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Comparison of Epithor clinical national database and medico-administrative database to identify the influence of case-mix on the estimation of hospital outliers
BACKGROUND: The national Epithor database was initiated in 2003 in France. Fifteen years on, a quality assessment of the recorded data seemed necessary. This study examines the completeness of the data recorded in Epithor through a comparison with the French PMSI database, which is the national medi...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6655697/ https://www.ncbi.nlm.nih.gov/pubmed/31339906 http://dx.doi.org/10.1371/journal.pone.0219672 |
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author | Bernard, Alain Falcoz, Pierre-Emmanuel Thomas, Pascal Antoine Rivera, Caroline Brouchet, Laurent Baste, Jean Marc Puyraveau, Marc Quantin, Catherine Pages, Pierre Benoit Dahan, Marcel |
author_facet | Bernard, Alain Falcoz, Pierre-Emmanuel Thomas, Pascal Antoine Rivera, Caroline Brouchet, Laurent Baste, Jean Marc Puyraveau, Marc Quantin, Catherine Pages, Pierre Benoit Dahan, Marcel |
author_sort | Bernard, Alain |
collection | PubMed |
description | BACKGROUND: The national Epithor database was initiated in 2003 in France. Fifteen years on, a quality assessment of the recorded data seemed necessary. This study examines the completeness of the data recorded in Epithor through a comparison with the French PMSI database, which is the national medico-administrative reference database. The aim of this study was to demonstrate the influence of data quality with respect to identifying 30-day mortality hospital outliers. METHODS: We used each hospital’s individual FINESS code to compare the number of pulmonary resections and deaths recorded in Epithor to the figures found in the PMSI. Centers were classified into either the good-quality data (GQD) group or the low-quality data (LQD) group. To demonstrate the influence of case-mix quality on the ranking of centers with low-quality data, we used 2 methods to estimate the standardized mortality rate (SMR). For the first (SMR1), the expected number of deaths per hospital was estimated with risk-adjustment models fitted with low-quality data. For the second (SMR2), the expected number of deaths per hospital was estimated with a linear predictor for the LQD group using the coefficients of a logistic regression model developed from the GQD group. RESULTS: Of the hospitals that use Epithor, 25 were classified in the GQD group and 75 in the LQD group. The 30-day mortality rate was 2.8% (n = 300) in the GQD group vs. 1.9% (n = 181) in the LQD group (P <0.0001). The between-hospital differences in SMR1 appeared substantial (interquartile range (IQR) 0–1.036), and they were even higher in SMR2 (IQR 0–1.19). SMR1 identified 7 hospitals as high-mortality outliers. SMR2 identified 4 hospitals as high-mortality outliers. Some hospitals went from non-outlier to high mortality and vice-versa. Kappa values were roughly 0.46 and indicated moderate agreement. CONCLUSION: We found that most hospitals provided Epithor with high-quality data, but other hospitals needed to improve the quality of the information provided. Quality control is essential for this type of database and necessary for the unbiased adjustment of regression models. |
format | Online Article Text |
id | pubmed-6655697 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-66556972019-08-07 Comparison of Epithor clinical national database and medico-administrative database to identify the influence of case-mix on the estimation of hospital outliers Bernard, Alain Falcoz, Pierre-Emmanuel Thomas, Pascal Antoine Rivera, Caroline Brouchet, Laurent Baste, Jean Marc Puyraveau, Marc Quantin, Catherine Pages, Pierre Benoit Dahan, Marcel PLoS One Research Article BACKGROUND: The national Epithor database was initiated in 2003 in France. Fifteen years on, a quality assessment of the recorded data seemed necessary. This study examines the completeness of the data recorded in Epithor through a comparison with the French PMSI database, which is the national medico-administrative reference database. The aim of this study was to demonstrate the influence of data quality with respect to identifying 30-day mortality hospital outliers. METHODS: We used each hospital’s individual FINESS code to compare the number of pulmonary resections and deaths recorded in Epithor to the figures found in the PMSI. Centers were classified into either the good-quality data (GQD) group or the low-quality data (LQD) group. To demonstrate the influence of case-mix quality on the ranking of centers with low-quality data, we used 2 methods to estimate the standardized mortality rate (SMR). For the first (SMR1), the expected number of deaths per hospital was estimated with risk-adjustment models fitted with low-quality data. For the second (SMR2), the expected number of deaths per hospital was estimated with a linear predictor for the LQD group using the coefficients of a logistic regression model developed from the GQD group. RESULTS: Of the hospitals that use Epithor, 25 were classified in the GQD group and 75 in the LQD group. The 30-day mortality rate was 2.8% (n = 300) in the GQD group vs. 1.9% (n = 181) in the LQD group (P <0.0001). The between-hospital differences in SMR1 appeared substantial (interquartile range (IQR) 0–1.036), and they were even higher in SMR2 (IQR 0–1.19). SMR1 identified 7 hospitals as high-mortality outliers. SMR2 identified 4 hospitals as high-mortality outliers. Some hospitals went from non-outlier to high mortality and vice-versa. Kappa values were roughly 0.46 and indicated moderate agreement. CONCLUSION: We found that most hospitals provided Epithor with high-quality data, but other hospitals needed to improve the quality of the information provided. Quality control is essential for this type of database and necessary for the unbiased adjustment of regression models. Public Library of Science 2019-07-24 /pmc/articles/PMC6655697/ /pubmed/31339906 http://dx.doi.org/10.1371/journal.pone.0219672 Text en © 2019 Bernard et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Bernard, Alain Falcoz, Pierre-Emmanuel Thomas, Pascal Antoine Rivera, Caroline Brouchet, Laurent Baste, Jean Marc Puyraveau, Marc Quantin, Catherine Pages, Pierre Benoit Dahan, Marcel Comparison of Epithor clinical national database and medico-administrative database to identify the influence of case-mix on the estimation of hospital outliers |
title | Comparison of Epithor clinical national database and medico-administrative database to identify the influence of case-mix on the estimation of hospital outliers |
title_full | Comparison of Epithor clinical national database and medico-administrative database to identify the influence of case-mix on the estimation of hospital outliers |
title_fullStr | Comparison of Epithor clinical national database and medico-administrative database to identify the influence of case-mix on the estimation of hospital outliers |
title_full_unstemmed | Comparison of Epithor clinical national database and medico-administrative database to identify the influence of case-mix on the estimation of hospital outliers |
title_short | Comparison of Epithor clinical national database and medico-administrative database to identify the influence of case-mix on the estimation of hospital outliers |
title_sort | comparison of epithor clinical national database and medico-administrative database to identify the influence of case-mix on the estimation of hospital outliers |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6655697/ https://www.ncbi.nlm.nih.gov/pubmed/31339906 http://dx.doi.org/10.1371/journal.pone.0219672 |
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