Imperial College London
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Dr Nina J. Zhu

Faculty of Medicine, Department of Infectious Disease

Research Associate
 
 
 
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Contact

 

jiayue.zhu09 Website

 
 
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Location

 

Commonwealth BuildingHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Naylor:2018:10.1186/s13756-018-0336-y,
author = {Naylor, NR and Atun, R and Zhu, N and Kulasabanathan, K and Silva, S and Chatterjee, A and Knight, G and Robotham, J},
doi = {10.1186/s13756-018-0336-y},
journal = {Antimicrobial Resistance and Infection Control},
title = {Estimating the burden of antimicrobial resistance: a systematic literature review},
url = {http://dx.doi.org/10.1186/s13756-018-0336-y},
volume = {7},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Background: Accurate estimates of the burden of antimicrobial resistance (AMR) are needed to establish the magnitude of this global threat in terms of both health and cost, and to enable cost-effectiveness evaluations of interventions aiming to tackle the problem. This review aimed to establish the alternative methodologies used in estimating AMR burden in order to appraise the current evidence base.  Methods: MEDLINE, EMBASE, Scopus, EconLit, PubMed and grey literature were searched. English language studies evaluating the impact of AMR (from any microbe) on patient, payer/provider and economic burden published between January 2013 and December 2015 were included. Independent screening of title/abstracts followed by full texts was performed using pre-specified criteria. A study quality score (from zero to one) was derived using Newcastle-Ottawa and Phillips checklists. Extracted study data were used to compare study method and resulting burden estimate, according to perspective. Monetary costs were converted into 2013  USD. Results: Out of 5,187 unique retrievals, 214 studies were included.  187 studies estimated patient health, 75 studies estimated payer/provider and 11 studies estimated economic burden. 64% of included studies were single centre. The majority of studies estimating patient or provider/payer burden used regression techniques. 48% of studies estimating mortality burden found a significant impact from resistance, excess healthcare system costs ranged from non-significance to $1 billion per year, whilst economic burden ranged from $21,832 per case  to over $3 trillion in GDP loss. Median quality scores (interquartile range) for patient, payer/provider and economic burden studies were 0.67 (0.56-0.67), 0.56 (0.46-0.67) and 0.53 (0.44-0.60) respectively. Conclusions: This study highlights what methodological assumptions and biases can occur dependent on chosen outcome and perspective. Currently, there is considerable variability in burden estimates, whi
AU  - Naylor,NR
AU  - Atun,R
AU  - Zhu,N
AU  - Kulasabanathan,K
AU  - Silva,S
AU  - Chatterjee,A
AU  - Knight,G
AU  - Robotham,J
DO  - 10.1186/s13756-018-0336-y
PY  - 2018///
SN  - 2047-2994
TI  - Estimating the burden of antimicrobial resistance: a systematic literature review
T2  - Antimicrobial Resistance and Infection Control
UR  - http://dx.doi.org/10.1186/s13756-018-0336-y
UR  - http://hdl.handle.net/10044/1/57796
VL  - 7
ER  -
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