Faculty’s Life Sciences team is using data science to highlight inequalities in US clinical trial access. Our work highlights the impacts of poor access to drive change so that more people can benefit from better representation in trials. Access to these trials is important for patients who have exhausted conventional routes, so it’s vital that these trials represent the whole population and that everyone can have equal access.
Leveraging existing data to identify healthcare inequalities
The life science industry invests billions in research and development for new therapies every year. Clinical trials are a key part of this, and trial efficacy is dependent on a representative sample of the general population. If a trial only includes a narrow sample from a certain group, its findings will have limited real-world applicability.
Efforts have been made to address the challenge of representation in trials, such as selecting sites in varied locations. But urban areas remain the standard trial location. Currently, the majority of clinical trials are held in the largest metropolitan areas in the US, including California, New York, and Miami.
To help tackle this, our research combined public datasets, including the FDA and World Health Organisation, to generate maps showing trial access relative to population density and poverty. We mapped trial access based on these two factors as they are critical determinants for access.
It is important to identify if populations in less dense, more disadvantaged areas have comparable access to those in cities so we can better understand research inequality.
Narrowing in on US healthcare data
The US benefits from leading research expertise, but does this research fairly represent its population?
These maps showcase our findings and enable exploration county by county. For our research, we define poverty as the number of people under the Supplemental Nutrition Assistance Program (SNAP, formerly the Food Stamp Program) by county population.
Our findings point to striking correlations:
- People in major population centres have far greater access to clinical trials.
- For every 1% increase in a county’s social welfare use, access to trials decreased by 4%.
An obvious reason for lower access in rural areas is that many trials take place in cities or academic medical centres, where access to specialised facilities and healthcare is more available. The map shows how highly populated areas, especially along the coasts, benefit from strong access to trials.
In contrast, much of the Midwest lacks any access at all. This means people in rural areas, or those unable to travel to trial sites, are often left out.
Similarly, the correlation between increased poverty and reduced trial access shows inequality in research. Many counties with SNAP rates of around 40% do not have access to trials, especially those in the South. This compares unfavourably with coastal areas, including in Florida and California, which have SNAP rates closer to 20% and far greater access.
The impact of inequality in research
Geography and economic status clearly influence the representation of US clinical trials. Population health varies across the US and can be impacted by socio-economic status.
Populations from counties with higher poverty rates are more likely to lack health insurance access and have higher rates of diabetes, depression, and malnutrition. And failing to include these individuals from poorer areas risks missing the impacts of treatments on those with common conditions in that demographic.
Real world therapy effectiveness can differ significantly from trial findings. This can be compounded if the sample population does not reflect the breadth of society.
The next frontier in healthcare data science
Equality in health research is vital, both for people to have fair access to new treatments and for researchers to have representative samples for their studies. Our analysis shows clear shortcomings in trial access. Exposing these inequalities is necessary in understanding how research can be done better and in more effective ways.
This analysis shows how data science can be applied to existing data to offer new insights. But identifying where and how clinical trials should take place is just one use case. By leveraging existing data, we can better appreciate a range of challenges in life science research and develop novel solutions to address them.
Interested in learning more about Faculty’s work in health and life sciences? Read about our work here