Rural Disadvantage in Glioblastoma Concentrates in the Early Postoperative Period: A Single-Center, Treatment-Standardized Cohort Study

BackgroundGlioblastoma (GB) is the most common and most aggressive malignant primary brain tumor, with 5-year survival rates around 5%. While treatment-related factors significantly impact outcomes, the influence of sociodemographic variables remains unclear, with prior studies showing inconsistent findings. These inconsistencies may stem from underrepresentation of rural patients within national databases and from comparison across centers with variable treatment practices. ObjectiveTo evaluate whether population size, geographic distance, and regional-average income influence short- and long-term survival among GB patients when treatment delivery is uniform. MethodsWe analyzed 248 patients who underwent surgical resection at a single publicly funded tertiary neurosurgical center serving all of Southeastern Ontario (2017-2023). Multivariable logistic regression assessed 90-day mortality, while Cox proportional hazards models with time-varying treatment covariates evaluated overall survival (OS). ResultsAmong 248 patients, 71.8% resided in communities with <50,000 residents. Residing in areas with [&ge;]50,000 residents was associated with 63% lower odds of 90-day mortality (OR 0.37; 95% CI: 0.17-0.81). Each 10-mile increase in distance was associated with 5% increased odds of 90-day mortality (OR 1.05; 95% CI: 1.01-1.10). Regional-average income showed no association with 90-day mortality (OR 0.96; 95% CI: 0.51-1.81). None of these variables significantly affected OS in multivariable models. Conclusions: When treatment practices are uniform, population size and geographic distance independently influence early post-operative mortality but not long-term survival. These findings suggest that improving equity in GB care requires targeted interventions during the critical first 90 days post-surgery, extending beyond geographic access alone to address challenges inherent to low-population-density communities. Key PointsO_LIRural residence and greater distance from tertiary care independently increase 90-day mortality after glioblastoma surgery, even when treatment practices are uniform, suggesting distinct mechanisms requiring separate interventions. C_LIO_LIThese sociodemographic factors do not affect overall survival when treatment access is equalized, indicating vulnerability occurs specifically during the early post-operative period rather than throughout the disease course. C_LIO_LIRegional-average income did not influence survival within our universal healthcare system, demonstrating that socioeconomic disparities in GB outcomes may be modifiable through system-level policies ensuring equitable treatment access. C_LI Importance of StudyPersistent outcome disparities between rural and urban glioblastoma patients have long been attributed to unequal access to oncologic therapy. By studying a cohort where all patients received care at the same tertiary center with uniform treatment practices, this study isolates the independent effects of geography from treatment variation. Our findings reveal that population size and distance primarily affect 90-day post-operative mortality rather than long-term survival, fundamentally reframing the mechanism through which geography influences these outcomes. This challenges the assumption that simply bringing care closer to rural communities will eliminate disparities. Instead, health systems must develop comprehensive interventions targeting the critical early post-operative period, including enhanced care coordination, proactive follow-up systems, and community-based support programs tailored to rural populations. The null effect of income in our publicly funded system provides compelling evidence that socioeconomic disparities observed in private healthcare systems are modifiable through universal coverage policies. This methodological approach, combining time-varying treatment analysis, uniform care delivery, and representative sampling of underserved populations, offers a replicable framework for investigating disparities in other cancer types and healthcare contexts.