Association between socioeconomic factors at diagnosis and survival in breast cancer: A population‐based study

Background The associations between socioeconomic statuses and survival outcomes of breast cancer remain unclear. No model has included both histological and socioeconomic factors to predict the survival of breast cancer. This study was designed to develop nomograms to predict breast cancer–specific survival (BCSS) and overall survival (OS) with consideration of socioeconomic factors for breast cancer patients. Materials and methods We included a total of 207 749 female patients, diagnosed with malignant breast cancer between 2007 and 2012 from the Surveillance, Epidemiology, and End Results database. BCSS and OS were evaluated with Gray's test and log‐rank tests, respectively. Marital statuses, insurance statuses, residence, median household income, poverty rate, unemployment rate, and education level were included as socioeconomic factors in univariate and multivariate Cox regression analyses. Clinicopathological factors and socioeconomic factors were integrated to construct nomograms. Calibration plots and concordance indexes (C‐indexes) were used to evaluate the accuracy and discrimination of the models. Results Four and three socioeconomic factors were involved in constructing the nomograms for 3‐, 5‐, and 7‐year BCSS and OS, respectively. The C‐indexes of the final nomograms were higher than those of the TNM staging system for predicting BCSS (0.835 vs 0.782; P
The findings from this population‐based study identify important socioeconomic factors at diagnosis, namely insurance status, marital status, median household income, educational level, and residence, which are closely associated with the survival of breast cancer. A survival prediction model for breast cancer patients was developed and validated based on nomograms, and for convenience of the users, we transferred models into a user‐friendly online tool. Both doctors and patients can use this online tool for free to predict survival from perspectives of pathology and socioeconomics, which is more accurate than traditional TNM staging.