and equity frameworks can serve several purposes, such as informing research agendas, serving as boundary-spanning tools for engagement, helping organize thoughts and shape narratives, and raising awareness of the interconnections that affect health and equity. She summarized a review of 27 graphic representations from the population health community published during the 21st century (Givens et al., 2020): few articulated underlying theories; most were found in publicly available grey literature, but only eight were published in peer-reviewed literature; earlier frameworks were intended to guide policy development or research, and more recent frameworks focused on community practice or research; and more than half acknowledged the existence of inequities in determinants or policies, while half mentioned multiple disparity domains. Most did not address how health outcomes or determinants are distributed across populations or the drivers that influence variation in those distributions. Only nine frameworks identified some drivers as “fundamental” or “root” causes of health inequity, and the terminology varied (i.e., only nine explicitly named “racism,” and five included political or institutional “power” as drivers of health and equity). Two of the frameworks that explicitly named racism as fundamental or sociocultural drivers were those developed by Schulz and Northridge (2004) and Hill and colleagues (2015). Frameworks that named power as a fundamental driver include those developed by the University of Wisconsin (2019), Public Health Scotland (2016), and ChangeLab Solutions (2019). She explained that the variation across these frameworks suggests that the population health community has not reached consensus on the drivers of health and equity, which has implica-

tions for measurement, methods, and understanding of the mechanisms that link racism to population health and well-being over time.

In closing, Givens shared an excerpt from a poem by Ryan Petteway (2022) entitled “Something something something by race, 2021,” which offers commentary on the dominant paradigm for the production of racial health inequities: “structural racism [is] not just a thing ‘out there’ to study in relation to health inequities, but also a thing ‘in here’ that shapes how we do what we do and who gets to do it.” She described this poem as a call to action for researchers. Givens outlined next steps for the population health community to better harness data for structural racism research: (1) use collective power and political will to mobilize the full range of data and research tools; (2) look inward, at history, and toward the future to improve methods and measurement; and (3) align practices and tools more effectively to leverage the power of data and narrative.

A RESEARCH OPPORTUNITY: A NEW COHORT OF THE NATIONAL LONGITUDINAL SURVEY OF YOUTH

Seth Sanders (Ronald Ehrenberg professor of economics at Cornell University) indicated that population representative panel datasets (i.e., data collected by following people over long periods of time) appear to be well-suited to studying the effects of structural racism because they create opportunities for linkages (e.g., criminal justice outcomes to late labor market success) and for the measurement of cumulative effects; however, these studies are not ideal for that purpose. He asserted that a new cohort of the National Longitudinal Survey of Youth (NLSY) could mitigate existing issues and become valuable for structural racism research.

Sanders highlighted six key strengths of national longitudinal surveys in relation to the study of structural racism.

1. Data collection typically begins when respondents are young. For example, four cohorts began in 1967–1968 with young men and women aged 14–24. The next cohort started in 1979 with men and women aged 14–21, who are still being followed today. Another cohort started in 1997 with adolescents aged 12–16. A new cohort is planned for 2026, for which the age range has not yet been determined (but will likely be similar to that of the 1997 cohort);
2. Since these surveys follow people throughout their life course, they are useful indicators of cumulative disadvantage, which is a key feature of structural racism;
3. The annual or biannual data collection in multiple domains enables studies of how racism moves from one set of institutions

3. to another—for example, education, training, and achievement scores; employment; household, geography, and contextual variables; family background; dating, marriage, cohabitation, sexual activity, pregnancy and fertility, and children; income, assets, and program participation; and attitudes and expectations. However, because this survey is sponsored by the Bureau of Labor Statistics, health has been studied less extensively and crime and substance use have rarely been studied;
4. The oversample of racial/ethnic minorities in the NLSY enables important subgroup analysis in the study of structural racism;
5. Some intergenerational aspects can also be studied; for example, NLSY79 has a companion survey for the children of its female cohort participants, and NLSY97 had a parent survey in the first year of the project; and
6. The sampling design of the NLSY allows for the study of siblings and cousins, with controls for family background.

