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Title: Improving the estimation of the global burden of antimicrobial resistant infections
Authors: Direk Limmathurotsakul
Susanna Dunachie
Keiji Fukuda
Nicholas A. Feasey
Iruka N. Okeke
Alison H. Holmes
Catrin E. Moore
Christiane Dolecek
H. Rogier van Doorn
Nandini Shetty
Alan D. Lopez
Sharon J. Peacock
Public Health England
The University of Hong Kong Li Ka Shing Faculty of Medicine
Malawi-Liverpool-Wellcome Trust Clinical Research Programme
University of Cambridge
University of Oxford
University of Melbourne
Imperial College London
Mahidol University
University of Ibadan
Oxford University Clinical Research Unit
Keywords: Medicine
Issue Date: 1-Nov-2019
Citation: The Lancet Infectious Diseases. Vol.19, No.11 (2019), e392-e398
Abstract: © 2019 Elsevier Ltd Estimating the global burden of disease from infections caused by pathogens that have acquired antimicrobial resistance (AMR) is essential for resource allocation and to inform AMR action plans at national and global levels. However, the scarcity of robust and accepted methods to determine burden is widely acknowledged. In this Personal View, we discuss the underlying assumptions, characteristics, limitations, and comparability of the approaches used to quantify mortality from AMR bacterial infections. We show that the global burdens of AMR estimated in previous studies are not comparable because of their different methodological approaches, assumptions, and data used to generate the estimates. The analytical frameworks from previous studies are inadequate, and we conclude that a new approach to the estimation of deaths caused by AMR infection is needed. The innovation of a new approach will require the development of mechanisms to systematically collect a clinical dataset of substantial breadth and quality to support the accurate assessment of burden, combined with decision-making and resource allocation for interventions against AMR. We define key actions required and call for innovative thinking and solutions to address these problems.
ISSN: 14744457
Appears in Collections:Scopus 2019

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