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Breast cancer survival among economically disadvantaged women: the influences of delayed diagnosis and treatment on mortality.

Publication ,  Journal Article
Smith, ER; Adams, SA; Das, IP; Bottai, M; Fulton, J; Hebert, JR
Published in: Cancer Epidemiol Biomarkers Prev
October 2008

Breast cancer affects thousands each year in the United States, and disproportionately affects certain subgroups. For example, the incidence of breast cancer in South Carolina is lower in African American compared with European American women by approximately 12% to 15%, but their mortality rate is twice as high as in European American women. The purpose of the study was to assess factors associated with breast cancer mortality between African American and European American women. Participants (n=314) in South Carolina's Breast and Cervical Cancer Early Detection Program (SCBCCEDP), which provides breast cancer screening and treatment services, during the years 1996-2004 were included in the study. Data, including tumor characteristics, delay intervals, and race, were examined using the chi(2) test and the Wilcoxon rank-sum test. Cox regression modeling was used to assess the relationship between delay intervals and other factors. No racial differences were found in age at diagnosis, tumor characteristics, or delay intervals. Time delay intervals did not explain differences and mortality rates by race. Survival, however, was affected by prognostic factors as well as by a significant interaction between hormone-receptor status and race. Despite the excellent record of the SCBCCEDP in screening and diagnostic or treatment referrals, the racial disparities in breast cancer mortality continue to exist in South Carolina. These findings highlight the need for future research into the etiology of racial differences, and their impact on breast cancer survival.

Duke Scholars

Published In

Cancer Epidemiol Biomarkers Prev

DOI

ISSN

1055-9965

Publication Date

October 2008

Volume

17

Issue

10

Start / End Page

2882 / 2890

Location

United States

Related Subject Headings

  • White People
  • Time Factors
  • Survival Analysis
  • Statistics, Nonparametric
  • South Carolina
  • Socioeconomic Factors
  • Registries
  • Proportional Hazards Models
  • Middle Aged
  • Humans
 

Citation

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Chicago
ICMJE
MLA
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Smith, E. R., Adams, S. A., Das, I. P., Bottai, M., Fulton, J., & Hebert, J. R. (2008). Breast cancer survival among economically disadvantaged women: the influences of delayed diagnosis and treatment on mortality. Cancer Epidemiol Biomarkers Prev, 17(10), 2882–2890. https://doi.org/10.1158/1055-9965.EPI-08-0221
Smith, Emily Rose, Swann Arp Adams, Irene Prabhu Das, Matteo Bottai, Jeanette Fulton, and James R. Hebert. “Breast cancer survival among economically disadvantaged women: the influences of delayed diagnosis and treatment on mortality.Cancer Epidemiol Biomarkers Prev 17, no. 10 (October 2008): 2882–90. https://doi.org/10.1158/1055-9965.EPI-08-0221.
Smith ER, Adams SA, Das IP, Bottai M, Fulton J, Hebert JR. Breast cancer survival among economically disadvantaged women: the influences of delayed diagnosis and treatment on mortality. Cancer Epidemiol Biomarkers Prev. 2008 Oct;17(10):2882–90.
Smith, Emily Rose, et al. “Breast cancer survival among economically disadvantaged women: the influences of delayed diagnosis and treatment on mortality.Cancer Epidemiol Biomarkers Prev, vol. 17, no. 10, Oct. 2008, pp. 2882–90. Pubmed, doi:10.1158/1055-9965.EPI-08-0221.
Smith ER, Adams SA, Das IP, Bottai M, Fulton J, Hebert JR. Breast cancer survival among economically disadvantaged women: the influences of delayed diagnosis and treatment on mortality. Cancer Epidemiol Biomarkers Prev. 2008 Oct;17(10):2882–2890.

Published In

Cancer Epidemiol Biomarkers Prev

DOI

ISSN

1055-9965

Publication Date

October 2008

Volume

17

Issue

10

Start / End Page

2882 / 2890

Location

United States

Related Subject Headings

  • White People
  • Time Factors
  • Survival Analysis
  • Statistics, Nonparametric
  • South Carolina
  • Socioeconomic Factors
  • Registries
  • Proportional Hazards Models
  • Middle Aged
  • Humans