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Racial disparity in breast cancer survival: the impact of pre-treatment hematologic variables.

Publication ,  Journal Article
Wang, C; Civan, J; Lai, Y; Cristofanilli, M; Hyslop, T; Palazzo, JP; Myers, RE; Li, B; Ye, Z; Zhang, K; Xing, J; Yang, H
Published in: Cancer Causes Control
January 2015

PURPOSE: A survival disparity of black versus white breast cancer patients has been extensively documented but not adequately explained. Blacks and whites also have significant differences in hematologic traits including hemoglobin (HGB). However, a link between survival disparity and hematologic differences has not been reported. We aimed to explore the effect of pre-treatment hematologic variables on this survival disparity. METHODS: We sequentially matched 443 black patients, using a minimum distance approach, to four different sets of 443 whites on demographics (age, year of diagnosis, smoking, and drinking status), tumor presentation (all demographic variables plus tumor stage, grade, and hormone receptor status), treatment (all presentation variables plus surgery, chemotherapy, radiation therapy, and hormone therapy), and presentation plus pre-treatment hematologic variables. Racial survival for each matched dataset was analyzed by Cox proportional hazards model. RESULTS: We found that white patients matched on demographic characteristics had more favorable survival than blacks [hazard ratio (HR) 0.57, 95 % confidence interval (CI) 0.42-0.77, p log-rank = 0.0002]. Presentation match diminished this disparity [HR 0.72 (0.54-0.95), p log-rank = 0.0199], which was not further reduced in treatment match [HR 0.73 (0.55-0.96), p log-rank = 0.0249]. However, the survival disparity was largely reduced when pre-treatment level of HGB or red blood cell distribution width was further matched in addition to presentation match [HR 0.83 (0.64-1.09), p log-rank = 0.1819 and HR 0.83 (0.64-1.09), p log-rank = 0.1760, respectively]. CONCLUSIONS: We found that in our patient population, differences in tumor presentation and certain pre-treatment hematologic traits, but not treatment, were associated with the survival disparity between black and white breast cancer patients.

Duke Scholars

Published In

Cancer Causes Control

DOI

EISSN

1573-7225

Publication Date

January 2015

Volume

26

Issue

1

Start / End Page

45 / 56

Location

Netherlands

Related Subject Headings

  • United States
  • Survival Analysis
  • Proportional Hazards Models
  • Middle Aged
  • Incidence
  • Humans
  • Health Status Disparities
  • Female
  • Ethnicity
  • Epidemiology
 

Citation

APA
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ICMJE
MLA
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Wang, C., Civan, J., Lai, Y., Cristofanilli, M., Hyslop, T., Palazzo, J. P., … Yang, H. (2015). Racial disparity in breast cancer survival: the impact of pre-treatment hematologic variables. Cancer Causes Control, 26(1), 45–56. https://doi.org/10.1007/s10552-014-0481-4
Wang, Chun, Jesse Civan, Yinzhi Lai, Massimo Cristofanilli, Terry Hyslop, Juan P. Palazzo, Ronald E. Myers, et al. “Racial disparity in breast cancer survival: the impact of pre-treatment hematologic variables.Cancer Causes Control 26, no. 1 (January 2015): 45–56. https://doi.org/10.1007/s10552-014-0481-4.
Wang C, Civan J, Lai Y, Cristofanilli M, Hyslop T, Palazzo JP, et al. Racial disparity in breast cancer survival: the impact of pre-treatment hematologic variables. Cancer Causes Control. 2015 Jan;26(1):45–56.
Wang, Chun, et al. “Racial disparity in breast cancer survival: the impact of pre-treatment hematologic variables.Cancer Causes Control, vol. 26, no. 1, Jan. 2015, pp. 45–56. Pubmed, doi:10.1007/s10552-014-0481-4.
Wang C, Civan J, Lai Y, Cristofanilli M, Hyslop T, Palazzo JP, Myers RE, Li B, Ye Z, Zhang K, Xing J, Yang H. Racial disparity in breast cancer survival: the impact of pre-treatment hematologic variables. Cancer Causes Control. 2015 Jan;26(1):45–56.
Journal cover image

Published In

Cancer Causes Control

DOI

EISSN

1573-7225

Publication Date

January 2015

Volume

26

Issue

1

Start / End Page

45 / 56

Location

Netherlands

Related Subject Headings

  • United States
  • Survival Analysis
  • Proportional Hazards Models
  • Middle Aged
  • Incidence
  • Humans
  • Health Status Disparities
  • Female
  • Ethnicity
  • Epidemiology