Insurance status and survival among female breast cancer patients in Florida
Methods: Data from the Florida Cancer Data System (FCDS), the Agency for Health Care Administration (in- and out-patient treatment records), and the Census were linked (n=111,822). Survival time by insurance type was our primary endpoint, with adjustments for sociodemographic status, a neighborhood-based poverty measure, clinical characteristics, and co-morbidities. Insurance type was defined as private, defense/military/veteran, Indian Health Service, Medicaid, Medicare, other insurance, or uninsured. Cox regression models were performed to determine the hazard ratio (HR). Results: In the univariate model, compared to private insurance Medicaid (HR 2.42; 95% Confidence Interval=2.28-2.56), Medicare ([2.07]; 2.02-2.13), Indian Health Service ([2.03]; 1.61-2.55), and uninsured ([1.91]; 1.8-2.03) had increased risk of reduced survival. In the fully adjusted model, women with Indian Health Service insurance had the largest risk of reduced survival [1.72]; 1.33-2.23); those with Medicaid ([1.62]; 1.50-1.75), uninsured ([1.41]; 1.27-1.57), and Medicare ([1.10]; 1.06-1.15) were also at increased risk (all p<0.001). These risks were not significantly different for women with defense/military/veteran ([1.08]; 0.93-1.25). Conclusion: Insurance type at the time of diagnosis is predictive of survival in women with breast cancer. Studies are needed to identify strategies to reduce the excess mortality burden in women with Indian Health Service and Medicaid insurance.
Learning Areas:Chronic disease management and prevention
Diversity and culture
Compare the risk of reduced survival to the reference group, private insurance, for all insurance types (including uninsured). Discuss reasons why women from the Indian Health Service would be at the highest risk for reduced survival in the fully adjusted model, followed by Medicaid, uninsured, and Medicare. Describe how linkage of the three datasets (Florida Cancer Data System, Florida Agency for Health Care Administration, and the US Census) aided the analysis of the fully adjusted model predicting survival when compared to those who were privately insured.
Qualified on the content I am responsible for because: I am qualified to be an abstract author because I am a co-investigator on this grant and have focused on cancer and disparities by race, ethnicity, and socioeconomic status in other grants as well.
Any relevant financial relationships? No
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