02 April 2019 : Clinical Research
Impact of Insurance Status on Stage, Treatment, and Survival in Patients with Colorectal Cancer: A Population-Based AnalysisWei Sun1ABCD, Minghua Cheng1BCDE, Shaohui Zhuang1CDE, Zeting Qiu1ABEF*
Med Sci Monit 2019; 25:2397-2418
BACKGROUND: This study aimed to analyze data from the Surveillance, Epidemiology, and End Results (SEER) program to identify patients with colorectal cancer (CRC) who had specific insurance details and the effects of stage at diagnosis, definitive treatment, and survival outcome with insurance status.
MATERIAL AND METHODS: Between 2007 and 2009, SEER database analysis identified 54,232 patients with CRC. Logistic models examined the associations between insurance status and disease stage and definitive treatment. Kaplan-Meier analysis, the Cox model, and the Fine and Gray model were used to compare the tumor cause-specific survival (TCSS) for patients with different insurance status.
RESULTS: Insured patients were more likely to have earlier tumor stage at diagnosis when compared with patients receiving Medicaid (adjusted OR, 1.318; 95% CI, 1.249–1.391; P<0.001) and when compared with uninsured patients (adjusted OR, 1.479; 95% CI, 1.352–1.618; P<0.001). Insured patients were significantly more likely to undergo definitive treatment when compared with patients receiving Medicaid (adjusted OR, 0.591; 95% CI, 0.470–0.742; P<0.001) and compared with patients who were uninsured (adjusted OR, 0.404; 95% CI, 0.282–0.579; P<0.001). Insured patients had a significantly increased TCSS when compared with patients receiving Medicaid (HR, 1.298; 95% CI, 1.236–1.363; P<0.001) and compared with patients who were uninsured (HR 1.195, 95% CI, 1.100–1.297; P<0.001).
CONCLUSIONS: Insurance status was a significant factor that determined early diagnosis, definitive treatment, and clinical outcome and was an independent factor for TCSS in patients with CRC.
Keywords: Colorectal Neoplasms, Insurance Coverage, Neoplasm Staging, Survival Rate, Treatment Outcome, Adult, Aged, Databases, Factual, Insurance, Health, Kaplan-Meier Estimate, Medicaid, Medically Uninsured, Middle Aged, Prognosis, Proportional Hazards Models, SEER Program, United States
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