Logo

Real-World Data is Essential for Illuminating and Addressing Disparities in Care

Real-world data (RWD) is quickly becoming a fundamental pillar of the life sciences industry. With growing acceptance from physicians, researchers, patients, regulators, and pharma stakeholders, RWD is taking on an increasingly prominent role in everything from clinical trial design to medical device approvals.

But the utility of RWD goes far beyond the development of drugs and devices. RWD can help us address one of the most challenging systemic issues in healthcare: disparities in health outcomes perpetuated by uneven access to services and the unconscious bias of humans and technology.

Providers, payers, regulators, and solutions developers frequently suffer from blind spots when addressing the needs of individuals across the racial, ethnic, gender, and socioeconomic spectrum, creating deeply entrenched disparities with far-reaching consequences.

These inequities range from differences in insurance coverage and chronic disease prevalence to the lack of healthcare access and worse outcomes from COVID-19.

For example, non-White individuals are more likely to suffer from some chronic conditions, including asthma and hypertension. An African American child is nearly eight times more likely than a non-Hispanic white child to die of asthma. And while African American, Asian and Hispanic adults are diagnosed with hypertension significantly more often than non-Hispanic white adults, they are much less likely to have their condition under control.

Similar differences are starkly visible in cancer care, maternal health, mental and behavioral healthcare, and even trauma care. The list goes on.

To start solving the problem, we must uncover the “why” behind these disparities.  And to do that, we need comprehensive, accurate, multisource data about the people and processes involved.

Real-world data, including EHR data, claims, patient-reported data, and disease registries, can provide many of the answers we’re looking for. With access to a variety of digital data sources – and the use of advanced analytics to aggregate, clean, and curate the data for use – researchers may be better able to compile and compare diverse cohorts, identify treatment patterns, analyze outcomes, and apply their learnings to real-life care situations.

The Study of Women’s Health Across the Nation (SWAN) initiative is one promising example of how RWD can help unveil insights into the experiences of traditionally marginalized groups. Cosponsored by several government agencies, including the National Institutes of Health (NIH), the SWAN study leveraged RWD to find that women of color tend to enter menopause earlier than white women and face more severe symptoms. The data also revealed racial and ethnic disparities in sleep quality and statin use among middle-aged women, all of which may contribute to higher risks of cardiovascular disease.

COTA has also recently published research, in conjunction with several leading cancer centers, using RWD to show clear disparities in access to cancer care for African American patients with multiple myeloma. The study revealed that compared to white patients, African Americans experienced a notably longer time to receive treatment for this cancer, even though the same therapies were available to both groups.

Since African Americans are two to three times more likely than white Americans to develop multiple myeloma, and delayed care often leads to worse outcomes, it is critical that oncologists understand and address this difference as quickly as possible.

As we continue to develop the ability to curate high-value data and apply actionable insights to crucial questions of care delivery and outcomes, we will certainly uncover even more disparities that need our attention. RWD will be a crucial tool in the fight to reduce disparities, ensure that underserved groups have access to necessary services, and provide effective, evidence-based care equally to all individuals who interact with the healthcare system.