title: Race, socioeconomic status and survival in three female cancers.
abstract: OBJECTIVE
  Although many studies have reported that socioeconomic status (SES) and race affect cancer survival, researchers have not established whether SES and race affect survival independently. The research reported here addresses this question with special attention to cancers affecting large numbers of women in the US.
  
  
  METHODS
  The authors analyzed data on survival among patients in the Centralized Cancer Patient Data System (CCPDS) with cancers of the breast (n = 6896), cervix (n = 2209) and uterine corpus (n = 1492).
  
  
  RESULTS
  According to Cox proportional hazards models, race predicted survival in all three cancers, while socioeconomic status predicted survival for cancers of the breast and uterine corpus. Interaction effects between race and SES were generally not statistically significant. This study includes larger numbers of observations within specific forms of cancer and covers a broader patient population than most previous investigations. These features promote detectability of SES effects, comparability among disease sites, and generalizability to cancer patients throughout the US.
  
  
  CONCLUSIONS
  Findings imply that SES and race affect cancer mortality risk independently of each other, and that the impact of SES and race may vary by malignancy. Survival disadvantages due to race-which may be more pronounced among women than men-should remain a continuing concern.
authors.affiliation: University of Southern California, University Park, Los Angeles 90089, USA.
authors.forename: H P
authors.initials: HP
authors.lastname: Greenwald
authors.forename: N L
authors.initials: NL
authors.lastname: Polissar
authors.forename: H H
authors.initials: HH
authors.lastname: Dayal
mesh_terms: D000328:Adult; D000368:Aged; D044383:Black People; D001943:Breast Neoplasms / Q000208:ethnology* / Q000401:mortality; D003431:Cross-Cultural Comparison; D005260:Female; D006801:Humans; D008875:Middle Aged; D016016:Proportional Hazards Models; D012042:Registries / Q000706:statistics & numerical data; D012044:Regression Analysis; D012959:Socioeconomic Factors; D015996:Survival Rate; D014481:United States / Q000453:epidemiology; D002583:Uterine Cervical Neoplasms / Q000208:ethnology* / Q000401:mortality; D014594:Uterine Neoplasms / Q000208:ethnology* / Q000401:mortality; D044465:White People; D001741:Black or African American