Modern medical practice is built on the foundation of using measurements and data to inform clinical decision making. From basic tests of blood pressure or heart rate through to more recent innovations such as genomic analysis or continuous glucose monitoring, a world without data-informed care seems almost unimaginable.
Sadly, the same cannot be said for how we diagnose and treat problems at a population health level. The reality of addressing serious public health issues such as the pervasive racial health inequalities in our health and care system is that there are significant gaps in the data that is available.
Imagine a surgeon beginning their incisions without the benefit of imaging, or an oncologist unable to monitor the bloodwork of their patient to see if a medicine is having an effect on their cancer. Too often, the fog of missing, incomplete or poor-quality data is what those tasked with solving racial health inequalities are faced with.
Data tells a story. The facts and figures it generates provide critical insights into demographics and medical information, enabling us to analyse, assess, and understand experiences, access, and disparities across communities. In healthcare, the potential of data to improve services across the NHS is undeniable. Particularly in addressing health inequalities, data plays a crucial role in identifying knowledge gaps. Through rigorous analysis, we can gain a deeper understanding of the challenges faced by specific communities, allowing for the development of tailored interventions to address inequalities in access, experiences, and outcomes.
NHS England has prioritised complete and timely datasets as part of its five key strategies to reduce healthcare inequalities. The goal is to better understand the challenges faced by marginalised communities, including Black, Asian, and minoritised ethnic populations, and to develop targeted interventions based on these insights. However, significant gaps remain in accessing the right insights across the NHS, hindering progress in closing the healthcare inequality gap.
Data, Ethnicity, and Health Inequalities: London Anti-Racism Collaboration for Health
On October 21, the Health Innovation Network South London and Race Equality Foundation launched the first in a series of learning and engagement sessions as part of the London Anti-Racism Collaboration for Health (LARCH). LARCH, a Greater London Authority (GLA) funded initiative, aims to improve the health and wellbeing of London’s Black, Asian, and minoritised ethnic communities. The event highlighted the role of ethnicity data in driving anti-racist strategies and advancing health equity.
The event featured a distinguished panel of experts, including:
- Tracey Bignall, Director of Policy and Engagement, Race Equality Foundation
- Brenda Hayanga, expert on ethnic inequalities in healthcare use and care equality for people with multiple long-term conditions, City University
- Macius Kurowski, Royal Free London NHS Foundation Trust, and Manal Sadik, North Middlesex University Hospital, discussing data-led approaches to reducing race-related health inequalities
- Mary Hill, NHS England, Head of Policy, Healthcare Inequalities Improvement, discussing data, ethnicity recording, and coding
Key Barriers to Improvement
Collecting ethnicity data is vital for understanding the unique health experiences of different ethnic groups. For instance, we know that 1 in 4 Black men will develop prostate cancer earlier in life, compared to 1 in 8 White men. Yet despite its importance, research reveals significant issues with the quality of data being collected.
Across the board, there is inconsistency. Research from the Nuffield Trust highlights that up to 40% of patients were coded as ‘any other ethnic group,’ even when a more specific ethnic group code would have provided deeper insights. Furthermore, the research from the Race Equality Foundation on the recording of ethnicity in health settings found variation in ethnic categories used with most settings where the 2001 and 2011 censuses are used inconsistently. Many people also wish to be identified by more specific ethnic terms, like “Hong Kongese,” but the options provided are often too generic.
Another challenge is the use of arbitrary codes like ‘not asked,’ ‘not stated,’ or ‘unknown,’ which do not contribute meaningfully to population insights. These categories might meet organisational reporting requirements but reflect the discomfort or lack of training among staff in asking about ethnicity.
Barriers for Patients and Staff
Research in partnership with the Wellcome Trust shows that while communities are generally willing to share their demographic information, many are unclear on how the data will be used or fear it may be used in discriminatory ways. This lack of trust is reinforced by findings in the Earning Trust: A Foundation for Health Equity report, which noted that despite growing diversity in the NHS workforce, a 2022 review found that only 14% of NHS board members came from Black and minority ethnic backgrounds, while these groups constitute at least 18% of the population of England and Wales. The findings demonstrate that leadership isn’t reflective of the communities they serve and therefore strategies to see improvements are lagging behind.
Staff uncertainty also hinders progress. The Race Equality Foundation found that employees are unsure about how the data they collect will be used, and there is little evidence that managers emphasise the importance of ethnicity data collection during supervision. Some staff also feel awkward asking for this information, fearing they are being invasive, and lack the confidence to explain why it is important.
Recommendations moving forward
A system-wide approach is urgently needed to improve ethnicity data collection, monitor inequalities, and train staff in data capture. Poor-quality ethnicity data is masking health inequalities, and addressing this requires concerted effort.
Having more rigorous ethnicity data collection provides a more nuanced understanding of the issues facing minority populations and enables healthcare systems to focus their efforts on addressing the issues at hand. We need to be led by the data in order to solve health inequalities.
We can do this by driving:
- Confidence: Build confidence in using data to understand experiences, assess progress, and reduce health inequalities.
- Guidance: Provide clear guidance to improve the understanding, collection, and recording of ethnicity data.
- Procedures: Ensure there are robust procedures in place to monitor the quality of ethnicity data.
Between now and March 2025, LARCH will be having more learning and engagement events. Stay tuned for future events.
