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Updated: Sep 27, 2026

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 2, 2013
Healthcare System Drivers of Prostate Cancer Disparities: Real-World Evidence, Informatics Pathways, and Policy
Chen Yang1,2, Zelin Guo3, Fan Cheng1,4,5
1Department of Urology, Renmin Hospital of Wuhan University, Wuhan 430060, China.
Abstract:
Background: Racial and socioeconomic differences in prostate cancer outcomes are often described as survival disparities, but observed differences may also reflect healthcare access, treatment delivery, and resource allocation. Methods: NCDB and SEER-Medicare were treated as independent, complementary retrospective data sources without individual-level linkage. The analytic material comprised 111,396 database records from 2010 to 2020 across two independent source files; this is a cross-source record count, not a deduplicated count of unique persons. Harmonized descriptive analyses characterized stage, treatment, and treatment timing, while multivariable Cox proportional hazards modeling evaluated prostate cancer-specific survival (CSS) in outcome-eligible SEER-Medicare records. The CSS model denominator is therefore substantially smaller than the total record count, and the two should not be confused. Results: Records for non-Hispanic Black (NHB) men more often showed stage III-IV disease than records for non-Hispanic White (NHW) men (26.9% vs. 18.7%) and lower receipt of guideline-concordant first-course management initiated within 90 days, particularly in the low-SES subgroup (51.4% vs. 84.3% among high-SES NHW men). In the adjusted CSS model, NHB race was associated with higher mortality (HR = 1.32; 95% CI: 1.24-1.41). Uninsured status (HR = 1.47; 95% CI: 1.33-1.62), low income (HR = 1.28; 95% CI: 1.19-1.37), and rural residence (HR = 1.14; 95% CI: 1.06-1.22) were also associated with higher mortality. Conclusions: The findings support a healthcare-system interpretation of prostate cancer disparities and identify measurable targets for prospective evaluation, including EHR-enabled monitoring, nurse navigation, telehealth-supported follow-up, and facility-level treatment-concordance review.
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