Abstract
Background/Aim: Mapping crude and adjusted geographic distributions of disease outcomes is a useful tool for identifying risk factors of public health concern. The crude spatial pattern of disease is often what is observed by public health practitioners, but these patterns may be due to important spatially-varying predictors such as socioeconomic status, race/ethnicity, or environmental exposures. Individual-level spatial analyses can provide insight regarding geographic disparities in disease risk by adjusting for these variables without aggregation bias. Methods: We conducted a spatial analysis of cervical cancer survival among 17,541 cases diagnosed throughout California United States from 1995-2009 using MapGAM, an R package that estimates spatial hazard ratios (HR) and corresponding 95% confidence intervals (CI) in a generalized additive model framework with a non-parametric bivariate smooth term of location. Survival models were adjusted for patient age, tumor characteristics, race, socioeconomic status, care at a high-volume hospital (HVH), non-adherence to treatment guidelines, distance to receive care, and distance to closest HVH. Results were mapped in R also using MapGAM. Results: After controlling for patient and tumor characteristics, we observed significant areas of low HR in the San Francisco Bay area, Ventura and Los Angeles Counties. When distance and quality care variables were included in the model, HR northeast of Sacramento became elevated and significant due to reverse spatial confounding. Living within 30 miles of a HVH was protective [HR:0.86, 95% CI:0.77,0.97]. The fully adjusted spatial analysis reveals geographic disparities not due to quality of care. Conclusions: Quality of care indicators were the most influential on geographic disparities, but areas of poorer cervical cancer survival may be related to spatially-varying social or environmental stressors that warrant further investigation. The use of a bivariate smoother of location within the survival model allows for more advanced spatial analyses for exploring potential predictors of geographic disparities.
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