Insurance Is No Longer a Determinant to Improve Overall Survival for Patients Diagnosed with HL, DLBCL or PCNS in an Enriched Hispanic Community

Background Variables that determine overall survival (OS) in patients diagnosed with Hodgkin (HL) and Non-Hodgkin lymphomas (NHL) have been widely studied in the United States. Inequalities in survival has been noted when patients lack insurance (Cancer PMID: 26058564). However, healthcare disparities exist within the different cancer subtypes and ethnic minorities. In the case of Follicular lymphoma, uninsurance has been linked with worse outcomes (Blood PMC6137560). For Burkitt and plasmablastic lymphomas, insurance does not seem to have a repercussion in survival (Blood 136 Supplement 1:45-46) (Leuk Lymphoma PMC6923579). In subgroup analysis, Hispanics (HI) have been noted to have higher rate of uninsurance with no significant distinction in OS in patients with DLBCL (Blood136 Supplement 1: 9). There is a need to understand determinants in ethnic disparities in outcomes for HL and NHL. This is the first large statewide population-based study differentiating ethnicity, insurance status and survival for HL, diffuse large B cell lymphoma (DLBCL) and primary central nervous system lymphoma (PCNS) in Texas. Material and Methods A retrospective analysis of patients diagnosed with HL, DLBCL, PCNS recorded in the Texas Cancer Registry from 2006-2017 was carried out. Inclusion criteria was histopathologic proven HL, DLBCL, and PCNS. Patients were divided into HI and non-Hispanics (NH), and subsequently in insured (i) and uninsured (un), for a total of four cohorts for comparison: iHI, unHI, iNH and unNH. Survival time was measured using the day of diagnosis to last date of follow up or death. For each cohort, median survival (MS) and analysis at 2,5 and 10 years (y) was calculated. Survival distribution were determined based on Kaplan-Meier curves. Results From 2006-2017, 21,229 patients with HL, DLBCL, PCNS were diagnosed in Texas. Of these, 6,004 patients (iHI n=1,369, unHI n=376, iNH n=3,781, unNH n=478) were diagnosed with HL (Graph 1), 14,366 patients (iHI n=2,810, unHI n=635, iNH n=10,273, unNH n=648) were diagnosed with DLBCL (Graph 2) and 859 patients (iHI n=195, unHI n=54, iNH n=559, unNH n=51) were diagnosed with PCNS (Graph 3). MS was outstanding for uninsured compared to insured patients with HL, DLBCL and PCSN. In HL, MS for iHI was 9.8 y, unHI was not reached, iNH was 10.3 y, and unNH was 10.8 y. In DLBC, MS was 3.7 y, 9.3 y, 4.2 y and 5.3 y, respectively. In PCNS, MS for these groups corresponded to 0.9 y, 0.8 y, 0.7 y and 3.2 y. Survival probability at 2-,5- and 10y among i vs un was noteworthy in HL, DLBCL and PCNS (Table 1). In HL, iHI was 0.762, 0.686 and 0.448; unHI was 0.873, 0.784 and N/A; iNH was 0.843, 0.765 and 0.584, and unNH was 0.846, 0.782 and 0.703, respectively. In DLBCL, for iHI it was 0.573, 0.456 and 0.222; unHI was 0.685, 0.631 and 0.350; iNH was 0.602, 0.469 and 0.174; unNH was 0.583, 0.510 and 0.239, accordingly. In PCNS, for iHI it was 0.374, 0.219 and N/A; unHI was 0.314, 0.174 and N/A; iNH was 0.354, 0.229 and 0.061; unNH was 0.516, 0.473 and 0.473, correspondingly. Overall Survival (OS) was statistically significant for iHI vs unHI vs iNH vs unNH when comparing HL, DLBCL and PCNS, with p values of <0.0001, <0.0001 and 0.037, respectively (Graph1-3). In HL the group with the best OS was unHI. This was also evidenced for DLBCL. However, for PCNS this trend was noted in unNH. In both three malignancies, the highest OS rate was reported in uninsured population. Conclusions For HL, DLBCL and PCNS the uninsured population has statistically significant better OS at 10 y. Interestingly for HL and DLBCL this corresponded to unHI while for PCNS was unNH. This paroxysmal finding may be due to standardized treatment, immediate healthcare enrolling after diagnosis and/or different community healthcare practices. Additionally, this population may have unique behaviors such as higher rate of compliance/adherence, environmental exposures or genetic predisposition to improved survival. Nonetheless, lack of insurance may delay diagnosis, need for multiple lines of chemotherapies, increase the rate of metastatic disease or recurrences. Accordingly, as more expensive and personalized therapies evolve, insurance status can limit access to these treatment regimens. Therefore, although insurance is no longer a determinant for improving OS for patients diagnosed with HL, DLBCL or PCNS, it can have implications for other oncological outcomes. Figure 1 Figure 1. Disclosures Diaz Duque: Incyte: Consultancy; Morphosys: Speakers Bureau; Astra Zeneca: Research Funding; Hutchinson Pharmaceuticals: Research Funding; Epizyme: Consultancy; ADCT: Consultancy.

