Insurance status and survival among lung cancer patients in Florida
Wednesday, November 6, 2013
: 10:30 a.m. - 10:50 a.m.
Insurance type at time of diagnosis has previously been shown to influence quality of care among lung cancer patients, and can be used to define populations at increased risk of poor treatment outcomes. We examined associations between insurance type and survival for patients diagnosed with lung cancer in Florida between 1996 and 2007. Methods:
Lung cancer data from the Florida Cancer Data System, the Agency for Health Care Administration (in- and out-patient treatment records), and the Census were linked (n=148,140). 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, or uninsured. Univariate and multivariate Cox regression models were performed. Results:
In the univariate model, compared to private insurance, worse survival was seen for Medicaid (hazard ratio [HR] 1.37; [95% Confidence interval=1.33-1.41]), Medicare (1.21; [1.19-1.22]), Defense/Military/Veteran (1.13; [1.08-1.18]), Indian Health Service (1.25; [1.08-1.44]), and uninsured (1.52; [1.47-1.56]). In the fully adjusted model, significant differences for worse survival remained for Medicaid (1.19; [1.15-1.23]) and uninsured (1.19; [1.14-1.24]) patients compared to patients with private insurance. Conclusion:
Lung cancer patients who were uninsured or on Medicaid had worse survival than those with private insurance, even after adjusting for myriad sociodemographic, clinical, treatment, tumor and comorbidity characteristics. Studies are needed to identify the causes of, and strategies to reduce the excess mortality burden in these patients.
Chronic disease management and prevention
Diversity and culture
Provision of health care to the public
Compare the survival risk in the univariate and multivariate models. Explain the retention of Medicaid and uninsured as significant in the fully adjusted model. Describe the adjustments in the model to control for the confounding variables.
Presenting author's disclosure statement:
Qualified on the content I am responsible for because: I am qualified to be abstract author because I have been the principal investigator of multiple federally funded grants focusing on health disparities in breast and lung cancer. I am the principal investigator on the grant from which this presentation is related.
Any relevant financial relationships? No
I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines,
and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed
in my presentation.