Background
Breast cancer is a heterogeneous disease composed of molecular subtypes with distinct prognostic and clinical implications. Survival outcomes are shaped by both tumor biology and timeliness of care, particularly stage at diagnosis. Understanding how molecular subtype distribution interacts with stage and survival at the population level is critical for guiding equitable early detection strategies and health-system planning, especially in settings confronting aggressive disease and diagnostic delays.

Objectives
To characterize the distribution of breast cancer molecular subtypes and quantify their independent impact on survival at the population level, generating evidence relevant to equitable early detection and global cancer control strategies.

Methods
We analyzed a cohort of 1,035 women diagnosed with invasive breast cancer and followed for up to 10 years. Tumors were classified into luminal A, luminal B, HER2-enriched, and triple-negative subtypes using immunohistochemical markers. Survival differences were assessed using Kaplan–Meier methods and multivariable Cox proportional hazards models adjusting for age, stage at diagnosis, body mass index, and menopausal status.

Results
Luminal A was the most common molecular subtype (41.7%), followed by luminal B (23.4%), triple-negative (19.5%), and HER2-enriched disease (15.4%). Molecular subtype was an independent determinant of survival. Compared with luminal A tumors, HER2-enriched and triple-negative subtypes were associated with significantly poorer survival. Advanced stage at diagnosis substantially magnified these subtype-specific disparities, with the worst outcomes observed among late-stage HER2-enriched and triple-negative cancers, highlighting the combined impact of tumor biology and delayed detection.

Conclusions/Implications
This population-level analysis reveals that survival inequalities in breast cancer are driven by the intersection of aggressive tumor biology, particularly HER2-enriched and triple-negative subtypes, and advanced stage at diagnosis. These findings support integrating molecular subtype information into population-based cancer control strategies to tailor screening, strengthen early detection, and guide efficient resource allocation. Subtype-informed approaches are essential for building resilient, equitable health systems and improving outcomes across diverse global settings.