Does Medicaid expansion influence county health spending? A case of New York counties

We investigated the impacts of Medicaid expansion on New York county total health spending and specifics of health spending, including health services, public health facilities and public health administration. Little research considered the financial effect of Medicaid expansion on local governments while well reported are its influences on uninsured rates and health services utilization. New York counties have contributed to health in their boundaries by providing or funding public health services, and supporting a part of the non-federal share of Medicaid expenditures and uncompensated care. Medicaid expansion can reduce the size of county expenditures for health by enrolling more previously uninsured population in the program and offering more generous federal funding for the expanded Medicaid. We offer empirical evidence that Medicaid expansion was associated with reduced county health spending.

Access to kidney transplantation in Mexico, 2007–2019: a call to end disparities in transplant care

Background Access to kidney transplantation is limited to more than half of the Mexican population. A fragmented health system, gender, and sociocultural factors are barriers to transplant care. We analyzed kidney transplantation in Mexico and describe how public policies and sociocultural factors result in these inequities. Methods Kidney transplant data between 2007 to 2019 were obtained from the National Transplant Center database. Transplant rates and time spent on the waiting list, by age, gender, health system, and insurance status, were estimated. Results During the study period 34,931 transplants were performed. Recipients median age was 29 (IQR 22–42) years, 62.4% were males, and 73.9% were insured. 72.7% transplants were from living-donors. Annual transplant rates increased from 18.9 per million population (pmp) to 23.3 pmp. However, the transplant rate among the uninsured population remained low, at 9.3 transplants pmp. In 2019, 15,890 patients were in the waiting list; 60.6% were males and 88% were insured. Waiting time to transplant was 1.55 (IQR 0.56–3.14) years and it was shorter for patients listed in the Ministry of Health and private facilities, where wait lists are smaller, and for males. Deceased-organ donation rates increased modestly from 2.5 pmp to 3.9 pmp. Conclusions In conclusion, access to kidney transplantation in Mexico is unequal and restricted to patients with medical insurance. An inefficient organ procurement program results in low rates of deceased-donor kidneys. The implementation of a comprehensive kidney care program, recognizing kidney transplantation as the therapy of choice for renal failure, offers an opportunity to correct these inequalities.

