Mapping breast cancer survival can help cancer control programs prioritize efforts with limited resources. We used Bayesian spatial models to identify whether breast cancer survival among patients in New Jersey (NJ) varies spatially after adjusting for key individual (age, stage at diagnosis, molecular subtype, race/ethnicity, marital status, and insurance) and neighborhood measures of poverty and economic inequality [index of concentration at the extremes (ICE)].
Survival time was calculated for all NJ women diagnosed with invasive breast cancer between 2010 and 2014 and followed to December 31, 2015 (N = 27,078). Nonlinear geoadditive Bayesian models were used to estimate spatial variation in hazard rates and identify geographic areas of higher risk of death from breast cancer.
Significant geographic differences in breast cancer survival were found in NJ. The geographic variation of hazard rates statewide ranged from 0.71 to 1.42 after adjustment for age and stage, and were attenuated after adjustment for additional individual-level factors (0.87–1.15) and neighborhood measures, including poverty (0.9–1.11) and ICE (0.92–1.09). Neighborhood measures were independently associated with breast cancer survival, but we detected slightly stronger associations between breast cancer survival, and the ICE compared to poverty.
The spatial models indicated breast cancer survival disparities are a result of combined individual-level and neighborhood socioeconomic factors. More research is needed to understand the moderating pathways in which neighborhood socioeconomic status influences breast cancer survival.
More effective health interventions aimed at improving breast cancer survival could be developed if geographic variation were examined more routinely in the context of neighborhood socioeconomic inequalities in addition to individual characteristics.