Background: Lung cancer is the leading cause of cancer-related death in the United States, but early diagnosis and evidence-based guideline-concordant treatment (GCT) can improve prognosis. However, disparities exist in who receives GCT for lung cancer which may be attributable to a patient’s geography, social environment, or provider relationship. We studied the relative contribution of travel time, neighborhood diversity, and healthcare provider engagement on disparities in GCT among non-small cell lung cancer (NSCLC) patients in California. Methods: For Aims 1 and 2, we analyzed geocoded California Cancer Registry linked American Community Survey data for ~23,000 stage I-II NSCLC patients (2006-2015). In Aim 3, we additionally linked these data to electronic health records (EHRs) for ~1,000 patients from a large healthcare delivery system. GCT was defined based on National Comprehensive Cancer Network guidelines. Driving and public transit travel times were estimated from a patient’s residence to their treatment facility, neighborhood diversity was based on the racial/ethnic composition of the patient’s neighborhood, and EHR variables reflecting healthcare engagement included provider sex and enrollment in an online patient portal. We used adjusted regression models to quantify the relative risks for undertreatment and delay (treatment initialization >45 days from diagnosis) associated with our target variables, stratified by detailed patient race/ethnicity. Results: In Aim 1, we observed that longer travel times reduced risk of undertreatment and delay. This counterintuitive result, which we call a ‘Travel Time Paradox’, did not benefit all patients, with longer travel times leading to reduced quality care for some racial/ethnic groups. In Aim 2, we observed that patients living in neighborhoods that are mixed or discordant from their race/ethnicity increased risk of undertreatment and delay, but these findings also varied across race/ethnicities with some non-White patient groups living in racial/ethnic concordant neighborhoods at increased risk for undertreatment and delay. In Aim 3 we observed that patients enrolled in the online patient portal were at substantially decreased risk for undertreatment and delay, but most patients were not enrolled. Conclusion: These results support the role of contextual drivers of inequitable treatment for cancer and highlight the importance of evaluating risk heterogeneity among multiethnic populations.