Overview:

American Indian/Alaska Native (AI/AN) persons disproportionately suffer from diabetes and end-stage kidney disease (ESKD) compared to non-Hispanic whites (NHW). Over two-thirds of all ESKD in AI/AN persons is caused by diabetes (ESKD-D). Kidney transplant is the most desirable option to treating ESKD, but kidney transplant remains unattainable for many AI/AN persons, especially in South Dakota (SD). Furthermore, palliative and hospice care options for AI/AN with any serious illness in SD is largely inaccessible. For kidney transplant recipients, the likelihood of receiving palliative or hospice care is likely less because of the desire to prolong transplant function as long as possible. This presentation explores the identified disparities in hospice use among American Indian persons with ESKD-D to non-Hispanic Whites who have received a transplant, and those with no history of transplant.

Objectives:

1. Describe hospice use disparities for American Indian/Alaska Native persons with end-stage kidney disease.
2. Identify strategies to address hospice access disparities.

Presenter: Brandon Varilek, Assistant Professor, University of Nebraska Medical Center

Dr. Brandon Varilek is an Assistant Professor at the University of Nebraska Medical Center College of Nursing. His program of research is focused on mitigating health disparities and addressing social determinants of health that affect South Dakota and Nebraska minoritized populations. Much of this work centers around persons with cancer or end-stage kidney disease. Dr. Varilek is also passionate about palliative care research and aims to expand his program of research to develop palliative care interventions for those on dialysis. Dr. Varilek is PI or Co-PI on two NIH grants. 1) NIDDK/CAIANDTR (P30) to explore survival & health disparities between American Indian/Alaska Native and non-Hispanic white populations with end-stage kidney disease; 2) AIM-AHEAD Consortium Development Award through NIH Office of Strategic Data Science to develop an explainable machine learning algorithm to generate mortality risk score based on social determinants of health for integration into clinical partner EMR systems.