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. 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

Joseph Lipscomb  1 , Steven T Fleming  2 , Amy Trentham-Dietz  3 , Gretchen Kimmick  4 , Xiao-Cheng Wu  5 , Cyllene R Morris  6 , Kun Zhang  7 , Robert A Smith  8 , Roger T Anderson  9 , Susan A Sabatino  10 ; Centers for Disease Control and Prevention National Program of Cancer Registries Patterns of Care Study Group
Affiliations

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

Joseph Lipscomb et al. Cancer Epidemiol Biomarkers Prev. 2016 Apr.

Abstract

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.

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Conflict of interest statement

Disclosure of Potential Conflicts of Interest

No potential conflicts of interest were disclosed.

Figures

Figure 1.
Figure 1.
Breast cancer stage at diagnosis: conceptual framework.
Figure 2.
Figure 2.
Conceptualizing the statistical analysis of the determinants of stage: transforming the framework to reflect estimation via 2SRI IV model.
See this image and copyright information in PMC

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