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A Risk Assessment of Antibiotic Pan-Drug-Resistance in the UK: Bayesian Analysis of an Expert Elicitation Study.

Carter, D; Charlett, A; Conti, S; Robotham, JV; Johnson, AP; Livermore, DM; Fowler, T; Sharland, M; Hopkins, S; Woodford, N; et al. Carter, D; Charlett, A; Conti, S; Robotham, JV; Johnson, AP; Livermore, DM; Fowler, T; Sharland, M; Hopkins, S; Woodford, N; Burgess, P; Dobra, S (2017) A Risk Assessment of Antibiotic Pan-Drug-Resistance in the UK: Bayesian Analysis of an Expert Elicitation Study. Antibiotics (Basel), 6 (1). p. 9. ISSN 2079-6382 https://doi.org/10.3390/antibiotics6010009
SGUL Authors: Sharland, Michael Roy

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Abstract

To inform the UK antimicrobial resistance strategy, a risk assessment was undertaken of the likelihood, over a five-year time-frame, of the emergence and widespread dissemination of pan-drug-resistant (PDR) Gram-negative bacteria that would pose a major public health threat by compromising effective healthcare delivery. Subsequent impact over five- and 20-year time-frames was assessed in terms of morbidity and mortality attributable to PDR Gram-negative bacteraemia. A Bayesian approach, combining available data with expert prior opinion, was used to determine the probability of the emergence, persistence and spread of PDR bacteria. Overall probability was modelled using Monte Carlo simulation. Estimates of impact were also obtained using Bayesian methods. The estimated probability of widespread occurrence of PDR pathogens within five years was 0.2 (95% credibility interval (CrI): 0.07-0.37). Estimated annual numbers of PDR Gram-negative bacteraemias at five and 20 years were 6800 (95% CrI: 400-58,600) and 22,800 (95% CrI: 1500-160,000), respectively; corresponding estimates of excess deaths were 1900 (95% CrI: 0-23,000) and 6400 (95% CrI: 0-64,000). Over 20 years, cumulative estimates indicate 284,000 (95% CrI: 17,000-1,990,000) cases of PDR Gram-negative bacteraemia, leading to an estimated 79,000 (95% CrI: 0-821,000) deaths. This risk assessment reinforces the need for urgent national and international action to tackle antibiotic resistance.

Item Type: Article
Additional Information: © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Keywords: Bayesian modelling, antibiotic resistance, risk assessment
SGUL Research Institute / Research Centre: Academic Structure > Infection and Immunity Research Institute (INII)
Journal or Publication Title: Antibiotics (Basel)
ISSN: 2079-6382
Language: eng
Dates:
DateEvent
7 March 2017Published
1 March 2017Accepted
Publisher License: Creative Commons: Attribution 4.0
PubMed ID: 28272350
Go to PubMed abstract
URI: http://openaccess.sgul.ac.uk/id/eprint/108769
Publisher's version: https://doi.org/10.3390/antibiotics6010009

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