County-Level Socioeconomic Status Adjustment of Acute Myocardial Infarction Mortality Hospital Performance Measure in the U.S.

The U.S. Centers for Medicare and Medicaid Services’ (CMS’s) Hospital Compare (HC) data provides a collection of risk-adjusted hospital performance metrics intended to allow comparison of hospital-provided care. However, CMS does not adjust for socioeconomic status (SES) factors, which have been fou...

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Autores principales:	Sean Daley, Bakthameera Kajendrakumar, Samyuktha Nandhakumar, Christine Personett, Michael Sholes, Swornim Thapa, Chen Xue, Michael Korvink, Laura H. Gunn
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	risk-adjusted acute myocardial infarction mortality rate socioeconomic status hospital performance metric Medicine R
Acceso en línea:	https://doaj.org/article/5243ba3c522c44ae9a7d564f367b7822
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spelling	oai:doaj.org-article:5243ba3c522c44ae9a7d564f367b78222021-11-25T17:43:45ZCounty-Level Socioeconomic Status Adjustment of Acute Myocardial Infarction Mortality Hospital Performance Measure in the U.S.10.3390/healthcare91114242227-9032https://doaj.org/article/5243ba3c522c44ae9a7d564f367b78222021-10-01T00:00:00Zhttps://www.mdpi.com/2227-9032/9/11/1424https://doaj.org/toc/2227-9032The U.S. Centers for Medicare and Medicaid Services’ (CMS’s) Hospital Compare (HC) data provides a collection of risk-adjusted hospital performance metrics intended to allow comparison of hospital-provided care. However, CMS does not adjust for socioeconomic status (SES) factors, which have been found to be associated with disparate health outcomes. Associations between county-level SES factors and CMS’s risk-adjusted 30-day acute myocardial infarction (AMI) mortality rates are explored for <i>n</i> = 2462 hospitals using a variety of sources for county-level SES information. Upon performing multiple imputation, a stepwise backward elimination model selection approach using Akaike’s information criteria was used to identify the optimal model. The resulting model, comprised of 14 predictors mostly at the county level, provides an additional 8% explanatory power to capture the variability in 30-day risk-standardized AMI mortality rates, which already account for patient-level clinical differences. SES factors may be an important feature for inclusion in future risk-adjustment models, which will have system and policy implications for distributing resources to hospitals, such as reimbursements. It also serves as a stepping stone to identify and address long-standing SES-related inequities.Sean DaleyBakthameera KajendrakumarSamyuktha NandhakumarChristine PersonettMichael SholesSwornim ThapaChen XueMichael KorvinkLaura H. GunnMDPI AGarticlerisk-adjustedacute myocardial infarction mortality ratesocioeconomic statushospital performance metricMedicineRENHealthcare, Vol 9, Iss 1424, p 1424 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	risk-adjusted acute myocardial infarction mortality rate socioeconomic status hospital performance metric Medicine R
spellingShingle	risk-adjusted acute myocardial infarction mortality rate socioeconomic status hospital performance metric Medicine R Sean Daley Bakthameera Kajendrakumar Samyuktha Nandhakumar Christine Personett Michael Sholes Swornim Thapa Chen Xue Michael Korvink Laura H. Gunn County-Level Socioeconomic Status Adjustment of Acute Myocardial Infarction Mortality Hospital Performance Measure in the U.S.
description	The U.S. Centers for Medicare and Medicaid Services’ (CMS’s) Hospital Compare (HC) data provides a collection of risk-adjusted hospital performance metrics intended to allow comparison of hospital-provided care. However, CMS does not adjust for socioeconomic status (SES) factors, which have been found to be associated with disparate health outcomes. Associations between county-level SES factors and CMS’s risk-adjusted 30-day acute myocardial infarction (AMI) mortality rates are explored for <i>n</i> = 2462 hospitals using a variety of sources for county-level SES information. Upon performing multiple imputation, a stepwise backward elimination model selection approach using Akaike’s information criteria was used to identify the optimal model. The resulting model, comprised of 14 predictors mostly at the county level, provides an additional 8% explanatory power to capture the variability in 30-day risk-standardized AMI mortality rates, which already account for patient-level clinical differences. SES factors may be an important feature for inclusion in future risk-adjustment models, which will have system and policy implications for distributing resources to hospitals, such as reimbursements. It also serves as a stepping stone to identify and address long-standing SES-related inequities.
format	article
author	Sean Daley Bakthameera Kajendrakumar Samyuktha Nandhakumar Christine Personett Michael Sholes Swornim Thapa Chen Xue Michael Korvink Laura H. Gunn
author_facet	Sean Daley Bakthameera Kajendrakumar Samyuktha Nandhakumar Christine Personett Michael Sholes Swornim Thapa Chen Xue Michael Korvink Laura H. Gunn
author_sort	Sean Daley
title	County-Level Socioeconomic Status Adjustment of Acute Myocardial Infarction Mortality Hospital Performance Measure in the U.S.
title_short	County-Level Socioeconomic Status Adjustment of Acute Myocardial Infarction Mortality Hospital Performance Measure in the U.S.
title_full	County-Level Socioeconomic Status Adjustment of Acute Myocardial Infarction Mortality Hospital Performance Measure in the U.S.
title_fullStr	County-Level Socioeconomic Status Adjustment of Acute Myocardial Infarction Mortality Hospital Performance Measure in the U.S.
title_full_unstemmed	County-Level Socioeconomic Status Adjustment of Acute Myocardial Infarction Mortality Hospital Performance Measure in the U.S.
title_sort	county-level socioeconomic status adjustment of acute myocardial infarction mortality hospital performance measure in the u.s.
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5243ba3c522c44ae9a7d564f367b7822
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County-Level Socioeconomic Status Adjustment of Acute Myocardial Infarction Mortality Hospital Performance Measure in the U.S.

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