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‘Objective’ Science and White Bias: BAME Under-Representation in Biomedical Research (Part 1)

By Amber Roguski



This two-part blog series is written within the context of United Kingdom (UK) institutions and research, though much can be applied to Global North biomedical research.

Minority Ethnic’ in this blog post describes individuals who do not identify as one of the majority White populations of the UK: English / Welsh / Scottish / Northern Irish / British.

Biomedical research is a broad term used to describe scientific enquiry into health and disease.


Everything that we know about humans is the result of observation. For example, we can see that humans have different colour hair, and that the hair colour of a couple influences the hair colour of their children. Sometimes, we have to do complicated experiments to help us observe something about a person. The discovery of inheritance, genes and DNA has led to the development of methods which make it possible to sequence a person’s entire genetic makeup (their genome) and identify mutations which put them at risk of developing particular diseases.

Nowadays, powerful and precise technologies can do most of the observation work for us. Yet, despite all the advances in technology, our observations of humans are not as representative as we might think. This is because the majority of biomedical research has involved observing White people.

This first blog post explores under-representation of Minority Ethnic individuals in biomedical research, using a top-down explanation. Because, as with most things, this is a phenomenon which starts at the top, with institutional racism.


Part 1: Researchers and funding

Who ‘Runs’ Biomedical Research?

“Who runs biomedical research” can be divided into two topics: who funds research, and who spends that funding?

UK biomedical research is funded by Government-funded research bodies, industry (e.g. pharmaceutical and technology companies), charities and other Non-Governmental Organisations. These multiple funding sources all share roughly the same downstream processes (who they fund, who conducts the research, who participates), so for simplicity, this blog post will focus on publicly-funded (i.e. Government-funded) awards.

To receive funding, researchers submit their study plans to funding bodies for consideration, in a competitive process. In theory, funding decisions should be objective as these organisations put in place rigorous procedures to try and eliminate any bias in awarding their money to researchers. For example, the initial funding proposals may be assessed ‘blind’ so that the authors of the proposal are anonymised. Nevertheless, in practice, the majority of funding is still awarded to White researchers. One explanation for this is that the majority of applications come from White researchers. It is true that most applicants are White, in part because the UK is White-majority but also due to the fact that institutional racism means that White people are more likely to professionally progress to a position where they would be lead applicant (Chief/Principal Investigator; CI/PI) or co-applicants on a funding application [1].

Analyses show that White applicants (who are more likely to be applying anyway) also have a disproportionately higher award success rate than Minority Ethnic individuals, with the gap in awards widening in recent years [2]. Minority Ethnic applicants also receive smaller sums of money compared to their White counterparts (£564,000 compared to £700,000, respectively [3]). A more likely explanation for the bias seen in funding awards lies with the panel members who decide who is funded. As with most committees, the majority of funding panel members are White, who bring their personal unconscious and conscious biases into their decision making.

Often, UK funding bodies group applicants into three ethnicity categories along the lines of ‘White’, ‘Black/Asian/Minority Ethnic (BAME)’ and ‘Not Disclosed’. When funding statistics are given for Minority Ethnic individuals as a group, without considering the different component ethnicities, data richness is lost. For example, in 2015-16, the Medical Research Council (MRC) report awarding 11.6% of its funds to applicants from ‘Asian/Black/Chinese/Mixed/Other’ backgrounds, but do not provide any further ethnicity details. The complexity of omissions of this kind were highlighted recently in a joint funding call between the National Institute for Health Research (NIHR) and UK Research & Innovation (UKRI) to investigate the disproportionate impact of COVID-19 on Black, Asian and Minority Ethnic (BAME) individuals and communities. Of the seven PIs who were awarded funds, none were Black [4]. In the same call, one of the decision-making panel members, Professor Kamlesh Khunti, was also a named co-investigator on three of the seven funded proposals. This incident demonstrates how misleading it can be to report statistics for Minority Ethnic people as one homogenous group and highlights the lack of Black academics in UK higher education [5]. Additionally, we see how individual politics can have a strong influence on funding panel decisions.

