County Rankings Have Limited Utility When Predicting Liver Transplant Outcomes

Background Evidence of geographical differences in liver transplantation (LT) outcomes has been proposed as a reason to include community characteristics in risk adjustment of transplant quality metrics. However, consistency and utility of rankings in LT outcomes for counties have not been demonstra...

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Bibliographic Details
Published in:Digestive diseases and sciences 2020-01, Vol.65 (1), p.104-110
Main Authors: Akateh, Clifford, Miller, Rebecca, Beal, Eliza W., Tumin, Dmitry, Tobias, Joseph D., Hayes, Don, Black, Sylvester M.
Format: Article
Language:English
Subjects:
Adult
Analysis
Anesthesiology
Biochemistry
Female
Gastroenterology
Health aspects
Health Status Indicators
Healthcare Disparities
Hepatology
Humans
Liver
Liver diseases
Liver Transplantation - adverse effects
Liver Transplantation - mortality
Liver transplants
Male
Medical research
Medicine
Medicine & Public Health
Medicine, Experimental
Middle Aged
Mortality
Oncology
Original Article
Outcome and Process Assessment, Health Care
Patients
Pediatrics
Registries
Residence Characteristics
Risk Assessment
Risk Factors
Social Class
Social Determinants of Health
Socioeconomic factors
Surgery
Transplant Surgery
Transplantation
Transplants & implants
Treatment Outcome
Trends
United States
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Summary:Background Evidence of geographical differences in liver transplantation (LT) outcomes has been proposed as a reason to include community characteristics in risk adjustment of transplant quality metrics. However, consistency and utility of rankings in LT outcomes for counties have not been demonstrated. Aims We sought to evaluate the utility of county rankings (county socioeconomic status (SES) or county health scores (CHS)) on outcomes after LT. Methods Using the United Network for Organ Sharing Registry, adults ≥ 18 years of age undergoing LT between 2002 and 2014 were identified. County-specific 1-year survival was calculated using the Kaplan–Meier method for counties with ≥ 5 LT performed during this period. Agreement between high-risk designation by 1-year mortality rate and county ranking was calculated using the Spearman correlation coefficient. Results The analysis included 47,769 LT recipients in 1092 counties. County 1-year mortality rates were not correlated with county CHS (Spearman ρ  = 0.01, p  = 0.694) or county SES (Spearman ρ  = − 0.01, p  = 0.734). After controlling for individual-level covariates, a statistically significant variability in mortality hazards across counties ( p  
ISSN:0163-2116
1573-2568
DOI:10.1007/s10620-019-05734-z