Modeling Geospatial Patterns of Late-Stage Diagnosis of Breast Cancer in the US
AbstractIn the US, about one-third of new breast cancers (BCs) are diagnosed at a late stage, where morbidity and mortality burdens are higher. Health outcomes research has focused on the contribution of measures of social support, particularly the residential isolation or segregation index, on propensity to utilize mammography and rates of late-stage diagnoses. Although inconsistent, studies have used various approaches and shown that residential segregation may play an important role in cancer morbidities and mortality. Some have focused on any individuals living in residentially segregated places (place-centered), while others have focused on persons of specific races or ethnicities living in places with high segregation of their own race or ethnicity (person-centered). This paper compares and contrasts these two approaches in the study of predictors of late-stage BC diagnoses in a cross-national study. We use 100% of U.S. Cancer Statistics (USCS) Registry data pooled together from 40 states to identify late-stage diagnoses among ~1 million new BC cases diagnosed during 2004–2009. We estimate a multilevel model with person-, county-, and state-level predictors and a random intercept specification to help ensure robust effect estimates. Person-level variables in both models suggest that non-White races or ethnicities have higher odds of late-stage diagnosis, and the odds of late-stage diagnosis decline with age, being highest among the <age 50 group. After controlling statistically for all other factors, we examine place-centered isolation and find for anyone living in an isolated Asian community there is a large beneficial association (suggesting lower odds of late-stage diagnosis) while for anyone living in an isolated White community there is a large detrimental association (suggesting greater odds of late-stage diagnosis). By contrast, living in neighborhoods among others of one’s own race or ethnicity (person-centered isolation) is associated with greater odds of late-stage diagnosis, as this measure is dominated by Whites (the majority). At the state level, living in a state that allows unfettered access to a specialist is associated with a somewhat lower likelihood of being diagnosed at a late stage of BC. Geographic factors help explain the likelihood of late-stage BC diagnosis, which varies considerably across the U.S. as heterogeneous compositional and contextual factors portray very different places and potential for improving information and outcomes. The USCS database is expanding to cover more states and is expected to be a valuable resource for ongoing and future place-based cancer outcomes research. View Full-Text
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Mobley, L.R.; Kuo, T.-M.; Scott, L.; Rutherford, Y.; Bose, S. Modeling Geospatial Patterns of Late-Stage Diagnosis of Breast Cancer in the US. Int. J. Environ. Res. Public Health 2017, 14, 484.
Mobley LR, Kuo T-M, Scott L, Rutherford Y, Bose S. Modeling Geospatial Patterns of Late-Stage Diagnosis of Breast Cancer in the US. International Journal of Environmental Research and Public Health. 2017; 14(5):484.Chicago/Turabian Style
Mobley, Lee R.; Kuo, Tzy-Mey; Scott, Lia; Rutherford, Yamisha; Bose, Srimoyee. 2017. "Modeling Geospatial Patterns of Late-Stage Diagnosis of Breast Cancer in the US." Int. J. Environ. Res. Public Health 14, no. 5: 484.
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