The introduction of spatial and temporal frailty parameters in survival models furnishes a way to represent unmeasured confounding in the outcome of interest. Using a Bayesian accelerated failure time model, we can flexibly address a wide range of spatial and temporal options for structuring frailties as well as examine the benefits of using these different structures in certain settings. Our results suggest that it is important to include these temporal frailties. Additionally, the frailties can correctly recover the truth imposed on simulated data without affecting the fixed effect estimates. In the case study involving Louisiana breast cancer-specific mortality, the temporal frailty played an important role in representing the unmeasured confounding related to improvements in knowledge, education, and disease screenings as well as the impacts of Hurricane Katrina and the passing of the Affordable Care Act. In conclusion, the incorporation of spatial and temporal frailties in survival analysis can lead to better fitting models and improved inference by representing spatially and temporally varying unmeasured risk factors and confounding that could impact survival.