Historic redlining, modern neighborhood structures, and mortality in children, adolescents, and young adults with cancer.

11037 Background: Historic redlining, referring to the Home Owners Loan Corporation (HOLC) program’s racially biased risk monitoring maps in the 1930’s, has indelible impacts on modern day neighborhood characteristics and health outcomes. The impact of redlining, a form of structural racism, on children, adolescents, and young adults with cancer (CAYACs) is unknown. This retrospective cohort study evaluates the association between redlining and mortality in CAYACs. Methods: Using the California Cancer Registry, we identified cases <25 years old diagnosed with first primary malignant cancer between 2000-2019. Using merged HOLC maps and U.S. Census data we determined case redlining status (A “Best”, B “Still Desirable”, C “Declining”, D “Hazardous”, and N “Not graded”) by residence at diagnosis. We defined residents of D neighborhoods redlined-exposed and residents of A or B neighborhoods unexposed or referent. Kaplan Meier methods and multivariable Cox proportional hazard models were applied to calculate overall survival. Additional models accounted for census tract-level neighborhood SES (nSES, a composite measure incorporating seven SES domains), neighborhood racial and ethnic composition (or typology) and racialized economic segregation (via index of concentration at the extremes). Results: In total 8,210 cases were HOLC-graded, the median age was 16 years, 46% were female, and 48% had solid tumors. Overall survival at 5 years was inferior for D cases vs. A or B cases (OS 80.2%, 95% CI: 78.5-81.7 vs. OS 88.2, 95% CI: 84.0-91.3). Adjusting for individual clinical characteristics (age, sex, year of diagnosis, and cancer stage), D cases experienced greater mortality (HR 1.32, 95% CI: 1.12-1.55) compared to A or B cases. Additional adjustment for insurance and nSES attenuated the relationship (HR 1.17, 95%CI: 1.00-1.36 and HR 1.07, 95% CI: 0.92-1.24 respectively). Similar patterns were observed in models adjusting for racialized economic segregation (HR 1.07, 95% CI: 0.92-1.24) and typology (HR 1.10, 95% CI: 0.94-1.28) in place of nSES. Conclusions: In this population-based study, redlined-exposed CAYACs experienced greater mortality compared to unexposed CAYACs. Adjusting for insurance and contemporary nSES attenuated the association between redlining and mortality. Findings suggest current neighborhood attributes may be downstream manifestations of structural racism with survival implications for CAYACs.