Excess resource use and cost of drug-resistant infections for six key pathogens in Europe: a systematic review and Bayesian meta-analysis.

Antimicrobial resistance Bayesian meta-analysis Costs Length of stay Resource use

Journal

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases
ISSN: 1469-0691
Titre abrégé: Clin Microbiol Infect
Pays: England
ID NLM: 9516420

Informations de publication

Date de publication:
Mar 2024
Historique:
received: 05 12 2023
revised: 05 12 2023
accepted: 11 12 2023
pubmed: 22 12 2023
medline: 22 12 2023
entrez: 21 12 2023
Statut: ppublish

Résumé

Quantifying the resource use and cost of antimicrobial resistance establishes the magnitude of the problem and drives action. Assessment of resource use and cost associated with infections with six key drug-resistant pathogens in Europe. A systematic review and Bayesian meta-analysis. MEDLINE (Ovid), Embase (Ovid), Econlit databases, and grey literature for the period 1 January 1990, to 21 June 2022. Resource use and cost outcomes (including excess length of stay, overall costs, and other excess in or outpatient costs) were compared between patients with defined antibiotic-resistant infections caused by carbapenem-resistant (CR) Pseudomonas aeruginosa and Acinetobacter baumannii, CR or third-generation cephalosporin Escherichia coli (3GCREC) and Klebsiella pneumoniae, methicillin-resistant Staphylococcus aureus, and vancomycin-resistant Enterococcus faecium, and patients with drug-susceptible or no infection. All patients diagnosed with drug-resistant bloodstream infections (BSIs). NA. An adapted version of the Joanna Briggs Institute assessment tool, incorporating case-control, cohort, and economic assessment frameworks. Hierarchical Bayesian meta-analyses were used to assess pathogen-specific resource use estimates. Of 5969 screened publications, 37 were included in the review. Data were sparse and heterogeneous. Most studies estimated the attributable burden by, comparing resistant and susceptible pathogens (32/37). Four studies analysed the excess cost of hospitalization attributable to 3GCREC BSIs, ranging from -€ 2465.50 to € 6402.81. Eight studies presented adjusted excess length of hospital stay estimates for methicillin-resistant S. aureus and 3GCREC BSIs (4 each) allowing for Bayesian hierarchical analysis, estimating means of 1.26 (95% credible interval [CrI], -0.72 to 4.17) and 1.78 (95% CrI, -0.02 to 3.38) days, respectively. Evidence on most cost and resource use outcomes and across most pathogen-resistance combinations was severely lacking. Given the importance of this evidence for rational policymaking, further research is urgently needed.

Sections du résumé

BACKGROUND BACKGROUND
Quantifying the resource use and cost of antimicrobial resistance establishes the magnitude of the problem and drives action.
OBJECTIVES OBJECTIVE
Assessment of resource use and cost associated with infections with six key drug-resistant pathogens in Europe.
METHODS METHODS
A systematic review and Bayesian meta-analysis.
DATA SOURCES METHODS
MEDLINE (Ovid), Embase (Ovid), Econlit databases, and grey literature for the period 1 January 1990, to 21 June 2022.
STUDY ELIGIBILITY CRITERIA METHODS
Resource use and cost outcomes (including excess length of stay, overall costs, and other excess in or outpatient costs) were compared between patients with defined antibiotic-resistant infections caused by carbapenem-resistant (CR) Pseudomonas aeruginosa and Acinetobacter baumannii, CR or third-generation cephalosporin Escherichia coli (3GCREC) and Klebsiella pneumoniae, methicillin-resistant Staphylococcus aureus, and vancomycin-resistant Enterococcus faecium, and patients with drug-susceptible or no infection.
PARTICIPANTS METHODS
All patients diagnosed with drug-resistant bloodstream infections (BSIs).
INTERVENTIONS METHODS
NA.
ASSESSMENT OF RISK OF BIAS UNASSIGNED
An adapted version of the Joanna Briggs Institute assessment tool, incorporating case-control, cohort, and economic assessment frameworks.
METHODS OF DATA SYNTHESIS UNASSIGNED
Hierarchical Bayesian meta-analyses were used to assess pathogen-specific resource use estimates.
RESULTS RESULTS
Of 5969 screened publications, 37 were included in the review. Data were sparse and heterogeneous. Most studies estimated the attributable burden by, comparing resistant and susceptible pathogens (32/37). Four studies analysed the excess cost of hospitalization attributable to 3GCREC BSIs, ranging from -€ 2465.50 to € 6402.81. Eight studies presented adjusted excess length of hospital stay estimates for methicillin-resistant S. aureus and 3GCREC BSIs (4 each) allowing for Bayesian hierarchical analysis, estimating means of 1.26 (95% credible interval [CrI], -0.72 to 4.17) and 1.78 (95% CrI, -0.02 to 3.38) days, respectively.
CONCLUSIONS CONCLUSIONS
Evidence on most cost and resource use outcomes and across most pathogen-resistance combinations was severely lacking. Given the importance of this evidence for rational policymaking, further research is urgently needed.

