Dmitry Tumin1, Jessica Horan2, Emily A Shrider3, Sakima A Smith4, Joseph D Tobias5, Don Hayes6, Randi E Foraker7. 1. Department of Anesthesiology and Pain Medicine, Nationwide Children's Hospital, Columbus, OH; Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH. Electronic address: tumin.1@osu.edu. 2. Division of Epidemiology, The Ohio State University College of Public Health, Columbus, OH. 3. Department of Sociology, The Ohio State University, Columbus, OH. 4. Department of Internal Medicine, The Ohio State University College of Medicine, Columbus, OH. 5. Department of Anesthesiology and Pain Medicine, Nationwide Children's Hospital, Columbus, OH; Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH; Department of Anesthesiology, The Ohio State University College of Medicine, Columbus, OH. 6. Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH; Section of Pulmonary Medicine, Nationwide Children's Hospital, Columbus, OH; Department of Surgery, The Ohio State University College of Medicine, Columbus, OH. 7. Division of Epidemiology, The Ohio State University College of Public Health, Columbus, OH; Department of Internal Medicine, The Ohio State University College of Medicine, Columbus, OH.
Abstract
BACKGROUND: Geographic disparities in survival after heart transplantation have received mixed support in prior studies, and specific geographic characteristics that might be responsible for these differences are unclear. We tested for differences in heart transplant outcomes across United States (US) counties after adjustment for individual-level covariates. Our secondary aim was to evaluate whether specific county-level socioeconomic characteristics explained geographic disparities in survival. METHODS: Data on patients aged ≥18 years undergoing a first-time heart transplant between July 2006 and December 2014 were obtained from the United Network for Organ Sharing. Residents of counties represented by <5 patients were excluded. Patient survival (censored in March 2016) was analyzed using multivariable Cox regression. Shared frailty models were used to test for residual differences in overall all-cause mortality across counties after adjusting for recipient and donor characteristics. Measures of county economic disadvantage, inequality, and racial segregation were obtained from US Census data and coded into quintiles. A likelihood ratio test determined whether adjusting for each county measure improved the fit of the Cox model. RESULTS: Multivariable analysis of 10,879 heart transplant recipients found that, adjusting for individual-level characteristics, there remained statistically significant variation in mortality hazard across US counties (P=.004). Adjusting for quintiles of community disadvantage, economic inequality, or racial segregation did not significantly improve model fit (likelihood ratio test P=.092, P=.273, and P=.107, respectively) and did not explain residual differences in patient survival across counties. CONCLUSIONS: Heart transplantation outcomes vary by county, but this difference is not attributable to county-level socioeconomic disadvantage.
BACKGROUND: Geographic disparities in survival after heart transplantation have received mixed support in prior studies, and specific geographic characteristics that might be responsible for these differences are unclear. We tested for differences in heart transplant outcomes across United States (US) counties after adjustment for individual-level covariates. Our secondary aim was to evaluate whether specific county-level socioeconomic characteristics explained geographic disparities in survival. METHODS: Data on patients aged ≥18 years undergoing a first-time heart transplant between July 2006 and December 2014 were obtained from the United Network for Organ Sharing. Residents of counties represented by <5 patients were excluded. Patient survival (censored in March 2016) was analyzed using multivariable Cox regression. Shared frailty models were used to test for residual differences in overall all-cause mortality across counties after adjusting for recipient and donor characteristics. Measures of county economic disadvantage, inequality, and racial segregation were obtained from US Census data and coded into quintiles. A likelihood ratio test determined whether adjusting for each county measure improved the fit of the Cox model. RESULTS: Multivariable analysis of 10,879 heart transplant recipients found that, adjusting for individual-level characteristics, there remained statistically significant variation in mortality hazard across US counties (P=.004). Adjusting for quintiles of community disadvantage, economic inequality, or racial segregation did not significantly improve model fit (likelihood ratio test P=.092, P=.273, and P=.107, respectively) and did not explain residual differences in patient survival across counties. CONCLUSIONS: Heart transplantation outcomes vary by county, but this difference is not attributable to county-level socioeconomic disadvantage.
Authors: Randi E Foraker; Mehul D Patel; Eric A Whitsel; Chirayath M Suchindran; Gerardo Heiss; Kathryn M Rose Journal: Am Heart J Date: 2012-11-20 Impact factor: 4.749
Authors: R Cutler Quillin; Gregory C Wilson; Koffi Wima; Samuel F Hohmann; Jeffrey M Sutton; Joshua J Shaw; Ian M Paquette; E Steve Woodle; Daniel E Abbott; Shimul A Shah Journal: Clin Gastroenterol Hepatol Date: 2014-06-04 Impact factor: 11.382
Authors: T P Singh; D C Naftel; L Addonizio; W Mahle; M T Foushee; S Zangwill; E D Blume; J K Kirklin; R Singh; J K Johnston; R Chinnock Journal: Am J Transplant Date: 2010-09 Impact factor: 8.086
Authors: Jonathan D W Evans; Stephen Kaptoge; Rishi Caleyachetty; Emanuele Di Angelantonio; Clive Lewis; K Jayan Parameshwar; Stephen J Pettit Journal: Circ Cardiovasc Qual Outcomes Date: 2016-11-01
Authors: Clifford Akateh; Rebecca Miller; Eliza W Beal; Dmitry Tumin; Joseph D Tobias; Don Hayes; Sylvester M Black Journal: Dig Dis Sci Date: 2019-07-22 Impact factor: 3.199
Authors: Elizabeth Golembiewski; Katie S Allen; Amber M Blackmon; Rachel J Hinrichs; Joshua R Vest Journal: JMIR Public Health Surveill Date: 2019-10-07