Background: Previous studies suggest spatial differences in mortality for many types of cancer, including breast cancer. Identifying explanations for these spatial differences results in a better understanding of what leads to longer survival time.
Methods: We used a Bayesian accelerated failure time model with spatial frailty terms to investigate potential spatial differences in breast cancer mortality following breast cancer diagnosis using 2000-2013 Louisiana SEER data.
Results: There are meaningful spatial differences in breast cancer mortality across the parishes of Louisiana, even after adjusting for known demographic and clinical risk factors. For example, the average survival time of a woman diagnosed in Orleans parish was 1.51 times longer than that of a woman diagnosed in Terrebonne parish. Additionally, there is evidence to suggest shorter survival times in lower income parishes along the Red and Mississippi Rivers, as well as parishes with lower socioeconomic status, less access to care and fresh food, worse quality of care, and more workers in certain industries.
Conclusion: The addition of spatial frailties to account for an individual's geographic location is useful when analyzing breast cancer mortality data. Our findings suggest that survival following breast cancer diagnosis could potentially be improved if socioeconomic status differences were addressed, healthcare improved in quality and became more accessible, and certain industrial situations were improved for individuals diagnosed in parishes identified as having shorter average survival times.
Keywords: Accelerated failure time model; Breast cancer mortality; SEER; Spatial frailty; Survival analysis.
Published by Elsevier Ltd.