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Geographic Disparities in Readmissions for Peripheral Artery Disease in South Carolina
Readmissions constitute a major health care burden among peripheral artery disease (PAD) patients. This study aimed to 1) estimate the zip code tabulation area (ZCTA)-level prevalence of readmission among PAD patients and characterize the effect of covariates on readmissions; and (2) identify hotspo...
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/PMC8751080/ https://www.ncbi.nlm.nih.gov/pubmed/35010545 http://dx.doi.org/10.3390/ijerph19010285 |
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author | Witrick, Brian Kalbaugh, Corey A. Shi, Lu Mayo, Rachel Hendricks, Brian |
author_facet | Witrick, Brian Kalbaugh, Corey A. Shi, Lu Mayo, Rachel Hendricks, Brian |
author_sort | Witrick, Brian |
collection | PubMed |
description | Readmissions constitute a major health care burden among peripheral artery disease (PAD) patients. This study aimed to 1) estimate the zip code tabulation area (ZCTA)-level prevalence of readmission among PAD patients and characterize the effect of covariates on readmissions; and (2) identify hotspots of PAD based on estimated prevalence of readmission. Thirty-day readmissions among PAD patients were identified from the South Carolina Revenue and Fiscal Affairs Office All Payers Database (2010–2018). Bayesian spatial hierarchical modeling was conducted to identify areas of high risk, while controlling for confounders. We mapped the estimated readmission rates and identified hotspots using local Getis Ord (G*) statistics. Of the 232,731 individuals admitted to a hospital or outpatient surgery facility with PAD diagnosis, 30,366 (13.1%) experienced an unplanned readmission to a hospital within 30 days. Fitted readmission rates ranged from 35.3 per 1000 patients to 370.7 per 1000 patients and the risk of having a readmission was significantly associated with the percentage of patients who are 65 and older (0.992, 95%CI: 0.985–0.999), have Medicare insurance (1.013, 1.005–1.020), and have hypertension (1.014, 1.005–1.023). Geographic analysis found significant variation in readmission rates across the state and identified priority areas for targeted interventions to reduce readmissions. |
format | Online Article Text |
id | pubmed-8751080 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87510802022-01-12 Geographic Disparities in Readmissions for Peripheral Artery Disease in South Carolina Witrick, Brian Kalbaugh, Corey A. Shi, Lu Mayo, Rachel Hendricks, Brian Int J Environ Res Public Health Article Readmissions constitute a major health care burden among peripheral artery disease (PAD) patients. This study aimed to 1) estimate the zip code tabulation area (ZCTA)-level prevalence of readmission among PAD patients and characterize the effect of covariates on readmissions; and (2) identify hotspots of PAD based on estimated prevalence of readmission. Thirty-day readmissions among PAD patients were identified from the South Carolina Revenue and Fiscal Affairs Office All Payers Database (2010–2018). Bayesian spatial hierarchical modeling was conducted to identify areas of high risk, while controlling for confounders. We mapped the estimated readmission rates and identified hotspots using local Getis Ord (G*) statistics. Of the 232,731 individuals admitted to a hospital or outpatient surgery facility with PAD diagnosis, 30,366 (13.1%) experienced an unplanned readmission to a hospital within 30 days. Fitted readmission rates ranged from 35.3 per 1000 patients to 370.7 per 1000 patients and the risk of having a readmission was significantly associated with the percentage of patients who are 65 and older (0.992, 95%CI: 0.985–0.999), have Medicare insurance (1.013, 1.005–1.020), and have hypertension (1.014, 1.005–1.023). Geographic analysis found significant variation in readmission rates across the state and identified priority areas for targeted interventions to reduce readmissions. MDPI 2021-12-28 /pmc/articles/PMC8751080/ /pubmed/35010545 http://dx.doi.org/10.3390/ijerph19010285 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 Witrick, Brian Kalbaugh, Corey A. Shi, Lu Mayo, Rachel Hendricks, Brian Geographic Disparities in Readmissions for Peripheral Artery Disease in South Carolina |
title | Geographic Disparities in Readmissions for Peripheral Artery Disease in South Carolina |
title_full | Geographic Disparities in Readmissions for Peripheral Artery Disease in South Carolina |
title_fullStr | Geographic Disparities in Readmissions for Peripheral Artery Disease in South Carolina |
title_full_unstemmed | Geographic Disparities in Readmissions for Peripheral Artery Disease in South Carolina |
title_short | Geographic Disparities in Readmissions for Peripheral Artery Disease in South Carolina |
title_sort | geographic disparities in readmissions for peripheral artery disease in south carolina |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8751080/ https://www.ncbi.nlm.nih.gov/pubmed/35010545 http://dx.doi.org/10.3390/ijerph19010285 |
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