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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2012
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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. |
format | Online Article Text |
id | pubmed-3551201 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
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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