It should also be noted here that, from the outset, there is a lack of biomedical research focusing on ethnic diversity as few funding streams specifically ask for proposals looking at ethnic or racial differences.

So, what we are left with is a majority-White funding panel, awarding money to majority-White researchers who may have no contractual or personal interest on researching Minority Ethnic populations.


Who Performs Biomedical Research?

It is important to consider who plans and performs research. Most will be undertaken by researchers in the early stages of their careers (PhD students, postdoctoral researchers, and research assistants), which in the UK means they will most likely be White or working within a White-dominated environment [6][7].

Although science prides itself on objectivity, the reality is that scientists are humans with their own biases (conscious or not), which inevitably influence the research they conduct. This can manifest in several ways, including White researchers being potentially less likely to recruit participants from Minority Ethnic backgrounds. The UK has a majority White population, and Minority Ethnic individuals have lower engagement with medical research (discussed further in Part 2). This means that most medical research studies struggle to recruit an appropriate sample from Minority Ethnic backgrounds. A problem arises if a (often White) researcher sees themselves as represented in the study and therefore does not think to challenge the study population’s demographics. Similarly, some biomedical technologies or procedures may not be suitable for, or may actively discriminate against, select Minority Ethnic groups and the researcher may be unaware of this, leaving the methodology unchallenged.

One example of discriminatory practices in technologies is in artificial intelligence (AI) and the application of machine learning algorithms to data for healthcare predictions. Obermeyer et al. [8] showed how a widely-used US healthcare algorithm, used to identify patients requiring specialised ‘high-risk care management’, was less likely to refer Black patients for the additional healthcare, despite them being just as ill as their White counterparts who were referred. The consequence of this discrimination means Black patients are less likely to receive the treatment needed to address complex health needs and are therefore more likely to have worse healthcare outcomes. This is an entirely preventable situation: the study found that if the algorithm were adjusted to become unbiased, through measures such as increasing the risk-threshold, the number of Black patients referred for specialist services would increase from 17.7% to 46.5% [8]. Though people often like to think that such examples of discrimination in AI are the result of complex and objective mathematics, and that these results give credibility to the racial differences we see in our societies, the truth is that these algorithms are created by humans who decide what to include and exclude from their mathematical formulae. As such, mathematical formulae are a reflection of what the scientist thinks is important in the given context. If they are White and/or naïve to social determinants of health, this means they might be less likely to consider how institutional racism will impact healthcare outcomes for Minority Ethnic individuals.

In all of the above situations, being unaware of the situation or complacent with the status quo ends with non-inclusive research and which may not be generalisable to the population.

Challenging the status quo of research methodologies is no small task, nor should it be the burden of those who identify with a Minority Ethnic group. A diverse, inclusive academic environment yields inclusive and representative data. Employing researchers from diverse backgrounds and supporting them fully, while ensuring all researchers are educated in the biases inherent to their respective fields are the first steps in creating and upholding this environment.

Recognition of the importance of inclusive research study design is ever-increasing, especially in light of COVID-19 and its disproportionate impact on BAME communities. The Centre for BME Health’s toolkit for Increasing Participation of BAME Groups in Social and Healthcare Research [9] gives comprehensive and clear guidance for researchers for every stage of the study design process and engagement of BAME communities. One of the highlights of this resource is the in-depth consideration and importance of inclusive Patient & Public Involvement (PPI) groups in research design. PPI groups are made up of members of the public and patients with health conditions relevant to the research field in question, who discuss the proposed design of research studies and give their opinions on study documents (such as participant information sheets) and whether research techniques or topics are suitable for the study participants. It is incredibly important to make sure Minority Ethnic individuals are represented in PPI groups, as they can give opinion on aspects of the study such as appropriate recruitment procedures and the cultural relevance of the work within their communities. BAME individuals in PPI groups may also be more likely to identify aspects the study design which are influenced by unconscious biases, such as groups assigned to participants by the researcher.

