Evidence map›Paper›PMID 27821153›Full record

ArticleSystematic reviews2016

Methods for estimating the burden of antimicrobial resistance: a systematic literature review protocol.

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

Open access · goldAbstract read
In one paragraph

Article in Systematic reviews, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 5 pooled it
1.4field-weighted citation impact, top 14% of its field
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

12 citing papers in PubMed, 5 syntheses or guidelines pooled it, 16 citations in OpenAlex.

  1. Pooled it
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  5. Pooled it
  6. [Health economic significance of antimicrobial resistance].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 2 countries.

Nichola R NaylorNational Institute for Health Research Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College, Hammersmith Campus, London, W12 0NN, UK. n.naylor@imperial.ac.uk.
Sachin SilvaImperial College London, Sir Alexander Fleming Building, South Kensington Campus, London, UK.
Kavian KulasabanathanImperial College London, Sir Alexander Fleming Building, South Kensington Campus, London, UK.
Rifat AtunNational Institute for Health Research Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College, Hammersmith Campus, London, W12 0NN, UK.
Nina ZhuNational 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 KnightNational 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 RobothamNational Institute for Health Research Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College, Hammersmith Campus, London, W12 0NN, UK.
Imperial College London · GBHarvard University · USNational Institute for Health Research · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEstimates of the burden of antimicrobial resistance (AMR) are needed to ascertain AMR impact, to evaluate interventions, and to allocate resources efficiently. Recent studies have estimated health, cost, and economic burden relating to AMR, with outcomes of interest ranging from drug-bug resistance impact on mortality in a hospital setting to total economic impact of AMR on the global economy. However, recent collation of this information has been largely informal, with no formal quality assessment of the current evidence base (e.g. with predefined checklists). This review therefore aims to establish what perspectives and resulting methodologies have been used in establishing the burden of AMR, whilst also ascertaining the quality of these studies.

methodsThe literature review will identify relevant literature using a systematic review methodology. MEDLINE, EMBASE, Scopus and EconLit will be searched utilising a predefined search string. Grey literature will be identified by searching within a predefined list of organisational websites. Independent screening of retrievals will be performed in a two-stage process (abstracts and full texts), utilising a pre-defined inclusion and exclusion criteria. Data will be extracted into a data extraction table and descriptive examination will be performed. Study quality will be assessed using the Newcastle-Ottawa scales and the Philips checklists where appropriate. A narrative synthesis of the results will be presented. DISCUSSION: This review will provide an overview of previous health, cost and economic definitions of burden and the resultant impact of these different definitions on the burden of AMR estimated. The review will also explore the methods that have been used to calculate this burden and discuss resulting study quality. This review can therefore act as a guide to methods for future research in this area. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42016037510.

Indexed as

Drug Resistance, Multiple, BacterialPublic HealthAnti-Bacterial AgentsCost-Benefit AnalysisCross InfectionHumansMicrobial Sensitivity TestsPractice Guidelines as TopicPublic Health PracticeRisk FactorsSystematic Reviews as TopicAnti-Bacterial AgentsAntimicrobial resistanceBurdenMethodsSystematic review

Identifiers

PMID27821153
PMCPMC5100234
OpenAlexW2539537051

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.