The COVID-19 global health crisis brought widespread attention to the issue of health care disparity, but it is not a new problem – health care disparity has a decades-long history rooted in structural and systemic inequities. Addressing these inequities would result in an overall improvement in public health and reduce unnecessary health care costs. But disparities are not limited to the delivery of health care. Disparities also exists in the way clinical trials are structured, leading top pharmaceutical companies to examine how to improve demographic data in clinical trials and design more successful studies while making sure that HIPAA compliance requirements are met.
What factors contribute to health care disparities?
Health care disparities are driven by social and economic inequities. These drivers are complex and inter-related, and in recent years have moved health systems, patient advocacy groups, and life science companies to create more patient-focused programs that work to reduce such barriers, which include:
- Economic stability
- Location and physical environment
- Food security and access to nourishing food
- Community, safety, and social context
- Health coverage, access to providers and pharmacies, and quality of care
Many of these factors also contribute to the disparity in clinical trial enrollment and participation, and here again, a patient-centric approach is helping many pharmaceutical organizations take steps to make clinical studies more accessible and equitable to a variety of patients around the world. Specific challenges facing medical research teams in working toward more diverse clinical trial design include:
- Logistical and financial barriers for patients in marginalized communities or underrepresented patient populations
- Lack of engagement with minority communities and too few minority clinical investigators who trial participants may be more likely to relate to and trust
- Language and cultural barriers
- Trials repeatedly being held in the same general locations near large hospitals, academic centers, and professional for-profit clinical trial units, which may be too far for some patients to travel
What is demographic data in clinical trials?
In a research context, demographic data refers to factors like age, race, and sex. Other types of demographic data can include socioeconomic information like employment, education, income, marital status, and more. This information is used by governments, corporations, and other entities to learn more about demographic characteristics for various purposes, like policy development and market research.
According to reporting in JAMA Network, the FDA has had an increased focus on improving demographic representation in clinical trials, and in 2014 presented an action plan to improve demographic subgroup analysis in the evaluation of new therapeutics, with specific guidelines for improving the quality of demographic data collected in postmarketing surveillance systems.
But clinical trial data sharing shows that these efforts have not come far enough, and it can be difficult to even gain visibility into the full scope of the problem. According to a 2019 JAMA study, out of 230 trials leading to FDA oncology drug approvals over the past decade, participant race was only reported by 63%. And experts believe that it’s increasingly important to collect data that goes beyond race, ethnicity, and location. According to Laura Meloney, program manager at MRCT Harvard:
“What we see with people making [comparisons] or assumptions that you will be miseducated or of lower education status if you were from a certain zip code, I think is incorrect. And I think that some of these things need to be readjusted, and we need to look at the whole portfolio of variables instead of saying that this is going to be a proxy for this other thing.”
Addressing gaps in data and high shares of unknown or missing data across clinical trial data sets can contribute to a higher degree of personalized clinical trials and health care in the future. And like other applications where data can easily be misinterpreted, the use of big data analytics for clinical trials may help life science and research organizations gather more accurate and wide-ranging information on trial participants in future studies. Electronic data capture or EDC clinical trials may also help to gather and preserve more complete information as trials progress.
Why is demographic data important in clinical trials?
Most clinical trials are not representative of the population as a whole, or even of people who live with a particular disease. According to HBR:
“Clinical trials primarily enroll white, male patients, with consistent underrepresentation of women, the elderly, and people of color – especially Black and Hispanic patients. While people of color make up about 39% of the US population, these groups represent from 2% to 16% of patients in trials.”
The most recent notable example of this problem occurred during the trial of Moderna’s COVID-19 vaccine, in which Black Americans represented just 7% of the trial versus 13% of the US population. To rectify this, Moderna slowed the trial and recruited more people of color to participate. However, companies rarely face public scrutiny in trials at this stage and can be reluctant to bog down an already slow and expensive process to recruit more participants.
Adding to the problem is the fact that drug manufacturers and research companies are not mandated to conduct diverse trials, and for many, the issue is not a priority over other factors that go into trial design and execution. In the Moderna example, there was a difference between trial sites managed by commercial subcontractors and those administered by other organizations. For example, the National Institutes of Health has invested in clinical trial sites with outreach programs staffed by doctors and nurses with ties to minority communities. “That’s not something that is part of the business model of commercial research organizations,” says Dr. Larry Corey, co-leader of COVID-19 Prevention Trials Network.
Problems like the Moderna recruitment example go beyond representation – COVID-19 infects Black Americans at nearly three times the rate of white Americans, and Black patients are twice as likely to die from the virus, according to a report from the National Urban League. Ensuring adequate clinical trial participation can result in drugs that more effectively treat people who are disproportionately affected by various conditions.
Steps to improve demographic data in clinical trials
With a renewed patient focus, life science companies and clinical research subcontractors can turn the tide on a lack of enrollment diversity in clinical trials and strive to narrow health disparity. Reporting in HBR names the following as positive steps toward more representative trial data:
- Know what representation looks like for the condition being studied
- Set intentional recruitment goals
- Use patient registry datasets as the basis for trials
- Go beyond traditional academic medical centers to access patients from different demographic subgroups
Starting with a more inclusive approach, and recruiting with intentionality, can have a remarkable impact on patient lives. In one notable example, when the Multiple Myeloma Research Foundation learned that about 20% of patients with the disease are Black, the organizations made a point to strive for a representative population in all of its studies.
To learn more about clinical trial design and development and how technology can shorten clinical study timelines, visit within3.com.