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Case-mix adjusted hospital mortality is a poor proxy for preventable mortality: a modelling study

Risk-adjustment schemes are used to monitor hospital performance, on the assumption that excess mortality not explained by case mix is largely attributable to suboptimal care. We have developed a model to estimate the proportion of the variation in standardised mortality ratios (SMRs) that can be ac...

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Autores principales: Girling, Alan J, Hofer, Timothy P, Wu, Jianhua, Chilton, Peter J, Nicholl, Jonathan P, Mohammed, Mohammed A, Lilford, Richard J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3551201/
https://www.ncbi.nlm.nih.gov/pubmed/23069860
http://dx.doi.org/10.1136/bmjqs-2012-001202
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author Girling, Alan J
Hofer, Timothy P
Wu, Jianhua
Chilton, Peter J
Nicholl, Jonathan P
Mohammed, Mohammed A
Lilford, Richard J
author_facet Girling, Alan J
Hofer, Timothy P
Wu, Jianhua
Chilton, Peter J
Nicholl, Jonathan P
Mohammed, Mohammed A
Lilford, Richard J
author_sort Girling, Alan J
collection PubMed
description Risk-adjustment schemes are used to monitor hospital performance, on the assumption that excess mortality not explained by case mix is largely attributable to suboptimal care. We have developed a model to estimate the proportion of the variation in standardised mortality ratios (SMRs) that can be accounted for by variation in preventable mortality. The model was populated with values from the literature to estimate a predictive value of the SMR in this context—specifically the proportion of those hospitals with SMRs among the highest 2.5% that fall among the worst 2.5% for preventable mortality. The extent to which SMRs reflect preventable mortality rates is highly sensitive to the proportion of deaths that are preventable. If 6% of hospital deaths are preventable (as suggested by the literature), the predictive value of the SMR can be no greater than 9%. This value could rise to 30%, if 15% of deaths are preventable. The model offers a ‘reality check’ for case mix adjustment schemes designed to isolate the preventable component of any outcome rate.
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spelling pubmed-35512012013-01-23 Case-mix adjusted hospital mortality is a poor proxy for preventable mortality: a modelling study Girling, Alan J Hofer, Timothy P Wu, Jianhua Chilton, Peter J Nicholl, Jonathan P Mohammed, Mohammed A Lilford, Richard J BMJ Qual Saf Research and Reporting Methodology Risk-adjustment schemes are used to monitor hospital performance, on the assumption that excess mortality not explained by case mix is largely attributable to suboptimal care. We have developed a model to estimate the proportion of the variation in standardised mortality ratios (SMRs) that can be accounted for by variation in preventable mortality. The model was populated with values from the literature to estimate a predictive value of the SMR in this context—specifically the proportion of those hospitals with SMRs among the highest 2.5% that fall among the worst 2.5% for preventable mortality. The extent to which SMRs reflect preventable mortality rates is highly sensitive to the proportion of deaths that are preventable. If 6% of hospital deaths are preventable (as suggested by the literature), the predictive value of the SMR can be no greater than 9%. This value could rise to 30%, if 15% of deaths are preventable. The model offers a ‘reality check’ for case mix adjustment schemes designed to isolate the preventable component of any outcome rate. BMJ Publishing Group 2012-12 2012-10-16 /pmc/articles/PMC3551201/ /pubmed/23069860 http://dx.doi.org/10.1136/bmjqs-2012-001202 Text en Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/3.0/ and http://creativecommons.org/licenses/by-nc/3.0/legalcode
spellingShingle Research and Reporting Methodology
Girling, Alan J
Hofer, Timothy P
Wu, Jianhua
Chilton, Peter J
Nicholl, Jonathan P
Mohammed, Mohammed A
Lilford, Richard J
Case-mix adjusted hospital mortality is a poor proxy for preventable mortality: a modelling study
title Case-mix adjusted hospital mortality is a poor proxy for preventable mortality: a modelling study
title_full Case-mix adjusted hospital mortality is a poor proxy for preventable mortality: a modelling study
title_fullStr Case-mix adjusted hospital mortality is a poor proxy for preventable mortality: a modelling study
title_full_unstemmed Case-mix adjusted hospital mortality is a poor proxy for preventable mortality: a modelling study
title_short Case-mix adjusted hospital mortality is a poor proxy for preventable mortality: a modelling study
title_sort case-mix adjusted hospital mortality is a poor proxy for preventable mortality: a modelling study
topic Research and Reporting Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3551201/
https://www.ncbi.nlm.nih.gov/pubmed/23069860
http://dx.doi.org/10.1136/bmjqs-2012-001202
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