title: Using residential segregation to predict colorectal cancer stage at diagnosis: two different approaches.
abstract: Studies have found a variety of evidence regarding the association between residential segregation measures and health outcomes in the United States. Some have focused on any individuals living in residentially segregated places, whereas others have examined whether persons of specific races or ethnicities living in places with high segregation of their own race or ethnicity have differential outcomes. This article compares and contrasts these two approaches in the study of predictors of late-stage colorectal cancer (CRC) diagnoses in a cross-national study. We argue that it is very important when interpreting results from studies like this to carefully consider the geographic scope of the analysis, which can significantly change the context and meaning of the results.
  
  
  
  We use US Cancer Statistics Registry data from 40 states to identify late-stage diagnoses among over 500,000 CRC cases diagnosed during 2004-2009. We pool data over the states and estimate a multilevel model with person, county, and state levels and a random intercepts specification to ensure robust effect estimates. The isolation index of residential segregation is defined for racial and ethnic groups at the county level using Census 2000 data. The association between isolation indices and late-stage CRC diagnosis was measured by (1) anyone living in minority-segregated areas (place-centered approach) and by (2) individuals living in areas segregated by one's own racial or ethnic peers (person-centered approach).
  
  
  
  Findings from the place-centered approach suggest that living in a highly segregated African American community is associated with lower likelihood of late-stage CRC diagnosis, whereas the opposite is true for people living in highly segregated Asian communities, and living in highly segregated Hispanic communities has no significant association. Using the person-centered approach, we find that living in places segregated by one's racial or ethnic peers is associated with lower likelihood of late-stage CRC diagnosis.
  
  
  
  In a model that covers a large geographic area across the nation, the place-centered approach is most likely picking up geographic disparities that may be deepened by targeted interventions in minority communities. By contrast, the person-centered approach provides a national average estimate suggesting that residential isolation may confer community cohesion or support that is associated with better CRC prevention.
authors.affiliation: School of Public Health, Georgia State University, Atlanta. Electronic address: lmobley@gsu.edu.
authors.forename: Lee R
authors.initials: LR
authors.lastname: Mobley
authors.affiliation: School of Public Health, Georgia State University, Atlanta.
authors.forename: Lia
authors.initials: L
authors.lastname: Scott
authors.affiliation: School of Public Health, Georgia State University, Atlanta.
authors.forename: Yamisha
authors.initials: Y
authors.lastname: Rutherford
authors.affiliation: Lineberger Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill.
authors.forename: Tzy-Mey
authors.initials: TM
authors.lastname: Kuo
mesh_terms: D000328:Adult; D001741:Black or African American / Q000706:statistics & numerical data; D000368:Aged; D001213:Asian / Q000706:statistics & numerical data; D015179:Colorectal Neoplasms / Q000175:diagnosis / Q000453:epidemiology* / Q000473:pathology*; D016208:Databases, Factual; D005006:Ethnicity / Q000706:statistics & numerical data*; D005260:Female; D006630:Hispanic or Latino / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009361:Neoplasm Invasiveness; D009367:Neoplasm Staging; D011237:Predictive Value of Tests; D012042:Registries; D012111:Residence Characteristics; D012189:Retrospective Studies; D018570:Risk Assessment; D000072699:Social Segregation; D014481:United States / Q000453:epidemiology; D044465:White People / Q000706:statistics & numerical data
keywords: Cancer control; Geographic disparities; Health disparities; Multilevel analysis