What Predicts an Advanced-Stage Diagnosis of Breast Cancer? Sorting Out the Influence of Method of Detection, Access to Care, and Biologic Factors.

Journal Article (Journal Article)

BACKGROUND: Multiple studies have yielded important findings regarding the determinants of an advanced-stage diagnosis of breast cancer. We seek to advance this line of inquiry through a broadened conceptual framework and accompanying statistical modeling strategy that recognize the dual importance of access-to-care and biologic factors on stage. METHODS: The Centers for Disease Control and Prevention-sponsored Breast and Prostate Cancer Data Quality and Patterns of Care Study yielded a seven-state, cancer registry-derived population-based sample of 9,142 women diagnosed with a first primary in situ or invasive breast cancer in 2004. The likelihood of advanced-stage cancer (American Joint Committee on Cancer IIIB, IIIC, or IV) was investigated through multivariable regression modeling, with base-case analyses using the method of instrumental variables (IV) to detect and correct for possible selection bias. The robustness of base-case findings was examined through extensive sensitivity analyses. RESULTS: Advanced-stage disease was negatively associated with detection by mammography (P < 0.001) and with age < 50 (P < 0.001), and positively related to black race (P = 0.07), not being privately insured [Medicaid (P = 0.01), Medicare (P = 0.04), uninsured (P = 0.07)], being single (P = 0.06), body mass index > 40 (P = 0.001), a HER2 type tumor (P < 0.001), and tumor grade not well differentiated (P < 0.001). This IV model detected and adjusted for significant selection effects associated with method of detection (P = 0.02). Sensitivity analyses generally supported these base-case results. CONCLUSIONS: Through our comprehensive modeling strategy and sensitivity analyses, we provide new estimates of the magnitude and robustness of the determinants of advanced-stage breast cancer. IMPACT: Statistical approaches frequently used to address observational data biases in treatment-outcome studies can be applied similarly in analyses of the determinants of stage at diagnosis. Cancer Epidemiol Biomarkers Prev; 25(4); 613-23. ©2016 AACR.

Full Text

Duke Authors

Cited Authors

  • Lipscomb, J; Fleming, ST; Trentham-Dietz, A; Kimmick, G; Wu, X-C; Morris, CR; Zhang, K; Smith, RA; Anderson, RT; Sabatino, SA; Centers for Disease Control and Prevention National Program of Cancer Registries Patterns of Care Study Group,

Published Date

  • April 2016

Published In

Volume / Issue

  • 25 / 4

Start / End Page

  • 613 - 623

PubMed ID

  • 26819266

Pubmed Central ID

  • PMC8638656

Electronic International Standard Serial Number (EISSN)

  • 1538-7755

Digital Object Identifier (DOI)

  • 10.1158/1055-9965.EPI-15-0225


  • eng

Conference Location

  • United States