Minimally important difference in cost savings: Is it possible to identify an MID for cost savings?

Cost savings MID Minimally important differences

Journal

Health services & outcomes research methodology
ISSN: 1387-3741
Titre abrégé: Health Serv Outcomes Res Methodol
Pays: Netherlands
ID NLM: 9815809

Informations de publication

Date de publication:
2021
Historique:
received: 30 04 2020
revised: 18 11 2020
accepted: 25 11 2020
pubmed: 14 1 2021
medline: 14 1 2021
entrez: 13 1 2021
Statut: ppublish

Résumé

As healthcare costs continue to increase, studies assessing costs are becoming increasingly common, but researchers planning for studies that measure costs differences (savings) encounter a lack of literature or consensus among researchers on what constitutes "small" or "large" cost savings for common measures of resource use.  Other fields of research have developed approaches to solve this type of problem. Researchers measuring improvement in quality of life or clinical assessments have defined minimally important differences (MID) which are then used to define magnitudes when planning studies. Also, studies that measure cost effectiveness use benchmarks, such as cost/QALY, but do not provide benchmarks for cost differences. In a review of the literature, we found no publications identifying indicators of magnitude for costs. However, the literature describes three approaches used to identify minimally important outcome differences: (1) anchor-based, (2) distribution-based, and (3) a consensus-based Delphi methods. In this exploratory study, we used these three approaches to derive MID for two types of resource measures common in costing studies for: (1) hospital admissions (high cost); and (2) clinic visits (low cost). We used data from two (unpublished) studies to implement the MID estimation. Because the distributional characteristics of cost measures may require substantial samples, we performed power analyses on all our estimates to illustrate the effect that the definitions of "small" and "large" costs may be expected to have on power and sample size requirements for studies. The anchor-based method, while logical and simple to implement, may be of limited value in cases where it is difficult to identify appropriate anchors. We observed some commonalities and differences for the distribution and consensus-based approaches, which require further examination. We recommend that in cases where acceptable anchors are not available, both the Delphi and the distribution-method of MID for costs be explored for convergence.

Identifiants

pubmed: 33437174
doi: 10.1007/s10742-020-00233-5
pii: 233
pmc: PMC7790477
doi:

Types de publication

Journal Article

Langues

eng

Pagination

131-144

Subventions

Organisme : NIDDK NIH HHS
ID : P30 DK123704
Pays : United States
Organisme : NIGMS NIH HHS
ID : U54 GM104941
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001450
Pays : United States

Informations de copyright

© The Author(s) 2021.

Déclaration de conflit d'intérêts

Conflict of interestThe authors declare that they have no conflict of interest.

Auteurs

Mary Dooley (M)

Departments of Health Science and Research and Healthcare Leadership and Management, College of Health Professions, Medical University of South Carolina, Charleston, USA.

Annie N Simpson (AN)

Departments of Health Science and Research and Healthcare Leadership and Management, College of Health Professions, Medical University of South Carolina, Charleston, USA.

Paul J Nietert (PJ)

Department of Public Health Sciences, College of Medicine, Medical University of South Carolina, Charleston, USA.

Dunc Williams (D)

Departments of Health Science and Research and Healthcare Leadership and Management, College of Health Professions, Medical University of South Carolina, Charleston, USA.

Kit N Simpson (KN)

Departments of Health Science and Research and Healthcare Leadership and Management, College of Health Professions, Medical University of South Carolina, Charleston, USA.
Department of Public Health Sciences, College of Medicine, Medical University of South Carolina, Charleston, USA.

Classifications MeSH