Nonetheless, several weaknesses have restricted the usefulness of national longitudinal surveys for the study of structural racism, Sanders explained. For example, the NLSY effectively links contextual data spatially but lacks the ability to link data in other dimensions—without school, firm, health care provider, and law enforcement agency IDs, important institutions where structural racism could vary are not considered. Inter-generational data are also limited in the NLSY. Historically, diversity in the planning process and design team has not been a priority for the NLSY, which likely affected survey content and design. He suggested that leadership and collective action could address this weakness, with the research community actively providing input and the Bureau of Labor Statistics staff justifying the content and design decisions. More specific areas of weakness include that although the NLSY has measured outcomes effectively (e.g., when a person was arrested), it has not successfully measured the processes leading to those outcomes (e.g., why the person was arrested). Additionally, because the NLSY is somewhat of a general-purpose survey, content relevant to all participants is prioritized over content that might be highly relevant to studies of structural racism but less relevant generally. Furthermore, he continued, although the representative oversamples are useful, a question remains as to whether they are large enough; for example, historically, sample sizes of minorities with high socioeconomic status have been small. Although there have been efforts to collect data specifically on health every 10 years beginning at ages 30 and 40 in the NLSY79 and NLSY97, respectively, the health data are generally weak in the NLSY relative to those in health surveys. He underscored that this is a missed opportunity to observe the effects of structural racism on health that begin

far earlier in life. Additionally, although the NLSY97 collects stressors more effectively than the NLSY79, it still does not collect race-specific stressors. No biomarker assessments are included in the NLSY, he continued, and self-reports on biomarkers are limited, which conflates health access with health conditions. Lastly, the NLSY lacks assessments that are typically important in health surveys (e.g., measured height, weight, blood pressure, and pulse, as well as genetic data). However, despite the aforementioned weaknesses, he pointed out that health scientists are the fastest-growing set of NLSY users.

In closing, Sanders detailed the new NLSY cohort, for which the planning process is underway for 2026 data collection. Its planners are prioritizing diversity and engaging a wide set of stakeholders by involving (1) content panels with subject matter experts in family background and cognition, K–12 education and health, the environment, and Department of Defense interests; (2) listening sessions on childhood and family retrospective, mental health, physical health and the environment; (3) listening sessions on innovations in international surveys and the nature of the work, as well as data needs of think tanks, nonprofits, and research organizations; (4) invitations to registered NLSY users to participate in the user survey; and (5) analysis of alternative data sources and underused variables. Sanders expressed his hope that the 2026 NLSY cohort will better meet the needs of a diverse research community and be more impactful for the study of structural racism.

STRENGTHENING INFRASTRUCTURE FOR RESEARCH ON STRUCTURAL RACISM AND AGING

Jennifer Manly (workshop planning committee member and professor of neuropsychology at Columbia University) emphasized that because racism is a “fundamental cause of disease and death” and understanding racism is key to eliminating health inequities, the investigation of the systems that cause harm through structural racism is within the purview of the National Institutes of Health (NIH). Accordingly, NIH has made a commitment to end structural racism in the biomedical research enterprise, which requires robust health equity research (Collins et al., 2021).¹ Manly highlighted a March 2021 NIH-wide call for applications to understand and address the impact of structural racism and discrimination on minority health and health disparities.² However, its August 2021 deadline did not provide enough time for researchers without already-funded projects or an existing research infrastructure to submit an R01 with preliminary results.

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¹ See also https://www.nih.gov/ending-structural-racism/unite

² See https://grants.nih.gov/grants/guide/rfa-files/rfa-md-21-004.html

She underscored that this created a significant missed opportunity for many interdisciplinary teams to participate in this important research.

Manly asserted that to build capacity for structural racism research, institutions have to provide resources for assessing and eliminating racism, align promotion and tenure with best practices for health equity, and sustain this commitment over the long term. Furthermore, she continued, an institution’s commitment to antiracism should become a score-driving criterion for institutional resources in grants. Part of building this new research infrastructure includes building competencies among research teams to do health equity research within a framework of race-making (historical, dynamic, relational, contextual); creating a score-driving team that represents communities in the study with proven success in community engagement; encouraging personal awareness and adapting behaviors among researchers who might lack appropriate expertise (see Lett et al., 2022); building multidisciplinary teams; and understanding how to prioritize community ownership of research goals, resources, and capacities.