The NIHR’s INCLUDE project has also produced excellent guidance to ensure inclusion of under-served individuals in research [10] (with specific guidance for the context of COVID-19 research in [11]), with considerations for both funders and researchers. This guidance recognises an under-served group as having characteristics such as ‘lower inclusion in research than one would expect from population estimates’ and ‘high healthcare burden that is not matched by the volume of research designed for the group’ and provides examples of under-served groups and barriers to their inclusion in biomedical and healthcare research. The numerous under-served groups identified in this resource reminds us that a Minority Ethnic background is just one (very broad) aspect of being under-served and the intersectional approach researchers must take to ensuring their work is inclusive of the many characteristics that might apply to one individual.

There are many reasons for the healthcare inequity that affects individuals from a Minority Ethnic background. Both a lack of funding for biomedical research focusing on racial and ethnic differences, as well as under-representation of Minority Ethnic individuals in senior and junior research positions are contributing factors to these health inequities. The issues discussed in this post are just the tip of the iceberg- their causes are rooted deep in our research institutions, can often be subtle, and often require money and political power to be effectively challenged. Eliminating structural racism in biomedical science funding requires self-reflection and transparency in order to quantify the magnitude of the problem. Research institutions must take responsibility for the comprehensive training of researchers in inclusive study design. For both funding and research organisations, employment of BAME individuals is one step towards inclusive research but alone it is not enough: BAME employees must be fully supported and their needs recognised by their colleagues and institutions throughout their careers to create a truly diverse, inclusive and rich research environment.


References

[1] The position of women and BME staff in professorial roles in UK HEIs. University and College Union, January 2013. https://www.ucu.org.uk/bmewomenreport. Accessed 10.09.20

[2] Diversity Results for UKRI Funding Data (2014-15 to 2018-19). United Kingdom Research and Innovation (UKRI), June 2020. https://www.ukri.org/about-us/equality-diversity-and-inclusion/diversity-data/. Accessed 10.09.20

[3] UKRI Letter to Chair RE: Impact of Funding on Equality, Diversity, Inclusion and Accessibility. United Kingdom Research and Innovation (UKRI), October 2019. https://drive.google.com/file/d/18hMHKhZX5UIM9RKNvHj55G2iNH0QJDIV/view. Accessed 10.09.20

[4] Fresh row over review process for Covid-19 BAME grants. Research Professional, August 2020. https://www.researchprofessionalnews.com/rr-news-uk-research-councils-2020-8-fresh-row-over-review-process-for-covid-19-bame-grants/. Accessed 10.09.20

[5] Equality in higher education: statistical report 2019. Advance HE, September 2019. https://www.advance-he.ac.uk/knowledge-hub/equality-higher-education-statistical-report-2019. Accessed 10.09.20

[6] A Picture of the UK Scientific Workforce. The Royal Society, 2014. https://royalsociety.org/topics-policy/diversity-in-science/uk-scientific-workforce-report/. Accessed 10.09.20

[7] Higher Education Staff Statistics: UK, 2018/19. Higher Education Statistics Agency (HESA), January 2020. https://www.hesa.ac.uk/news/23-01-2020/sb256-higher-education-staff-statistics. Accessed 10.09.20

[8] Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science. https://doi.org/10.1126/science.aax2342

[9] Increasing Participation of Black, Asian and Minority Ethnic (Bame) Groups in Health and Social Care Research. Centre for BME Health, December 2018. https://centreforbmehealth.org.uk/resources/toolkits/. Accessed 10.09.20

[10] Improving inclusion of under-served groups in clinical research: Guidance from INCLUDE project. National Institute of Health Research, August 2020. https://www.nihr.ac.uk/documents/improving-inclusion-of-under-served-groups-in-clinical-research-guidance-from-include-project/25435. Accessed 10.09.20

[11] Ensuring that COVID-19 Research is Inclusive: Guidance from the NIHR CRN INCLUDE project. National Institute of Health Research, August 2020. https://www.nihr.ac.uk/documents/ensuring-that-covid-19-research-is-inclusive-guidance-from-the-nihr-crn-include-project/25441. Accessed 10.09.20

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