Does Insurance Status Interfere with Outcomes in Patients with Hodgkin and Non-Hodgkin Lymphoma?: The UT Health San Antonio Experience

Introduction: Historically, a lack of health insurance has been reported to correlate with decreased access to medical care, a delay in cancer treatment and poorer outcomes overall. Furthermore, access to preventive services for cancer screening also decrease with lack of medical insurance (1, 2). Also, studies report that an increase in Medicaid expansion help reduce racial disparities previously seen between African American and Caucasian patients (3). The aim of this study was to present and analyze vitality data based on insurance coverage among Hispanic (HI) and non-Hispanic (NH) population at the only NCI designated cancer center of South Texas primarily serving Hispanics. Methods: This is a retrospective observational study of a cohort of patients seen with diagnosis of lymphoma by International Classification of Diseases (ICD) codes from 2008 to 2018 at UT Health San Antonio. Diffuse Large B Cell Lymphoma (DLBCL) cases were not included. Variables included age of diagnosis, lymphoma subtype, stage at diagnosis, comorbidities, treatment received, lines of therapy, B symptoms present, death, and cause of death and current vitality status. Continuously distributed outcomes were summarized with the mean and standard deviation and categorical outcomes were summarized with frequencies and percentages. The significance of variation in the mean with disease category was assessed with one-way ANOVA and the significance of associations between categorical outcomes was assessed with Pearson's Chi Square or Fisher's Exact test as appropriate. Multivariate logistic regression was used to model binary outcomes in terms of covariates and indicators of disease. All statistical testing was two-sided with a significance level of 5%. R1 was used throughout. Primary end point was to characterize insurance status. Secondary end points - overall 3 and 5-year survival based on insurance and demographics. Results: A total of 477 patients with lymphoma were identified. Hodgkin lymphoma (HL)( n = 116, 24%), non-Hodgkin lymphoma (NHL) (n = 308, 65%), T cell lymphoma (TCL) ( n = 53, 11%). Subtypes for all indolent lymphomas ( n = 217), of which included; Follicular lymphoma (FL) ( n = 123), Marginal Zone lymphoma (MZL) ( n = 53), Nodular Lymphocyte Predominant Hodgkin Lymphoma (NLPHL) (n = 8), Small lymphocytic lymphoma (SLL) ( n = 28). Overall mean age of diagnosis for all lymphoma subtypes was 51, male patients (n = 244, 51%), female patients (n = 232, 49%), HI (n = 263, 56