Concerns Regarding Assumptions of Racial Monoliths in Lewis et al.’s Study on COVID-19 Disparities

PI-Supervised Commentary by Grok

The study by Lewis et al. (2025), titled "Beyond Fear of Backlash: Effects of Messages about Structural Drivers of COVID-19 Disparities among Large Samples of Asian, Black, Hispanic, and White Americans," represents a significant effort to understand how diverse racial and ethnic groups respond to messages about health disparities during the COVID-19 pandemic. By employing large, purposive samples of Asian, Black, Hispanic, and White Americans, the study seeks to address gaps in prior research that predominantly focused on White perspectives. However, a critical concern arises in the study’s implicit assumption of racial and ethnic groups as monoliths—homogeneous entities with uniform experiences, beliefs, and responses. This assumption oversimplifies the complex, heterogeneous realities within these groups and risks undermining the study’s validity and applicability. Drawing on insights from the Critical Race (CR) Framework, which emphasizes rigorous conceptualization and operationalization of race in research, this essay explores the problematic nature of assuming racial monoliths in Lewis et al.’s study, its implications for research quality, and potential pathways for improvement.

The Problem of Assuming Racial Monoliths

The assumption of racial monoliths manifests in Lewis et al.’s study through the treatment of racial and ethnic categories—Asian, Black, Hispanic, and White—as cohesive groups with shared characteristics and responses to health disparity messaging. While the study’s purposive sampling is a strength, aiming to capture diverse perspectives, it does not sufficiently account for the heterogeneity within these categories. For instance, the category "Hispanic" encompasses individuals from diverse national origins (e.g., Mexican, Puerto Rican, Cuban), each with distinct historical, cultural, and socioeconomic contexts that shape their experiences of health disparities. Similarly, "Asian" includes groups as varied as Chinese, Indian, and Filipino Americans, whose differing immigration histories, languages, and socioeconomic statuses influence their interactions with health systems and responses to public health messages. By aggregating these subgroups into broad racial categories, the study risks obscuring critical within-group variations that could affect outcomes such as causal attributions, emotional responses, or support for mitigation policies.

The CR Framework, developed to standardize critical evaluation of studies using racial taxonomy, underscores the importance of conceptual clarity in defining race and ethnicity (Williams, 2024). It critiques the common practice in public health research of treating race as a proxy for uniform social, cultural, or biological traits without acknowledging its constructed and context-dependent nature. Lewis et al.’s study, while addressing structural drivers of disparities, does not explicitly define what race represents in their analysis—whether it proxies systemic racism, cultural identity, or socioeconomic conditions. This lack of clarity exacerbates the monolithic assumption, as it implies that all members of a racial group experience these structural factors similarly. Such an approach aligns with critiques in the CR Framework, which argue that poor conceptual clarity of race weakens research quality by introducing measurement error and reducing interpretative precision (Martinez et al., 2022).

Implications for Research Quality

The assumption of racial monoliths has significant implications for the reliability, validity, internal validity, and external validity of Lewis et al.’s findings, as highlighted by the CR Framework’s four pillars of critical appraisal.

1. Reliability: The reliance on self-reported race in Lewis et al.’s study introduces potential measurement error, particularly when racial categories are treated as monolithic. Self-identification can vary based on context, personal identity, or social pressures, and individuals with multiracial or intersectional identities may not fit neatly into the provided categories (Saperstein & Penner, 2012). For example, a participant identifying as "Hispanic" might prioritize their Mexican heritage in one context but their American identity in another, affecting their responses to disparity messages. The CR Framework emphasizes that reliable racial measures require consistent and contextually relevant categorizations, which Lewis et al.’s broad groupings may not achieve.

2. Validity: The validity of racial constructs in the study is compromised by the lack of a clear operational definition of race. The CR Framework argues that race should not be assumed to capture a singular construct (e.g., racism exposure) without explicit justification (Jones, 2001). By treating racial groups as monoliths, Lewis et al. risk misrepresenting the mechanisms driving responses to disparity messages. For instance, Black Americans’ responses may differ significantly based on regional histories of segregation or socioeconomic status, yet these nuances are not explored. This oversight limits the study’s ability to accurately capture the intended constructs, such as structural attributions or emotional responses.

3. Internal Validity: The assumption of monoliths threatens internal validity by ignoring potential confounders within racial groups. The CR Framework highlights the need to control for within-group heterogeneity, such as differences in education, income, or exposure to discrimination, which could influence outcomes (LaVeist, 1994). For example, Hispanic participants in densely populated urban areas may attribute COVID-19 disparities to structural factors like housing more strongly than those in rural settings, but Lewis et al.’s analysis does not disaggregate these effects. Without addressing such confounders, the study’s causal inferences about message effects may be overstated.

4. External Validity: The generalizability of Lewis et al.’s findings is constrained by the monolithic treatment of racial groups. The CR Framework stresses that external validity depends on contextualizing findings within specific populations and settings (McDermott, 2011). By aggregating diverse subgroups, the study may not accurately reflect how specific communities—such as Korean Americans or Afro-Latinos—would respond to similar messages in real-world public health campaigns. This limits the applicability of the findings to tailored interventions, a critical goal of health disparities research.

