Community economic factors influence outcomes for patients with primary malignant glioma. Issue 4 (24th March 2020) - Bower, Aaron; Hsu, Fang-Chi; Weaver, Kathryn E; Yelton, Caleb; Merrill, Rebecca; Wicks, Robert; Soike, Mike; Hutchinson, Angelica; McTyre, Emory; Laxton, Adrian; Tatter, Stephen; Cramer, Christina; et al.
Abstract: Background: Community economics and other social health determinants influence outcomes in oncologic patient populations. We sought to explore their impact on presentation, treatment, and survival in glioma patients. Methods: A retrospective cohort of patients with glioma (World Health Organization grades III–IV) diagnosed between 1999 and 2017 was assembled with data abstracted from medical record review. Patient factors included race, primary care provider (PCP) identified, marital status, insurance status, and employment status. Median household income based on zip code was used to classify patients as residing in high-income communities (HICs; ie, above the median state income) or low-income communities (LICs; ie, below the median state income). The Kaplan–Meier method was used to assess overall survival (OS); Cox proportional hazards regression was used to explore associations with OS. Results: Included were 312 patients, 73% from LICs. Survivors residing in LICs and HICs did not differ by age, sex, race, tumor grade, having a PCP, employment status, insurance, time to presentation, or baseline performance status. Median OS was 4.1 months shorter for LIC patients (19.7 vs 15.6 mo; hazard ratio [HR], 0.75; 95% CI: 0.56–0.98, P  = 0.04); this difference persisted with 1-year survival of 66% for HICs versus 61% for LICs at 1 year, 34% versus 24% at 3 years, and 29% versus 17% at 5 years. Multivariable analysis controlling for age, grade, and chemotherapy treatment showed a 25% lower risk of death for HIC patients (HR, 0.75; 95% CI: 0.57–0.99, P  < 0.05). Conclusions: The economic status of a glioma patient's community may influence survival. Future efforts should investigate potential mechanisms such as health care access, stress, treatment adherence, and social support.