Medical Insurance Coverage and Clinical Outcomes for Patients with Hematological Malignancies in a Rural State

Abstract

Background Hematological malignancies, such as leukemia, lymphoma, and multiple myeloma, affect blood, bone marrow, and/or the lymphatic system. They can be difficult to treat and often require prolonged and expensive interventions such as chemotherapy, radiation therapy, stem cell transplant, and/or targeted immunotherapy. Due to the complexity and cost of treatment, they pose a significant financial burden on patients and their families. Medical insurance coverage plays a crucial role in providing access to these treatments. This is particularly important for individuals of lower socioeconomic status, and patients in rural states. Aim This retrospective study is focused at examining the relationship between various medical insurance coverage options and/or its absence in influencing the treatment regime and clinical outcomes of patients with hematological malignancies in a statewide cancer registry. Methods This is a retrospective cohort study, conducted by analyzing data in the cancer registry of patients with various hematological malignancies in a single rural state between 2015 and 2022. The patients were divided into study groups based on their insurance providers (Medicare, Medicaid, Private, Military, and Other). Demographic data, cancer treatment data as well as survival data was collected for all patients included in the study. Descriptive statistics were calculated for the overall sample and for each Insurance type subgroup. Due to normality violations, Kruskal Wallis tests (a non-parametric alternative to Analysis of Variance) were utilized to determine if there were statistically significant differences in age, number of treatments, and survival months between the Insurance type groups. To quantify the magnitude of the effect, epsilon-squared (2) was calculated for all between-group comparisons, as a recommended effect size for Kruskal-Wallis tests. Post-hoc group comparisons were conducted when necessary. All categorical variables were evaluated to determine if there were differences present between Insurance type groups using chi-square tests, with post-hoc group comparisons made when necessary. A log-rank test was utilized to compare the survival curves between the Insurance type groups. Univariable and multivariable Cox proportional-hazards regression analysis was used to examine the relationship between survival and Insurance type, when additionally accounting for demographics such as age, race, and sex. All analyses were conducted using R Version 4.3.1 with statistical significance defined as p<0.05. Results The study cohort included 2,472 patients from a single state cancer registry. The study groups differed significantly on age, race, current cancer status, overall treatment summary, chemotherapy, hormone treatment, immunotherapy, hematologic transplant and endocrine treatment, other/palliative treatment, number of treatments, survival length, and overall survival (Table 1). In addition, the survival curves significantly differed by insurance type (Figure 1). Independent predictors of death were age, sex, and insurance type. Multivariable analysis revealed only age and insurance were significant predictors of death with increased risk for older individuals relative to younger and increased risk of death for Medicare, Medicaid, and those that listed their insurance as ‘Other’ relative to those with Private insurance. All assumptions for Cox proportional-hazards regression were tenable. Conclusion There is a strong correlation between insurance types and the outcomes in cancer patients. It revealed that patients with private insurance were more likely to live longer and have more treatment options compared to patients without insurance or other types of coverage. This is reflected in the stratified insurance cohort which showed a 72.8% 5-year survival rate for patients with private insurance, as opposed to Medicare patients who ranked lowest at 49.3%. In addition, the study also highlighted racial disparities in insurance coverage. For instance, evidencing that black & white patients were more likely to be covered in equally by Medicare, while the white patients were over 50% more likely to be covered by other plans as compared to their black counterparts. Overall, the study thoroughly highlights the impact a patient's choice of insurance could portend in ensuring their longevity when battling hematological malignancies