Identifiants

pubmed: 38128781
pii: S1198-743X(23)00603-1
doi: 10.1016/j.cmi.2023.12.013
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

S26-S36

Investigateurs

None Lorenzo Argante
Benedetta Barana (B)
Eva Cappelli (E)
Maria Elena De Rui (ME)
Liliana Galia (L)
Jeroen Geurtsen (J)
Mariana Guedes (M)
Jorly Mejia (J)
Andrea Palladino (A)
Maria Diletta Pezzani (MD)
Alen Piljic (A)

Informations de copyright

Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.

Auteurs

Rhys Kingston (R)

Field Service Data Science Team, UK Health Security Agency, London, UK.

Venanzio Vella (V)

GSK, Siena, Italy.

Koen B Pouwels (KB)

Nuffield Department of Population Health, Health Economics Research Centre, University of Oxford, Oxford, UK.

Johannes E Schmidt (JE)

GSK, Siena, Italy.

Radwa A Abdelatif El-Abasiri (RA)

Nuffield Department of Population Health, Health Economics Research Centre, University of Oxford, Oxford, UK.

Eduardo Reyna-Villasmil (E)

Infectious Diseases and Microbiology Division, Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen Macarena, Department of Medicine, University of Sevilla/CSIC, Sevilla, Spain.

Nasreen Hassoun-Kheir (N)

Infection Control Program, Geneva University Hospitals and Faculty of Medicine, WHO Collaborating Center, Geneva, Switzerland.

Stephan Harbarth (S)

Infection Control Program, Geneva University Hospitals and Faculty of Medicine, WHO Collaborating Center, Geneva, Switzerland.

Jesús Rodríguez-Baño (J)

Infectious Diseases and Microbiology Division, Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen Macarena, Department of Medicine, University of Sevilla/CSIC, Sevilla, Spain.

Evelina Tacconelli (E)

Infectious Diseases, Department of Diagnostics and Public Health, University of Verona, Verona, Italy.

Fabiana Arieti (F)

Infectious Diseases, Department of Diagnostics and Public Health, University of Verona, Verona, Italy.

Beryl Primrose Gladstone (BP)

Department of Internal Medicine, DZIF-Clinical Research Unit, Infectious Diseases, University Hospital Tübingen, Tübingen, Germany.

Marlieke E A de Kraker (MEA)

Infection Control Program, Geneva University Hospitals and Faculty of Medicine, WHO Collaborating Center, Geneva, Switzerland.

Nichola R Naylor (NR)

HCAI, Fungal, AMR, AMU, & Sepsis Division, UK Health Security Agency, London, UK.

Julie V Robotham (JV)

HCAI, Fungal, AMR, AMU, & Sepsis Division, UK Health Security Agency, London, UK. Electronic address: julie.robotham@ukhsa.gov.uk.

Classifications MeSH