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Understanding cost data collection tools to improve economic evaluations of health interventions

Micro-costing data collection tools often used in literature include standardized comprehensive templates, targeted questionnaires, activity logs, on-site administrative databases, and direct observation. These tools are not mutually exclusive and are often used in combination. Each tool has unique...

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Autores principales: Chapel, John M, Wang, Guijing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6979867/
https://www.ncbi.nlm.nih.gov/pubmed/32030205
http://dx.doi.org/10.1136/svn-2019-000301
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author Chapel, John M
Wang, Guijing
author_facet Chapel, John M
Wang, Guijing
author_sort Chapel, John M
collection PubMed
description Micro-costing data collection tools often used in literature include standardized comprehensive templates, targeted questionnaires, activity logs, on-site administrative databases, and direct observation. These tools are not mutually exclusive and are often used in combination. Each tool has unique merits and limitations, and some may be more applicable than others under different circumstances. Proper application of micro-costing tools can produce quality cost estimates and enhance the usefulness of economic evaluations to inform resource allocation decisions. A common method to derive both fixed and variable costs of an intervention involves collecting data from the bottom up for each resource consumed (micro-costing). We scanned economic evaluation literature published in 2008-2018 and identified micro-costing data collection tools used. We categorized the identified tools and discuss their practical applications in an example study of health interventions, including their potential strengths and weaknesses. Sound economic evaluations of health interventions provide valuable information for justifying resource allocation decisions, planning for implementation, and enhancing the sustainability of the interventions. However, the quality of intervention cost estimates is seldom addressed in the literature. Reliable cost data forms the foundation of economic evaluations, and without reliable estimates, evaluation results, such as cost-effectiveness measures, could be misleading. In this project, we identified data collection tools often used to obtain reliable data for estimating costs of interventions that prevent and manage chronic conditions and considered practical applications to promote their use.
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spelling pubmed-69798672020-02-06 Understanding cost data collection tools to improve economic evaluations of health interventions Chapel, John M Wang, Guijing Stroke Vasc Neurol Review Micro-costing data collection tools often used in literature include standardized comprehensive templates, targeted questionnaires, activity logs, on-site administrative databases, and direct observation. These tools are not mutually exclusive and are often used in combination. Each tool has unique merits and limitations, and some may be more applicable than others under different circumstances. Proper application of micro-costing tools can produce quality cost estimates and enhance the usefulness of economic evaluations to inform resource allocation decisions. A common method to derive both fixed and variable costs of an intervention involves collecting data from the bottom up for each resource consumed (micro-costing). We scanned economic evaluation literature published in 2008-2018 and identified micro-costing data collection tools used. We categorized the identified tools and discuss their practical applications in an example study of health interventions, including their potential strengths and weaknesses. Sound economic evaluations of health interventions provide valuable information for justifying resource allocation decisions, planning for implementation, and enhancing the sustainability of the interventions. However, the quality of intervention cost estimates is seldom addressed in the literature. Reliable cost data forms the foundation of economic evaluations, and without reliable estimates, evaluation results, such as cost-effectiveness measures, could be misleading. In this project, we identified data collection tools often used to obtain reliable data for estimating costs of interventions that prevent and manage chronic conditions and considered practical applications to promote their use. BMJ Publishing Group 2019-12-01 /pmc/articles/PMC6979867/ /pubmed/32030205 http://dx.doi.org/10.1136/svn-2019-000301 Text en © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Review
Chapel, John M
Wang, Guijing
Understanding cost data collection tools to improve economic evaluations of health interventions
title Understanding cost data collection tools to improve economic evaluations of health interventions
title_full Understanding cost data collection tools to improve economic evaluations of health interventions
title_fullStr Understanding cost data collection tools to improve economic evaluations of health interventions
title_full_unstemmed Understanding cost data collection tools to improve economic evaluations of health interventions
title_short Understanding cost data collection tools to improve economic evaluations of health interventions
title_sort understanding cost data collection tools to improve economic evaluations of health interventions
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6979867/
https://www.ncbi.nlm.nih.gov/pubmed/32030205
http://dx.doi.org/10.1136/svn-2019-000301
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