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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8620965/ https://www.ncbi.nlm.nih.gov/pubmed/34828471 http://dx.doi.org/10.3390/healthcare9111424 |
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author | Daley, Sean Kajendrakumar, Bakthameera Nandhakumar, Samyuktha Personett, Christine Sholes, Michael Thapa, Swornim Xue, Chen Korvink, Michael Gunn, Laura H. |
author_facet | Daley, Sean Kajendrakumar, Bakthameera Nandhakumar, Samyuktha Personett, Christine Sholes, Michael Thapa, Swornim Xue, Chen Korvink, Michael Gunn, Laura H. |
author_sort | Daley, Sean |
collection | PubMed |
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 n = 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 | Online Article Text |
id | pubmed-8620965 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86209652021-11-27 County-Level Socioeconomic Status Adjustment of Acute Myocardial Infarction Mortality Hospital Performance Measure in the U.S. Daley, Sean Kajendrakumar, Bakthameera Nandhakumar, Samyuktha Personett, Christine Sholes, Michael Thapa, Swornim Xue, Chen Korvink, Michael Gunn, Laura H. Healthcare (Basel) Article 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 n = 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. MDPI 2021-10-22 /pmc/articles/PMC8620965/ /pubmed/34828471 http://dx.doi.org/10.3390/healthcare9111424 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Daley, Sean Kajendrakumar, Bakthameera Nandhakumar, Samyuktha Personett, Christine Sholes, Michael Thapa, Swornim Xue, Chen Korvink, Michael Gunn, Laura H. County-Level Socioeconomic Status Adjustment of Acute Myocardial Infarction Mortality Hospital Performance Measure in the U.S. |
title | 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_short | 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. |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8620965/ https://www.ncbi.nlm.nih.gov/pubmed/34828471 http://dx.doi.org/10.3390/healthcare9111424 |
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