Is There an Association Between Insurance Status and Survival and Treatment of Primary Bone and Extremity Soft-tissue Sarcomas? A SEER Database Study.

BACKGROUND: Several recently published population-based studies have highlighted the association between insurance status and survival in patients with various cancers such as breast, head and neck, testicular, and lymphoma [22, 24, 38, 41]. Generally, these studies demonstrate that uninsured patients or those with Medicaid insurance had poorer survival than did those who had non-Medicaid insurance. However, this discrepancy has not been studied in patients with primary bone and extremity soft-tissue sarcomas, a unique oncological population that typically presents late in the disease course and often requires referral and complex treatment at tertiary care centers-issues that health insurance coverage disparities could aggravate. QUESTIONS/PURPOSES: (1) What is the relationship between insurance status and cause-specific mortality? (2) What is the relationship between insurance status and the prevalence of distant metastases? (3) What is the relationship between insurance status and the proportion of limb salvage surgery versus amputation?
METHODS: The Surveillance, Epidemiology, and End Results database (SEER) was used to identify a total of 12,008 patients: 4257 patients with primary bone sarcomas and 7751 patients with extremity soft-tissue sarcomas, who were diagnosed and treated between 2007 and 2014. Patients were categorized into one of three insurance groups: insured with non-Medicaid insurance, insured with Medicaid, and uninsured. Patients without information available regarding insurance status were excluded (2.7% [113 patients] with primary bone sarcomas and 3.1% [243 patients] with extremity soft-tissue sarcomas.) The association between insurance status and survival was assessed using a Cox proportional hazards regression analysis adjusted for patient age, sex, race, ethnicity, extent of disease (lymph node and metastatic involvement), tumor grade, tumor size, histology, and primary tumor site.
RESULTS: Patients with primary bone sarcomas with Medicaid insurance had reduced disease-specific survival than did patients with non-Medicaid insurance (hazard ratio 1.3 [95% confidence interval 1.1 to 1.6]; p = 0.003). Patients with extremity soft-tissue sarcomas with Medicaid insurance also had reduced disease-specific survival compared with those with non-Medicaid insurance (HR 1.2 [95% CI 1.0 to 1.5]; p = 0.019). Patients with primary bone sarcomas (relative risk 1.8 [95% CI 1.3 to 2.4]; p < 0.001) and extremity soft-tissue sarcomas (RR 2.4 [95% CI 1.9 to 3.1]; p < 0.001) who had Medicaid insurance were more likely to have distant metastases at the time of diagnosis than those with non-Medicaid insurance. Patients with primary bone sarcomas (RR 1.8 [95% CI 1.4 to 2.1]; p < 0.001), and extremity soft-tissue sarcomas (RR 2.4 [95% CI 1.9 to 3.0]; p < 0.001) that had Medicaid insurance were more likely to undergo amputation than patients with non-Medicaid insurance. Patients with primary bone and extremity soft-tissue sarcomas who were uninsured were not more likely to have distant metastases at the time of diagnosis and did not have a higher proportion of amputation surgery as compared with patients with non-Medicaid insurance. However, uninsured patients with extremity soft-tissue sarcomas still displayed reduction in disease-specific survival (HR 1.6 [95% CI 1.2 to 2.1]; p = 0.001).
CONCLUSIONS: Disparities manifested by differences in insurance status were correlated with an increased risk of metastasis at the time of diagnosis, reduced likelihood of treatment with limb salvage procedures, and reduced disease-specific survival in patients with primary bone or extremity soft-tissue sarcomas. Although several potentially confounding variables were controlled for, unmeasured confounding played a role in these results. Future studies should seek to identify what factors drive the finding that substandard insurance status is associated with poorer survival after a cancer diagnosis. Candidate variables might include medical comorbidities, treatment delays, time to first presentation to medical care and time to diagnosis, type of treatment received, distance travelled to treatments and transportation barriers, out-of-pocket payment burden, as well as educational and literacy status. These variables are almost certainly associated with socioeconomic deprivation in a vulnerable patient population, and once identified, treatment can become targeted to address these systemic inequities. LEVEL OF EVIDENCE: Level III, therapeutic study.