'Around 90 percent of human genetic samples come from people of European ancestry,' Dr Brieuc Lehmann, a statistical science lecturer at University College London, said as a guest on an episode of the G Word 'The need for health equity,' a podcast series from Genomics England with the aim of making genomics more accessible to all.
It was not until the last ten minutes that Dr Lehmann revealed this startling statistic, which, in reality, is what makes health equity so difficult to repair. Without access to care, many minorities (including racial minorities, gay, queer, and transgender people) are excluded from data samples, leaving them underrepresented. These gaps in data increase the risk of improper diagnoses and poorer care for underrepresented populations.
The podcast episode, filmed during pride month and data month, explored the complex questions surrounding health equity. While one 37-minute podcast episode cannot answer every complicated question (some of which data scientists do not yet know the answers to), the host, Dr Maxine Mackintosh, the programme lead for diverse data at Genomics England, and her guests, Dr Alisha Davies, from Public Health Wales and the Alan Turing Institute, and Dr Lehmann, made a notable effort to do just that.
But first, Dr Mackintosh guided her guests to help non-experts understand what health equity entails. Dr Lehmann explained that health equity aims to 'disentangle the complex factors' and 'complex feedback groups' and 'use data to begin to bridge some of these gaps.'
The two guests, Dr Lehmann and Dr Davie identified socioeconomic deprivation as a determinant and catalyst for poor health. Many minority groups have less access to health and well-being resources, such as doctors, childcare, and healthy foods. With less access to resources comes a decrease in life expectancy. 'People living in poorer areas not only die sooner, but they also spend more of their shorter lives living in poorer health,' Dr Davies explained.
Refreshingly, the panelists did not dwell on the effects that SARS-CoV-2, the virus that leads to COVID-19, had on health equity. They simply mentioned its effect, reminded listeners that this problem existed before the pandemic, and moved on to discuss what steps could be taken to improve equality in healthcare. All three of them made these complex ideas digestible for the lay listener throughout most of the podcast.
Dr Mackintosh redirected the conversation many times in order to keep the podcast accessible to all. As the discussion became more complex, though, the language used became less applicable to the general public. For a podcast attempting to destigmatise the G word, genomics, I felt that it could have used less jargon and intricate explanations in order to engage a wider audience. They used technical terms, such as 'structured missingness', which describes data that is expected to be missing, and 'imputation', when scientists infer based on the DNA surrounding the missing data. While they did provide definitions for these terms, it was easy to become lost and misunderstand the conversation that followed.
It was hard to ignore the dissonance between the message and the messengers. The all white panelists were speaking about inequalities that affect minority groups, which they may not belong to. While this shouldn't immediately dismiss them, the host had an opportunity to promote diversity in a conversation centred around minority groups. It seems that they may have unintentionally emphasised the problem with the lack of diversity in data science and health science when choosing guests for this episode.
Although voices could have been diversified, their perspectives did add to a larger discussion of health equity. Their array of expertise ranging from statistics to genomics to public health, gave this conversation nuance. The host and guests all approached this debate with care and understanding. They recognised that the first hurdle to cross is merely detecting gaps in data. Doing so might sound simple but in reality means fixing years of flawed data collection.
Dr Davies, who now focuses on research and evaluation linked to data and health, explained that 'it could take years, generations to make a tangible difference' in correcting the holes in data samples. Instead of leaving it at that, she took a realistic stance: 'There needs to be a balance of innovation in that data science space but also helping getting on and doing,' she concluded.
The intersection between data science and health equity might not be the most invigorating topic, but this podcast highlighted why these mundane areas of research are critical to the most vulnerable people – and emphasised the need for diverse voices in this conversation.