Geographic disparities in late-stage breast cancer diagnosis in California.

Using cancer registry data for the population of California women aged 67+ with breast cancers, we estimated random intercept logistic models to examine how two socio-ecological predictors (residential isolation and poverty) were associated with probability of late-stage diagnosis for breast cancer. Using the multilevel modeling results, we calculated fully adjusted predicted probabilities associated with women in each Medical Service Study Area (MSSA) in California and classified the areas into two distinct groups: MSSAs with predicted rates below the 25th percentile (presumably the better outcome areas) and MSSAs with predicted rates above the 75th percentile (presumably the worse outcome areas) for two minority groups. Some areas had better outcomes for one group but worse outcomes for the other, suggesting that interventions to improve outcomes need different strategies for different groups in the same areas. Using information from geographic risk factors and multilevel modeling, this study informs interventions designed to reduce disparities in breast cancer outcomes. Copyright Â