Harnessing Social Determinants of Health - Unlocking Value and Best Practices in Health Economics and Outcomes Research

Harnessing Social Determinants of Health 

Social determinants of health (SDOH) data is becoming increasingly critical in health economics and outcomes research (HEOR). As diverse and inclusive research practices grow in awareness and importance, SDOH factors such as socioeconomic status, education, neighborhood and physical environment, employment and social support networks – along with access to healthcare – have become profound influencers on health outcomes and healthcare costs. 

Understanding and incorporating SDOH insights provide a more comprehensive and holistic view of patients beyond clinical metrics, enabling more accurate assessments of treatment effectiveness, healthcare use and overall well-being. Additionally, these insights can be useful in developing patient-centered treatments to understand which additional services or value-based contracting might be needed.  

Further, the growing investment in innovative research models, such as community-based participatory research, decentralized models, digital twin research and real-world evidence studies emphasizes inclusivity and the experiences of a broader range of patients. SDOH data provides the context to highlight disparities, inequalities and other barriers to patient care. 

At a time when HEOR studies are charged with assuring that research used to generate robust and generalizable findings is comprehensive, equitable and applicable to diverse populations, SDOH ensures that healthcare delivery is equitable, efficient and patient-centered. 

SDOH plays a critical role in advancing health equity and HEOR goals 

Unfortunately, health disparities have long been the norm in the United States. Factors such as financial stability, access to care and geographic location are integral to understanding why these disparities exist. However, disclosure of these factors may unintentionally contribute to stigmatization and discrimination against historically marginalized groups. Although SDOH spotlights these disparities, the data can help to overcome the distrust in research and its effect on reporting biases. 

Diverse representation in clinical research is, after all, a step in earning and building trust and driving health equity. Advancing health equity requires HEOR researchers to adopt a more equitable approach to data collection and HEOR efforts – including more diverse populations in clinical research and utilizing SDOH data in retrospective analyses to disaggregate outcomes. 

HEOR teams need more holistic and accurate insights to better understand health disparities and to help drive policies and practices that advance health equity. A thorough understanding of the patient’s complete journey from prevention and diagnosis through treatment and long-term care, ensures patient-centered healthcare delivery that addresses healthcare access and outcomes disparities. Still, there are obstacles to the optimal utilization of SDOH. 

SDOH can yield vast opportunities to advance equitable healthcare once operational challenges are overcome  

Data quality and standardization are key among the challenges. The way data is collected and formatted may vary. Extenuating factors such as distrust or moment-in-time data capturing may influence and bias data quality.  

Research teams need patient-level profiles of SDOH factors that can be filtered or configured to align with standard frameworks and defined domains of social determinants. Data quality considerations range from incomplete data to self-report biases, timeliness, data granularity (for example, insights on whether a patient with low socioeconomic status is financially unstable, experiences transportation challenges- or lives in an unstable home) and missing data. 

With data quality, bias concerns and standardization issues in the spotlight, research teams that collect SDOH data on their own frequently find that self-reporting methods are time-consuming and fraught with bias, inaccuracies and maintenance difficulties. Traditional survey methodologies may fail to capture the full range of social determinants and patient experience. 

Fortunately, collaboration with data vendors to link and enrich clinical data sources or patient-reported outcomes can enhance the comprehensiveness and accuracy of the data, ultimately improving the accuracy of research findings. With the right data and resources in place, using SDOH data to solve HEOR challenges can become reality to advance the future of healthcare. 

Data vendors should possess robust data integration capabilities to effectively combine and link disparate datasets while maintaining standardization, accuracy and patient privacy. Scalability is also vital, given the large volumes and complexity of data involved in SDOH analysis. After securing the data, researchers can design studies and build models to explore select factors and their impact on health outcomes. 

Download our white paper Harnessing Social Determinants of Health - Unlocking Value and Best Practices in Health Economics and Outcomes Research to drive better decisions and outcomes with LexisNexis® Health Equity and Inclusion Insights and Social Determinants of Health.