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The ABCs of SDOH: Meaningful strategies to improve patient outcomes

In the ever-evolving healthcare landscape, social determinants of health (SDOH) are pivotal factors in influencing patient outcomes. As biopharmaceutical and medical device companies increasingly recognize the impact of SDOH, a question emerges: how can these sponsors effectively integrate SDOH data into clinical trial recruitment and real-world evidence strategies? To find out, I spoke with Andrew Goldstein, VP of Client Partnerships at Panalgo, to discuss the core strategies and considerations essential for harnessing SDOH to drive meaningful improvements in patient care.

Q: More and more, biopharmaceutical and medical device companies are realizing the importance of SDOH, but they’re not quite sure how to incorporate SDOH into their data lake and research strategies. What’s your advice for getting started?

If you don’t have top-down support and buy-in from senior levels, programs die on the vine. So first, you must figure out what your leadership knows about SDOH, educate them, and look for their backing. The most important thing is assessing what your specific patient population looks like and how SDOH may (or may not) be a factor in their access to care. It’s very hard at a corporate level to enact meaningful strategies that will impact specific patient segments, so a brand-level approach, understanding how SDOH impacts a specific patient population, is a good place to start. Then SDOH value could trickle down into the various groups that align to a brand. Examples are:  1) patient services who develop and deliver programs designed to reduce barriers to care that are impacting patients, 2) HEOR teams that analyze data to determine the impact specific SDOH factors have on outcomes, 3) market access groups that partner with payers, and 4) health systems and physicians who want to better understand their patient populations and the steps needed to keep patients adherent to treatment and out of emergency departments.

Q: Leveraging SDOH data to diversify clinical trials is one important use case for SDOH, as the FDA is now mandating it. What are some key considerations for clinical trials diversity?

The diversity mandate is about making sure clinical trials are more racially diverse, and that’s a numbers game. It’s about how to power a study with X number of patients so that, after drop-out, you have Y number of patients who complete the study. Incorporating these numbers into statistical analysis for your clinical trial and carrying it through to site selection gets more challenging. You need to design your clinical trial to make it easier for patients who have might have difficulty accessing care due to economic issues, travel, or family life constraints. There are rules on what you can and can’t offer a patient, but you can help with transportation, food vouchers, or daycare for working parents. Perhaps patients live in areas where WiFi is limited and all your trial ads are digital, so you need to consider printed materials. Perhaps English isn’t your patients’ first language and so, materials need to be in other languages, such as Spanish or Mandarin. It’s about identifying your patient population and figuring out how to get patients to the site and keep them returning.

Real-world data can be helpful in identifying racial and ethnic factors in a disease population to diversify clinical trials, such as when a global top 10 pharma company used Panalgo’s IHD platform to look into a claims dataset and identify disease prevalence benchmarks. By using these benchmarks in their planning, they were able to identify more diverse populations for their clinical trials.

Q: The traditional classifications of SDOH are race, gender and ethnicity, but there are other factors that biopharma and medical device companies should consider. What are they, and how can they be used to improve clinical trials design?

When you’re planning a clinical trial and looking at disease criteria, how the disease presents and progresses, then understanding race, ethnicity, age, and gender are all important. There are well-defined SDOH domains such as financial strain, food insecurity, housing instability, transportation barriers, health literacy, and social connectedness, to name a few. Each one of those is important in terms of access to and delivery of care. For clinical trials, financial strain or transportation barriers (for example, if a clinical trial is at the Cleveland Clinic but the patient lives in Florida) are easy things to identify. But let’s say there’s a woman with cancer who understands her medications and her diagnosis, and who can take non-pharmacologic actions to treat her disease. She might choose counseling therapy, physical therapy, eating a healthy diet, and lowering her stress by taking a sabbatical from work. All of these things can be quite beneficial over and above her medical care. Those looking at her clinical trial data will see that the medication appears to be working well, but they’re not seeing all the other interventions that are positively influencing her outcomes. The outcomes-based side of SDOH is important in any treatment setting, because if you’re not eating a healthy diet, managing your stress or seeing a therapist, it could have a negative effect on your outcomes. In clinical trials this can be especially important when trying to assess the effectiveness of a medication. Because of this, the FDA may start to look for sub-population analyses of post-clinical trial data. Manufacturers are starting to look at ‘negative patients’ in their clinical trials to see if there’s an SDOH component that might have impacted those patient’s outcomes.

Q: What are some challenges claims data providers face in getting better SDOH data, and is there a way for them to address these challenges?

EHRs aren’t set up to ask the right questions to ascertain SDOH parameters, and there are limited reasons and no incentives for physicians to input these data it into the EHR. Outside of race and gender, there’s a question of how credible SDOH data are. Data vendors are gathering as much SDOH data as they can and putting it into their commercial datasets, but what you can find right now is rather limited. A lot of data are available via government research: companies are taking census data, for example, and analyzing them to characterize the SDOH landscape in the U.S. Still, these types of data do not easily fit into a typical data providers’ framework.

If biopharma and medical device companies haven’t figured out what they need and what they’ll pay for, it’s harder for data vendors to invest accordingly. Right now, data providers are sticking to proven ways of gathering and selling data they know the market will consume, thus limiting options for SDOH-complemented data.

Q: How can SDOH data help physicians make better decisions along the patient journey to improve outcomes

Physicians want to understand their patient populations. The better they understand them, the more effectively they can make decisions on treatment. Factors such as social connectedness  – whether the patient is going home to a family member or caregiver who makes sure they’re taking their medication appropriately and eating healthy – these things really matter. If you send a patient home with a diabetes drug and they only have access to highly processed fast food, they’re going to wind up in the hospital. Doctors make different decisions to make sure patients’ outcomes are positive, like connecting them with a diabetes nurse to talk to them remotely. SDOH data also help physicians understand how the patient got there, what their next treatment steps should be, and what barriers they need to overcome throughout their patient journey. Most situations aren’t as easy as prescribing an antibiotic. More sophisticated diseases require a more holistic view of the patient so you can understand what’s going on around them and how it can impact overall care. Physicians are starting to pay more attention to SDOH factors to improve patient outcomes. Widespread disasters such as Hurricane Katrina and the COVID-19 pandemic have helped to bring SDOH data to the forefront as healthcare providers needed a ‘search and rescue’ protocol for the most vulnerable and at-risk populations. SDOH data are helpful in so many ways and on so many levels, and we’re sure to see more deployment in the coming years.

Ready to enhance your SDOH data? Contact us to find out how Panalgo’s IHD Analytics can work for you.