Background

Racial bias in research can manifest in various ways, including systematic under-representation of ethnic minorities and inappropriate interpretation of disparities between socially-defined racial and ethnic groups. Misunderstandings can arise when the social construct of race is incorrectly equated to biological differences.

Racial bias may be especially prevalent in observational and qualitative research, where interpretation is subjective, and in experimental studies, where researchers might unintentionally favour one group over another.

Bias can stem from systemic, institutional, interpersonal or individual forms of prejudice against a socially-defined race or ethnicity, affecting all stages of a health research project. Prejudice can arise from a variety of sources, including lack of diversity in research teams, language barriers, and historical misconceptions of race as a biological construct.

Underrepresentation of racial and ethnic minorities in research is a significant issue. It can be challenging to identify when socially-constructed racial and/or ethnic data are not reported. Misinterpretations can occur when research links biological outcomes to race without considering that race is a social construct with health determinants. Genetic ancestry can affect health outcomes, but should not be conflated with race, nor used to support conclusions of biological race-based differences.

Health system algorithms can perpetuate racial bias and health inequities when race is uncritically accepted as biological. Clinicians influenced by such conclusions risk incorporating race into decision-making tools as if it were a biological fact, rather than a societal construct. Recognising race as a social construct is key to combating racial bias and improving health outcomes.

Example

In a systematic review of trials leading to FDA approvals for cancer drugs, race wasn’t reported in over a third of trials. Black and Hispanic populations were significantly underrepresented in those that did. These trials, crucial for new drug approvals, show a concerning underrepresentation of Black and Hispanic people (22% and 44% respectively) considering their higher cancer risk. This raises questions about generalisability and equal distribution of scientific advancements, especially as risk factors like smoking and obesity vary by race.

The implications of race-based medicine, where race is misinterpreted as biological or a genetic ancestry proxy, are evident in treatment algorithms rooted in outdated research or biased data. For instance, a breast cancer risk calculator discourages rigorous screening for non-white women by overestimating white women’s risk. A cardiac surgery risk calculator, despite unknown underlying mechanisms, places ethnic minorities at higher risk, dissuading them from surgery. The US Kidney Donor Risk Index tool predicts graft failure based on African American race, reducing transplant opportunities for this population.

A study by Obermeyer et al. (2019) highlighted racial bias in a widely used health system algorithm predicting patients’ healthcare costs, not illness severity. This flawed process is exacerbated by Black patients often receiving lower quality of care than white patients with similar health issues, leading to inaccurate predictions and compromised care.

Impact

Goyal et al (2023) reviewed perinatal mental health research during the COVID-19 pandemic, finding recruitment rates for Black, Indigenous, and other women of colour at just 23.5%. In 13 studies (52%), white participants made up over 80% of the sample. Moreover, 68% of studies lacked detailed descriptions of non-white participants, using broad categories like ‘ethnic minorities’. While anonymity concerns may restrict racial data reporting, this under-representation is concerning given the disproportionate COVID-19 impact on ethnic minorities, notably Black women’s elevated postpartum depression risk and maternal death rates.

Historical misconduct like the Tuskegee trial may contribute to low ethnic minority participation in health research. The pandemic’s classification of these women as essential workers could have posed research participation challenges and implicit bias in study design and data reporting may also lead to under-representation. The prevalence of cross-sectional designs and social media recruitment could limit participation from lower-income individuals.

A well-known example of race-based medicine in recent years is the Bidil study, a clinical trial that aimed to test the efficacy of a heart failure drug exclusively in self-identified African Americans, without genetic testing, and although self-identified race is an inadequate proxy for genetic ancestry or variation. The study reinforced harmful racial stereotypes regarding race-based genetic differences while failing to adequately consider ancestral genetics and the complex social and environmental factors that could have contributed to disparities in health outcomes.

Preventive steps

The causes of racial bias in research are numerous and complex, and preventing it demands a multifaceted approach on all levels. For individual researchers and groups, a key aspect of such an approach is to ensure diversity in their research teams, as this can enhance the integrity of data.

A well-designed protocol can help ensure that the research is conducted in a rigorous, objective, and culturally sensitive manner, which minimises the possibility that racial bias will influence the results. Protocols should include a clear, unbiased research question, non-discriminatory inclusion/exclusion criteria, inclusive recruitment strategies, community engagement, randomisation and blinding where appropriate, guidelines for data analysis that take an intersectional approach and include measures validated for diverse populations and ethical considerations. If genetic testing is considered as part of the research protocol, it should be approached with caution, ensuring that it is linked to individual ancestry rather than relying on broad racial classifications, in order to avoid perpetuating harmful racial stereotypes while still providing personalised insights into potential benefits or harms of the intervention.

Developing trusting relationships within diverse communities and using culturally sensitive recruitment strategies are crucial to increasing the participation of women from ethnic minorities in research.

The NIHR INCLUDE guideline was created to support researchers in improving the inclusion of under-served groups in research. The STRIDE Project has produced eight recommendations specifically focused on enhancing the inclusion of ethnic groups in clinical trials. Finally, the Critically Appraising for Antiracism Tool has been developed to aid researchers by identifying and addressing concerns related to racial bias in research.