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Exploring variation in low-value care: a multilevel modelling study

BACKGROUND: Whether patients receive low-value hospital care (care that is not expected to provide a net benefit) may be influenced by unmeasured factors at the hospital they attend or the hospital’s Local Health District (LHD), or the patients’ areas of residence. Multilevel modelling presents a me...

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Autores principales: Badgery-Parker, Tim, Feng, Yingyu, Pearson, Sallie-Anne, Levesque, Jean-Frederic, Dunn, Susan, Elshaug, Adam G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6543591/
https://www.ncbi.nlm.nih.gov/pubmed/31146744
http://dx.doi.org/10.1186/s12913-019-4159-1
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author Badgery-Parker, Tim
Feng, Yingyu
Pearson, Sallie-Anne
Levesque, Jean-Frederic
Dunn, Susan
Elshaug, Adam G.
author_facet Badgery-Parker, Tim
Feng, Yingyu
Pearson, Sallie-Anne
Levesque, Jean-Frederic
Dunn, Susan
Elshaug, Adam G.
author_sort Badgery-Parker, Tim
collection PubMed
description BACKGROUND: Whether patients receive low-value hospital care (care that is not expected to provide a net benefit) may be influenced by unmeasured factors at the hospital they attend or the hospital’s Local Health District (LHD), or the patients’ areas of residence. Multilevel modelling presents a method to examine the effects of these different levels simultaneously and assess their relative importance to the outcome. Knowing which of these levels has the greatest contextual effects can help target further investigation or initiatives to reduce low-value care. METHODS: We conducted multilevel logistic regression modelling for nine low-value hospital procedures. We fit a series of six models for each procedure. The baseline model included only episode-level variables with no multilevel structure. We then added each level (hospital, LHD, Statistical Local Area [SLA] of residence) separately and used the change in the c statistic from the baseline model as a measure of the contribution of the level to the outcome. We then examined the variance partition coefficients (VPCs) and median odds ratios for a model including all three levels. Finally, we added level-specific covariates to examine if they were associated with the outcome. RESULTS: Analysis of the c statistics showed that hospital was more important than LHD or SLA in explaining whether patients receive low-value care. The greatest increases were 0.16 for endoscopy for dyspepsia, 0.13 for colonoscopy for constipation, and 0.14 for sentinel lymph node biopsy for early melanoma. SLA gave a small increase in c compared with the baseline model, but no increase over the model with hospital. The VPCs indicated that hospital accounted for most of the variation not explained by the episode-level variables, reaching 36.8% (95% CI, 31.9–39.0) for knee arthroscopy. ERCP (8.5%; 95% CI, 3.9–14.7) and EVAR (7.8%; 95% CI, 2.9–15.8) had the lowest residual variation at the hospital level. The variables at the hospital, LHD and SLA levels that were available for this study generally showed no significant effect. CONCLUSIONS: Investigations into the causes of low-value care and initiatives to reduce low-value care might best be targeted at the hospital level, as the high variation at this level suggests the greatest potential to reduce low-value care. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12913-019-4159-1) contains supplementary material, which is available to authorized users.
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spelling pubmed-65435912019-06-04 Exploring variation in low-value care: a multilevel modelling study Badgery-Parker, Tim Feng, Yingyu Pearson, Sallie-Anne Levesque, Jean-Frederic Dunn, Susan Elshaug, Adam G. BMC Health Serv Res Research Article BACKGROUND: Whether patients receive low-value hospital care (care that is not expected to provide a net benefit) may be influenced by unmeasured factors at the hospital they attend or the hospital’s Local Health District (LHD), or the patients’ areas of residence. Multilevel modelling presents a method to examine the effects of these different levels simultaneously and assess their relative importance to the outcome. Knowing which of these levels has the greatest contextual effects can help target further investigation or initiatives to reduce low-value care. METHODS: We conducted multilevel logistic regression modelling for nine low-value hospital procedures. We fit a series of six models for each procedure. The baseline model included only episode-level variables with no multilevel structure. We then added each level (hospital, LHD, Statistical Local Area [SLA] of residence) separately and used the change in the c statistic from the baseline model as a measure of the contribution of the level to the outcome. We then examined the variance partition coefficients (VPCs) and median odds ratios for a model including all three levels. Finally, we added level-specific covariates to examine if they were associated with the outcome. RESULTS: Analysis of the c statistics showed that hospital was more important than LHD or SLA in explaining whether patients receive low-value care. The greatest increases were 0.16 for endoscopy for dyspepsia, 0.13 for colonoscopy for constipation, and 0.14 for sentinel lymph node biopsy for early melanoma. SLA gave a small increase in c compared with the baseline model, but no increase over the model with hospital. The VPCs indicated that hospital accounted for most of the variation not explained by the episode-level variables, reaching 36.8% (95% CI, 31.9–39.0) for knee arthroscopy. ERCP (8.5%; 95% CI, 3.9–14.7) and EVAR (7.8%; 95% CI, 2.9–15.8) had the lowest residual variation at the hospital level. The variables at the hospital, LHD and SLA levels that were available for this study generally showed no significant effect. CONCLUSIONS: Investigations into the causes of low-value care and initiatives to reduce low-value care might best be targeted at the hospital level, as the high variation at this level suggests the greatest potential to reduce low-value care. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12913-019-4159-1) contains supplementary material, which is available to authorized users. BioMed Central 2019-05-30 /pmc/articles/PMC6543591/ /pubmed/31146744 http://dx.doi.org/10.1186/s12913-019-4159-1 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Badgery-Parker, Tim
Feng, Yingyu
Pearson, Sallie-Anne
Levesque, Jean-Frederic
Dunn, Susan
Elshaug, Adam G.
Exploring variation in low-value care: a multilevel modelling study
title Exploring variation in low-value care: a multilevel modelling study
title_full Exploring variation in low-value care: a multilevel modelling study
title_fullStr Exploring variation in low-value care: a multilevel modelling study
title_full_unstemmed Exploring variation in low-value care: a multilevel modelling study
title_short Exploring variation in low-value care: a multilevel modelling study
title_sort exploring variation in low-value care: a multilevel modelling study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6543591/
https://www.ncbi.nlm.nih.gov/pubmed/31146744
http://dx.doi.org/10.1186/s12913-019-4159-1
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