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Cancer Epidemiol Biomarkers Prev. 2016 Apr;25(4):613-23. doi: 10.1158/1055-9965.EPI-15-0225. Epub 2016 Jan 27.

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

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Department of Health Policy and Management, Rollins School of Public Health, Winship Cancer Institute, Emory University, Atlanta, Georgia.
Department of Epidemiology, University of Kentucky College of Public Health, Lexington, Kentucky.
University of Wisconsin Carbone Cancer Center, Madison, Wisconsin.
Department of Internal Medicine, Medical Oncology, Duke University Medical Center and Multidisciplinary Breast Cancer Program, Duke Cancer Institute, Durham, North Carolina.
Epidemiology Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, Louisiana.
California Cancer Registry, Institute for Population Health Improvement, UC Davis Health System, Sacramento, California.
Department of Health Policy and Management, Rollins School of Public Health, Emory University, Atlanta, Georgia.
American Cancer Society, Atlanta, Georgia.
Department of Public Health Sciences, University of Virginia School of Medicine, and UVA Cancer Center, Charlottesville, Virginia.
Division of Cancer Prevention and Control, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia.



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.


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.


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.


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.


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.

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