Manly stressed that diversity does not automatically equate with equity, which is an important concept to understand both when forming research teams (Jeske et al., 2022) and when selecting research participants. For instance, recruiting diverse research team members without allowing those individuals to serve in leadership roles or to guide study design does not lead to equitable research. She highlighted the importance of “disrupting the power differential” to better protect Black, Indigenous, and Latinx researchers at all stages of their careers. She added that irresponsible research approaches have lasting impacts.

Manly also noted that “community mistrust of the medical system and of research is not a fundamental driver of health inequalities.” To engage communities around Alzheimer’s research, for example, Green-Harris and colleagues (2019) incorporated the community’s value system in their work, became a part of the “community fabric” by offering services for older adults, met community needs, and established relationships. Manly emphasized that to engage with a community at this level, funding support beyond that of NIH is required. She also suggested that funding not be awarded to teams unwilling to engage with communities. As an example of a successful research project that included interdisciplinary teams and engaged communities in structural racism research, she described the Investment in Communities Offspring Study, to which a social work team was built in to the funded staff to help research participants navigate community resources related to housing, food assistance, and mental health services.

Manly emphasized the need to raise the bar for both research products (see Boyd et al., 2020) and research teams, and indicated that an ahistorical approach to understanding racial health inequalities is unacceptable (see

Williams, 2019). She articulated that a sustainable data infrastructure for structural racism research on health and aging requires:

1. Representative, not convenience, samples (i.e., oversampling techniques reflect heterogeneity within groups);
2. Funding for life-course and longitudinal design;
3. Core support for multiple sources of data (e.g., residential history, individual sources of stress and positive well-being, biomarker collection, in-home assessment, and administrative linkages); and
4. Incentives to focus research frameworks on structural and policy change.

INSIGHTS FROM THE CENTER FOR ANTIRACISM RESEARCH FOR HEALTH EQUITY

Rachel Hardeman (associate professor and Blue Cross endowed professor of health and racial equity in the Division of Health Policy and Management, University of Minnesota School of Public Health) explained that the Center for Antiracism Research for Health Equity (CARHE) pursues health equity and justice for every individual, family, and community in Minnesota and beyond. It works to identify, understand, and dismantle structural racism through multidisciplinary antiracist and collaborative research, education and training, authentic community engagement, and narrative change, as well as by serving as a trusted resource for members of the public health and policy communities. Fostering authentic community engagement and serving as a trusted resource are of particular importance, Hardeman continued. To achieve those two goals, she proposed that multidisciplinary teams engaged in research reflect the communities harmed by structural racism. She asserted that much work remains to measure and combat structural racism as researchers continue to leverage existing expertise and innovation.

Hardeman reflected on the status of naming and measuring structural racism in the public health scholarship. For example, Hardeman and colleagues (2018) found that among all articles published in the top 50 highest-impact public health journals from 2002 to 2015, only 25 articles named “structural,” “systemic,” or “institutional” racism in the title or abstract. Groos and colleagues (2018) found only 20 articles that included measures of structural racism, and these measures of structural racism were in the following domains: residential housing, social institutions, immigration and border enforcement, political participation, socioeconomic status, criminal justice, and the workplace environment. She pointed out that many of the measures in these 20 articles were chosen by their authors based on the availability of public data, which were then analyzed in the context of

differential experiences attributed to structural racism. Hardeman encouraged researchers to continue to leverage available data to move the field forward.