A Plan-Do-Study-Act Approach to the Development, Implementation and Evaluation of a Patient Navigation Program to Reduce Breast Cancer Screening Disparities in Un- and Under-Insured, Racially and Ethnically Diverse Urban Women

Introduction: For the over 28 million Americans without health insurance, there is a great need to develop programs that help meet the health needs of the uninsured population.Materials and Methods: We applied the Plan-Do-Study-Act (PDSA) quality improvement framework to the development, implementation, and evaluation of a breast cancer screening navigation program for un- and under-insured women.Results: Six critical steps emerged: (1) obtain program funding; (2) navigator training; (3) establish a referral base network of community partners that serve the un- and under-insured women; (4) implement a process to address the barriers to accessing mammography; (5) develop a language- and culturally-tailored messaging and media campaign; and (6) develop measures and process evaluation to optimize and expand the program's reach.Discussion: A Plan-Do-Study-Act approach allowed identification of the key elements for successful development, implementation and optimization of a breast cancer screening navigation program aimed at reaching and screening un- and underinsured women.

Patient Satisfaction With Telehealth and Experiences During the COVID-19 Pandemic Among Uninsured Free Clinic Patients

The COVID-19 pandemic is a significant public health issue especially for underserved populations. Little is known about patient satisfaction with telehealth among free clinic patients or other underserved populations. The purpose of this study is to examine factors associated with patient satisfaction with in-person services and telehealth during the pandemic and describe the experiences during the pandemic among free clinic patients. Data were collected from 628 uninsured English- and Spanish-speaking patients of a free clinic using an online survey from June to August in 2020. Free clinic patients are satisfied both with in-person services and telehealth. Factors associated with satisfaction were slightly different for in-person services and telehealth. The major experiences during the pandemic were related to food/diet and physical inactivity. This study examined a new trend in patient satisfaction and is important because telehealth may be a stepping-stone on how to handle future doctor visits for underserved populations. Furthermore, as the pandemic rapidly develops and changes daily life experiences, the uninsured population faces imminent impacts in various aspects of their life experiences.

Predictors for County Level Variations in Initial 4-week COVID-19 Incidence and Case Fatality Risk in the United States

While studies indicate differences in incidence and case fatality risk of COVID-19, few efforts have shed light on regional variations in the intensity of initial community spread. We conducted a nationwide study using county-level data on COVID-19 from Center for Systems Science and Engineering at Johns Hopkins University. We characterized intensity of initial community COVID-19 attack by calculating the incidence and case fatality risk (CFR) for the first 4-week period of COVID-19 spread in each county. We used multivariate multilevel multinomial logistic regression to estimate the association of county-level characteristics with COVID-19 incidence and CFR. Of 3,143 counties, we included 1,052 with at least 100 reported cases on June 1st. Median incidence was 193.4 per 100,000 population (IQR: 94.2-397.5). Median case fatality risk was 3.6% (IQR: 1.4–7.3). Median age, rural population, population density, lower education, uninsured population, obesity, COPD prevalence were positively associated, while population, female sex, races (Asian, white), higher education, excessive drinking were negatively associated with initial COVID-19 incidence. Median age, female sex, Asian race, population density, higher education, excessive drinking, Intensive Care Unit beds, airborne infection isolation rooms were positively associated, while Hispanic ethnicity, lower education, obesity (paradox), uninsured population were negatively associated with initial COVID-19 CFR.

Education and Experience as Determinants of Micro Health Insurance Enrolment

Background: India faces a formidable challenge of providing universal health coverage to its uninsured population in the informal sector of the economy. Numerous micro health insurance (MHI) schemes have emerged as health financing mechanisms to reduce medical-illness-induced poverty. Existing research shows that the purchase of health insurance is most likely to be determined by health status, expected healthcare expenditure, and past health experiences in addition to socio-economic variables. We add to the understanding of various factors influencing enrolment in MHI from an Indian perspective. Methods: A survey was carried out to collect quantitative data in three districts in the state of Karnataka, India. Results: We show that education does not matter as significantly as experience does, in the determination of new insurance purchases. In other words, the importance of new insurance is not understood by those who are merely educated, but by those who have either fallen ill, or have previously seen the hazards of usurious borrowing. Conclusion: Our study provides deeper insights into the role of usurious borrowing and past illness in determining insurance purchases and highlights the formidable challenge of financial sustainability in the MHI market of India.