How Real-World Data Is Helping Close the Rural-Urban Health Divide

For too long, rural and underserved communities have been left behind in medical research, clinical trials, and access to the latest medicines. While the easier to reach, urban populations are overrepresented in clinical trials, rural areas with fewer academic research centers and fewer participating physicians remain vastly understudied. Real-world data provides a powerful tool to bridge this gap and generate insights to improve health equity for our most isolated populations. 

By gathering data from electronic health records, insurance claims and patient wearables, researchers can now study treatment effects and outcomes across a broad set of demographics without geographic limitations. For example, real-world analysis can provide insights into how factors largely out of rural residents’ control – such as lack of access to healthy food, limited infrastructure, provider shortages, and demographic and socioeconomic disparities – impact health outcomes and access to medical care. 

Identifying gaps in quality care 

In the Appalachia region of the U.S., researchers with the National Cancer Institute have spent more than three decades working with this community to address disparities in cancer rates, leveraging real-world data to address and test ways to improve the rate of rural cancer screenings. 

In the Appalachian program, researchers surveyed community leaders and residents regarding motivators and barriers to seeking healthcare, especially preventive care such as vaccinations. Identifying how to break through to different communities in ways that resonate culturally and are sensitive to societal nuances can be key to improved participation. 

Clinical trial enrollment is one of the largest challenges we face in ensuring rural patients are adequately represented. We learned this with Covid-19. Initially, trials were held in large cities with easy access to patients, most of whom were older, white, and middle class. Covid-19 was new and certainly not exclusive to those demographics. As an industry, we had to pivot quickly to ensure we included patients of all races, ages, and socioeconomic statuses. I think it is fair to say that the Covid-19 trials were the most significant, widely observed trials in human history.  Patients were identified and efforts were made to have those rural patients included. This collaborative approach has offered a global solution that changed the trajectory of getting the virus to an endemic status and has saved countless lives.

Empowering remote communities

Additionally, real-world data research positions clinical trials more effectively. Retrospective and prospective studies bring objective interpreted data to those that approve and manage the outcomes of these efforts. 

These real-world data assets, specifically, electronic health records, can be analyzed to understand the prevalence of nutrition-related illnesses, rates of preventive care usage, availability of specialty care and transportation barriers facing rural populations. These data sources can also unearth disparities in health outcomes and mortality rates based on socioeconomic status in rural communities. 

Identifying effective solutions 

Such real-world data analyses require only the costs of data acquisition and analytics – a fraction of what would be required to investigate these topics through new clinical trials or primary data collection. With real-world evidence, researchers can maximize limited rural health patient access to gain data-driven insights into environmental, systemic, and socioeconomic factors underlying rural health challenges. The evidence then can inform policy changes and resource allocation to improve equity and access. 

Research has found that rural communities have higher uninsured rates compared to their urban counterparts. Studies have found that this is, in part, due to a lack of insurers participating in rural marketplaces. (Indeed, roughly 10% of rural participants in Affordable Care Act marketplaces had access to only one participating insurer.) In response, many states have bolstered health insurance coverage and waived many cost requirements for state Medicaid programs and other state-regulated insurance plans.

Addressing nuanced challenges 

Other initiatives have championed the use of real-world data to improve conditions that contribute to health challenges in rural areas. The Robert Wood Johnson Foundation, for example, provides grants to rural communities to develop creative solutions to their healthcare challenges. Funded projects have addressed issues such as food insecurity, transportation barriers, provider shortages and mental health. 

In addition to addressing the needs of rural communities at large, many other initiatives have leveraged real-world data to address the unique challenges of specific cohorts. The Veteran’s Administration’s Office of Rural Health has partnered with several outside organizations to look at key issues impacting rural vets, including barriers to mental health treatment, risks of substance abuse and challenges with care coordination. By investigating the actual experiences and distinct obstacles rural veterans face through data analysis and community-based research, the initiative continues to inform tailored policies and programs to improve access, quality, and outcomes of VA care for this underserved group. 

Capturing experiences, outcomes

Real-world data gives us an invaluable peek into the realities of people’s day-to-day lives – the ups, the downs and everything in between. In a controlled study, we only see a narrow slice of participants’ experiences. But real-world data casts a much wider net. It captures all kinds of health journeys, not just the prescribed path laid out by researchers. This makes real-world data crucial for really understanding health outcomes across the board.

Real-world data is also inclusive in a way that traditional studies often aren’t. It draws from people who tend to get left out of research – those juggling multiple chronic conditions, seniors, patients in rural towns and low-income neighborhoods. Real-world data ensures their specific challenges are contemplated and studied, too. That matters because it democratizes the benefits of medical research reach more people, not just select groups.

Randomized trials still have a key role to play. But let’s not underestimate real-world data. It offers a 360-degree view of health that studies in controlled settings lack. This wider lens makes real-world data a powerful asset as we work to promote health equity and optimal wellbeing for all. With real-world evidence guiding the way, we can build a future where your ZIP code or bank account doesn’t determine your health – where everyone has a fair shot at living their healthiest life possible.

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