As the focus on personalized, equitable healthcare continues to intensify, social determinants of health (SDoH) have attracted significant attention in healthcare and the life sciences. Studies are increasingly focused on integrating information about patient ethnicity, economic background, and education levels to understand more fully how different socio-economic factors affect health.
The parallel shift to using real-world data (RWD) provides the ideal framework for further investigation into SDoH. However, researchers need access to top-quality and far-reaching data so that studies incorporating SDoH can be structured against a firm evidence base.
Why is SDoH important?
Research into social factors that indirectly or directly impact an individual鈥檚 likelihood of accessing care, chance of developing a disease, or their response to a medication are not new.Analyzing measures of SDoH, such as race and ethnicity, household income, and community wealth, can highlight differences in hospitalization rates, healthcare utilization, and the risk of preterm birth.
While health researchers were well-aware of SDoH before COVID-19, the pandemic served as a reminder of their importance and highlighted how much work was still left to do in relieving health inequity. Where people lived and what type of healthcare facilities were accessible to them were factors in the type of care that they received. Through the pandemic, the value of SDoH data, which had often been siloed in the past, was highlighted on a global scale. There was a revived focus on how integrating SDoH data into studies can reveal critical insights into health outcomes.
Better insights into how SDoH affects health can highlight the systemic influences that leave certain groups with worse health outcomes than others, despite similar health insurance coverage or access. Such information can inform policy and encourage healthcare systems to determine how they can improve access or adapt services in certain communities to better serve patient groups that may otherwise be overlooked. Essentially, understanding SDoH elements can help target individual care and help tailor treatments to improve health outcomes for patients.
What are the challenges with real-world SDoH data?
Growing interest in SDoH aligns with the rising use of RWD, which offers valuable insights into diverse populations. It can also help reduce costs, making trials more efficient, and support better patient outcomes. Additionally, RWD can help uncover groups, such as those with a chronic illness or those living in an underserved community, that could benefit from improved treatment.
Just because we now have more data sources under the SDoH umbrella and the technology to connect databases doesn鈥檛 mean it鈥檚 easy. SDoH can mean different things depending on the questions being asked. Figuring out if the data exists, its detail level, and how to combine it with current data while protecting patient privacy is tricky. But tackling these challenges is crucial to better understanding important health issues.
For example, it鈥檚 important to consider whether someone has insurance (public or private), a job, and what their income may be. These factors can all affect an individual鈥檚 access to quality healthcare and the ability to pay for care. But even so it鈥檚 not enough to have access to this data 鈥� you need the right people on hand who know how to use it appropriately.
How can databases improve health outcomes?
MarketScan庐, a Truven company, has several research databases that can fill this gap. With over 30 years of experience in data linking and RWD, MarketScan鈥檚 capabilities and staff expertise have been instrumental to a variety of studies incorporating SDoH. Its unique SDoH Database integrates information for several variables with the data from individuals found in other comprehensive MarketScan databases. For example, the MarketScan Commercial Database is an employer-sourced claims database, meaning it covers employees, their families, and comes from a plurality of insurance plans and companies. Encompassing data from people from all 50 U.S. states and for various age groups, its broad demographic representation provides researchers with a complete spectrum of socioeconomic data.
MarketScan showed how data can be used to better understand healthcare utilization patterns at the . This is important because anyone from healthcare providers to employers may want to know what factors may influence an individual鈥檚 choice for types of treatment. SDoH can influence diabetes risk and management, and access to healthcare can also affect disease management and progression. One study dove into whether annual income influenced choice in GLP-1 RA therapy for patients with Type 2 Diabetes.
Researchers found that 鈥渃hoice in treatment should consider disease status, patient preference, and SDoH (e.g., medication accessibility), especially in cases where there are multiple in-class agents.鈥� Additionally, the study determined that factors other than wage, such as access to care, are playing a role in Type 2 Diabetes medication choice and utilization.
MarketScan datasets are being used to better understand patient outcomes and improve access to care. Studies incorporating SDoH data are needed more than ever to expedite health equity, and to highlight the nuanced factors at a population and individual level that can impact health. MarketScan provides researchers with the necessary resources to navigate large datasets, enhance their studies, and contribute to better healthcare systems for patients.
To learn more about MarketScan, download the free one-pager below.