Hardeman remarked that the first step in conducting research on structural racism is to develop an antiracist research framework that centers on (1) the notion that racism is a fundamental cause of health inequities at the margins; (2) the notion that systems, policies, social structures, and history create the conditions that allow inequities to persist; and (3) reconsideration of which evidence is “real.” She underscored that researchers should work authentically with communities to cocreate evidence that will dismantle the existing societal structure organized around a “powerful center” and “the margins.” She indicated that CARHE held several virtual community conversations with people across varied racial backgrounds to better understand their experiences with structural racism and their thoughts about how it could be measured. These conversations, as well as the work of Chambers and colleagues (2021) on Black women’s perspectives in particular, have revealed domains of structural racism that have not yet been captured quantitatively. Moving forward, Hardeman continued, incorporating and leading with community voices can inform the development of a data infrastructure for structural racism measurement. Accordingly, CARHE has an objective to build a nationally, publicly available data repository that could be used by researchers, community members, and policy makers to understand how structural racism is operating and its impact on population health and well-being—The MeasuringRacism Data Portal^® (Figure 3-1). This approach centers at the margins, with community voices guiding and holding researchers accountable.

REFLECTIONS AND DISCUSSION

Serving as the discussant for this session of the workshop, Margaret Hicken (workshop planning committee member and research associate professor in the Institute for Social Research at the University of Michigan) highlighted three themes that emerged during the presentations:

1. Data and narrative are influenced by those with power, yet collaborative research includes those who are surveilled in decisions about what information is collected;
2. A very small group determines what is included in large, expensive panel datasets; however, more people could be involved in determining what data are collected and how they are linked; and
3. Gatekeeping continues to be a barrier to funding and publication. Remaining questions include how academic success is defined, who develops research questions, and how those questions are tested.

Hicken also identified an overarching theme of the workshop: research teams comprised of scholars with diverse experiences are best suited to understand the drivers of structural racism. She advocated for scholars trained in population health, and thus who are relatively new to this research, to look beyond the scholarship in their own field—humanists, humanistic social scientists, and artists have been studying racism for centuries. She also emphasized the value of expanding place-based research methods, because a place has a “living history” that shapes health variations within and between communities. Neighborhood plays a key role in studies of structural racism, she continued, as residential segregation is a tool for those in power to invest and disinvest in different groups of people.

A participant requested examples of research that has successfully assessed racism at the structural level. Sanders recalled, as one example, the scholarship of presenter Jamein Cunningham (assistant professor in the Jeb E. Brooks School of Public Policy at Cornell University) on how police contracts affect community outcomes. He also championed the use of creative data collection and linkage for nonspatial inequalities and institutions.

Hicken pointed out that no data are perfect and advocated for the use of theory-informed interdisciplinary frameworks for guiding research questions before data are collected and linked.

A participant inquired about specific priorities for data linkage. Manly supported the practice of collecting data on life experiences to better understand a community’s residential history (e.g., number of moves people have made as well as the dates they were in certain locations), which enables place-based linkages that could provide additional information about displacement, for instance. She also reflected on the Jackson Heart Study,³ which asked people about their experiences with stress and racism, as a model of effective survey research. Sanders explained that because all data linkages require informed consent, building trust is the first step in encouraging people to share their personal information with the research community. As an example of creative data linkage, he referenced Lisa Cook’s scholarship on the timing and spatial variation in locations of Civil War Confederate statues as a measure of structural racism and its effects on socioeconomic outcomes.

Another participant asked how best to incentivize data sharing. Givens described an ongoing movement toward data transparency, which could motivate more people to recognize that data are a public good not meant to be “held behind walls.” Hardeman suggested a focus on “data for action,” as well as building infrastructure so that data sharing becomes an easy, trusted process.

A participant posed a question about the future of interdisciplinary training for population health scholars. Givens encouraged researchers to reflect on their identities: honest and difficult conversations are critical in helping people understand how to use their influence in the workforce. Hardeman said that, after this critical self-reflection, if each person enters the training knowing their “why,” the training most suitable to achieve this why can be selected. Hicken wondered how to build diverse, interdisciplinary teams when academia remains segregated. Manly inspired researchers to be creative and disruptive: those with tenure and R01 funding could use their privilege and power to change the academic landscape, where “Whiteness is a credential” (Ray, 2019). She stressed that innovation and time, as well as shifts in reviewer and promotion and tenure structures could enable substantive change in recognizing and rewarding diverse teams.

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³ See https://www.jacksonheartstudy.org

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