Financial toxicity in cancer care: Experience from a tertiary care cancer center.

e23199 Background: Financial toxicity (FT) is associated with poor quality of life (QOL) and survival and is a well-recognized, critical issue for cancer patients (pts). Challenges remain to effectively address FT. The Cleveland Clinic Taussig Cancer Institute Financial Navigation (FN) program uses the Comprehensive Score for financial Toxicity (COST) to screen for FT and assists with needs including copays, drug costs, and transportation. We aimed to better understand risk factors for FT, including patient-reported outcomes (PROs), in our population to improve the impact of FN programs on FT. Methods: We identified 16,423 pts scheduled for first cancer treatment between 6/2021-12/2023. Of those, 11,497 completed at least one PRO measure (PROMIS10, PHQ9, and NCCN DT), and 1906 pts completed COST at first treatment. Data collection included Area Deprivation Index (ADI), social determinants of health, and disease/treatment characteristics. The association of these factors and COST scores was analyzed. Sensitivity analysis explored if pts with COST assessment were representative of the whole cancer population. Results: Key demographics of pts completing COST included: median age 65 years (IQR 56-72), 54% female, 82% white, 57% married, 47% insured privately and 35% with Medicare. Sensitivity analysis demonstrated some differences between pts with (N = 1906) and without (N = 14,517) a COST measure: age (median 65 vs 69 years), race/ethnicity (non-Hispanic white 82% vs 78%, and Hispanic 2.4% vs 5.4%), insurance (Medicare 35% vs 13%, private 47% vs 71%), respectively, p < 0.001 for all. Among pts with a COST, risk factor analysis identified younger age, non-white and Hispanic race/ethnicity, non-private insurance, tobacco use, and higher deprivation as significantly associated with higher FT (Table). Higher distress (NCCN DT, N = 1001) and lower physical (N = 1474) and mental (N = 1505) health (PROMIS10) were also associated with increased FT, but these PROs were highly correlated with ADI. Conclusions: Our study provides insights into several risk factors for FT in a large cancer population. These findings suggest a disproportionate effect of FT on vulnerable pts. Further analysis of FT in specific diseases and treatments, as well as impact of FN on FT longitudinally, is ongoing. Multivariable analysis of risk factors for FT. Comparison Odds Ratio Lower Limit Upper Limit p-value Age per ↑ 5 years 0.85 0.81 0.89 <0.001 Race/Ethnicity: NHB vs NHW 1.62 1.19 2.20 0.002 Race/Ethnicity: Hispanic vs NHW 2.00 1.11 3.59 0.02 Insurance: Medicaid vs Private 2.35 1.68 3.29 <0.001 Insurance: Medicare vs Private 1.85 1.47 2.33 <0.001 Insurance: Other vs Private 1.85 1.28 2.69 0.001 Smoking: Former vs Never 1.34 1.08 1.66 0.007 Smoking: Current vs Never 1.41 1.05 1.89 0.02 ADI (per 5 %-ranks) 1.04 1.02 1.06 <0.001 NHB: non-Hispanic Black; NHW: non-Hispanic White; ADI: Area Deprivation Index. All estimates are cumulative odds ratios and adjusted for diagnosis.