Exploring variation in low-value care: a multilevel modelling study.
Adolescent
Adult
Aged
Colonoscopy
/ statistics & numerical data
Deglutition Disorders
/ etiology
Delivery of Health Care
/ standards
Early Detection of Cancer
/ statistics & numerical data
Endoscopy, Digestive System
/ statistics & numerical data
Epidemiologic Methods
Female
Hospitalization
Hospitals
/ standards
Humans
Male
Melanoma
/ diagnosis
Middle Aged
New South Wales
Quality of Health Care
/ statistics & numerical data
Sentinel Lymph Node Biopsy
/ statistics & numerical data
Skin Neoplasms
/ diagnosis
Young Adult
Choosing wisely
Disinvestment
Low-value care
Multilevel logistic regression
Journal
BMC health services research
ISSN: 1472-6963
Titre abrégé: BMC Health Serv Res
Pays: England
ID NLM: 101088677
Informations de publication
Date de publication:
30 May 2019
30 May 2019
Historique:
received:
19
10
2018
accepted:
10
05
2019
entrez:
1
6
2019
pubmed:
31
5
2019
medline:
20
8
2019
Statut:
epublish
Résumé
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. 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. 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. 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.
Sections du résumé
BACKGROUND
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
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
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
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.
Identifiants
pubmed: 31146744
doi: 10.1186/s12913-019-4159-1
pii: 10.1186/s12913-019-4159-1
pmc: PMC6543591
doi:
Types de publication
Journal Article
Langues
eng
Pagination
345Subventions
Organisme : Capital Markets CRC Limited
ID : -
Organisme : Capital Markets CRC Limited
ID : -
Organisme : NSW Ministry of Health
ID : -
Organisme : NSW Ministry of Health
ID : -
Organisme : The University of Sydney
ID : -
Organisme : The University of Sydney
ID : -
Organisme : HCF Research Foundation
ID : -
Organisme : National Health and Medical Research Council
ID : 1109626
Organisme : National Health and Medical Research Council
ID : 1109626
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