Sociodemographic differences in treatment and survival for patients who receive genomic tumor testing in a rural state.

170 Background: New cancer technologies like genomic tumor testing (GTT) make targeted treatments and improved outcomes possible. However, access to technologies like GTT is often inequitably skewed such that it is most available to urban and socially advantaged individuals, which may contribute to cancer disparities in disadvantaged groups. This study aims to assess the impact of differential resource allocation by examining whether patient rurality, education, or income are associated with whether patients receive genome matched treatment (GMT) and their survival. Methods: Patients with cancer ( n=1,258) enrolled in the Maine Cancer Genomics Initiative, which provided free GTT across a rural state Demographic and clinical data was collected from surveys and electronic medical records. We used multivariable logistic regression models to examine whether receipt of GMT differs by patient rurality, education, and income. Kaplan-Meier survival (KM) curves and multivariable Cox proportional hazards regression were conducted to explore the relationship between 12-month mortality and rurality, education, and income. We fit both unadjusted and adjusted (age, gender, physical quality of life, cancer stage and type) models. Results: After completing GTT, ~16% of patients received at least one GMT. Rurality, education, or income did not predict receipt of GMT. 41.4% of the lower educational level group versus 32.2% of the higher education group died within 365 days of enrollment (unadjusted HR: 1.37; 95% CI: 1.12 to 1.68; p=0.002). This difference was driven by patients who did not receive GMT, where those in the lower education group had increased mortality (unadjusted HR: 1.43, 95% CI: 1.15 to 1.78, p=0.001). For patients who received GMT, there was no difference in mortality between the education groups (unadjusted HR: 1.03, 95% CI: 0.58 to 1.83, p=0.9). No significant differences in mortality were observed for rurality or income. Conclusions: Lower educational attainment was associated with worse survival for patients who did not receive GMT. Education and specialized health care are differentially available to those in urban and rural areas and can cause health disparities. Sustaining initiatives that provide access to novel technologies is critical to address cancer disparities. To account for the intersection of social and psychological factors on cancer disparities, future analyses will include patients’ perceptions of GTT.