Introduction: The field of neuro-oncology has experienced marked advances in the understanding of clinical, surgical, and molecular underpinnings of brain cancer. However, non-medical, socioeconomic drivers of patient outcomes remains poorly understood. We aim to assess neurosurgical oncologist density, travel distance, and their link to disease-specific mortality in the US.
Methods: County-level data was collected from two national data registries: Centers for Medicare & Medicaid Services (CMS) and Surveillance, Epidemiology, and End Results (SEER) database. County catchment areas were calculated and described based on three primary outcomes of interest: neurosurgical oncologist per capita, average distance to subspecialist care, and incidence-based mortality of diffuse glioma. K-means clustering was performed on catchment areas to describe distinct “phenotypes” of regional catchment areas. Univariate and multivariate linear regressions was performed to identify unique variables associated with the study’s primary outcomes.
Results: A total of 112 catchment areas were constructed. Mean number of subspecialty providers per capita was 0.9 and the average distance traveled to the nearest provider across the US was 49.9 miles (5.9–151.5 mi). Mean age-adjusted glioma mortality rate among all catchment areas represented was 2.41 deaths per 1000 person-years. Catchment areas with the higher average income were associated with a greater number of subspecialty providers (95% CI 2.13-3.44, p< 0.027). Among all US regions there were no correlation between mortality and provider density or average distance to care. Average income (p=0.02), poverty (p=0.02), and vacancy rate (p < 0.01) were significant predictors of subspecialty provider density while American Indian patients had a significantly higher association with larger distance to travel to the nearest subspecialist.
Conclusion : This population-level study illustrates significant disparities in access to neurosurgical oncology care across the United States, driven by geographic, demographic, and socioeconomic factors. Addressing these inequities will require targeted interventions in workforce planning and healthcare infrastructure to ensure more equitable care delivery for glioma patients.
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