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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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4 2016
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Source: Cancer Epidemiol Biomarkers Prev. 25(4):613-623
[PDF-481.50 KB]
Details:
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Alternative Title:Cancer Epidemiol Biomarkers Prev
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Personal Author:
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Description: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.
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Pubmed ID:26819266
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Pubmed Central ID:PMC8638656
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Document Type:
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Funding:
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Volume:25
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Issue:4
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