County-Level Characteristics Associated with Variation in ESKD Mortality in the United States, 2010–2018

Abstract

Background: Geographic and neighborhood-level factors such as poverty and education have been associated with an increased risk for incident end-stage kidney disease (ESKD), likelihood of receiving pre-ESKD care, and likelihood of receiving a transplant. However, few studies have examined whether these same factors are associated with ESKD mortality. In this study, we examined county-level variation in ESKD mortality and identified county-level characteristics associated with this variation. Methods: We identified 1,515,986 individuals (aged 18-84) initiating renal replacement therapy (dialysis or transplant) between 2010 and 2018 using the United States Renal Data System. Among 2,781 counties, we estimated county-level all-cause age-standardized mortality rates (ASMR) among patients with ESKD. We then identified county-level demographic (e.g., % female), socioeconomic (e.g., % unemployed), health care (e.g., % without health insurance), and health behavior (e.g., % current smokers) characteristics associated with ASMR using multivariable hierarchical linear mixed models and quantified the percentage of ASMR variation explained by county-level characteristics. Results: County-level ESKD ASMR ranged from 45 to 1,022 per 1,000 person-years (PY) (mean, 119/1,000 PY). ASMRs were highest in counties located in the Tennessee Valley and Appalachia regions, and lowest in counties located in New England, Pacific Northwest, and southern California. In fully adjusted models, county-level characteristics significantly associated with higher ESKD mortality included a lower percentage of Black residents (-4.94/1,000 PY), lower transplant rate (-4.08/1,000 PY), and higher health care expenditures (5.21/1,000 PY). Overall, county-level characteristics explained 18.9% of variation in ESKD mortality. Conclusions: Counties with high ESKD-related mortality may benefit from targeted and multi-level interventions that combine knowledge from a growing evidence base on the interplay between individual and community-level factors associated with ESKD mortality.

Geography, inequities, and the social determinants of health in transplantation

“…Despite these advances, there remains a deep skepticism of the use of area-level measures in clinical research when used to reflect conditions shared by an administrative or geographic boundary such as state, region, ZIP code, census tract, or block group. In fact, many papers that model individual-level health outcomes using multi-level regression still describe the ecological fallacy in their limitations sections ( 11 , 12 ). The ecological fallacy now seems to be shorthand for concern around the validity and utility of area-level measures.…”

Early prediction of end-stage kidney disease using electronic health record data: a machine learning approach with a 2-year horizon

“…This increase is largely due to an aging population and higher rates of diabetes, hypertension, and chronic kidney disease (CKD). 2 Treatment and management of ESKD patients require dialysis or kidney transplant, representing a significant healthcare expenditure. 3 Indeed, according to the United States Renal Data System (USRDS), 7% of all Medicare claims are due to ESKD.…”

Time to Hyperkalemia and Other Outcomes Post-Implementation of the 2021 CKD-EPI Creatinine Equation: An Observational Cohort Study

“…Like differences in diagnosis, ESKD treatment and outcomes vary by demographics and social determinants of health (SDOH) such as age, race, geography, and access to care. (6,7)…”

“…Like differences in diagnosis, ESKD treatment and outcomes vary by demographics and social determinants of health (SDOH) such as age, race, geography, and access to care. (6,7) Sixty-six percent of hemodialysis patients have an arteriovenous graft fistula (AVF), the gold standard method for providing hemodialysis. (8-10) Yet, many patients have significant morbidity prior to needing dialysis including having an increased risk of hyperkalemia; medications to treat CKD and comorbidities (e.g.…”