Journal of surgical oncology · 2025
Income and Rurality Impact Overall Survival but not Cause-Specific Survival in Patients With Chondrosarcoma: A Population-Based Study From the SEER Database.
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Lin Adrian J, Joachim Kole, Gettleman Brandon, Hamad Christopher, Perrotta Amanda, Jeong Sumin, Fice Michael, Wessel Lauren E, Bernthal Nicholas M, Christ Alexander B
Аннотация
BACKGROUND AND OBJECTIVES: Previous studies evaluating socioeconomic status (SES) in bone malignancies such as chondrosarcoma used the Cox Proportional Hazards model, which might overestimate risk compared to cause-specific models like the Fine-Gray model. This study aims to evaluate the prognostic significance of income status in chondrosarcoma using both models.
METHODS: We performed a retrospective cohort study using the Surveillance, Epidemiology, and End Results (SEER) database for patients diagnosed with chondrosarcoma. Patients were stratified by SES indicators, including income levels (low: < $55 000, middle: $55 000-$70 000, high: > $70 000) and rurality (urban vs. rural). Survival analysis was conducted using Cox Proportional Hazards and Fine-Gray models.
RESULTS: The inclusion criteria were met by 3678 patients with income distributions as follows: 72.5% high-income, 18.3% middle-income, and 9.2% low-income. Cox analysis identified low-income (hazard ratio [HR] = 1.43, 95%-confidence interval [95%-CI]: 1.10-1.84, p = 0.006) and rurality (HR = 0.71, 95%-CI: 0.55-0.90, p = 0.006) as significant prognostic survival factors. Fine-Gray modeling attenuated the findings for low-income (sub-hazard ratio [SHR] = 1.36, 95%-CI: 0.95-1.94, p = 0.089) and rurality (SHR = 0.76, 95%-CI: 0.54-1.07, p = 0.122).
CONCLUSION: SES influences chondrosarcoma survival, but its effect on cause-specific mortality decreases when competing risks are considered. Fine-gray modeling reveals critical nuances in survival analysis, stressing the need for appropriate statistical methods to interpret SES-related disparities.
METHODS: We performed a retrospective cohort study using the Surveillance, Epidemiology, and End Results (SEER) database for patients diagnosed with chondrosarcoma. Patients were stratified by SES indicators, including income levels (low: < $55 000, middle: $55 000-$70 000, high: > $70 000) and rurality (urban vs. rural). Survival analysis was conducted using Cox Proportional Hazards and Fine-Gray models.
RESULTS: The inclusion criteria were met by 3678 patients with income distributions as follows: 72.5% high-income, 18.3% middle-income, and 9.2% low-income. Cox analysis identified low-income (hazard ratio [HR] = 1.43, 95%-confidence interval [95%-CI]: 1.10-1.84, p = 0.006) and rurality (HR = 0.71, 95%-CI: 0.55-0.90, p = 0.006) as significant prognostic survival factors. Fine-Gray modeling attenuated the findings for low-income (sub-hazard ratio [SHR] = 1.36, 95%-CI: 0.95-1.94, p = 0.089) and rurality (SHR = 0.76, 95%-CI: 0.54-1.07, p = 0.122).
CONCLUSION: SES influences chondrosarcoma survival, but its effect on cause-specific mortality decreases when competing risks are considered. Fine-gray modeling reveals critical nuances in survival analysis, stressing the need for appropriate statistical methods to interpret SES-related disparities.