Avalere analyzed how area-level measures of sociodemographic factors relate to Medicare patients’ adherence to multiple myeloma (MM) treatment regimens. To understand how demographic factors and social determinants of health (SDOH) affect patients with varying out-of-pocket costs, Avalere assessed these sociodemographic factors both for standard Medicare Part D beneficiaries and for Part D beneficiaries who receive low-income subsidies. Adherence challenges may differ between oral and intravenous therapies, and patients prescribed combination treatments that include both modalities may face particular challenges.

Avalere calculated a proportion-of-days-covered metric to quantify the percentage of days in the measurement period in which a patient received coverage for one of three selected oral oncolytic therapies used to treat MM. To analyze adherence according to patient characteristics, Avalere analyzed household SDOH factors, by ZIP-code level averages, including ratio of income to federal poverty level, educational attainment, percentage of households containing a personal computer, and census division. Sociodemographic characteristics at the ZIP-code neighborhood level can act as proxies for SDOH characteristics at the member level and contextualize adherence and outcomes for MM patients.

Key Findings

Results show inconsistencies in how SDOH and demographics correlate to therapy adherence for Medicare beneficiaries with MM. Key findings include:

Avalere’s analysis classified patients into three groups. For both multi-adult households and households by PC ownership, the results consider analysis between the highest (≥75 percentile) and lowest (≤25 percentile) group for each SDOH factor.

These results indicate that SDOH are a factor in patients’ treatment adherence. However, results revealed substantial variation, with some factors—such as living in a multi-adult household and having a personal computer in the home—more influential in impacting adherence in Medicare patients diagnosed with MM. This may indicate that socioeconomic issues such as transportation, childcare, and familial support systems may be key contributing factors to lower adherence rates. Furthermore, since there was inconsistency in the findings, there might be additional factors such as a patient’s health literacy level, transportation, access to technology, and specific treatment regimen impacting how adherent patients are to their therapies, which could be further explored in additional analyses. Future analyses also could consider how these measures may be benchmarked to the broader Medicare population to understand if health disparities are more prevalent among patients with MM and how SDOH variables contribute to adherence relative to other measures such as access to care (e.g., pharmacy access) and out-of-pocket costs.

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Methodology

Fee-for-service (FFS) beneficiaries were identified using the 100% file of Medicare FFS Parts A, B, and D data, accessed via a research-focused data use agreement with the Centers for Medicare & Medicaid Services. Medicare Advantage members were identified using medical and pharmacy claims from the Inovalon MORE² Registry®, a large scale, real-world, multi-payer dataset comprising medical, pharmacy, lab claims, and clinical data on more than 332 million de-identified patients. Avalere leveraged the Acxiom database to assess socioeconomic factors in the relevant geographic areas in which each individual resides at the ZIP-code level.