Broader Implications for Health Disparities Research

The assumption of racial monoliths in Lewis et al.’s study reflects a broader issue in health disparities research, where race is often used as a convenient but crude variable. The CR Framework’s review of 20 health disparities studies found that most exhibited low quality due to insufficient discussion of race’s conceptualization and measurement (Williams, 2024). This pattern, evident in Lewis et al.’s work, perpetuates a cycle of research that fails to capture the nuanced realities of marginalized communities. For instance, assuming that all Black Americans share similar experiences of COVID-19 disparities ignores the distinct challenges faced by Black immigrants versus native-born Black Americans, or those in urban versus rural settings. Such oversimplifications can lead to misguided public health interventions that fail to address specific community needs.

Moreover, the monolithic assumption risks reinforcing stereotypes and essentializing racial groups, contrary to the study’s aim of advancing equity. Critical Race Theory (CRT), which informs the CR Framework, emphasizes centering marginalized perspectives and acknowledging the multiplicity of experiences within racial groups (Ford et al., 2010). Lewis et al.’s focus on structural drivers is a step toward this goal, but without disaggregating racial categories or exploring intersectional factors (e.g., race with gender or class), the study misses opportunities to fully engage with CRT’s principles. This gap is particularly concerning given the study’s findings that Black and Hispanic participants showed stronger structural attributions and advocacy intentions, which may reflect diverse lived experiences not captured by broad categorizations.

Recommendations for Addressing Monolithic Assumptions

To mitigate the concerns raised by assuming racial monoliths, Lewis et al. could adopt several strategies, informed by the CR Framework and CRT principles:

1. Refine Conceptualization of Race: Explicitly define what race represents in the study, whether as a marker of systemic racism, cultural identity, or socioeconomic conditions. Drawing on CRT’s race consciousness principle, the study could frame race as a dynamic, context-dependent construct shaped by historical and structural factors (Ford et al., 2010). This would provide a clearer theoretical foundation for interpreting findings.

2. Disaggregate Racial Categories: Incorporate subgroup analyses to explore variations within racial groups, such as by national origin, immigration status, or geographic region. For example, comparing responses from Mexican versus Puerto Rican Hispanics could reveal distinct patterns in attributions or policy support, enhancing the study’s precision and relevance.

3. Account for Intersectionality: Examine how race intersects with other factors like gender, income, or education to influence responses. The CR Framework encourages multidimensional approaches to race, aligning with CRT’s emphasis on centering marginalized perspectives (Crenshaw, 2015). This could involve stratified analyses or qualitative follow-ups to capture intersectional experiences.

4. Enhance Measurement Reliability: Use more granular or context-specific racial measures, such as allowing participants to select multiple identities or specify subgroups. The CR Framework suggests validating such measures through pilot testing to ensure consistency across diverse populations (Williams, 2024).

5. Contextualize Generalizability: Acknowledge the limitations of applying findings to specific subgroups or future contexts. The CR Framework emphasizes temporal and ecological validity, urging researchers to clarify the boundaries of their conclusions (McDermott, 2011). Discussing how findings might vary across different phases of the pandemic or health issues would strengthen external validity.

Conclusion

Lewis et al.’s study makes a commendable contribution to health disparities research by prioritizing diverse perspectives and structural explanations for COVID-19 outcomes. However, the assumption of racial monoliths undermines its scientific rigor and practical utility, a concern illuminated by the CR Framework’s emphasis on critical appraisal of race in research. By treating racial groups as homogeneous, the study risks oversimplifying complex social realities, introducing measurement error, and limiting the applicability of its findings. These issues reflect broader challenges in health disparities research, where race is often used without sufficient scrutiny. To address these concerns, future iterations of the study should refine the conceptualization of race, disaggregate categories, account for intersectionality, enhance measurement reliability, and contextualize generalizability. By doing so, Lewis et al. can align more closely with the CR Framework’s call for rigorous, equitable research that honors the diversity of lived experiences and advances public health practice.

References

• Crenshaw, K. (2015). Demarginalizing the Intersection of Race and Sex: A Black Feminist Critique of Antidiscrimination Doctrine, Feminist Theory and Antiracist Politics. The University of Chicago Legal Forum, 1989(1).

• Ford, C. L., & Airhihenbuwa, C. O. (2010). Critical Race Theory, Race Equity, and Public Health: Toward Antiracism Praxis. American Journal of Public Health, 100(S1), S693-S698.

• Jones, C. P. (2001). Invited Commentary: “Race,” Racism, and the Practice of Epidemiology. American Journal of Epidemiology, 154(4), 299-304.

• LaVeist, T. A. (1994). Beyond Dummy Variables and Sample Selection: What Health Services Researchers Ought to Know About Race as a Variable. Health Services Research, 29(1), 1-16.

• Lewis, N. A., Jr., et al. (2025). Beyond Fear of Backlash: Effects of Messages about Structural Drivers of COVID-19 Disparities among Large Samples of Asian, Black, Hispanic, and White Americans. Social Science & Medicine, 377, 118096.

• Martinez, R. A. M., et al. (2022). Conceptualization, Operationalization, and Utilization of Race and Ethnicity in Major Epidemiology Journals 1995-2018: A Systematic Review. American Journal of Epidemiology, kwac146.

• McDermott, R. (2011). Internal and External Validity. Cambridge Handbook of Experimental Political Science, 27-40.

• Saperstein, A., & Penner, A. M. (2012). Racial Fluidity and Inequality in the United States. American Journal of Sociology, 118(3), 676-727.

• Williams, C. (2024). The Critical Race Framework Study: Standardizing Critical Evaluation for Research Studies That Use Racial Taxonomy. Doctoral dissertation, University of Maryland, College Park.