Background: Rural disparities in cancer outcomes have been widely evaluated, but limited evidence is available to characterize what characteristics of rural environments contribute to the increased risk of poor outcomes. Therefore, we propose to estimate the mediating effects of county characteristics on the relationship between urban/rural status and mortality among patients with cancer, identify county profiles, and determine problematic county profiles alongside rural settings. Methods: Patients with cancers (known to be associated with rural disparities) between 2000 and 2016 were assessed using Surveillance, Epidemiology and End Results data. We then linked these data to the 2010 County Health Rankings (CHR) which ranks US counties into quartiles within their state based on measures of health: morbidity, mortality, health behaviors, physical environment, socioeconomic status, clinical care environment. We dichotomized each domain as least healthy (quartiles 3 and 4) and most healthy (quartiles 1 and 2). Rurality was defined using the 2010 Rural-Urban Commuting Codes. Using mediation analyses, we estimated 1) the direct contribution of rurality to five-year all-cause and cancer-specific mortality and 2) the contribution of the rural effect indirectly through each CHR domain. We then used latent class analysis to identify county groupings based on rankings. Using flexible parametric survival models, we estimated the hazard of mortality associated with class membership, rurality and the interaction between rurality and class. Results: Among the 757,655 patients representing 596 counties, 51% were female, the most frequent diagnoses were breast and prostate (21%, respectively), 52% were diagnosed with localized disease, and 54% were diagnosed between 55y and 65y. When setting the mediator at least healthy (quartiles 3 and 4) premature death, clinical care, and physical environment, the rural patients had 17.9% to 20.2% less survival time compared to those in rural counties. Of this, 4.1% to 12.6% of the total excess risk was mediated by these characteristics. Three county classes were identified: Class 1 (28%) had consistently poor rankings, Class 2 (32%) had moderate rankings but poor clinical care, and Class 3 (41%) had consistently high rankings except for physical environment. Patients living in rural-high risk counties (class 1 and 2) saw higher all-cause mortality than those in urban-lower risk counties (reference class 3; HR, 1.04, 95%CI 1.01-1.08 and 1.07, 95%CI, 1.03-1.11). Conclusions: County characteristics mediated the relationship between rurality and mortality among patients with cancer. Counties with poor rankings had increased mortality risks regardless of rurality; however, the poor rankings elevated the mortality risk for those in rural counties. Access to care and physical environment may be actionable points of intervention in the pathway from rurality to mortality.