Evidence map›Paper›PMID 29713465›Full record

SynthesisAntimicrobial resistance and infection control2018

Estimating the burden of antimicrobial resistance: a systematic literature review.

Nichola R Naylor, Rifat Atun, Nina Zhu, Kavian Kulasabanathan, Sachin Silva, Anuja Chatterjee, Gwenan M Knight, Julie V Robotham

Abstract readSystematic Review
In one paragraph

Synthesis in Antimicrobial resistance and infection control, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 252 papers, 13 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
252citing papers in PubMed, 13 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

252 citing papers in PubMed, 13 syntheses or guidelines pooled it.

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192 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Nichola R Naylor1National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College, Hammersmith Campus, London, W12 0NN UK.ORCID 0000-0003-4122-6631
Rifat Atun3Harvard University, 665 Huntington Avenue, Boston, MA 02115 USA.
Nina Zhu1National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College, Hammersmith Campus, London, W12 0NN UK.
Kavian Kulasabanathan2Imperial College London, Sir Alexander Fleming Building, South Kensington Campus, London, UK.
Sachin Silva3Harvard University, 665 Huntington Avenue, Boston, MA 02115 USA.
Anuja Chatterjee1National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College, Hammersmith Campus, London, W12 0NN UK.
Gwenan M Knight1National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College, Hammersmith Campus, London, W12 0NN UK.
Julie V Robotham4Modelling and Economics Unit, National Infection Service, Public Health England, 61 Colindale Avenue, London, NW9 5EQ UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 paramaterise 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 Philips 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 5187 unique retrievals, 214 studies were included. One hundred eighty-seven 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, which can lead in-turn to inaccurate intervention evaluations and poor policy/investment decisions. Future research should utilise the recommendations presented in this review. Trial registration: This systematic review is registered with PROSPERO (PROSPERO CRD42016037510).

Indexed as

Anti-Bacterial AgentsBacterial InfectionsCost-Benefit AnalysisDrug Resistance, Multiple, BacterialFemaleHealth Care CostsHumansMaleTreatment OutcomeAnti-Bacterial AgentsAntibiotic resistanceAntimicrobial resistanceBurdenCost

Identifiers

PMID29713465
PMCPMC5918775

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.