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Using the best available data to estimate the cost of antimicrobial resistance: a systematic review

BACKGROUND: Valuation of the economic cost of antimicrobial resistance (AMR) is important for decision making and should be estimated accurately. Highly variable or erroneous estimates may alarm policy makers and hospital administrators to act, but they also create confusion as to what the most reli...

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Autores principales: Wozniak, Teresa M., Barnsbee, Louise, Lee, Xing J., Pacella, Rosana E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6359818/
https://www.ncbi.nlm.nih.gov/pubmed/30733860
http://dx.doi.org/10.1186/s13756-019-0472-z
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author Wozniak, Teresa M.
Barnsbee, Louise
Lee, Xing J.
Pacella, Rosana E.
author_facet Wozniak, Teresa M.
Barnsbee, Louise
Lee, Xing J.
Pacella, Rosana E.
author_sort Wozniak, Teresa M.
collection PubMed
description BACKGROUND: Valuation of the economic cost of antimicrobial resistance (AMR) is important for decision making and should be estimated accurately. Highly variable or erroneous estimates may alarm policy makers and hospital administrators to act, but they also create confusion as to what the most reliable estimates are and how these should be assessed. This study aimed to assess the quality of methods used in studies that quantify the costs of AMR and to determine the best available evidence of the incremental cost of these infections. METHODS: In this systematic review, we searched PubMed, Embase, Cinahl, Cochrane databases and grey literature sources published between January 2012 and October 2016. Articles reporting the additional burden of Enterococcus spp., Escherichia coli (E. coli), Klebsiella pneumoniae (K. pneumoniae), Pseudomonas aeruginosa (P. aeruginosa) and Staphylococcus aureus (S. aureus) resistant versus susceptible infections were sourced. The included studies were broadly classified as reporting oncosts from the healthcare/hospital/hospital charges perspective or societal perspective. Risk of bias was assessed based on three methodological components: (1) adjustment for length of stay prior to infection onset and consideration of time-dependent bias, (2) adjustment for comorbidities or severity of disease, and (3) adjustment for inappropriate antibiotic therapy. RESULTS: Of 1094 identified studies, we identified 12 peer-reviewed articles and two reports that quantified the economic burden of clinically important resistant infections. Two studies used multi-state modelling to account for the timing of infection minimising the risk of time dependent bias and these were considered to generate the best available cost estimates. Studies report an additional CHF 9473 per extended-spectrum beta-lactamases -resistant Enterobacteriaceae bloodstream infections (BSI); additional €3200 per third-generation cephalosporin resistant Enterobacteriaceae BSI; and additional €1600 per methicillin-resistant S. aureus (MRSA) BSI. The remaining studies either partially adjusted or did not consider the timing of infection in their analysis. CONCLUSIONS: Implementation of AMR policy and decision-making should be guided only by reliable, unbiased estimates of effect size. Generating these estimates requires a thorough understanding of important biases and their impact on measured outcomes. This will ensure that researchers, clinicians, and other key decision makers concerned with increasing public health threat of AMR are accurately guided by the best available evidence. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13756-019-0472-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-63598182019-02-07 Using the best available data to estimate the cost of antimicrobial resistance: a systematic review Wozniak, Teresa M. Barnsbee, Louise Lee, Xing J. Pacella, Rosana E. Antimicrob Resist Infect Control Research BACKGROUND: Valuation of the economic cost of antimicrobial resistance (AMR) is important for decision making and should be estimated accurately. Highly variable or erroneous estimates may alarm policy makers and hospital administrators to act, but they also create confusion as to what the most reliable estimates are and how these should be assessed. This study aimed to assess the quality of methods used in studies that quantify the costs of AMR and to determine the best available evidence of the incremental cost of these infections. METHODS: In this systematic review, we searched PubMed, Embase, Cinahl, Cochrane databases and grey literature sources published between January 2012 and October 2016. Articles reporting the additional burden of Enterococcus spp., Escherichia coli (E. coli), Klebsiella pneumoniae (K. pneumoniae), Pseudomonas aeruginosa (P. aeruginosa) and Staphylococcus aureus (S. aureus) resistant versus susceptible infections were sourced. The included studies were broadly classified as reporting oncosts from the healthcare/hospital/hospital charges perspective or societal perspective. Risk of bias was assessed based on three methodological components: (1) adjustment for length of stay prior to infection onset and consideration of time-dependent bias, (2) adjustment for comorbidities or severity of disease, and (3) adjustment for inappropriate antibiotic therapy. RESULTS: Of 1094 identified studies, we identified 12 peer-reviewed articles and two reports that quantified the economic burden of clinically important resistant infections. Two studies used multi-state modelling to account for the timing of infection minimising the risk of time dependent bias and these were considered to generate the best available cost estimates. Studies report an additional CHF 9473 per extended-spectrum beta-lactamases -resistant Enterobacteriaceae bloodstream infections (BSI); additional €3200 per third-generation cephalosporin resistant Enterobacteriaceae BSI; and additional €1600 per methicillin-resistant S. aureus (MRSA) BSI. The remaining studies either partially adjusted or did not consider the timing of infection in their analysis. CONCLUSIONS: Implementation of AMR policy and decision-making should be guided only by reliable, unbiased estimates of effect size. Generating these estimates requires a thorough understanding of important biases and their impact on measured outcomes. This will ensure that researchers, clinicians, and other key decision makers concerned with increasing public health threat of AMR are accurately guided by the best available evidence. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13756-019-0472-z) contains supplementary material, which is available to authorized users. BioMed Central 2019-02-01 /pmc/articles/PMC6359818/ /pubmed/30733860 http://dx.doi.org/10.1186/s13756-019-0472-z 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
Wozniak, Teresa M.
Barnsbee, Louise
Lee, Xing J.
Pacella, Rosana E.
Using the best available data to estimate the cost of antimicrobial resistance: a systematic review
title Using the best available data to estimate the cost of antimicrobial resistance: a systematic review
title_full Using the best available data to estimate the cost of antimicrobial resistance: a systematic review
title_fullStr Using the best available data to estimate the cost of antimicrobial resistance: a systematic review
title_full_unstemmed Using the best available data to estimate the cost of antimicrobial resistance: a systematic review
title_short Using the best available data to estimate the cost of antimicrobial resistance: a systematic review
title_sort using the best available data to estimate the cost of antimicrobial resistance: a systematic review
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6359818/
https://www.ncbi.nlm.nih.gov/pubmed/30733860
http://dx.doi.org/10.1186/s13756-019-0472-z
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