Why effective health equity interventions require accurate data
Social drivers of health (SDOH), such as income, education, housing, and geography, are increasingly recognized as important influences on health outcomes and access to care. However, their use in drug development and patient engagement remains limited.
This Deep Dive, which builds on research presented by Inovalon at ISPOR 2025, explores how more granular and linked SDOH data can enhance planning and support more equitable healthcare delivery. It draws on recent studies to examine how improved data integration can help identify disparities and support targeted, data-informed interventions.
Integrating SDOH into life sciences strategies
As healthcare systems place greater emphasis on improving outcomes and access, the integration of SDOH is becoming increasingly important across the product lifecycle. Despite their relevance, these non-clinical factors have often been underutilized in drug development and customer engagement strategies. This gap limits the ability to deliver equitable care and support data-driven decision-making across clinical and commercial settings. Addressing this shortfall by combining clinical and SDOH data offers a more complete view of patient populations, enabling more informed planning and more responsive, targeted interventions.
In many cases, interventions have historically been guided by generalized socioeconomic indicators or aggregate data that may not reflect the individual-level variation among patients. However, with access to more complete and context-specific data sources, including those captured at the point-of-care (e.g., z-codes) and through linked claims–SDOH data warehouses, there are new opportunities to design more informed and equitable interventions.
The following case studies highlight how variations in data granularity, source integration, and analytical depth influence the identification of disparities and the design of equity-focused interventions. The findings emphasize that while all levels of SDOH data provide value, enhancing completeness and specificity through integrated datasets enables a more nuanced understanding of patient experience.
Case study 1: Using multi-level SDOH to understand patient variation
Presented at ISPOR 2025, the study "Incorporating Social Drivers of Health Information Into Health Economics and Ou1tcomes Research: Neighborhood-Level Proxies Versus Individual-Level Data" explores how SDOH measured at different geographic resolutions relate to person-level characteristics. Drawing on a nationally representative sample of 100,000 patients from an all-payer claims database, researchers matched patients to SDOH data at three levels:
- 5-digit ZIP
- 9-digit ZIP (near-neighborhood)
- Individual-level data using tokenized identifiers
While each level of data provides useful insight, the study found that 9-digit ZIP-level data aligned more closely with individual values than broader 5-digit ZIP data; for example, near-neighborhood data explained up to 52% of the variation in certain characteristics such as net worth, whereas 5-digit ZIP explained roughly half as much variation in the corresponding person-level measures. These results highlight how increasing data granularity can improve the accuracy of patient segmentation and enable more precise interventions.
The ability to link SDOH to clinical and claims data at the near-neighborhood or individual patient level opens new possibilities for targeted strategies that address patients' needs with greater specificity, enhancing the effectiveness of HEOR and population health initiatives.
Case study 2: Socioeconomic position and race/ethnicity in cancer care access
Building on the value of refined geographic data, the second case study explores how socioeconomic factors interact with demographic characteristics to influence healthcare use. Published in Future Oncology, the study involved a retrospective analysis of more than 22,200 adults with hepatocellular carcinoma (HCC) using a multi-payer claims database linked to 9-digit ZIP code-level SDOH data. The objective was to examine the interaction between race/ethnicity and socioeconomic position (SEP,) and assess their potential differential impact on emergency department (ED) utilization.
Researchers hypothesized that racial differences in ED utilization may vary across income levels, independent of other SDOH. Higher SEP is generally associated with reduced ED use, often interpreted as an indicator of better access to coordinated care. However, the study revealed that these benefits were not uniformly distributed. Among Black and Hispanic patients, increased income was linked to smaller reductions in ED visits compared to White patients, a trend often referred to as "diminished returns."
These findings underscore the importance of evaluating how multiple SDOH intersect to affect outcomes. Integrating individual-level SDOH with clinical and claims data allows for the identification of disparities that may otherwise be obscured, supporting the design of more responsive and equitable interventions.
Case study 3: Breast cancer screening disparities across payer types
Adding to the understanding of persistent disparities across demographic groups, a third study presented at ISPOR 2025 examined 7 years of breast cancer screening (BCS) trends using a nationally representative dataset linked to 9-digit ZIP SDOH. Screening rates among women aged 40 and older were lowest among those enrolled in Medicaid, with consistent disparities across all SDOH strata including income, education, and language proficiency.
Even as screening rates improved overall between 2017 and 2023, the disparities between groups remained largely unchanged. For instance, Medicaid-insured women had screening rates as low as 40–55%, significantly below those with commercial insurance.
The granularity provided by 9-digit ZIP data allowed for refined stratification by region, payer, and social risk factor characteristics, enabling researchers to identify persistent disparities in access to breast cancer screening within specific at-risk populations. These findings illustrate how integrated claims and SDOH data can inform more targeted outreach and patient engagement strategies to achieve the best health outcomes for all patients.
Case study 4: Geographic and social barriers in Parkinson’s disease care
While the previous case study focused on preventive care disparities, this study, published in the Journal of Parkinson's Disease, shifts attention to treatment access. Reseachers evaluated disparities in access to device-aided therapies (DATs) for advanced Parkinson's disease (aPD) among traditional Medicare beneficiaries. Utilizing claims data from 2017 to 2020 linked to Inovalon’s SDOH warehouse, the analysis offered a comprehensive view of how patient characteristics, SDOH, and geographic location influence likelihood of receiving advanced therapies.
Even in states with numerous DAT facilities, long travel distances often presented a barrier, particularly for women, racial minorities, and people with low SEP. By including geographic concentration analyses, the study identified regions with large aPD populations but insufficient local access to DAT facilities, revealing actionable insights into unmet geographic needs.
This case highlights the value of combining geographic data with SDOH and clinical records to pinpoint underserved populations and support shared decision-making between providers and patients.
Advancing equity with integrated, actionable data
Together, these case studies reinforce the importance of complete, specific data in designing effective health equity interventions. For life sciences companies, access to granular SDOH is not only a tool for addressing disparities but also a valuable resource for strategic planning across the product lifecycle.
Inovalon’s SDOH Market Insights™ platform, built on the MORE² Registry®, integrates more than 50 person- and community-level SDOH variables across 169 million US lives. These data span key domains such as education, housing, food security, transportation, and financial stress to enable rich segmentation, actionable risk stratification, and targeted decision-making across clinical development, commercial strategy, and market access.
By incorporating SDOH insights into real-world evidence frameworks, life sciences teams can better align their efforts with population needs, supporting informed decisions that contribute to more equitable and effective healthcare delivery. In a healthcare landscape increasingly shaped by personalization and measurable value, investing in data integration and granularity is a practical step toward improving access, outcomes, and overall impact.
Sponsorship for this Deep Dive was provided by Inovalon, Inc.